The indispensable guide to effective corporate AI policy

Artificial Intelligence (AI) has become the cornerstone of modern innovation, permeating diverse sectors of the economy and revolutionizing the way we live and work. Today, we stand at a crucial crossroads: one where the path we choose determines if AI fosters a brighter future or casts a long shadow of ethical quandaries. To embrace the former, we must equip ourselves with a moral compass – a comprehensive guide to developing and deploying AI with trust and responsibility at its core. This Entefy policy guide provides a practical framework for organizations dedicated to fostering ethical, trustworthy, and responsible AI.

According to version 1.0 of its AI Risk Management Framework (AI RMF), the U.S. Department of Commerce’s National Institute of Standards and Technology (NIST) views trustworthy AI systems as those sharing a number of characteristics. Trustworthy AI systems are typically “valid and reliable, safe, secure and resilient, accountable and transparent, explainable and interpretable, privacy-enhanced, and fair with harmful bias managed.” Further, validity and reliability are required criteria for trustworthiness while accountability and transparency are connected to all other characteristics. Naturally, trustworthy AI isn’t just about technology; it is intricately connected to data, organizational values, as well as the human element involved in designing, building, and managing such systems.

The following principles can help guide every step of development and usage of AI applications and systems in your organization:

1. Fairness and Non-Discrimination

Data represents the bloodline of AI. And regrettably, not all datasets are created equal. In many cases, bias in data can translate into bias in AI model behavior. Such biases can have legal or ethical implications in areas such as crime prediction, loan scoring, or job candidate assessment. Therefore, actively seek and utilize datasets that reflect the real world’s diversity and the tapestry of human experience. To promote fairness and avoid perpetuating historical inequalities, try to go beyond readily available data and invest in initiatives that collect data from underrepresented groups.

Aside from using the appropriate datasets, employing fairness techniques in algorithms can shield against hidden biases. Techniques such as counterfactual fairness or data anonymization can help neutralize biases within the algorithms themselves, ensuring everyone is treated equally by AI models regardless of their background. Although these types of techniques represent positive steps forward, they are inherently limited since recognizing perfect fairness may not be achievable.

Regular bias audits are also recommended to stay vigilant against unintended discrimination. These audits can be conducted by independent experts or specialized internal committees consisting of members who represent diverse perspectives. To be effective, such audits should include scrutinizing data sources, algorithms, and outputs, identifying potential biases, and recommending mitigation strategies.

2. Transparency and Explainability

Building trust in AI requires transparency and explainability in how these intelligent systems make decisions. In many cases, advanced models using deep learning such as large language models (LLMs) are categorized as impenetrable black boxes. Black box AI is a type of artificial intelligence system that is so complex that its decision-making or internal processes cannot be easily explained by humans, thus making it challenging to assess how the outputs were created. This lack of transparency can erode trust and lead to poor decision-making.

Promoting transparency and explainability in AI models is essential for responsible AI development. To whatever extent practicable, use interpretable models, explainable AI (XAI)— a set of tools and techniques that helps people understand and trust the output of machine learning algorithms—and modular architecture where the model is divided into smaller, more understandable components. Visual dashboards can present data trends and model behavior in easier-to-understand formats as well.

Building trust in AI requires openness and inclusivity. Start with demystifying the field by inviting diverse voices and perspectives into the conversation. This means engaging with communities most likely to be impacted by AI, fostering public dialogue about its benefits and risks, and proactively addressing concerns.

Transparency and explainability need to be part of continuous improvement to foster trust, allowing users to engage with AI as informed partners. Encourage user feedback on the clarity and effectiveness of explanations, continuously refining the efforts to make AI more understandable.

3. Privacy and Security

AI’s dependence on sensitive or personal data raises significant concerns about privacy and security. Implementing robust data protection frameworks is crucial for ensuring user privacy and safeguarding against data breaches or criminal misuse.

Machine learning models trained on private datasets can expose private information in surprising ways. It is not uncommon for AI models, including large language models (LLMs), to be trained on private datasets, which may include personally identifiable information (PII).   Research has exposed cases where “an adversary can perform a training data extraction attack to recover individual training examples by querying the language model.” 

Privacy-preserving machine learning (PPML) can be used to help in maintaining confidentiality of private and sensitive information. PPML is a collection of techniques that allow machine learning models to be trained and used without revealing the sensitive, private data that they were trained on. PPML practices, including data anonymization, differential privacy, and federated learning, among others, help protect identities and proprietary information while preserving valuable insights for analysis.

In a world where AI holds the keys to intimate and, in some cases, critical data, strong encryption and access controls are vital in safeguarding user privacy. The regulatory landscape for data protection and security has been evolving over the years but now with the latest advances in machine learning, AI-specific regulations are taking center stage globally. The effectiveness of these regulations, however, depends on enforcement mechanisms and industry self-regulation. Collaborative efforts among governments, businesses, and researchers are crucial to ensure responsible AI development that respects data privacy and security.

In addition to regulatory pressures, organizations are learning the benefits of providing clear and accessible privacy policies to their customers, employees, and other stakeholders, obtaining informed consent for data collection and usage, and offering mechanisms for users to access, rectify, or delete their data.

Beyond technical, regulatory, or policy measures, organizations need to also build a culture of privacy. This involves continual employee training on security and data privacy best practices, conducting internal audits to identify and address vulnerabilities, and proactively communicating credible threats or data breaches to stakeholders.

4. Accountability and Human Oversight

Even the best intended AI models can stray in terms of results or decisions. This is where human oversight is key, ensuring responsible AI at every stage. Clearly defined roles and responsibilities ensure that individuals are held accountable for ethical oversight, compliance, and adherence to established ethical standards throughout the AI lifecycle. Ethical review boards comprising multidisciplinary experts play a pivotal role in evaluating the ethical implications of AI projects. These boards provide invaluable insights, helping align initiatives with organizational values and responsible AI guidelines.

Continual risk assessment plus maintaining comprehensive audit trails and documentation are equally important. In assessing the risks, consider not just technical implications but also potential social, environmental, and ethical impact of AI systems.

Each organization can benefit from clear protocols for human intervention in AI decision-making. This involves establishing human-in-the-loop systems for critical decisions, setting thresholds for human intervention when certain parameters are met, or creating mechanisms for users to appeal or challenge AI decisions.

5. Safety and Reliability

To truly harness the power of AI without unleashing its potential dangers, rigorous safety and reliability measures must be included in an organization’s AI policies and practices. These safeguards should be multifaceted, ensuring not just technical accuracy but also ethical integrity.

Begin with stress testing and simulations of adversarial scenarios. Subject the AI systems to strenuous testing, including edge cases, unexpected inputs, and potential adversarial attacks. This stress testing identifies vulnerabilities and allows for implementation of safeguards. Build in fail-safe mechanisms that automatically intervene or shut down operations in case of critical errors. Consider redundancy mechanisms to maintain functionality even if individual components malfunction. In addition, actively monitor AI systems for potential issues, anomalies, or performance degradation. Conduct regular audits to assess their safety and reliability.

Safety-critical applications, such as those in healthcare, transportation, or energy demand even stricter testing protocols and fail-safe mechanisms to prevent even the most unlikely mishaps. In cases of malfunctions, the AI system should degrade to a safe state in order to prevent harm. Continuous monitoring and data collection allow for better problem detection and resolution to unforeseen issues. This necessitates building AI systems that generate logs and provide insights into their internal processes, enabling developers to identify anomalies and intervene promptly.

6. Human Agency and Control

As the field of machine intelligence evolves, the human-AI partnership grows stronger, yet more complex. Collaboration between people and intelligent machines can take many forms. AI can act as a tireless assistant, freeing up people’s time for more strategic or creative tasks. It can offer personalized recommendations or automate repetitive processes, enhancing overall efficiency. But the human element remains critical in providing context, judgment, and ethical considerations that AI, for now, still lacks.

In creating trustworthy AI, intelligent machines should empower, not replace, human agency. The goal is to design systems that augment or strengthen human capabilities, not usurp them. Design AI systems where the user has clear, accessible mechanisms to override AI decisions or opt-out of its influence. This involves providing user interfaces that have clear parameters for human control or creating AI systems that actively solicit user input before making critical decisions.

Embrace user-centered design to make AI interfaces intuitive and understandable. This provides users the ability to readily comprehend the reasoning behind AI recommendations and make informed decisions about whether to accept or override them. Ultimately, the relationship between humans and intelligent machines should be one of collaboration. AI remains a powerful tool at our service, empowering us to achieve more than we could alone while respecting our right to control and direct its actions.

7. Social and Environmental Impact

The ripples of AI extend far beyond the technical realm. It promises to power society in unprecedented ways and solve some of humanity’s longest-lasting challenges in medicine, energy, manufacturing, sustainability. The development and usage of responsible AI requires adoption of a holistic view. One that considers the potential for social and environmental implications. This requires a proactive approach, considering not only the intended benefits but also the unintended consequences of deploying AI systems.

Automation powered by AI could lead to significant job losses across various industries including manufacturing, transportation, media, legal, education, and finance. While new jobs may emerge in other sectors, the transition may be painful and disruptive for displaced workers and communities. Concerns arise about how to provide support and retraining for those affected, as well as ensuring equitable access to the new opportunities created by AI.

As part of the policies for creating trustworthy AI, sustainability serves as the North Star, guiding us towards solutions that minimize environmental and social harm while promoting responsible resource management. Properly designed, AI can server as a powerful tool for combatting climate change, optimizing resource utilization, and fostering sustainable development.

Conducting comprehensive impact assessments prior to AI deployment is imperative to gauge potential societal implications. Proactive measures to mitigate negative effects are necessary to ensure that AI advancements contribute positively to societal well-being. Remaining responsive to societal concerns and feedback is equally crucial. Organizations should demonstrate adaptability to evolving ethical standards and community needs, thereby fostering a culture of responsible AI usage.

8. Continuous Improvement

The quest for responsible AI isn’t a destination, but a continuous journey. Embrace a culture of learning and improvement, constantly seeking new tools, techniques, and insights to refine practices. Collaboration becomes the fuel that drives teams to learn from experts, partner with diverse voices, and engage in open dialogue about responsible AI development.

Sharing research findings, conducting public forums, and participating in industry initiatives are essential aspects of the trustworthy AI journey. Fostering an open and collaborative environment allows us to collectively learn from successes and failures, identify emerging challenges, and refine our understanding of responsible AI principles.

Continuous improvement doesn’t always translate to rapid advancement. Sometimes it requires taking a step back, reassessing approaches, and making necessary adjustments to ensure that the organization’s AI endeavors remain aligned with ethical principles and social responsibility.


Responsible AI development and usage at any organization requires team commitment, the willingness to embrace complex challenges, and agreement to continuous improvement as a foundational principle. By embedding these AI policy guidelines, your organization can build AI that isn’t only powerful, but also trustworthy, inclusive, and beneficial for all.

Begin your Enterprise AI journey, here, learn more about artificial general intelligence (AGI), and avoid the 5 common missteps in bringing AI projects to life.


Entefy is an enterprise AI software and hyperautomation company. Entefy’s patented, multisensory AI technology delivers on the promise of the intelligent enterprise, at unprecedented speed and scale.

Entefy products and services help organizations transform their legacy systems and business processes—everything from knowledge management to workflows, supply chain logistics, cybersecurity, data privacy, customer engagement, quality assurance, forecasting, and more. Entefy’s customers vary in size from SMEs to large global public companies across multiple industries including financial services, healthcare, retail, and manufacturing.

To leap ahead and future proof your business with Entefy’s breakthrough AI technologies, visit or contact us at

Navigating the labyrinth of fast-evolving AI regulation

The world stands at the precipice of a new era, one where artificial intelligence (AI) is poised to revolutionize every facet of human life. From healthcare to education, finance, retail, entertainment, and supply chain, AI promises to reshape our experiences in ways both subtle and profound. Yet, with such immense power comes an equally immense responsibility: to ensure that AI is developed and deployed responsibly, ethically, and in a manner that benefits all of humanity.

One of the most critical tools in this endeavor is effective regulation and guardrails designed to safeguard against individual and societal harm. Governments around the world are grappling with the complex and multifaceted challenge of crafting regulatory frameworks that foster innovation while mitigating the potential risks associated with AI and, more specifically, artificial general intelligence (AGI). This task is akin to navigating a labyrinth, where each turn presents new challenges and opportunities.

The regulatory landscape for building trustworthy AI

Building trustworthy AI involves addressing various concerns such as algorithmic biases, data privacy, transparency, accountability, and the potential societal impacts of AI. At present, multiple corporate and governmental initiatives are underway to create ethical guidelines, codes of conduct, and regulatory frameworks that promote fairness, accountability, and transparency in AI development and deployment. Collaborative efforts between industry leaders, policymakers, ethicists, and technologists aim to embed ethical considerations into the entire AI lifecycle, fostering the creation of AI systems that benefit society while respecting fundamental human values and rights. The goal is to navigate the complexities of AI advancements while upholding principles that prioritize human well-being and ethical standards.

Increasingly, large corporations and government entities alike are taking key steps aimed at protecting consumers and society at large. Leading the charge is perhaps the European Union, taking a bold step towards comprehensive regulation in the field with its EU AI Act. This legislation, the first of its kind on a global scale, establishes a risk-based approach to AI governance. By classifying AI systems into four risk tiers based on their potential impact, the Act imposes varying levels of oversight. Thus, promoting responsible development while encouraging innovation.

Across the Atlantic, the United States has taken a more decentralized approach to AI regulation. Rather than a single national law, the U.S. is relying on a patchwork of guidelines issued by different states. This fragmented approach can lead to inconsistencies and uncertainties, potentially hindering responsible AI development. In this regard, the U.S. Chamber of Commerce has raised concerns, stating that such a patchwork approach to AI regulation “threatens to slow the realization of [AI] benefits and stifle innovation, especially for small businesses that stand to benefit the most from the productivity boosts associated with AI.”

Here are several specific examples of initiatives worldwide that have emerged to help ensure ethical AI development:

  • As an early leader in this area, the European Commission drafted “The Ethics Guidelines for Trustworthy Artificial Intelligence (AI)” to promote ethical principles for organizations, developers, and society at large. On December 8, 2023, after intense negotiations among policymakers, the European Union reached agreement on its landmark AI legislation, the AI Act. This agreement clears the way for the most ambitious set of principles yet to help control the technology. “The proposed regulations would dictate the ways in which future machine learning models could be developed and distributed within the trade bloc, impacting their use in applications ranging from education to employment to healthcare.” 
  • The first comprehensive regulatory framework for AI was proposed in the EU in April 2021 and is expected to be adopted in 2024. This EU Artificial Intelligence Act represents the first official regulation in the field of AI aimed at the protection of the rights, health, and safety of its people. The new rules will categorize AI by risk levels and prohibit certain practices, with full bans on predictive policing and biometric surveillance, mandatory disclosure requirements by generative AI systems, and protection against AI systems that are used to sway elections or influence voters.
  • According to a joint paper, Germany, France, and Italy have reached agreement on the treatment of AI and how it should be regulated. “The three governments support ‘mandatory self-regulation through codes of conduct’ for so-called foundation models of AI, which are designed to produce a broad range of outputs.”
  • The Organisation for Economic Co-operation and Development (OECD) AI Principles call for accountability and responsibility in developing and deploying AI systems. These principles emphasize human-centered values and transparency, providing guidelines for policymakers, developers, and users.
  • Big tech, including Amazon, Google, Microsoft, and Meta, has agreed to meet a set of AI safeguards. President Biden recently “announced that his administration has secured voluntary commitments from seven U.S. companies meant to ensure that their AI products are safe before they release them. Some of the commitments call for third-party oversight of the workings of the next generation of AI systems.”
  • In 2023, the U.S. Department of Commerce’s National Institute of Standards and Technology (NIST) released the version 1.0 of its AI Risk Management Framework (AI RMF). “The AI RMF follows a direction from Congress for NIST to develop the framework and was produced in close collaboration with the private and public sectors. It is intended to adapt to the AI landscape as technologies continue to develop, and to be used by organizations in varying degrees and capacities so that society can benefit from AI technologies while also being protected from its potential harms.”

Key challenges in AI regulation

Delving deeper into the intricacies of AI regulation exposes a set of sophisticated challenges ahead:

Risk Assessment. Effectively managing the risks associated with AI requires robust risk assessment frameworks. Determining the level of risk posed by different AI systems is a complex task, demanding a nuanced and objective evaluation of potential harms.

Data Privacy and Security. AI’s dependence on personal or proprietary data raises significant concerns about privacy and security. Implementing robust data protection frameworks is crucial for ensuring user privacy and safeguarding against data breaches or criminal misuse.

Transparency and Explainability. Building trust in AI requires transparency and explainability in how these intelligent systems make decisions. In many cases, advanced models using deep learning such as large language models (LLMs) are categorized as black boxes. Black box AI is a type of artificial intelligence system that is so complex that its decision-making or internal processes cannot be easily explained by humans, thus making it challenging to assess how the outputs were created. Regulations mandating transparency are essential for responsible AI development and ensuring accountability for potential harm.

Algorithmic Bias and Discrimination. This is the invisible enemy in AI. Intelligent systems can inadvertently perpetuate harmful biases based on factors such as race, gender, and socioeconomic status. Addressing this issue necessitates policy and regulation that promotes fairness, transparency, and accountability in development and deployment of algorithmic models.

The case for ethical AI

The need for professional responsibility in the field of artificial intelligence cannot be understated. There are many high-profile cases of algorithms exhibiting biased behavior resulting from the data used in their training. The examples that follow add more weight to the argument that AI ethics are not just beneficial, but essential:

  • Data challenges in predictive policing. AI-powered predictive policing systems are already in use in cities including Atlanta and Los Angeles. These systems leverage historic demographic, economic, and crime data to predict specific locations where crime is likely to occur. However, the ethical challenges of these systems became clear in a study of one popular crime prediction tool. The predictive policing system developed by the Los Angeles Police Department in conjunction with university researchers, was shown to worsen the already problematic feedback loop present in policing and arrests in certain neighborhoods. An attorney from the Electronic Frontier Foundation said, “‘if predictive policing means some individuals are going to have more police involvement in their life, there needs to be a minimum of transparency.”’
  • Unfair credit scoring and lending. Operating on the premise that “all data is credit data,” machine learning systems are being designed across the financial services industry to determine creditworthiness using not only traditional credit data, but also social media profiles, browsing behaviors, and purchase histories. The goal on the part of a bank or other lender is to reduce risk by identifying individuals or businesses most likely to default. Research into the results of these systems has identified cases of bias such as two businesses of similar creditworthiness receiving different scores due to the neighborhood in which each business is located. According to Deloitte, the bias in AI can come from the input data, how the engineers may impact the model training, and in post-training where there is “continuous learning drift towards discrimination.
  • Biases introduced into natural language processing. Computer vision and natural language processing (NLP) are subfields of artificial intelligence that give computer systems digital eyes, ears, and voices. Keeping human bias out of those systems is proving to be challenging. One Princeton study into AI systems that leverage information found online showed that the biases people exhibit can make their way into AI algorithms via the systems’ use of Internet content. The researchers observed “that human-like semantic biases result from the application of standard machine learning to ordinary language—the same sort of language humans are exposed to every day.” This matters because other solutions and systems often use machine learning models that were trained on similar types of datasets.

    Today, large language models (LLMs), pre-trained on massive datasets with billions of parameters, are more powerful than ever and can generate new content. The positive opportunities created by LLMs and generative AI are endless but that all comes with a set of risks. These risks include discriminatory outputs, hallucinations where the LLMs generate false information, or “plausible-sounding reason, based on a process of predicting which words go together,” without actual reasoning. 
  • Limited effectiveness of health care diagnosis. There is limitless potential for AI-powered systems to improve patients’ lives using trustworthy and ethical AI. Entefy has written extensively on the topic, including the analysis of 9 paths to AI-powered affordable health care, how machine learning can outsmart coronavirus, improving the relationship between patients and doctors, and AI-enabled drug discovery and development.

    The ethical AI considerations in the health care industry emerge from the data and whether the data includes biases tied to variability in the general population’s access to and quality of health care. Data from past clinical trials, for instance, is likely to be far less diverse than the face of today’s patient population. Said one researcher, “At its core, this is not a problem with AI, but a broader problem with medical research and healthcare inequalities as a whole. But if these biases aren’t accounted for in future technological models, we will continue to build an even more uneven healthcare system than what we have today.” AI systems can reflect and perpetuate existing societal biases, leading to unfair outcomes for certain groups of people. This is particularly true for disadvantaged populations, who may receive inaccurate or inadequate care due to biased algorithmic predictions.
  • Impaired judgement in the criminal justice system. AI is performing a number of tasks for courts such as supporting judges in bail hearings and sentencing. One study of algorithmic risk assessment in criminal sentencing revealed the need for removing bias from some of their systems. Examining the risk scores of more than 7,000 people arrested in Broward County, Florida, the study concluded that the system was not only inaccurate but plagued with biases. For example, it was only 20% accurate in predicting future violent crimes and twice as likely to inaccurately flag African American defendants as likely to commit future crimes. Yet these systems contribute to sentencing and parole decisions. And in cases where policing is more active in some communities than others, biases may exist in the underlying data. “An algorithm trained on this data would pick up on these biases within the criminal justice system, recognize it as a pattern, and produce biased decisions based on that data.”

The road ahead for AI regulation will be paved by collaborative efforts towards responsible AI. As we navigate the road ahead, the landscape is expected to continue evolving rapidly. We can anticipate a global surge in the development and implementation of national and regional AI regulations, increased focus on advanced risk management and mitigation strategies, and continued collaboration on the development of international standards and best practices for responsible AI governance.


The effective regulation of AI is not simply a technical challenge; it is a call to action for all stakeholders, including governments, businesses, researchers, and individuals. By engaging in open dialogue, adopting responsible development practices, and actively participating in the regulatory process, we can collectively foster a future where AI serves as a force for good. A force that protects consumers, empowers innovation, and creates a more equitable and prosperous world for all.

To learn more, be sure to read Entefy’s guide to essential AI terms and our previous article about AI ethics and ways to ensure trustworthy AI for your organization.


Entefy is an enterprise AI software and hyperautomation company. Entefy’s patented, multisensory AI technology delivers on the promise of the intelligent enterprise, at unprecedented speed and scale.

Entefy products and services help organizations transform their legacy systems and business processes—everything from knowledge management to workflows, supply chain logistics, cybersecurity, data privacy, customer engagement, quality assurance, forecasting, and more. Entefy’s customers vary in size from SMEs to large global public companies across multiple industries including financial services, healthcare, retail, and manufacturing. 

To leap ahead and future proof your business with Entefy’s breakthrough AI technologies, visit or contact us at

AI Glossary: The definitive guide to essential terms in artificial intelligence

Artificial intelligence (AI) is the simulation of human intelligence in machines. Today, AI systems can learn and adapt to new information and can perform tasks that would normally require human intelligence. Machine learning is already having an impact in diagnosing diseases, developing new drugs, designing products, and automating tasks in a wide range of industries. It is also being used to create new forms of entertainment and education. As one of the most transformative technologies in history, advanced AI holds the potential to change our way of life and power society in ways previously unimaginable.

With the current pace of change, navigating the field of AI and machine intelligence can be daunting. To understand AI and its implications, it is important to have a basic understanding of the key terms and concepts. AI education and training can give you and your organization an edge. To this end, our team at Entefy has written this AI glossary to provide you with a comprehensive overview of practical AI terms. This glossary is intended for a broad audience, including students, professionals, and tech enthusiasts who are interested in the rapidly evolving world of machine intelligence.

We encourage you to bookmark this page for quick reference in the future.


Activation function. A mathematical function in a neural network that defines the output of a node given one or more inputs from the previous layer. Also see weight.

Algorithm. A procedure or formula, often mathematical, that defines a sequence of operations to solve a problem or class of problems.

Agent (also, software agent). A piece of software that can autonomously perform tasks for a user or other program(s).

AIOps. A set of practices and tools that use artificial intelligence capabilities to automate and improve IT operations tasks.

Annotation. In ML, the process of adding labels, descriptions, or other metadata information to raw data to make it more informative and useful for training machine learning models. Annotations can be performed manually or automatically. Also see labeling and pseudo-labeling.

Anomaly detection. The process of identifying instances of an observation that are unusual or deviate significantly from the general trend of data. Also see outlier detection.

Artificial general intelligence (AGI) (also, strong AI). The term used to describe a machine’s intelligence functionality that matches human cognitive capabilities across multiple domains. Often characterized by self-improvement mechanisms and generalization rather than specific training to perform in narrow domains.

Artificial intelligence (AI). The umbrella term for computer systems that can interpret, analyze, and learn from data in ways similar to human cognition.

Artificial neural network (ANN) (also, neural network). A specific machine learning technique that is inspired by the neural connections of the human brain. The intelligence comes from the ability to analyze countless data inputs to discover context and meaning.

Artificial superintelligence (ASI). The term used to describe a machine’s intelligence that is well beyond human intelligence and ability, in virtually every aspect.

Attention mechanism. A mechanism simulating cognitive attention to allow a neural network to focus dynamically on specific parts of the input in order to improve performance.

Autoencoder. An unsupervised learning technique for artificial neural network, designed to learn a compressed representation (encoding) for a set of unlabeled data, typically for the purpose of dimensionality reduction.

AutoML. The process of automating certain machine learning steps within a pipeline such as model selection, training, and tuning.


Backpropagation. A method of optimizing multilayer neural networks whereby the output of each node is calculated and the partial derivative of the error with respect to each parameter is computed in a backward pass through the graph. Also see model training.

Bagging. In ML, an ensemble technique that utilizes multiple weak learners to improve the performance of a strong learner with focus on stability and accuracy.

Bias. In ML, the phenomenon that occurs when certain elements of a dataset are more heavily weighted than others so as to skew results and model performance in a given direction.

Bigram. An n-gram containing a sequence of 2 words. Also see n-gram.

Black box AI. A type of artificial intelligence system that is so complex that its decision-making or internal processes cannot be easily explained by humans, thus making it challenging to assess how the outputs were created. Also see explainable AI (XAI).

Boosting. In ML, an ensemble technique that utilizes multiple weak learners to improve the performance of a strong learner with focus on reducing bias and variance.


Cardinality. In mathematics, a measure of the number of elements present in a set.

Categorical variable. feature representing a discrete set of possible values, typically classes, groups, or nominal categories based on some qualitative property. Also see structured data.

Centroid model. A type of classifier that computes the center of mass of each class and uses a distance metric to assign samples to classes during inference.

Chain of thought (CoT). In ML, this term refers to a series of reasoning steps that guides an AI model’s thinking process when creating high quality, complex output. Chain of thought prompting is a way to help large language models solve complex problems by breaking them down into smaller steps, guiding the LLM through the reasoning process.

Chatbot. A computer program (often designed as an AI-powered virtual agent) that provides information or takes actions in response to the user’s voice or text commands or both. Current chatbots are often deployed to provide customer service or support functions.

Class. A category of data indicated by the label of a target attribute.

Class imbalance. The quality of having a non-uniform distribution of samples grouped by target class.

Classification. The process of using a classifier to categorize data into a predicted class.

Classifier. An instance of a machine learning model trained to predict a class.

Clustering. An unsupervised machine learning process for grouping related items into subsets where objects in the same subset are more similar to one another than to those in other subsets.

Cognitive computing. A term that describes advanced AI systems that mimic the functioning of the human brain to improve decisionmaking and perform complex tasks.

Computer vision (CV). An artificial intelligence field focused on classifying and contextualizing the content of digital video and images. 

Convergence. In ML, a state in which a model’s performance is unlikely to improve with further training. This can be measured by tracking the model’s loss function, which is a measure of the model’s performance on the training data.   

Convolutional neural network (CNN). A class of neural network that utilizes multilayer perceptron, where each neuron in a hidden layer is connected to all neurons in the next layer, in conjunction with hidden layers designed only to filter input data. CNNs are most commonly applied to computer vision. 

Corpus. A collection of text data used for linguistic research or other purposes, including training of language models or text mining.

Central processing unit (CPU). As the brain of a computer, the CPU is the essential processor responsible for interpreting and executing a majority of a computer’s instructions and data processing. Also see graphics processing unit (GPU).

Cross-validation. In ML, a technique for evaluating the generalizability of a machine learning model by testing the model against one or more validation datasets.


Data augmentation. A technique to artificially increase the size and diversity of a training dataset by creating new data points from existing data. This can be done by applying various transformations to the existing data.

Data cleaning. The process of improving the quality of dataset in preparation for analytical operations by correcting, replacing, or removing dirty data (inaccurate, incomplete, corrupt, or irrelevant data).

Data preprocessing. The process of transforming or encoding raw data in preparation for analytical operations, often through re-shaping, manipulating, or dropping data.

Data curation. The process of collecting and managing data, including verification, annotation, and transformation. Also see training and dataset.

Data mining. The process of targeted discovery of information, patterns, or context within one or more data repositories.

DataOps. Management, optimization, and monitoring of data retrieval, storage, transformation, and distribution throughout the data life cycle including preparation, pipelines, and reporting.

Deepfake. Fabricated media content (such as image, video, or recording) that has been convincingly manipulated or generated using deep learning to make it appear or sound as if someone is doing or saying something they never actually did.    

Deep learning. A subfield of machine learning that uses neural networks with two or more hidden layers to train a computer to process data, recognize patterns, and make predictions.

Derived feature. A feature that is created and the value of which is set as a result of observations on a given dataset, generally as a result of classification, automated preprocessing, or sequenced model output.

Descriptive analytics. The process of examining historical data or content, typically for the purpose of reporting, explaining data, and generating new models for current or historical events. Also see predictive analytics and prescriptive analytics.

Dimensionality reduction. A data preprocessing technique to reduce the number of input features in a dataset by transforming high-dimensional data to a low-dimensional representation.

Discriminative model. A class of models most often used for classification or regression that predict labels from a set of features. Synonymous with supervised learning. Also see generative model.

Double descent. In machine learning, a phenomenon in which a model’s performance initially improves with increasing data size, model complexity, and training time, then degrades before improving again.


Ensembling. A powerful technique whereby two or more algorithms, models, or neural networks are combined in order to generate more accurate predictions.

Embedding. In ML, a mathematical structure representing discrete categorical variables as a continuous vector. Also see vectorization.

Embedding space. An n-dimensional space where features from one higher-dimensional space are mapped to a lower dimensional space in order to simplify complex data into a structure that can be used for mathematical operations. Also see dimensionality reduction.

Emergence. In ML, the phenomenon where a model develops new abilities or behaviors that are not explicitly programmed into it. Emergence can occur when a model is trained on a large and complex dataset, and the model is able to learn patterns and relationships that the programmers did not anticipate.

Enterprise AI. An umbrella term referring to artificial intelligence technologies designed to improve business processes and outcomes, typically for large organizations.

Expert System. A computer program that uses a knowledge base and an inference engine to emulate the decision-making ability of a human expert in a specific domain.

Explainable AI (XAI). A set of tools and techniques that helps people understand and trust the output of machine learning algorithms.

Extreme Gradient Boosting (XGBoost). A popularmachine learninglibrary based on gradient boosting and parallelization to combine the predictions from multiple decision trees. XGBoost can be used for a variety of tasks, including classification, regression, and ranking.


F1 Score. A measure of a test’s accuracy calculated as the harmonic mean of precision and recall.

Feature. In ML, a specific variable or measurable value that is used as input to an algorithm.

Feature engineering. The process of designing, selecting, and transforming features extracted from raw input to improve the performance of machine learning models. 

Feature vector (also, vector). In ML, a one-dimensional array of numerical values mathematically representing data points, features, or attributes in various algorithms and models.

Federated learning. A machine learning technique where the training for a model is distributed amongst multiple decentralized servers or edge devices, without the need to share training data.

Few-shot learning. A machine learning technique that allows a model to perform a task after seeing only a few examples of that task. Also see one-shot learning and zero-shot learning.

Fine-tuning. In ML, the process by which the hyperparameters of a model are adjusted to improve performance against a given dataset or target objective.

Foundation model. A large, sophisticated deep learning model pre-trained on a massive dataset (typically unlabeled), capable of performing a number of diverse tasks. Instead of training a single model for a single task, which would be difficult to scale across countless tasks, a foundation model can be trained on a broad dataset once and then used as the “foundation” or basis for training with minimal fine-tuning to create multiple task-specific models. Also see large language model.


Generative adversarial network (GAN). A class of AI algorithms whereby two neural networks compete against each other to improve capabilities and become stronger.

Generative AI (GenAI). A subset of machine learning with deep learning models that can create new, high-quality content, such as text, images, music, videos, and code. Generative AI models are trained on large datasets of existing content and learn to generate new content that is similar to the training data.

Generative model. A model capable of generating new data based on a given set of training data. Also see discriminative model.

Generative Pre-trained Transformer (GPT). A special family of models based on the transformer architecture—a type of neural network that is well-suited for processing sequential data, such as text. GPT models are pre-trained on massive datasets of unlabeled text, allowing them to learn the statistical relationships between words and phrases, and to generate text that is similar to the training data.

Graphics processing unit (GPU). A specialized microprocessor that accelerates graphics rendering and other computationally intensive tasks, such as training and running complex, large deep learning models. Also see central processing unit (CPU).

Gradient boosting. An ML technique where an ensemble of weak prediction models, such as decision trees, are trained iteratively in order to improve or output a stronger prediction model. Also see Extreme Gradient Boosting (XGBoost).

Gradient descent. An optimization algorithm that iteratively adjusts the model’s parameters to minimize the loss function by following the negative gradient (slope) of the functions. Gradient descent keeps adjusting the model’s settings until the error is very small, which means that the model has learned to predict the training data accurately.

Ground truth. Information that is known (or considered) to be true, correct, real, or empirical, usually for the purpose of training models and evaluating model performance.


Hallucination. In AI, a phenomenon wherein a model generates inaccurate or nonsensical output that is not supported by the data it was trained on.

Hidden layer. A construct within a neural network between the input and output layers which perform a given function, such as an activation function, for model training. Also see deep learning.

Hyperparameter. In ML, a parameter whose value is set prior to the learning process as opposed to other values derived by virtue of training.

Hyperparameter tuning. The process of optimizing a machine learningmodel’s performance by adjusting its hyperparameters.

Hyperplane. In ML, a decision boundary that helps classify data points from a single space into subspaces where each side of the boundary may be attributed to a different class, such as positive and negative classes. Also see support vector machine.


Inference. In ML, the process of applying a trained model to data in order to generate a model output such as a score, prediction, or classification. Also see training.

Input layer. The first layer in a neural network, acting as the beginning of a model workflow, responsible for receiving data and passing it to subsequent layers. Also see hidden layer and output layer.

Intelligent process automation (IPA). A collection of technologies, including robotic process automation (RPA) and AI, to help automate certain digital processes. Also see robotic process automation (RPA).


Jaccard index. A metric used to measure the similarity between two sets of data. It is defined as the size of the intersection of the two sets divided by the size of the union of the two sets. Jaccard index is also known as the Jaccard similarity coefficient.

Jacobian matrix. The first-order partial derivatives of a multivariable function represented as a matrix, providing critical information for optimization algorithms and sensitivity analysis.

Joins. In AI, methods to combine data from two or more data tables based on a common attribute or key. The most common types of joins include inner join, left join, right join, and full outer join.


K-means clustering. An unsupervised learning method used to cluster n observations into k clusters such that each of the n observations belongs to the nearest of the k clusters.

K-nearest neighbors (KNN). A supervised learning method for classification and regression used to estimate the likelihood that a data point is a member of a group, where the model input is defined as the k closest training examples in a data set and the output is either a class assignment (classification) or a property value (regression).

Knowledge distillation. In ML, a technique used to transfer the knowledge of a complex model, usually a deep neural network, to a simpler model with a smaller computational cost.


Labeling. In ML, the process of identifying and annotating raw data (images, text, audios, videos) with informative labels. Labels are the target variables that a supervised machine learning model is trying to predict. Also see annotation and pseudo-labeling.

Language model. An AI model which is trained to represent, understand, and generate or predict natural human language.

Large language model (LLM). A type of general-purpose language model pre-trained on massive datasets to learn the patterns of language. This training process often requires significant computational resources and optimization of billions of parameters. Once trained, LLMs can be used to perform a variety of tasks, such as generating text, translating languages, and answering questions.

Layer. In ML, a collection of neurons within a neural network which perform a specific computational function, such as an activation function, on a set of input features. Also see hidden layerinput layer, and output layer.

Logistic regression. A type of classifier that measures the relationship between one variable and one or more variables using a logistic function.

Long short-term memory (LSTM). A recurrent neural network (RNN) that maintains history in an internal memory state, utilizing feedback connections (as opposed to standard feedforward connections) to analyze and learn from entire sequences of data, not only individual data points.

Loss function. A function that measures model performance on a given task, comparing a model’s predictions to the ground truth. The loss function is typically minimized during the training process, meaning that the goal is to find the values for the model’s parameters that produce accurate predictions as represented by the lowest possible value for the loss function.


Machine learning (ML). A subset of artificial intelligence that gives machines the ability to analyze a set of data, draw conclusions about the data, and then make predictions when presented with new data without being explicitly programmed to do so.

Metadata. Information that describes or explains source data. Metadata can be used to organize, search, and manage data. Common examples include data type, format, description, name, source, size, or other automatically generated or manually entered labels. Also see annotation, labeling, and pseudo-labeling.

Meta-learning. A subfield of machine learning focused on models and methods designed to learn how to learn.

Mimi. The term used to refer to Entefy’s multimodal AI engine and technology.

MLOps. A set of practices to help streamline the process of managing, monitoring, deploying, and maintaining machine learning models.

Model training. The process of providing a dataset to a machine learning model for the purpose of improving the precision or effectiveness of the model. Also see supervised learning and unsupervised learning.

Multi-head attention. A process whereby a neural network runs multiple attention mechanisms in parallel to capture different aspects of input data.

Multimodal AI. Machine learning models that analyze and relate data processed using multiple modes or formats of learning.

Multimodal sentiment analysis. A type of sentiment analysis that considers multiple modalities, such as text, audio, and video, to predict the sentiment of a piece of content. This is in contrast to traditional sentiment analysis which only considers text data. Also see visual sentiment analysis.


N-gram. A token, often a string, containing a contiguous sequence of n words from a given data sample.

N-gram model. In NLP, a model that counts the frequency of all contiguous sequences of [1, n] tokens. Also see tokenization.

Naïve Bayes. A probabilistic classifier based on applying Bayes Rule which makes simplistic (naive) assumptions about the independence of features.

Named entity recognition (NER). An NLP model that locates and classifies elements in text into pre-defined categories.

Natural language processing (NLP). A field of computer science and artificial intelligence focused on processing and analyzing natural human language or text data.

Natural language generation (NLG). A subfield of NLP focused on generating human language text.

Natural language understanding (NLU). A specialty area within NLP focused on advanced analysis of text to extract meaning and context. 

Neural network (NN) (also, artificial neural network). A specific machine learning technique that is inspired by the neural connections of the human brain. The intelligence comes from the ability to analyze countless data inputs to discover context and meaning.

Neurosymbolic AI. A type of artificial intelligence that combines the strengths of both neural and symbolic approaches to AI to create more powerful and versatile AI systems. Neurosymbolic AI systems are typically designed to work in two stages. In the first stage, a neural network is used to learn from data and extract features from the data. In the second stage, a symbolic AI system is used to reason about the features and make decisions.


Obfuscation. A technique that involves intentional obscuring of code or data to prevent reverse engineering, tampering, or violation of intellectual property. Also see privacy-preserving machine learning (PPML).

One-shot learning. A machine learning technique that allows a model to perform a task after seeing only one example of that task. Also see few-shot learning and zero-shot learning.

Ontology. A data model that represents relationships between concepts, events, entities, or other categories. In the AI context, ontologies are often used by AI systems to analyze, share, or reuse knowledge.

Outlier detection. The process of detecting a datapoint that is unusually distant from the average expected norms within a dataset. Also see anomaly detection.

Output layer. The last layer in a neural network, acting as the end of a model workflow, responsible for delivering the final result or answer such as a score, class label, or prediction. Also see hidden layer and input layer.

Overfitting. In ML, a condition where a trained model over-conforms to training data and does not perform well on new, unseen data. Also see underfitting.


Parameter. In ML, parameters are the internal variables the model learns during the training process. In a neural network, the weights and biases are parameters. Once the model is trained, the parameters are fixed, and the model can then be used to make predictions on new data by using the parameters to compute the output of the model. The number of parameters in a machine learning model can vary depending on the type of model and the complexity of the problem being solved. For example, a simple linear regression model may only have a few parameters, while a complex deep learning model may have billions of parameters.

Parameter-Efficient Tuning Methods (PETM). Techniques used to improve the performance of a machine learning model by optimizing the hyperparameters (e.g. reducing the number of parameters required). PETM reduces computational cost, improves generalization, and improves interpretability.

Perceptron. One of the simplest artificial neurons in neural networks, acting as a binary classifier based on a linear threshold function.

Perplexity. In AI, a common metric used to evaluate language models, indicating how well the model predicts a given sample.

Precision. In ML, a measure of model accuracy computing the ratio of true positives against all true and false positives in a given class.

Predictive analytics. The process of learning from historical patterns and trends in data to generate predictions, insights, recommendations, or otherwise assess the likelihood of future outcomes. Also see descriptive analytics and prescriptive analytics.

Prescriptive analytics. The process of using data to determine potential actions or strategies based on predicted future outcomes. Also see descriptive analytics and predictive analytics.

Primary feature. A feature, the value of which is present in or derived from a dataset directly. 

Privacy-preserving machine learning (PPML). A collection of techniques that allow machine learning models to be trained and used without revealing the sensitive, private data that they were trained on. Also see obfuscation.

Prompt. A piece of text, code, or other input that is used to instruct or guide an AI model to perform a specific task, such as writing text, translating languages, generating creative content, or answering questions in informative ways. Also see large language model (LLM)generative AI, and foundation model.

Prompt design. The specialized practice of crafting optimal prompts to efficiently elicit the desired response from language models, especially LLMs.  Prompt design and prompt engineering are two closely related concepts in natural language processing (NLP).

Prompt engineering. The broader process of developing and evaluating prompts that elicit the desired response from language models, especially LLMs. Prompt design and prompt engineering are two closely related concepts in natural language processing (NLP).

Prompt tuning. An efficient technique to improve the output of a pre-trained foundation model or large language model by programmatically adjusting the prompts to perform specific tasks, without the need to retrain the model or update its parameters.

Pseudo-labeling. A semi-supervised learning technique that uses model-generated labeled data to improve the performance of a machine learning model. It works by training a model on a small set of labeled data, and then using the trained model to predict labels for the unlabeled data. The predicted labels are then used to train the model again, and this process is repeated until the model converges. Also see annotation and labeling.


Q-learning. A model-free approach to reinforcement learning that enables a model to iteratively learn and improve over time by taking the correct action. It does this by iteratively updating a Q-table (the “Q” stands for quality), which is a map of states and actions to rewards.


Random forest. An ensemble machine learning method that blends the output of multiple decision trees in order to produce improved results.

Recall. In ML, a measure of model accuracy computing the ratio of true positives guessed against all actual positives in a given class.

Recurrent neural network (RNN). A class of neural networks that is popularly used to analyze temporal data such as time series, video and speech data.

Regression. In AI, a mathematical technique to estimate the relationship between one variable and one or more other variables. Also see classification.

Regularization. In ML, a technique used to prevent overfitting in models. Regularization works by adding a penalty to the loss function of the model, which discourages the model from learning overly complex patterns, thereby making it more likely to generalize to new data.

Reinforcement learning (RL). A machine learning technique where an agent learns independently the rules of a system via trial-and-error sequences.

Robotic process automation (RPA). Business process automation that uses virtual software robots (not physical) to observe the user’s low-level or monotonous tasks performed using an application’s user interface in order to automate those tasks. Also see intelligent process automation (IPA).


Self-supervised learning. Autonomous Supervised Learning, whereby a system identifies and extracts naturally-available signal from unlabeled data through processes of self-selection.

Semi-supervised learning. A machine learning technique that fits between supervised learning (in which data used for training is labeled) and unsupervised learning (in which data used for training is unlabeled).

Sentiment analysis. In NLP, the process of identifying and extracting human opinions and attitudes from text. The same can be applied to images using visual sentiment analysis. Also see multimodal sentiment analysis.

Singularity. In AI, technological singularity is a hypothetical point in time when artificial intelligence surpasses human intelligence, leading to the rapid but uncontrollable increase in technological development.

Software agent (also, agent). A piece of software that can autonomously perform tasks for a user or other software program(s).

Strong AI. The term used to describe artificial general intelligence or a machine’s intelligence functionality that matches human cognitive capabilities across multiple domains. Often characterized by self-improvement mechanisms and generalization rather than specific training to perform in narrow domains. Also see weak AI.

Structured data. Data that has been organized using a predetermined model, often in the form of a table with values and linked relationships. Also see unstructured data.

Supervised learning. A machine learning technique that infers from training performed on labeled data. Also see unsupervised learning.

Support vector machine (SVM). A type of supervised learning model that separates data into one of two classes using various hyperplanes. 

Symbolic AI. A branch of artificial intelligence that focuses on the use of explicit symbols and rules to represent knowledge and perform reasoning. In symbolic AI, also known as Good Old-Fashioned AI (GOFAI), problems are broken down into discrete, logical components, and algorithms are designed to manipulate these symbols to solve problems. Also see neurosymbolic AI.

Synthetic data. Artificially generated data that is designed to resemble real-world data. It can be used to train machine learning models, test software, or protect privacy. Also see data augmentation.


Taxonomy. A hierarchal structured list of terms to illustrate the relationship between those terms. Also see ontology. 

Teacher-student model. A type of machine learning model where a teacher model is used to generate labels for a student model. The student model then tries to learn from these labels and improve its performance. This type of model is often used in semi-supervised learning, where a large amount of unlabeled data is available but labeling it is expensive.

Text-to-3D model. A machine learning model that can generate 3D models from text input.

Text-to-image model. A machine learning model that can generate images from text input.

Text-to-task model. A machine learning model that can convert natural language descriptions of tasks into executable instructions, such as automating workflows, generating code, or organizing data.

Text-to-text model. A machine learning model that can generate text output from text input.

Text-to-video model. A machine learning model that can generate videos from text input.

Time series. A set of data structured in spaced units of time.

TinyML. A branch of machine learning that deals with creating models that can run on very limited resources, such as embedded IoT devices.

Tokenization. In ML, a method of separating a piece of text into smaller units called tokens, representing words, characters, or subwords, also known as n-grams.

Training data. The set of data (often labeled) used to train a machine learning model.

Transfer learning. A machine learning technique where the knowledge derived from solving one problem is applied to a different (typically related) problem.

Transformer. In ML, a type of deep learning model for handling sequential data, such as natural language text, without needing to process the data in sequential order.

Tuning. The process of optimizing the hyperparameters of an AI algorithm to improve its precision or effectiveness. Also see algorithm.

Turing test. A test introduced by Alan Turing in his 1950 paper “Computing Machinery and Intelligence,” to determine whether a machine’s ability to think and communicate can match that of a human’s. The Turing test was originally named The Imitation Game.  


Underfitting. In ML, a condition where a trained model is too simple to learn the underlying structure of a more complex dataset. Also see overfitting.

Unstructured data. Data that has not been organized with a predetermined order or structure, often making it difficult for computer systems to process and analyze.

Unsupervised learning. A machine learning technique that infers from training performed on unlabeled data. Also see supervised learning.


Validation. In ML, the process by which the performance of a trained model is evaluated against a specific testing dataset which contains samples that were not included in the training dataset. Also see training.

Vector (also, feature vector). In ML, a one-dimensional array of numerical values mathematically representing data points, features, or attributes in various algorithms and models.

Vector database. A type of database that stores information as vectors or embeddings for efficient search and retrieval.

Vectorization. The process of transforming data into vectors.

Visual sentiment analysis. Analysis algorithms that typically use a combination of image-extracted features to predict the sentiment of a visual content. Also see multimodal sentiment analysis and sentiment analysis.


Weak AI. The term used to describe a narrow AI built and trained for a specific task. Also see strong AI.

Weight. In ML, a learnable parameter in nodes of a neural network, representing the importance value of a given feature, where input data is transformed (through multiplication) and the resulting value is either passed to the next layer or used as the model output.

Word Embedding. In NLP, the vectorization of words and phrases, typically for the purpose of representing language in a low-dimensional space.


XAI (explainable AI). A set of tools and techniques that helps people understand and trust the output of machine learning algorithms.

XGBoost (Extreme Gradient Boosting). A popularmachine learninglibrary based on gradient boosting and parallelization to combine the predictions from multiple decision trees. XGBoost can be used for a variety of tasks, including classification, regression, and ranking.

X-risk. In AI, a hypothetical existential threat to humanity posed by highly advanced artificial intelligence such as artificial general intelligence or artificial superintelligence.


YOLO (You Only Look Once). A real-time object detection algorithm that uses a single forward pass in a neural network to detect and localize objects in images.


Zero-shot learning. A machine learning technique that allows a model to perform a task without being explicitly trained on a dataset for that task. Also see few-shot learning and one-shot learning.


Entefy is an advanced AI software and process automation company, serving SME and large enterprise customers across diverse industries including financial services, health care, retail, and manufacturing.

2 AI winters and 1 hot AI summer

The field of artificial intelligence (AI) has its roots in the mid-20th century. The early development of AI can be traced back to the Dartmouth Conference in 1956, which is considered the birth of AI as a distinct field of research. The conference brought together a group of computer scientists who aimed to explore the potential of creating intelligent machines. Since its birth, AI’s journey has been long, eventful, and fraught with challenges. Despite early optimism about the potential of AI to match or surpass human intelligence in various domains, the field has undergone two AI winters and now, what appears to be, one hot AI summer.

In 1970, the optimism about AI and machine intelligence was so high that in an interview with Life Magazine, Marvin Minsky, one of the two founders of the MIT Computer Science and Artificial Intelligence Laboratory, predicted that within “three to eight years we will have a machine with the general intelligence of an average human being.” Although the timing of Minsky’s prophecy has been off by a long stretch, recent advances in computing systems and machine learning, including foundation models and large language models (LLMs), are creating renewed optimism in AI capabilities.

The first AI winter

AI winters refer to periods of time when public interest, funding, and progress in the field of artificial intelligence significantly decline. These periods are characterized by a loss of confidence in AI technologies’ potential and often follow periods of overhype and unrealistic expectations.

The first AI winter occurred in the 1970s, following the initial excitement that surrounded AI during the 50s and 60s. The progress did not meet the high expectations, and many AI projects failed to deliver on their promises. Funding for AI research decreased, and interest waned as the technology entered its first AI winter, facing challenges such as:

  1. Technical limitations: AI technologies had met significant technical obstacles. AI research had difficulty representing knowledge in ways that could be readily understood by machines and the R&D was exceedingly limited by the computing power available at the time. This in turn restricted the complexity and scale of learning algorithms. In addition, many AI projects were constrained by limited access to data and encountered difficulties in dealing with real-world complexity and unpredictability, making it challenging to develop effective AI systems.
  2. Overhype and unmet expectations: Early excitement and excessive optimism about AI’s potential to achieve “human-level” intelligence had led to unrealistic expectations. When AI projects faced major hurdles and failed in delivery, it led to disillusionment and a loss of confidence in the technology.
  3. Constraints in funding and other resources: As the initial enthusiasm for AI subsided and results were slow to materialize, funding for AI research plunged. Government agencies and private investors became more cautious about investing in AI projects, leading to resource constraints for institutions and researchers alike.
  4. Lack of practical applications: AI technologies had yet to find widespread practical applications. Without tangible benefits to businesses, consumers, or government entities, interest in the field faded.
  5. Criticism from the scientific community: Some members of the scientific community expressed skepticism about the approach and progress of AI research. Critics argued that the foundational principles and techniques of AI were too limited to achieve human-level intelligence, and they were doubtful about the possibility of creating truly intelligent machines.

    For example, “after a 1966 report by the Automatic Language Processing Advisory Committee (ALPAC) of the National Academy of Sciences/National Research Council, which saw little merit in pursuing [machine translation], public-sector support for practical MT in the United States evaporated…” The report indicated that since fully automatic high-quality machine translation was impossible, the technology could never replace human translators. They said that funds would therefore be better spent on basic linguistic research and machine aids for translators.”

    Another example is the Lighthill report, published in 1973, which was a critique of AI research in the UK. It criticized AI’s failure to achieve its ambitious goals and concluded that AI offered no unique solution that is not achievable in other scientific disciplines. The report highlighted the “combinatorial explosion” problem, suggesting that many AI algorithms were only suitable for solving simplified problems. As a result, AI research in the UK faced a significant setback, with reduced funding and dismantled projects.

Despite the multiple challenges facing AI at the time, the first AI winter lasted for less than a decade (est. 1974-1980). The end of the first AI winter was marked by a resurgence of interest and progress in the field. Several factors contributed to this revival in the early 1980s. These included new advancements in machine learning and neural networks, expert systems that utilized large knowledge bases and rules to solve specific problems, increased computing power, focused research areas, commercialization of and successes in practical AI applications, funding by the Defense Advanced Research Projects Agency (DARPA), and growth in data. The combined impact of these factors led to a reinvigorated interest in AI, signaling the end of the first AI winter. For a few years, AI research continued to progress and new opportunities for AI applications emerged across various industries. When this revival period ended in the late 1980s, the second AI winter set in.

The second AI winter

It is generally understood that the second AI winter started in 1987 and ended in the early 1990s. Similar to the first period of stagnation and decline in AI interest and activity in the mid 70s, the second AI winter was caused by hype cycles and outsized expectations that AI research could not meet. Once again, government agencies and the private sector became cautious about technical limitations, doubts in ROI (Return on Investment), and criticism from the scientific community. At the time, the expert systems proved to be brittle, rigid, and difficult to maintain. Scaling AI systems proved challenging as well since they needed to be able to learn from large data sets which was computationally expensive. This made it difficult to deploy AI systems in real-world applications.

Symbolic AI’s reliance on explicit, rule-based representations was criticized for being inflexible and unable to handle the complexity and ambiguity of everyday data and knowledge. Symbolic AI, also known as Good Old-Fashioned AI (GOFAI), is an approach to AI that focuses on the use of explicit symbols and rules to represent knowledge and perform reasoning. In symbolic AI, problems are broken down into discrete, logical components, and algorithms are designed to manipulate these symbols to solve problems. The fundamental idea behind symbolic AI is to create systems that can mimic human-like cognitive processes, such as logical reasoning, problem-solving, and decision-making. These systems use symbolic representations to capture knowledge about the world and apply rules for manipulating that knowledge to arrive at conclusions or make predictions.

While symbolic AI was a dominant approach in the early days of AI research, it faced certain limitations, such as difficulties in handling uncertainty, scalability issues, and challenges in learning from data. These limitations, along with the emergence of alternative AI paradigms, such as machine learning and neural networks, contributed to the rise of other AI approaches and the decline of symbolic AI in some areas. That said, symbolic AI continues to be used in specific applications and as part of hybrid AI systems that combine various techniques to address complex problems.

During the second AI winter, the AI fervor was not only waning in the United States, but globally as well. In 1992, the government of Japan “formally closed the books on its vaunted ‘Fifth Generation’ computer project.” The decade-long research initiative was aimed at developing “a new world of computers that could solve problems with human-style reasoning.” After spending $400+ million, most of the ambitious goals did not materialize and the effort ended with little impact on the computer market.

The second AI winter was a setback for the field of AI, but it also led to some important progress. AI researchers learned from their past experiments, and they developed new approaches to improve output and performance. These new approaches, such as deep learning, have led to a resurgence of interest in AI research ever since.

While the second AI winter was beginning to thaw in the early 1990s, other technical, political, and societal transformations were taking hold in parallel. The Cold War had come to an end with the collapse of the Soviet Union in December 1991 and, from the ashes, 15 newly independent nations rose. In the same year, Tim Berners-Lee introduced the World Wide Web—the Internet we know today. And in 1992, the Mosaic browser (later Netscape) was born. Users could “see words and pictures on the same page for the first time and to navigate using scrollbars and clickable links.” In the same decade, Linux, today’s most commonly known and used open source operating system, was launched. The first SMS text message was sent. was founded, disrupting the retail industry starting with books. Palm’s PDA (Personal Digital Assistant) devices gave us a glimpse of what’s to come in mobile computing. Google introduced its now dominant web search engine. And, yes, blogs became a thing, opening up opportunities for people to publish anything online.

As a result of all these changes, the world became more connected than ever, and data grew exponentially larger and richer. In the ensuing decade, the data explosion, key advancements to computing hardware, and breakthroughs in machine learning research would all prove to be perfect enablers of the next generation of AI. The AI spring which followed the second AI winter indicated renewed optimism for intelligent machines that can help radically improve our lives and solve major challenges.  

Today’s hot AI summer

Fast forward to present where the application of AI is more than just a thought exercise. The latest upgrades to AI include a set of technologies which are preparing us for artificial general intelligence (AGI). Also known as strong AI, AGI is used to describe a machine’s intelligence functionality that matches human cognitive capabilities across multiple domains and modalities. AGI is often characterized and judged by self-improvement mechanisms and generalizability rather than specific training to perform in narrow domains. Also see Entefy’s multisensory Mimi AI Engine.

In the context of AI, an AI summer characterizes a period of heightened interest and abundant funding directed towards the development and implementation of AI technology. There’s an AI gold rush underway and the early prospectors and settlers are migrating to the field en masse—investors, the world’s largest corporations, entrepreneurs, innovators, researchers, and tech enthusiasts. Despite certain limitations, the prevailing sentiment within the industry suggests that we are currently living through an AI summer, and here are a few reasons why:

  1. The new class of generative AI: Generative AI is a subset of deep learning, using expansive artificial neural networks to “generate” new data based on what it has learned from its training data. Traditional AI systems are designed to extract insights from data, make predictions, or perform other analytical tasks. In contrast, generative AI systems are designed to create new content based on diverse inputs. By applying different learning techniques to massive data sets such as unsupervised, semi-supervised, or self-supervised learning, today’s generative AI systems can produce realistic and compelling new content based on the patterns and distributions the models learn during training. The user input for and the content generated by these models can be in the form of text, images, audio, code, 3D models, or other data.

    Underpinning generative AI are the new foundation models that have the potential to transform not only our digital world but society at large. Foundation models are sophisticated deep learning models trained on massive amounts of data (typically unlabeled), capable of performing a number of diverse tasks. Instead of training a single model for a single task (which would be difficult to scale across countless tasks), a foundation model can be trained on a broad data set once and then used as the “foundation” or basis for training with minimal fine-tuning to create multiple task-specific models. In this way, foundation models can be adapted to a wide variety of use cases.

    Mainstream examples of foundation models include large language models (LLMs). Some of the better-known LLMs include GPT-4, PaLM 2, Claude, and LLaMA. LLMs are trained on massive data sets typically consisting of text and programing code. The term “large” in LLM refers to both the size of the training data and the number of model hyperparameters. As with most foundation models, the training process for an LLM can be computationally intensive and expensive. It can take weeks or months to train a sophisticated LLM on large data sets. However, once an LLM is trained they can solve common language problems. For example, generating essays, poems, code, scripts, musical pieces, emails, and summaries. LLMs can also be used to translate languages or answer questions.
  2. Better computing: In general, AI is computationally intensive, especially during model training. “It took the combination of Yann LeCun’s work in convolutional neural nets, Geoff Hinton’s back-propagation and Stochastic Gradient Descent approach to training, and Andrew Ng’s large-scale use of GPUs to accelerate deep neural networks (DNNs) to ignite the big bang of modern AI — deep learning.” And deep learning models fueling the current AI boom, with millions and billions of parameters, require robust computing power. Lots of it.

    Fortunately, technical advances in chips and cloud computing are all improving the way we can all access and use computing power. The enhancements to the microchips over the years have fueled scientific and business progress in every industry. Computing power has increased “one trillion-fold” from 1956 to 2015. “The computer that navigated the Apollo missions to the moon was about twice as powerful as a Nintendo console. It had 32.768 bits of Random Access Memory (RAM) and 589.824 bits of Read Only Memory (ROM). A modern smartphone has around 100,000 times as much processing power, with about a million times more RAM and seven million times more ROM. Chips enable applications such as virtual reality and on-device artificial intelligence (AI) as well as gains in data transfer such as 5G connectivity, and they’re also behind algorithms such as those used in deep learning.”
  3. Better access to more quality data: The amount of data available to train AI models has grown significantly in recent years. This is due to the growth of the Internet, the proliferation of smart devices and sensors, as well as the development of new data collection techniques. According to IDC, from 2022 to 2026, data created, replicated, and consumed annually is expected to more than double in size. “The Enterprise DataSphere will grow more than twice as fast as the Consumer DataSphere over the next five years, putting even more pressure on enterprise organizations to manage and protect the world’s data while creating opportunities to activate data for business and societal benefits.”
  4. Practical applications across virtually all industries: Over the past decade, AI has been powering a number of applications and services we use every day. From customer service to financial trading, advertising, commerce, drug discovery, patient care, supply chain, and legal assistance, AI and automation have helped us gain efficiency. And that was before the recent introduction of today’s new class of generative AI. The latest generative AI applications can help users take advantage of human-level writing, coding, and designing capabilities. With the newly available tools, marketers can create content like never before; software engineers can document code functionality (in half the time), write new code (in nearly half the time), or refactor code “in nearly two-thirds the time“; artists can enhance or modify their work by incorporating generative elements, opening up new avenues for artistic expression and creativity; those engaged in data science and machine learning can solve critical data issues with synthetic data creation; and general knowledge workers can take advantage of machine writing and analytics to create presentations or reports. 

    These few examples only scratch the surface of practical use cases to boost productivity with AI.
  5. Broad public interest and adoption: In business and technology, AI has been making headlines across the board, and for good reason. Significant increases to model performance, availability, and applicability, have brought AI to the forefront of public dialogue. And this renewed interest is not purely academic. New generative AI models and services are setting user adoption records. For example, ChatGPT reached its first 1 million registered users within the first 5 days of release, growing to an estimated 100 million users after only 2 months, at the time making it the fastest-growing user base of any software application in history. For context, the previous adoption king, TikTok took approximately 9 months to reach the same 100 million user milestone, and it took 30 months for Instagram to do the same.

    In the coming years, commercial adoption of AI technology is expected to grow with “significant impact across all industry sectors.” This will allow businesses additional opportunities to increase operational efficiency and boost productivity that is “likely to materialize when the technology is applied across knowledge workers’ activities.” Early reports suggest that the total economic impact of AI on the global economy could range between $17.1 trillion to $25.6 trillion.


The history of AI has been marked by cycles of enthusiasm and challenges, with periods of remarkable progress followed by setbacks, namely two AI winters. The current AI summer stands out as a transformative phase, fueled by breakthroughs in deep learning, better access to data, powerful computing, significant new investment in the space, and widespread public interest.

AI applications have expanded across industries, revolutionizing sectors such as healthcare, finance, transportation, retail, and manufacturing. The responsible development and deployment of new AI technologies, guided by transparent and ethical principles, are vital to ensure that the potential benefits of AI are captured in a manner that mitigates risks. The resurgence of AI in recent years is a testament to the resilience of the field. The new capabilities are getting us closer to realizing the potential of intelligent machines, ushering in a new wave of productivity.

For more about AI and the future of machine intelligence, be sure to read Entefy’s important AI terms for professionals and tech enthusiasts, the 18 valuable skills needed to ensure success in enterprise AI initiatives, and the Holy Grail of AI, artificial general intelligence.


Entefy is an advanced AI software and process automation company, serving SME and large enterprise customers across diverse industries including financial services, health care, retail, and manufacturing.

Entefy co-founders speak on the fast rise of multisensory AI and automation

Entefy sibling co-founders, Alston Ghafourifar and Brienne Ghafourifar, spoke with Yvette Walker, Executive Producer and Host of ABC News & Talk – Southern California Business Report, on the rise of the fast evolving domain of multisensory AI and intelligent process automation. The 1-hour segment included focused discussion on AI applications that help optimize business operations across a number of industries and use cases, including those relevant to supply chains, healthcare, and financial services. Watch the full interview.

In an in-depth conversation about business operational efficiency and optimization across virtually every corner of an organization, the Ghafourifars shared their views on the importance of digital transformation and the intelligence enterprise in volatile times like these. Alston mentioned that AI and machine learning is all about “augmenting human power, allowing machines to do jobs that were traditionally reserved for individuals. And many of those jobs aren’t things that humans should spend their time doing or they’re not things we’re perfectly suited to doing.”

For example, organizations grappling with supply chain challenges are benefitting from machine intelligence and automation. This includes AI-powered applications dealing with everything from sourcing to manufacturing, costing, logistics, and inventory management. “Research shows that many people are now interested in how things are made…all the way from raw materials to how they end up on the shelf or how they’re delivered,” Brienne said. This is ultimately impacting consumer behavior and purchase decisions for many.

This episode also included discussions on blockchain and cryptocurrencies. In particular how AI-powered smart contracts and forecasting models can help reduce risks and increase potential return on investment (ROI).   

When it comes to AI, every organization is at a different point in their journey and readiness. Entefy’s technology platform, in particular, its multisensory AI capabilities, are designed to address digital complexity and information overload. To make this a reality, Entefy and its business customers are envisioning new ways to improve business processes, knowledge management system, as well as workflow and process automation.

For more about AI and the future of machine intelligence, be sure to read our previous blogs on the 18 valuable skills needed to ensure success in enterprise AI initiatives.


Entefy is an advanced AI software and process automation company, serving SME and large enterprise customers across diverse industries including financial services, health care, retail, and manufacturing.

Key technology trends that will dominate 2023

At the beginning of each new year, we conduct research and reflect on how digital technology will continue to evolve over the course of the year ahead. This past year, we observed clear trends in a number of technical areas including machine learning, hyperautomation, Web3, metaverse, and blockchain.

Broadly speaking, artificial intelligence (AI) continues its growth in influence, adoption, and in spending. As compared to five years ago, business adoption of AI globally in 2022 has increased by 250%. At enterprises, AI capabilities such as computer vision, natural language understanding, and virtual agents are being embedded across a number of departments and functional areas for a variety of use cases.

As the world looks past the global pandemic with an eager eye on the horizon, enterprises and governments are adjusting to the new reality with the help of innovation and advanced technologies. 2023 will be a crucial year for organizations interested in gaining and maintaining their competitive edge.

Here are key digital trends at the forefront in 2023 and beyond.

Artificial Intelligence

The artificial intelligence market has been on a rapid growth trajectory for several years, with forecasted market size of a whopping $1.8 trillion by 2030. Ever since the launch of AI as a field at the seminal Dartmouth Conference in 1956, the story of AI evolution has been centered around helping data scientists and the enterprise better understand existing data to improve operations at scale. This type of machine intelligence falls under the category of analytical AI which can outpace our human ability to analyze data. However, analytical AI falls short where humans shine—creativity. Thanks to new advances in the field, machines are becoming increasingly capable of creative tasks as well. This has led to a fast-emerging category referred to as generative AI.

Leveraging a number of advanced deep learning models including General Adversarial Networks (GANs) and transformers, generative AI helps produce a multitude of digital content. From writing to creating new music, images, software code, and more, generative AI is poised to play a strong role across any number of industries.

In the past, there has been fear over the prospect of AI potentially overtaking human capital and ultimately putting people out of work. That said, similar to other disruptive technologies in the past, analytical AI and generative AI can both complement human-produced output, all in the interest of saving time, effectively bettering lives for us all. 


Hyperautomation, as predicted last year, continues to be one of the most promising technologies as it affords enterprises several advantages to ultimately improve operations and save valuable time and resources.

Think of hyperautomation as intelligent automation and orchestration of multiple processes and tools. It’s key in replacing many rote, low-level human tasks with process and workflow automation, creating a more fluid and nimble organization. This enables teams and organizations to adapt quickly to change and operate more efficiently. The end result is increasing the pace of innovation for the enterprise.

With hyperautomation, workflows and processes become more efficient, effectively reducing speed to market with products and services. It also reduces workforce costs while increasing employee satisfaction. Hyperautomation can help organizations establish a baseline standard for operations. When standards are defined, organized, and adhered to, it allows for continual audit readiness and overall risk reduction with those audits. This becomes a centralized source of truth for the business.

Globally, the hyperautomation market is expected to continue expanding with diverse business applications, growing from $31.4 billion in 2021 with a CAGR of 16.5% until 2030.


Not to be confused with cryptocurrencies like Bitcoin or Ethereum (which are built using blockchain technology), blockchain continues to gain adoption as both an implemented technology and a topic of strategic discussion for organizations large and small.

When implemented effectively, blockchain technology has many uses and can provide a number of valuable benefits ranging from increased transparency to accurate transaction tracking, auditability, and overall cost savings for the organization. Blockchain technology appears even more promising when considering its implications as a solution to improve trust between parties such as customers, vendors, producers, wholesalers, regulators, and more. Therefore, enterprise investments in blockchain technology to improve business processes “may offer significantly higher returns for each investment dollar spent than most traditional internal investments.”

It is estimated that blockchain’s potential economic impact on global GDP could exceed $1.75 trillion and enhance 40 million jobs by the end of this decade. Much of this impact is likely to stem from the improved trust which blockchain technology can enable between businesses relating to trade, transaction management, and more. Further, perhaps due in large part to its open and decentralized nature, this economic impact is unlikely to be focused solely on specific regions or dominated by specific nations, with the U.S., Asia (led by China), and Europe all poised to contribute.

Financial services is well positioned to continue as the blockchain leader among industries, as it has in recent years. However, as blockchain technology becomes more prolific, the enterprise landscape is likely to witness major adoption in other areas including healthcare, logistics, legal, media, supply chain, automotive, voting systems, gaming, agriculture, and more. In fact, in 2021 alone, funding for blockchain startups rose by 713% YoY.

With regulatory challenges surrounding digital assets, along with abundant news focused on high profile collapses of cryptocurrency exchanges such as FTX, enterprises are still approaching blockchain adoption cautiously. However, like any technology, blockchain is fast redefining itself as more than just the tech behind Bitcoin. It is emerging as a dominant focus area for digitally-minded organizations.


Web3 (or Web 3.0) represents the next generation of web technologies. It’s decentralized and permissionless, allowing everyone to access, create, and own digital assets globally, without any intermediary. Effectively, this levels the global playing field and brings us back to the original promise of the World Wide Web.

Web 1.0 was the original Web, dominated by static pages and content. In the early days, there were relatively few content creators and publishers. The Web was fairly limited and disorganized back then, but quickly evolved to Web 2.0 which ushered in a new era of digital interactivity and social engagement with innumerable applications. With this new Web, people were free to publish articles, share comments, and engage with others, creating the user-generated content explosion we experience today. But despite enormous success, Web 2.0 has created serious challenges. For example, user data is largely centralized, controlled, and monetized by only a few big tech monopolies.

Web3, along with the underlying blockchain technology that enables it, aims to address these challenges by decentralizing information systems, giving control of data and standards back to the community. This shift from Web 2.0 to Web 3.0 also opens doors to new business models such as those governed by decentralized autonomous organizations (DAOs) that eliminate intermediaries through secure (smart contract) automation.

Web3 has attracted large pools of capital and engineering talent, exploring new business models as the industry is estimated to increase significantly to $33.5 billion by 2030, growing at a CAGR of 44.9% from 2022 to 2030. As with any burgeoning technology disruption, early adopters will almost certainly face challenges, including unclear and evolving regulation and immature and emerging technology platforms.

In 2023 and the years ahead, policy changes are expected, impacting Web3 creators in several ways. New legislation is likely to focus on asset classification, legality and enforceability of blockchain-based contracts, capital provisioning, accountability systems, and anti-money laundering standards.

Digital Assets

From cryptocurrencies to NFTs, digital assets continue to dominate news cycles—between the public collapse of FTX (a leading cryptocurrency exchange) and dramatic and sustained drops in market value for even the largest cryptocurrencies such as Bitcoin. This market activity and awareness helps solidify digital assets as an important topic to watch in 2023.

While much of the discussion surrounding digital assets is focused on the underlying technology, blockchain, it’s the financial and regulatory considerations that are most likely to see dramatic change in the coming year. These considerations will help re-establish trust in an otherwise battered market inundated with scams, fraud, theft, hacks, and more. According to a complaint bulletin published by the Consumer Financial Protection Bureau (CFPB), a U.S. government agency, of all consumer complaints submitted in the last 2 years related to crypto-assets, “the most common issue selected was fraud and scams (40%), followed by transaction issues (with 25% about the issue of ‘Other transaction problem,’ 16% about ‘Money was not available when promised,’ and 12% about ‘Other service problem’).”

Some regulators contend that the very definition of “digital assets” may need to evolve as major regulatory bodies struggle to provide clear and consistent guidance for the sector. Leading the way in digital asset regulation is the European Union. Shortly after the collapse of FTX, Mark Branson, president of BaFin (Germany’s financial market regulator), suggested that “a ‘crypto spring’ may follow what has been a ‘crypto winter’ but that the industry that emerges is likely to have more links with traditional finance, further increasing the need for regulation.” Thoughtful regulation can help establish trust in digital assets, beginning with risk management. An important method of rebuilding this trust is providing third-party validation of assets, liabilities, controls, and solvency. This is expected to be an important area of growth in 2023.

Organizations and governments are also looking to traditional (non-blockchain) financial services, standards, and providers to design security, controls, governance, and transparency with regard to digital assets. The early rise of central bank digital currencies (CBDCs), which are non-crypto assets, in countries such as Venezuela, Russia, China, and Iran, has pushed these countries to “adopt restrictive stances or outright bans on other cryptos.” Conversely, adoption of CBDCs by G7 countries has been “deliberately cautious […] particularly with regards to retail CBDCs used by the public.” Going forward, policymakers will continue to wrestle with these types of important regulatory considerations. They will assess how existing rules can be applied more effectively and design new rules to reinforce trust in the market without hamstringing innovation in the space.

Aside from regulatory changes, digital assets are likely to continue growing through demand driven by merchants and social media. For example, as some social media companies gradually release their own payment platforms for end users, the potential for new types of digital assets is only expanding—from processing cryptocurrency transactions to offering unique value through identity tokens, NFTs, and more. Additionally, with anticipated growth of metaverse and Web3, digital assets are poised to play an outsized role in developing consumer trust, generating tangible value, and promoting engagement in these new technical arenas.


The metaverse is often seen as the next natural evolution of the Internet. Despite challenges facing the metaverse—from technology to user experience—the space is benefiting from continued venture investments and growing attention from notable brands and celebrities. “Brands from Nike and Gucci to Snoop Dogg and TIME Magazine poured money into metaverse initiatives as a way of revolutionizing experiential brand engagement, while Meta doubled down on its Horizon Worlds experiment.”

Further, with a potential to generate up to $5 trillion in economic impact across multiple industries by 2030, the metaverse is too important to ignore. To ensure truly immersive experiences in the metaverse, a significant boost in demand is expected for a number of technical and creative services. In support of the metaverse, specialty services provided by digital designers, architects, software and machine learning engineers, to name a few, are predicted to remain in high demand in 2023 and beyond.

Use of specialized hardware such as AR (augmented reality), VR (virtual reality), MR (mixed reality) glasses and headsets is not strictly required to experience the metaverse. However, advancements in AR, VR, MR, and haptic technologies can dramatically improve the experience, which is likely to drive further engagement and adoption of the metaverse.

Part of the projected growth in the metaverse is born out of necessity. During the early days of the global pandemic, the majority of companies suddenly had to become comfortable with virtual collaboration and meetings via web conferencing. Virtual workspaces in the metaverse are a natural next step, driven by brands and corporations. By 2026, one out of four of us is expected to spend at least an hour each day in a metaverse. Further, nearly one third of organizations in the world are projected to have some sort of product or service ready for the metaverse.

In the metaverse, virtual spaces can be used for onboarding, brainstorming on-the-fly, and ongoing education and training. With high quality and immersive 3D experiences, the metaverse can allow for design and engineering collaborations—for example, the ability to realize concept designs in a virtual world before any expense is spent on real-world production. For brands, the metaverse will also see spaces created (almost as digital lounges) where enthusiastic evangelists can hang out and be part of virtual experiences, develop their own brand-centric communities, and participate in brand contests.

Autonomous Cyber

The explosion of digital networks, devices, applications, software frameworks, libraries, and the rise of consumer demand for everything digital has brought the topic of cybersecurity to the fore. Cybersecurity attacks in recent years have risen at an alarming rate. There are too many examples of significant exploits to list in this article, however, the Center for Strategic & International Studies (CSIS) provides a summary list of notable cybersecurity incidents where losses by incident exceed $1 million. CSIS’s list illustrates the rise in significant cyberattacks, ranging from DDoS attacks to ransomware targeting healthcare databases, banking networks, military communications, and much more.

Cybersecurity is a major concern for the private sector with 88% of boards considering it a business risk. On a broader scale, cybersecurity is considered a matter of national and international security. In 2018, the U.S. Department of Homeland Security established Cybersecurity & Infrastructure Security Agency (CISA) to lead “the Nation’s strategic and unified work to strengthen the security, resilience, and workforce of the cyber ecosystem to protect critical services and American way of life.” As more “sophisticated cyber actors and nation-states […] are developing capabilities to disrupt, destroy, or threaten the delivery of essential services,” the critical risks of cybercrime are felt not just by government agencies, but also by commercial organizations which provide those essential services.

The fast-evolving field of autonomous cyber is a direct response to this growing problem. As computer networks and physical infrastructure grow more vulnerable to cyber threats, the ability for traditional approaches to cybersecurity are proving insufficient. Today’s enterprises require smart, agile systems to monitor and act on the compounding volume of cyber activity across an ever-growing number of undefended areas (in codebases, systems, or processes). Further, as cybercriminals become more sophisticated with modern tools and resources, organizations are feeling the added pressure to fortify their digital and physical security operations quickly in order to ensure resiliency.

Autonomous cyber uses AI and machine intelligence to continuously monitor an enterprise’s digital activity. Advanced machine learning models can help identify abnormal activity which may present risk to an organization’s IT systems or any aspect of business operations. Coupled with the notion of CEOs creating corporate resilience to protect themselves from cybercrimes, among other threats, the atmosphere seems welcoming for embracing more complex security technologies such as AI-powered autonomous cyber. Autonomous cyber uses intelligent orchestration and complex automation to create alerts and take mitigation steps at speed and scale.


At Entefy we are passionate about breakthrough computing that can save people time so that they live and work better. The 24/7 demand for products, services, and personalized experiences is forcing businesses to optimize and, in many cases, reinvent the way they operate to ensure resiliency and growth.

AI and automation are at the core of many of the technologies covered in this article. To learn more, be sure to read our previous articles on key AI terms, beginning the enterprise AI journey, and the 18 skills needed to bring AI applications to life.


Entefy is an advanced AI software and process automation company, serving SME and large enterprise customers across diverse industries including financial services, health care, retail, and manufacturing.

Entefy hits key milestones with new patents issued by the USPTO

Entefy continues to expand its IP portfolio with another set of awarded patents by the USPTO

PALO ALTO, Calif. December 20, 2022. The U.S. Patent and Trademark Office (USPTO) issues four new patents to Entefy Inc. These new patents, along with the other USPTO awards cited in a previous announcement in Q3 of this year, further strengthen Entefy’s growing intellectual property portfolio in artificial intelligence (AI), information retrieval, and automation.

“We operate in highly competitive business and technical environments,” said Entefy’s CEO, Alston Ghafourifar. “Our team is passionate about machine intelligence and how it can positively impact our society. We’re focused on innovation that can help people live and work better using intelligent systems and automation.”

Patent No. 11,494,421 for “System and Method of Encrypted Information Retrieval Through a Context-Aware AI Engine” expands Entefy’s IP holdings in the field of privacy and security cognizant data discovery. This disclosure relates to performing dynamic, server-side search on encrypted data without decryption. This pioneering technology preserves security and data privacy of client-side encryption for content owners, while still providing highly relevant server-side AI-enabled search results.

Patent No. 11,496,426 for “Apparatus and Method for Context-Driven Determination of Optimal Cross-Protocol Communication Delivery” expands Entefy’s patent portfolio of AI-enabled universal communication and collaboration technology. This disclosure relates to optimizing the delivery method of communications through contextual understanding. This innovation simplifies user communication with intelligent delivery across multiple services, devices, and protocols while preserving privacy.

Patent No. 11,494,204 for “Mixed-Grained Detection and Analysis of User Life Events for Context Understanding” strengthens Entefy’s IP portfolio of intelligent personal assistant technology. This disclosure relates to correlation of complex, interrelated clusters of contexts for use by an intelligent interactive interface (“intelli-interface”) to perform actions on behalf of users. This invention saves users time in digesting and acting on the ever-expanding volume of information through contextually relevant automation.

The USPTO has also awarded Entefy Patent No. 11,409,576 for “Dynamic Distribution of a Workload Processing Pipeline on a Computing Infrastructure”, expands Entefy’s patent portfolio of AI-enabled virtual resource management. This disclosure relates to use of AI models to configure, schedule, and monitor workflows across virtualized computing resources. This innovation improves resource use and efficiency of software as a service (SaaS), platform as a service (PaaS), and infrastructure as a service (IaaS) technology.

Entefy’s proprietary technology is built on key inventions in a number of rapidly changing digital domains including machine learning, multisensory AI, dynamic encryption, universal search, and others. “Entefy AI and automation technology is designed to help businesses become more resilient and operate much more efficiently,” said Ghafourifar. “Our team is excited about innovation in this space and creating distinct competitive differentiation for Entefy and our customers.”


Entefy is an advanced AI software and process automation company, serving SME and large enterprise customers across diverse industries including financial services, health care, retail, and manufacturing.

The smarter grid: AI fuels transformation in the energy sector

When it comes to energy, much of the world is at a critical turning point. There is a noticeable momentum in favor of renewable energy, with scientists and the United Nations warning the world’s leaders that carbon emissions “must plummet by half by 2030 to avoid the worst outcomes.” Given the variability and the lack of predictability in certain energy sources such as solar and wind, there is a growing need for a new kind of “smart” electrical grid that can reliably and efficiently manage flows of electricity from renewable sources with those generated by conventional power plants. Smart grids leverage advanced technologies to ultimately reduce costs and reduce waste. Creating such grids requires massive amounts of data, predictive analytics, and automation which is ideally powered by artificial intelligence (AI).

Many of us might not be aware that the top 10 hottest years since human beings began keeping records have all occurred in the last decade or so. In June 2022, “among global stations with a record of at least 40 years, 50 set (not just tied) an all-time heat or cold record.” During the same month, Arctic sea ice extent “was the 10th-lowest in the 44-year satellite record” and Antarctic sea ice extent “was the lowest for any June on record, beating out 2019.”

In the United States, the costs related to major weather and climate disasters sustained since 1980 (the events that have caused damage with costs in excess of $1 billion) have already totaled more than $2.295 trillion. So far this year alone, the United States has witnessed 15 such costly events.

NOAA National Centers for Environmental Information (NCEI) U.S. Billion-Dollar Weather and Climate Disasters (2022)., DOI: 10.25921/stkw-7w73

On a global level, the international treaty on climate change, the Paris Agreement, has called on 196 countries to prevent the planet from warming more than 2 degrees Celsius (3.6 degrees Fahrenheit) above pre-industrial levels. This treaty is a major step toward achieving net-zero carbon emissions by 2050. If this target can’t be met, the resulting financial losses could be as high as $23 trillion, which could put the global economy in a tailspin similar to the most severe economic contractions experienced in the past.   

For the U.S., the second-highest ranking country in carbon emissions (second only to China), fulfilling the Paris Agreement will require herculean effort, including a complete reimagining of the electrical grid.

The Smart Grid

The 2021 failure of the Texas grid left millions of Southerners freezing without electricity for days while the Great Blackout of 2003 shut down electricity for tens of millions on the East Coast. The Texas power grid may be independent and isolated (the only one in the contiguous 48 states). However, it’s clear that along with much of the U.S. infrastructure, power grids across the nation are aging and in desperate need of modernization with deadly power outages occurring at more than double the pace in the past 6 years over the prior 6 years. And, as each day passes, this need for infrastructure modernization becomes even more critical, not just to ensure the safety and reliability of the power network, but also to handle the accelerating demand for electricity catalyzed by clean energy initiatives and electric vehicle (EV) adoption

Historically, electric grids have lacked dynamism and flexibility. They are instead built to achieve very large economies of scale with the main sources of electricity coming from non-renewables such as coal or natural gas. These traditional sources of energy are costly to operate efficiently and consistently. Further, increased regulation over time and higher costs have promoted vertically-integrated energy monopolies which have stifled innovation.

Renewable sources of energy have their own set of challenges. Solar and wind, for example, don’t provide the same level of consistency afforded by traditional large-scale energy production. This means that despite the fact large renewable energy plants (as well as individual homeowners or businesses) can produce power with their solar panels and wind turbines, they are not able to produce such power at a steady, controllable rate. For some, the erratic rate of energy production can sometimes translate into excess production which can in turn be fed into the electrical grid or stored for later use, utilizing on-site batteries. All of this creates opportunities for a new type of 21st-century power grid that is modular, multidirectional, and smart.

With the future of energy looking more and more decentralized, how can energy consumers and generators monitor and monetize the flow of electricity? How does the power grid provide resiliency and flexibility to properly store excess energy while simultaneously operating at the scale necessary to satisfy the ever-growing demand for electricity without interruption? For much of the history of renewable energy, these problems have been managed with computational models based on historical weather and electricity demand patterns, modified according to demographic and economic changes. But the rise of new technologies like real-time data transmission, edge computing, Internet of Things (IoT) sensors, and even AI-powered control systems has enabled the design of new “smart grid” frameworks able to manage and integrate diverse energy sources for a revolutionary new approach to power generation, storage, distribution, pricing, and regulation.

Energy Demand Forecasting

To avoid the dangers of blackouts, a smart power grid anticipates when electricity demand in a given region may be at peak levels. This allows control systems to ensure that adequate electricity is fed to the grid from available sources which can satisfy the demand. If need be, a fossil fuel plant can be activated in advance of any peak demand with sufficient notice. However, what happens when the power grid needs to react swiftly to unexpected disruptions (such as extreme weather changes, natural disasters, or dramatic reductions in capacity) which traditional forecast models are unable to predict?

With AI and process automation, systems can achieve accurate, minute-by-minute forecasts of energy demand through the real-time transmission of energy use data from smart meters. “In 2021, U.S. electric utilities had about 111 million advanced (smart) metering infrastructure (AMI) installations, equal to about 69% of total electric meters installations. Residential customers accounted for about 88% of total AMI installations, and about 69% of total residential electric meters were AMI meters.” AI control and monitoring systems can utilize the wealth of information available via smart meters to identify patterns in the data that coincide with high demand and automatically trigger the allocation of different energy sources to help prevent overload. Similarly, machine learning can also be used to monitor the stability of electricity flow using the rich data generated by IoT sensors throughout the grid.

Automated Energy Trading

When AI triggers the grid to draw on additional energy sources to prevent a blackout, those energy sources can command a price for their resources. From wind and solar farms to traditional fossil fuel power plants, as well as individual homeowners with solar panels or power generators, energy trading is growing in volume and complexity. With legacy electrical grids and traditional meters, there was relatively little communication between utilities and energy customers. Electricity flowed unidirectionally from plants to power lines to customers, with meters read manually without granular insights into energy usage. Smart grids, microgrids, broad-based deployment of AMI meters, and modern battery storage technologies have changed all of that. In combination, these advances now enable bidirectional communication and transactions between producers, consumers, and prosumers (consumers who also produce and store energy, often using solar panels and batteries) allowing for better energy management and stimulating energy trading.

Today, AI and machine learning is used to analyze signals from diverse data sources in order to provide more accurate pricing and energy predictions. Taking advantage of this, allows energy traders the opportunity to better balance their position. AI has also emerged as a catalyst for new business models including “community ownership models, peer-to-peer energy trading, energy services models, and online payment models.” These new models give energy producers the improved ability to sell their electricity to the grid at a competitive market price.

The volatility in energy supply due to network constraints and strong fluctuations in energy demand make forecasting of energy and prices challenging. Automated trading platforms use AI and deep neural networks to monitor the market for the best energy sales opportunities and conduct high frequency trading without the need for human input. This is similar to how Entefy’s Mimi AI Crypto Market Intelligence provides real-time price predictions and 24/7 automated trading functionality in the highly volatile cryptocurrency market.

According to McKinsey, the “deployment of advanced analytics can lead to a reduction of more than 30 percent in costs by optimizing bidding of renewable assets in day-ahead and intraday markets.” As a secondary benefit, fully automated processes leveraging advance AI resulted in “a productivity gain in intraday trading of 90 percent.” The key observation was that these additional forms of automation transformed the role of energy traders from “taking decisions and executing trades toward focusing on market analysis and improving advanced analytics models on a continual basis.”

Intelligent Grid Security

One of the most important features of a truly “smart” grid is its ability to fend off cyberattacks. Cybersecurity is a growing area of concern in many industries and sectors but cyberattacks in the energy sector are of special interest. The ransomware attack on Colonial Pipeline, the United States’ largest pipeline for refined oil products, was a reminder to us all how vulnerable we actually are. On May 7, 2021 a group of cybercriminals from Russia hacked Colonial Pipeline’s “IT network, crippling fuel deliveries up and down the East Coast.” As a result of that cyberattack, within just an hour of the ransomware attack, the management made the decision to shut down the company’s 5,500 miles of pipelines. In effect, this event let to a shutdown of approximately 50% of the East Coast’s gasoline and jet fuel supply.

Preventing cyberattacks will be especially important as more smart grid operations come online and massive amounts of data are generated, transmitted, and stored across multiple IT systems and cloud infrastructures. As covered in one of Entefy’s previous blogs, modern countermeasures include machine learning, hyperautomation, and zero trust systems. AI-powered tools are capable of processing complex and voluminous data infinitely faster (and often more accurately) than human security analysts ever could. With sufficient compute infrastructure, artificial intelligence can be put to work 24/7/365 to analyze trends and learn new patterns that can identify vulnerabilities before it’s too late. More sophisticated approaches such as Autonomous Cyber can go further to use the power of AI not only to continuously monitor data flow or network activity but also to take actions as needed (including alerts, notifications, blocking access, or shutting off services) to help mitigate risks. Active identification of issues in security systems and enabling automatic “self-healing” processes by instantly patching or fixing errors can dramatically reduce remediation time for organizations.


The energy sector is facing massive changes and moving toward a future that operates on clean, renewable energy, decentralization and distributed energy production, as well as intelligent systems that optimize energy production, consumption, and infrastructure. Smart grids are a big step forward in that future. They leverage machine learning, intelligent process automation, and other advanced technologies to deliver efficiency to the market, cut costs, and reduce waste.

For more about AI and the future of machine intelligence, be sure to read our previous blogs on the 18 valuable skills needed to ensure success in enterprise AI initiatives, the rise in cybersecurity threats, and AI ethics.


Entefy is an advanced AI software and process automation company, serving SME and large enterprise customers across diverse industries including financial services, health care, retail, and manufacturing.

AI and intelligent automation coming to a supply chain near you

For years, many Americans have consumed a vast, world-class variety of products on a day-to-day basis without necessarily thinking too much about the complex supply chains involved in these products.

All of that changed dramatically with the onset of the COVID-19 pandemic. Suddenly, critical factories and assembly lines came to a halt around the world while shipping containers were stranded at ports due to restrictions from lockdowns and travel bans. For the first time, a majority of the population became painfully aware of the incredible power the global supply chain has on everyday life. More than two years after the pandemic’s start, we continue to feel the impact of these supply chain disruptions via rising costs, product and inventory shortages, delivery delays, and dramatic shifts in consumer behavior. Add a growing number of extreme weather and geopolitical events to the mix and witness the global flow of goods face greater instability.

The best way to address this increasingly complex and volatile trade environment is to work smarter, not harder. And, that is where artificial intelligence (AI) and automation come in.

Supply chain professionals around the world are rapidly adopting AI and machine learning technologies to help them adapt and thrive in the midst of emergent uncertainty. According to IDC, automation using AI will drive “50% of all supply chain forecasts” by 2023. Further, by the end of this year, “chronic worker shortages will prompt 75% of supply chain organizations to prioritize automation investments resulting in productivity improvements of 10%.”

Here are a few examples of how AI and automation are already transforming the supply chain:

Predictive analytics for delivery and warehousing

One of the great ways AI is impacting the supply chain is via advanced analysis of data generated by the Internet of Things (IoT). Thanks to GPS (Global Positioning System) tracking, IoT sensors placed on raw materials, shipping containers, products, as well as communications throughout the logistics pipeline, providers now have real-time visibility into the movement of their goods. Better yet, advancements in machine learning over the last decade have allowed AI to use voluminous, complex data from shipments and IoT devices to accurately predict the arrival of packages and even monitor the storage conditions of products and raw materials.

AI-enabled dynamic routing is helping companies optimize delivery and logistics leading to significant cost savings and efficiency. UPS, for instance, has employed its on-road integrated optimization and navigation (ORION) system “to reduce the number of miles traveled, fuel consumed, and CO2 emitted.” And the impact is tangible. “According to UPS, ORION has saved it around 100 million miles per year since its inception, which translates into 10 million gallons of fuel and 100,000 metric tons of carbon dioxide emissions. Moreover, reducing just one mile each day per driver can save the company up to $50 million annually.”

Use of predictive analytics in warehousing is gaining adoption as well. Many consumers have personally experienced or observed AI leveraging vast amounts of digital data in order to make predictions. We may see this when an e-commerce service tries to anticipate a consumer’s next purchase using historical data. What we sometimes forget is that all this consumer data is tremendously helpful to warehouses and brick-and-mortar stores, which use machine learning and predictive analytics to determine which items will sell in a given week and where those items should be stocked.

Machine learning for streamlining processes and reducing human error

Machine learning also helps with the more technical and time-consuming aspects of moving products around the world. According to a recently published joint report by the World Trade Organization and the World Customs Organization, customs authorities have already embraced advanced technologies such as AI and machine learning. “Around half use some combination of big data, data analytics, artificial intelligence and machine learning. Those who do not currently use them have plans to do so in the future. The majority of customs authorities see clear benefits from advanced technologies, in particular with regard to risk management and profiling, fraud detection and ensuring greater compliance.”

AI has been able to automate the completion of lengthy international customs and clearance forms, providing accurate tallies of products in shipments, correcting mistakes made by human officials regarding country of origin or gross net weight, and providing a customs entry number when a shipment finally arrives at its destination country. Machine learning can also parse out shipment invoices and correctly load the data into a company’s accounting system.

Additional specific use cases include AI systems and applications that help automate supply chain processes and workflows in smart ways. For example, Entefy’s Mimi Ai Digital Supply Chain Hub which is used to optimize logistics, costing, sourcing, and strengthen supplier relationships. With such AI-powered tools, users can automate repetitive manual procedures, reduce errors, track key metrics in real-time (and in one place), and monitor the global supply chain across geographies, business units, vendors, suppliers, and products. Moreover, users can quickly uncover valuable business-critical insights from complex data streams that would be otherwise unfeasible via traditional manual analysis.

Automated decisions and demand forecasting

Producing, packaging, delivering, and selling products today are meeting new sets of customer expectations and behaviors. Simultaneously, the business climate is growing more complex and more volatile, making competition uniquely challenging in times like these. These conditions are leading to digital and AI transformations at an ever-increasing number of organizations worldwide. Supply chain leaders have begun to embrace the need for modernized IT in order to ensure resiliency and efficiency going forward. And this has led to a big shift toward intelligent systems and automation.

According to McKinsey, 80% of supply chain leaders “expect to or already use AI and machine learning” for demand, sales, and operations planning. The overarching objective is to help reduce costs and increase revenue. This is achieved by reducing reliance on human involvement, integrating analytics across the supply chain (from orders to production to demand forecasts), enabling process and workflow automation at virtually every step, and ultimately balancing inventory levels with service levels while reducing lead times from the moment of order to end delivery.

Improved quality control and waste reduction

Quality companies care about quality. For these organizations quality is important to their customers as well as their bottom line. They realize the financial and operational costs associated with poor quality and are taking additional steps to improve quality control and reduce waste.

What is the cost of poor quality? According to ASQ (American Society for Quality), a global membership society of quality professionals in more than 130 countries, costs incurred related to quality related activities “may be divided into prevention costs, appraisal costs, and internal and external failure costs.” It is estimated that quality-related costs are “as high as 15-20% of sales revenue, some going as high as 40% of total operations. A general rule of thumb is that costs of poor quality in a thriving company will be about 10-15% of operations. Effective quality improvement programs can reduce this substantially, thus making a direct contribution to profits.”

The reality is that today with advanced technologies, including AI and machine learning, waste and yield losses due to quality problems are in many ways avoidable. By taking a data-driven approach to the problem, analyzing mountains of data from multiple sources (such as sensors, plant logs, defect reports, service tickets, etc.), companies can better pinpoint hidden issues, quickly identify root causes, and predict (and avoid) vulnerabilities to reduce critical defects and waste. In combination, with AI and automation, companies can increase yields while reducing the overall cost of quality.


The recent disruptions to global supply chains have tangibly impacted the way we produce and consume products—from major delays in product shipments to escalating costs to inventory shortages to the shifts in consumer behavior. Companies have been thrown off balance and as a result have accelerated their adoption of advanced technologies to gain efficiency and resiliency. Using multi-modal machine learning and intelligent process automation to achieve supply chain optimization is something we take pride in at Entefy. Be sure to read our previous blogs about how to begin your Enterprise AI journey and the 18 valuable skills needed to ensure success in enterprise AI initiatives.


Entefy is an advanced AI software and process automation company, serving SME and large enterprise customers across diverse industries including financial services, health care, retail, and manufacturing.

Entefy awarded four new patents by USPTO

PALO ALTO, Calif. July 12, 2022. The U.S. Patent and Trademark Office (USPTO) issues four new patents to Entefy Inc. Entefy’s newly granted patents represent a range of novel software and intelligent systems that serve to expand and strengthen the company’s core technology in communication, search, security, data privacy, and multimodal AI.

“These newly issued patents highlight our team’s effort and commitment in delivering value through innovation,” said Entefy’s CEO, Alston Ghafourifar. “As a company and as a team, we’ve been focused on developing technologies that can power society through AI and automation that is secure and preserves data privacy.”

Patent No. 11,366,838 for “system and method of context-based predictive content tagging for encrypted data” expands Entefy’s patent portfolio of AI-enabled universal communication and collaboration technology. This patent relates to multi-format, multi-protocol message threading by stitching together related communications in a manner that is seamless from the user’s perspective. This innovation saves users time in identifying, digesting, and acting on the ever-expanding volume of information available through modern data networks while maintaining data privacy.

Patent No. 11,366,839 for “system and method of dynamic, encrypted searching with model driven contextual correlation” expands Entefy’s intellectual property holdings in the field of privacy-preserving data discovery. The disclosure relates to ‘zero-knowledge’ privacy systems, where the server-side searching of user encrypted data is performed without accessing the underlying private user data. This technology preserves security and privacy of client-side encryption for content owners, providing highly relevant server-side search results via the use of content correlation, predictive analysis, and augmented semantic tag clouds.

Patent No. 11,366,849 for “system and method for unifying feature vectors in a knowledge graph” is representative of expansion of Entefy’s patent portfolio in the space of data understanding. The disclosure provides advanced AI-powered techniques for identifying conceptually related information across different data types, representing a significant leap in the evolution of data mapping and processing.

Entefy was also awarded Patent No. 11,367,068 for “decentralized blockchain for artificial intelligence-enabled skills exchanges over a network.” Expanding on Entefy’s core universal communication and multimodal machine intelligence technology, this new direction of technological development allows for AI-powered agents to learn and provide specific skills or services directly to end users or other digital agents through a blockchain-enabled marketplace. By leveraging smart contracts and other blockchain technologies, this invention lets individual products and systems communicate and share skills in an autonomous fashion with cost and resource efficiency in mind.

Entefy’s intellectual property assets span a series of domains from digital communication to artificial intelligence, dynamic encryption, enterprise search, multimodal machine intelligence and others. “As a company, we are committed to developing new technologies that can deliver unprecedented efficiencies to businesses everywhere,” said Ghafourifar. “The investment in our patent portfolio is an integral part of bringing our vision to life.”


Entefy is an advanced AI software and process automation company, serving SME and large enterprise customers across diverse industries including financial services, health care, retail, and manufacturing.