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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

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.

Blockchain

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

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, 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.

Metaverse

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.

Conclusion

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.

ABOUT ENTEFY

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.

www.entefy.com
contact@entefy.com

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.”

ABOUT ENTEFY

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.

www.entefy.com
contact@entefy.com

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.

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 21^st-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.

Conclusion

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.

ABOUT ENTEFY

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.

www.entefy.com
contact@entefy.com

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.

Conclusion

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.

ABOUT ENTEFY

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.

www.entefy.com
contact@entefy.com

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.”

ABOUT ENTEFY

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.

• www.entefy.com
• pr@entefy.com

From Mary Shelley’s Frankenstein to James Cameron’s Terminator, the possibility of scientists creating an autonomous intelligent being has long fascinated humanity—as has the potential impact of such a creation. In fact, the invention of machine intelligence with broad, humanlike capabilities, often referred to as artificial general intelligence, or AGI, is an ultimate goal of the computer science field. AGI refers to “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.”

Some well-known thinkers such as Stephen Hawking have warned that a true artificial general intelligence could quickly evolve into a “superintelligence” that would surpass and threaten the human race. However, others have argued that a superintelligence could help us solve some of the greatest problems facing humanity. Via rapid and advanced information processing and decision making, AGI can help us complete hazardous jobs, accelerate research, and eliminate monotonous tasks. As global birthrates plummet and the old outnumber the young, at the bare minimum, robots with human-level intelligence might help address the shortage of workers in critical fields such as healthcare, education, and yes, software engineering.

AI all around us

Powerful real-life demonstrations of AI, from computers that beat world chess champions to virtual assistants in our devices that speak to us and execute tasks, may lead us to think that a humanlike AGI is just around the corner. However, it’s important to note that virtually all AI implementations and use cases you experience today are examples of “narrow” or “weak” artificial intelligence—that is, artificial intelligence built and trained for specific tasks.   

Narrow forms of AI that actually work can be useful because of their ability to process information exponentially faster (and with fewer errors) than human beings ever could. However, as soon as these forms of narrow AI come upon a new situation or variation of their expert tasks, their performance falters.

To understand how ambitious the notion of creating AGI truly is, it’s helpful to reflect on the vast complexity of human intelligence.

Complex and combinatorial intelligence

Father of computer science Alan Turing offered what is famously called the Turing Test for identifying AGI. He proposed that any machine that could imitate a human being to the point of fooling another human being could pass the test.

However, MIT roboticist Rodney Brooks has outlined four key developmental stages of human intelligence that can help us measure progress in machine intelligence:

1. A two-year-old child can recognize and find applications for objects. For example, a two-year-old can see the similarity between a chair and a stool, and the possibility of sitting on both a rock and a pile of pillows. While the visual object recognition and deep learning capabilities of AI have advanced dramatically since the 1990s, most forms of current AI aren’t able to apply new actions to dissimilar objects in this manner.
2. A four-year-old child can follow the context and meaning of human language exchange through multiple situations and can enter a conversation at any time as contexts shift and speakers change. To some degree, a four-year-old can understand unspoken implications, responding to speech as needed, and can spot and use both lying and humor. In a growing number of cases, AI is able to successfully converse and use persuasion in manners similar to human beings. That said, none currently has the full capacity to consistently give factual answers about the current human-social context in conversation, let alone display all the sophistication of a four-year-old brain.
3. A six-year-old child can master a variety of complex manual tasks in order to engage autonomously in the environment. Such tasks include self-care and goal-oriented tasks such as dressing oneself and cutting paper in a specific shape. A child at this age can also manually handle younger siblings or pets with elevated sensitivity. There is hope that AI might power robotic assistants to the elderly and disabled by the end of the decade.
4. An eight-year-old child can infer and articulate the motivations and goals of other human beings by observing their behavior in a given context. They are socially aware and can identify their own motivations and goals and explain them in conversations, in addition to understanding the personal ambitions explained by their conversation partners. Eight-year-olds can implicitly understand the goals and objectives of assignments others give them without a full spoken explanation of the purpose. In 2020, MIT scientists successfully developed a machine learning algorithm that could determine human motivation and whether a human successfully achieved a desired goal through the use of artificial neural networks. This represents a promising advancement in independent machine learning.

Why the current excitement over AGI?

Artificial general intelligence could revolutionize the way we work and live, rapidly accelerating solutions to many of the most challenging problems plaguing human society—from the need to reduce carbon emissions to the need to react to and manage disruptions to global health, the economy, and other aspects of society. Some have posited that it could even liberate human beings from all forms of taxing labor, leaving us free to pursue pleasurable pursuits full time with the help of a universal basic income.

For his 2018 book, Architects of Intelligence, futurist Martin Ford surveyed 23 of the leading scientists in AI and asked them how soon they thought research could produce a genuine AGI. “Researchers guess [that] by 2099, there’s a 50 percent chance we’ll have built AGI.”

So why do news headlines make it seem as though AGI may be only a few years off? Hollywood is partly to blame for both popularizing and glamorizing super-smart machines in pseudo realistic films. Think Iron Man’s digital sidekick J.A.R.V.I.S., Samantha in the movie Her, or Ava in the movie Ex Machina. There is also the dramatic progress in machine learning that has occurred over the past decade in which a combination of advances in big data, computer vision, and speech recognition, along with the application of graphic processing units (GPUs) to algorithmic design, allowed scientists to better replicate patterns of the human brain through artificial neural networks.

Recent big digital technology trends in machine learning, hyperautomation, and the metaverse, among others, have made researchers hopeful that another important scientific discovery could help the field of AI make a giant leap forward toward complex human intelligence. As with previous revolutions in computing and software, it’s magical and inspiring to witness the journey of machine intelligence from narrow AI to AGI and the remarkable ways it can power society.

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

As Entefy enters its next phase of growth, we’re excited to publicly unveil our new branding, complete with our redesigned logo, updated visual theme, and exclusive content, all of which can be seen on our brand new website. Reflecting on the evolution of our company and services over recent years, it’s time to update our look on the outside to echo the progress we’ve made on the inside. While this new branding changes our look and feel, our mission and core values remain unchanged. 

With our new visual theme, you’ll notice how we’ve emphasized clarity, simplicity, and efficiency, beginning with the black and white primary color palette. Our suite of 2D and 3D graphic assets are all inspired by geometric shapes and principles to create coherence and harmony, especially when dealing with more complex aspects of data and machine learning. This is Entefy’s fresh visual design language offering a fresh perspective on AI, automation, and the future of machine intelligence.

Entefy’s new website provides important information about the company’s core technologies, highlighted products and services, as well as our all-in-one bundled EASI subscription.

Why Entefy? What is the Mimi AI Engine? How does the Entefy’s 5-Layer Platform work? How do Entefy AI and automation products solve problems at organizations across diverse industries? How does the EASI Subscription work and what are some customer success stories? You’ll find answers to these questions and much more on our new website. We encourage you to visit the new site and blog to learn more about Entefy and a better way to AI.

Much has happened since the early days of Entefy. Technological breakthroughs and inventions. Key product milestones. Expansion of Entefy’s technical footprint across countries and industries. And a long list of amazing people (team members, investors, partners, advisors, and customers alike) who have joined Entefy in manifesting the future of machine intelligence in support of our mission—saving people time so they can work and live better.

Interested in digital and AI transformation for your organization? Request a demo today.

Got the Entefy moxie and interested in new career opportunities? If so, we want you on our team.

In the early days of the Internet, cybersecurity wasn’t the hot topic it is now. It was more of a nuisance or an inconvenient prank. Back then, the web was still experimental and more of a side hustle for a relatively narrow, techie segment of the population. Nothing even remotely close to the bloodline it is for modern society today. Cyber threats and malware were something that could be easily mitigated with basic tools or system upgrades. But as technology grew smarter and more dependent on the Internet, it became more vulnerable. Today, cloud, mobile, IoT technologies, as well as endless applications, software frameworks, libraries, and widgets, serve as a veritable buffet of access points for hackers to exploit. As a result, cyberattacks are now significantly more widespread and can damage more than just your computer. The acceleration of digital trends due to the COVID-19 pandemic has only exacerbated the problem.

What is malware

Malware, or malicious software, is a general industry term that covers a slew of cybersecurity threats. Hackers use malware and other sophisticated techniques (including phishing and social-engineering-based attacks) to infiltrate and cause damage to your computing devices and applications, or simply steal valuable data. Malware includes common threats such as ransomware, viruses, adware, and spyware as well as, the lesser known threats, such as trojan horses, worms, or crypto jacking. Cybercriminals hack to control data and systems for money, to make political statements, or just for fun.

Today, cybersecurity represents real risk to consumers, corporations, and governments virtually everywhere. The reality is that most systems are not yet capable of identifying or protecting against all cyber threats, and those threats are only rising in volume and sophistication.

Examples of cybersecurity attacks

Cyberattacks in recent years have risen at an alarming rate. There are too many examples of significant exploits to list here. However, the Center for Strategic & International Studies (CSIS) provides a 12-month summary illustrating the noticeable surge in significant cyberattacks, ranging from DDoS attacks to ransomware targeting health care databases, banking networks, military communications, and much more.

All manner of information is at risk in these sorts of attacks, including personally identifiable information, confidential corporate data, and government secrets and classified data. For instance, in February 2022, “a U.N. report claimed that North Korea hackers stole more than $50 million between 2020 and mid-2021 from three cryptocurrency exchanges. The report also added that in 2021 that amount likely increased, as the DPRK launched 7 attacks on cryptocurrency platforms to help fund their nuclear program in the face of a significant sanctions regime.”

Another example, is an exploitation in July 2021 by Russian hackers who “exploited a vulnerability in Kaseya’s virtual systems/server administrator (VSA) software allowing them to deploy a ransomware attack on the network. The hack affected around 1,500 small and midsized businesses, with attackers asking for $70 million in payment.”

Living in a digitally-interconnected world also means growing vulnerability for physical assets. Last year, you may recall hearing about the largest American fuel pipeline, the Colonial Pipeline, being the recipient of a targeted ransomeware attack. In May 2021, “the energy company shut down the pipeline and later paid a $5 million ransom. The attack is attributed to DarkSide, a Russian speaking hacking group.” 

Cybersecurity is a matter of national (and international) security

As a newer operational component of the Department of Homeland Security, the Cybersecurity & Infrastructure Security Agency (CISA) in the U.S. “leads the national effort to understand, manage, and reduce risk to our cyber and physical infrastructure.” Their work helps “ensure a secure and resilient infrastructure for the American people.” CISA was established in 2018 and has since issued a number of emergency directives to help protect information systems. One recent example is the Emergency Directive 22-02 aimed at the Apache Log4J vulnerability which threatened the global computer network. In December 2021, CISA concluded that Log4J security vulnerability posed “an unacceptable risk to Federal Civilian Executive Branch agencies and requires emergency action.” 

In the private sector, “88% of boards now view cybersecurity as a business risk.” Historically, protection against cyber threats meant overreliance on traditional rules-based software and human monitoring efforts. Unfortunately, those traditional approaches to cybersecurity are failing the modern enterprise. This is due to several factors that frankly, in combination, have surpassed our human ability to effectively manage. These factors include the sheer volume of digital activity, the growing number of unguarded vulnerabilities in computer systems (including IoT, BYOD, and other smart devices), code bases, operational processes, the divergence of the two “technospheres” (Chinese and Western), the apparent rise in the number of cybercriminals, and the novel approaches used by hackers to stay one step ahead of their victims.

Costs of cybercrimes 

Security analysts and experts are responsible for hunting down and eliminating potential security threats. But this is tedious and often strenuous work, involving massive sets of complex data, with plenty of opportunity for false flags and threats that can go undetected.

When critical cyber breaches are found, the remediation efforts can take 205 days, on average.  The shortcomings of current cybersecurity, coupled with the dramatic rise in cyberattacks translate into significant costs. “Cybercrime costs the global economy about $445 billion every year, with the damage to business from theft of intellectual property exceeding the $160 billion loss to individuals.”

Cybercrimes can cause physical harm too. This “shifts the conversation from business disruption to physical harm with liability likely ending with the CEO.” By 2025, Gartner expects cybercriminals to “have weaponized operational technology environments successfully enough to cause human casualties.” Further, it is expected that by 2024, the security incidents related to cyber-physical systems (CPSs) will create personal liablity for 75% of CEOs. And, without taking into account “the actual value of a human life into the equation,” by 2023, CPS attacks are predicted to create a financial impact in excess of $50 billion.

Modern countermeasures include zero trust systems, artificial intelligence, and automation

The key to effective cybersecurity is working smarter, not harder. And working smarter in cybersecurity requires a march toward Zero Trust Architecture and autonomous cyber, powered by AI and intelligent automation. Modernizing systems with the zero trust security paradigm and machine intelligence represents powerful countermeasures to cyberattacks.

The White House describes ‘Zero Trust Architecture’ in the following way:

“A security model, a set of system design principles, and a coordinated cybersecurity and system management strategy based on an acknowledgement that threats exist both inside and outside traditional network boundaries.  The Zero Trust security model eliminates implicit trust in any one element, node, or service and instead requires continuous verification of the operational picture via real-time information from multiple sources to determine access and other system responses.  In essence, a Zero Trust Architecture allows users full access but only to the bare minimum they need to perform their jobs.  If a device is compromised, zero trust can ensure that the damage is contained.  The Zero Trust Architecture security model assumes that a breach is inevitable or has likely already occurred, so it constantly limits access to only what is needed and looks for anomalous or malicious activity.  Zero Trust Architecture embeds comprehensive security monitoring; granular risk-based access controls; and system security automation in a coordinated manner throughout all aspects of the infrastructure in order to focus on protecting data in real-time within a dynamic threat environment.  This data-centric security model allows the concept of least-privileged access to be applied for every access decision, where the answers to the questions of who, what, when, where, and how are critical for appropriately allowing or denying access to resources based on the combination of sever.”

AI’s most significant capability is robust, lightning fast data analysis that can learn in much greater volume and in less time than human security analysts ever could. Further, with the right compute infrastructure, artificial intelligence can be at work around the clock without fatigue, analyzing trends and learning new patterns. As a security measure, AI has already been implemented in some small ways such as the ability to scan and process biometrics in mainstream smartphones or advanced analysis of vulnerability databases. But, as advanced technology adoption accelerates, autonomous cyber is likely to provide the highest level of protection against cyberattacks. It will add resiliency to critical infrastructure and global computer networks via sophisticated algorithms and intelligently orchestrated automation that can not only better detect threats but can also take corrective actions to mitigate risks. Actively spotting errors in security systems and automatic “self-healing” by patching them in real time, significantly reduces remediation time.

Conclusion

Accelerating digital trends and cyber threats have elevated cybersecurity to a priority topic for corporations and governments alike. Technology and business leaders are recognizing the limitations of their organizations’ legacy systems and processes. There is growing community consciousness about the notion that, in light of the ever-evolving threat vectors, static and rules-based approaches are destined to fail. Augmenting cybersecurity systems with AI and automation can mitigate risks and speed up an otherwise time-consuming and costly process by identifying breaches in security before the damage becomes too widespread.

Is your enterprise prepared for what’s next in cybersecurity, artificial intelligence, and automation? Begin your enterprise AI Journey and read about the 18 skills needed to materialize your AI initiative, from ideation to production implementation.

Today, artificial intelligence (AI) is among the top technologies transforming industries and disrupting software across the globe. Organizations are quickly realizing the incredible value that AI can add in process and workflow automation, workforce productivity, cost reduction, energy and waste optimization, customer engagement, and much more.

AI and machine learning rely on a special blend of science and engineering to create intelligence for machines—preparing data, choosing algorithms, training models, tuning parameters, testing performance, creating workflows, managing databases, developing software, and more. Building models and architecting thoughtful software orchestration are central to work in this field and continual experimentation is simply par for the course. However, this is only part of the AI journey.

At Entefy, we’re obsessed with machine intelligence and its potential to change lives for the better. Given our work in advanced AI and intelligent process automation over the years and various inventions in the field, we’ve had the good fortune of working with amazing people to build systems that bring unprecedented efficiency to operations.

So, what does it take to make it all work and work well? What are the proficiencies and competencies required to bring useful and scalable AI applications to life? Below are 18 valuable skills organizations need in order to successfully materialize each AI project from ideation to production implementation.

18 Skills needed for enterprise AI

Architecture: Design and specification of software subsystems, supporting technologies, and associated orchestration services to ensure scalable and performant delivery.

Infrastructure Engineering: Implementation and maintenance of physical and virtual resources, networks, and controls that support the flow, storage, and analysis of data.

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

Machine Learning Science: Research and development of machine learning models including designing, training, validation, and testing of algorithmic models.

Machine Learning Engineering: Development and software optimization of machine learning models for scalable deployment.

DevOps: Building, packaging, releasing, configuring, and monitoring of software to streamline the development process.

Backend Engineering: Development of server-side programs and services including implementation of core application logic, data storage, and APIs.

Frontend Engineering: Development of client-side applications and user interfaces such as those present in websites, desktop software, and mobile applications.

Security Engineering: Implementation and management of policies and technologies that protect software systems from threats.

Quality Assurance: Quality and performance testing that validates the business logic and ensures the proper product function.

Release Management: Planning, scheduling, automating, and managing the testing and deployment of software releases throughout a product lifecycle.

UI Design: Wireframing, illustrations, typography, image, and color specifications that help visually bring user interfaces to life. 

UX Design: Specification of the interaction patterns, flows, features, and interface behaviors that enhance accessibility, usability, and overall experience of the user interaction.  

Project Management: Coordination of human resources, resolution of dependencies, and procurement of tools to meet project goals, budget, and delivery timeline.

Product Management: Scoping user, business, and market requirements to define product features and manage plans for achieving business goals.

Technical Writing and Documentation: Authoring solution and software specifications, usage documentation, technical blogs, and data sheets. 

Compliance and Legal Operations: Creation and monitoring of policies and procedures to ensure a solution’s adherence to corporate and governmental regulations through development and deployment cycles, including IP rights, data handling, and export controls. 

Business Leadership: Strategic decision making, risk assessment, budgeting, staffing, vendor sourcing, and coordination of resources all in service of the core business and solution objectives.

Conclusion

It takes a village to nurture an AI project. Whether you have people within your team or rely on external support, the resources are available to bring your AI initiatives to life. Begin your enterprise AI journey here. And be sure to check out our other resources to help you shorten your project implementation time and avoid common AI missteps.

Cut through the noise with this list of 106 useful AI terms curated by Entefy ML scientists and engineers

Unless you live on a deserted island, you are likely to have heard about artificial intelligence (AI) and ways it continues to rapidly gain traction. AI has evolved from an academic topic into a rich field of practical applications for businesses and consumers alike. And, like any advanced technical field, it has its own lexicon of key terms and phrases. However, without deeper AI training and education, it can be quite challenging to stay abreast of the rapid changes taking place within the field.

So, to help demystify artificial intelligence and its many sub-components, our team has assembled this list of useful terms for anyone interested in practical uses of AI and machine learning. This list includes some of the most frequently-used terms as well as some which may not be used as often, but are important in understanding foundational AI concepts.

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

A

Activation function. A 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.

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.

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.

B

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.

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.

C

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

Categorical variable. A 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.

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. 

Convolutional neural network (CNN). A class of neural network that utilizes multilayer perceptrons, 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. 

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.

D

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.

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. Also see deep neural network.

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.

E

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.

F

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.

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.

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.

G

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

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

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.

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.

H

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.

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.

I

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).

K

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.

L

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 layer, input 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.

M

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.

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.

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

N

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

Naive Bayes. A probabilistic classifier based on applying Bayes Rule which makes strong (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 understanding (NLU). A specialty area within Natural Language Processing 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.

O

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.

P

Precision. In machine learning, 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. 

R

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

Recall. In machine learning, 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.

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).

S

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).

Strong AI (also, AGI). 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. 

T

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.

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.

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.

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.

U

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.

V

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.

Vectorization. The process of transforming data into vector representation using numbers.

W

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.