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In the early days of the Internet, being hacked wasn’t usually newsworthy. It was more of a nuisance or an inconvenient prank. Threats and malware were something that could be easily mitigated with basic tools or system upgrades. But as technology became smarter and more dependent on the Internet, it also became more vulnerable. Today, cloud and mobile technologies serve as a veritable buffet of access points for hackers to exploit, compared to the slimmer pickings of a decade or so ago. As a result, cyberattacks are now significantly more widespread, and can damage more than just a computer. Being hacked nowadays is much more than a mere annoyance. What started out historically as simple pranks now has the muscle to cripple hospitals, banks, power grids, and even governments. But where hacking has gotten more sophisticated, so has cybersecurity with the help of advanced machine learning.

Cybersecurity is the top concern of an economic, social, and political world that depends almost entirely on the Internet. But most systems aren’t yet capable of catching all the threats, which are growing more every day at an incredible rate—an average of 350,000 new varieties per day. Within the past two years, there have been a surge of significant cyberattacks, ranging from DDoS attacks to ransomware infections, targeting hospital patient databases, banking networks, and military communications. All manner of information is at risk in these sorts of attacks, including personal health and banking information, classified data, and government services. The city of Atlanta recently spent over $2.6 million recovering from a ransomware attack that crippled the city’s online services. A major search engine paid out $50 million in a settlement after a massive customer database breach. So it’s easy to glean that hacking has moved on from inconvenient to downright costly.

Security analysts and experts are responsible for hunting down and eliminating potential security threats. But this is tedious and often strenuous work. It involves massive sets of complex data, with plenty of opportunity for false flags and threats that can go undetected. And when breaches are found, the time it takes for a fix to be built and implemented varies, depending on industry, between 100-245 days. This is more than enough time for a hacker to cause serious and costly damage. The shortcomings of current cybersecurity, coupled with the dramatic rise in cyberattacks will mean that by 2021, 3.5 million cybersecurity jobs will be needed, and cyberattacks will cost an estimated $6 trillion per year. But because burnout is so common in the cybersecurity industry, it’s speculated that most of those jobs won’t be or remain filled.

The key to effective cybersecurity is to work smarter, not harder. Supplementing cybersecurity systems with AI can be an intelligent countermeasure to cyberattacks. AI as a security measure has already been implemented in some small ways in current technology. Its ability to scan and process biometrics in real time has already been implemented in mainstream smartphones. Large tech firms have already begun to use AI protection in their networks and cloud operations.

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. It can be trained by white hat hackers who develop malware for security firms to recognize malicious patterns and behaviors in software before conventional antivirus programs and firewalls can learn to identify them. Natural language processing can, by scanning news reports and articles for cyberattacks, prepare a system for incoming threats. Artificial intelligence can be at work around the clock without fatigue, analyzing trends and learning new patterns. Maybe most important, AI can actively spot errors in security systems and “self-heal” by patching them in real time, cutting back significantly on remediation time.

Cyberattacks may now be newsworthy for the damages they have caused but implementing AI into cybersecurity systems can greatly diminish the risk. Machine learning can speed up an otherwise time-consuming and costly process by identifying breaches in security before the damage becomes too widespread.

Alexandria Ocasio-Cortez is no stranger to making headlines. She made quite a few after her election to Congress in 2018, namely as a woman, a millennial, and a democratic socialist who had just upended a ten-term incumbent to secure a seat in one of the highest branches of government. And in 2019, she shows no signs of slowing down, particularly after her comments at SXSW about automation and the future of the American economy. She is not the first to remark on AI’s place in the world, but she is among the more high-profile politicians. Her comments, and the subsequent public response, brought AI to the American political platform more pointedly than before. It’s clear that automation is going to become a hot-button topic in the future of politics, not just in America, but around the globe, with opinions as divided as politics itself.

In recent years, AI has been making its way into politics worldwide in ways both blatant and subtle. In the Tama city area of Tokyo, Japan, Mitchido Matsuda ran a “fair and balanced” campaign for mayor that garnered 4,000 votes. That may not sound particularly impressive on its own, but is notable considering that Mitchido-san is a robot. On a larger scale, in Russia, an AI named Alisa not only managed to secure a nomination for president, she garnered more than 25,000 votes. She ultimately lost to Putin, but her nomination and the subsequent base of support highlight growing confidence in AI systems in governance and politics.

So far, AI has been primarily used to support the campaigns of flesh-and-blood politicians and machine learning algorithms have been instrumental in targeted political advertising and Internet-based opinion polls. But the appeal of AI politicians seems to be in how they can compensate for traits that often cost support for human politicians. AI has unending stamina, can quickly react to new events, and multitask in ways previously unimaginable. But is that enough to make an AI system a worthy policymaker and a viable threat to human politicians?

Ocasio-Cortez has called for the public to embrace automation in ways that free up our time to create and live as we choose. This was her response to concerns raised by some of her constituents and perhaps much of the country: will AI take over jobs? This concern exemplifies a powerful dichotomy when it comes to public opinion of AI. A recent study in Europe has uncovered that while the public is still fearful of what automation could do to the job market, “one in four Europeans would prefer artificial intelligence to make important decisions about the running of their country.” This may be in response to widespread frustration with government. However, what seems to be gaining ground is the belief that AI’s logic-based framework may be better suited to policymaking. Although Alisa and Mitchido-san didn’t win their political races, they’ve opened our eyes to new opportunities for AI in politics.

Of all the “F” words that raise our collective blood pressure, none does so quite as effectively as “fraud.” It’s a crime that predates currency and, alongside society and technology, has only gotten more sophisticated with time. The increase of online banking and ecommerce has only exacerbated the opportunities for fraud. It has become a pervasive and costly problem with depth and complexity that makes it almost impossible to prevent. Fortunately, with artificial intelligence and its potent data intelligence capabilities, early detection of fraud is becoming a reality.

Needless to say, wherever there’s money, there’s usually a system to monitor its activity and check for fraud. These types of systems often track and gauge transaction activities, spending habits, or react to reported cases of fraud by those impacted. There’s already a certain degree of automation integrated in anti-fraud systems, but overall, manual human reviews remain the primary line of defense. This is a challenge because manual reviews of volumous financial data are error-prone, repetitive, and time consuming, not to mention costly due to wages and ongoing mandatory training of staff. In fact, according to a North American business survey, “manual review staff account for the largest single slice of [the] fraud management budget.”

Historically, fraud detection has been focused on discovering fraud rather than preventing it. This practice often results in higher rates of false positives or falsely-flagged transactions, which can erode or even destroy consumer trust. Merchants and banks lose out too on revenue as well as customers who, as a result of false flags, are hesitant to use their services or trust in their ability to protect data. In financial services, the technology already deployed to mitigate fraud leaves much to be desired. The traditional paradigm for fraud detection lies in customer history, where rule-based automated systems react to certain preset parameters. This can be a challenge because “false positives occur regularly with traditional rule-based anti-fraud measures, where the system flags anything that falls outside a given set of parameters.” Anti-fraud measures based purely on rules are devoid of human sensibility which is often essential to the understanding and mitigation of fraud. 

What sets machine learning systems apart is their ability to learn from massively diverse sets of data points, rather than merely focusing on customer history or a set of predetermined parameters. This way, sophisticated algorithmic models can build a more comprehensive profile for each customer and provide the right context to their purchasing history. Financial service juggernauts have already begun utilizing deep learning in their fraud detection systems, and by doing so, one company was able to cut down their number of false positives in half.

The implementation of AI in anti-fraud processes doesn’t entirely eliminate the need for the human touch. The human-AI feedback cycle necessitates a strong collaboration between people and machines. Many AI systems remain dependent on human input, especially in its early phases, and for integrating diversity into its models and understanding of data. Highly skilled analysts retain the ability to think like fraudsters and understand the complexity of human emotions that can affect how and why fraud is committed. In return, AI can help process vast, complex sets of data in a fraction of the time and do so around the clock.

These sorts of advantages are exactly what the financial industry has been looking for, especially in the wake of notorious financial fraud cases making headlines around the world. We’ve previously discussed how advanced AI can help streamline compliance processes, but at a business level, it provides even more. Leveraging the power of AI is a smart way to combat fraud and give peace of mind to concerned customers who are tired of hearing the “F” word.

Type in “Will AI…” into a search engine, and the first suggestion you’ll likely see is “Will AI take over?” The second? “Will AI take over jobs?” So it’s easy to glean what the public’s biggest concern is as far as AI. But even though AI is quickly becoming a technological juggernaut, this type of concern isn’t entirely well-founded. After all, the job market will do as it has always done when faced with new technology – it will adapt, it will evolve, and it will ultimately thrive in spite of our fears.

The job market has always been dynamic, not static, with obsolete jobs often replaced with better jobs to reflect the change in times. And AI isn’t about to alter any of that. Jobs evolve and change with every generation, and panic is often the first response to this phenomenon. Popular perception is that AI technology will displace humans in their jobs, but this is the same perception of every major technological milestone that historically was believed to do the same.

It’s important to remember that a job is not a singular activity, but rather a collection of different tasks. When automation is proposed, it’s immediately assumed that the entire job will be at risk of automation rather than just one of the tasks. Additionally, such assumptions typically disregard the potential for job creation. Even the Oxford paper claiming that up to 47% of US employment is at risk of automation cautions that predicting technological progress and the job creation that comes with it is difficult. And historically speaking, automation of certain trades actually created more jobs and lowered the price of goods that became easier to produce.

A chief example is Henry Ford’s introduction of the moving-chassis assembly line to the automotive industry. At the time, this too was met with strong skepticism. But the newly-automated system reduced the production of Model Ts from 12 hours per car to merely 2.5 hours. This made the cars less expensive to make, which in turn made it more affordable for the average worker to purchase. Meanwhile, more jobs, at highly competitive wages, were now available at Ford’s factory for workers who could maintain the new machinery. These two outcomes effectively gave birth to a thriving American middle class. The continued success of Henry Ford and his employees depended on the automotive industry’s brave decision to embrace new technology instead of avoiding it.

Following Oxford’s earlier paper, McKinsey conducted a new study in 2017 which delved deeper into historical context and the nuance of the job market. By taking into account the fact that a job is a collection of tasks rather than just one, the study determined that less than 5% of jobs are at risk of being fully automated, rather than the more daunting 47% estimated in Oxford’s study. Automating a job by and large refers to the automation of some tasks.

AI is meant to augment human capabilities, not usurp them. Machine learning already shows tremendous promise in improving the healthcare industry. For instance, AI systems are streamlining processes bogged down by obsolete traditional methods and providing tools to assist doctors with diagnoses and other tasks. And all of this is being achieved without replacing a single doctor.

Historically speaking, transformative change is rarely met with broad-based enthusiasm, and this has been a trend since time immemorial. But in spite of our fears, advanced technology has helped reshape our way of life into greater health, wealth, and prosperity for more people around the globe. Keep that in mind, and our search suggestions may soon change from “Will AI take over jobs?” to “How will AI make jobs better?”

It’s no secret that healthcare in the United States is in crisis. With skyrocketing costs, dysfunctional health coverage, and overburdened doctors, hospitals, and staff, it’s hardly surprising that that only 39% of patients have ‘a great deal’ of confidence in the medical system. This disparity between patients and doctors could have lasting impact on our collective health (as recent resurgences in measles and other preventable diseases has shown). But artificial intelligence’s significant impact on the healthcare industry, and its potential for more, may just be the tool we need to reconcile the patient experience.

Machine learning has already gained a foothold in modern medicine as a powerful tool to help doctors diagnose, perform medical procedures, and prescribe medication more quickly and with greater accuracy. The boon for the healthcare economy is enormous, with growth expected to reach $6.6 billion by 2021 and an estimated $150 billion in annual savings by 2026 in the United States alone. For patients who fear that any unforeseen medical bill could spell bankruptcy, this should come as somewhat of a reprieve. But saving money alone may not be enough to build patient confidence; patient-doctor interaction suffers from critical symptoms as well.

With their doctors spending more of their time doing paperwork than interfacing with them, patients often feel neglected or outright ignored in the doctor’s office. With little knowledge shared between the two to build up a relationship, patient perception of doctors has become increasingly negative. Of course, doctors have their reasons, although they do little to appease anxious patients: decreased insurance payouts means that doctors, particularly specialists, need to take on as many patients as possible within increasingly shorter time spans in order to make enough money to run their offices. This, coupled with increasing need to spend more than “two-thirds of their time doing paperwork” on administrative tasks, including documentation, follow-up, and dealing with insurance companies and several other layers of bureaucracy. This results in interacting more with paperwork than patient and lead to job dissatisfaction that can translate in doctor-patient interactions. Research suggests that an important correlation may exist between a patient’s satisfaction and the professional satisfaction of their physicians. By improving physician satisfaction, frustration with the medical process may finally be curable. 

AI optimization has already made tremendous headway in terms of how quickly and accurately physicians can make diagnoses. But if AI could also make a dent in the paperwork problem, physicians could be afforded the time to take on more patients every week and spend more than the current average of 22 minutes per encounter with their existing patients. 

Additionally, AI technology that already exists could expedite the diagnostic progress even further, to allow doctors to forge the deeper connections their patients often long for. Wearables like the Apple Watch and FitBit already track metrics such as heartbeat and steps taken per day. Even cell phones have apps that can track the amount of REM sleep you achieve each night. If the technology continues to develop in this way, including the ability to track other metrics such as body temperature or even blood content levels, wearables can potentially work like a car’s computer, tracking and keeping record of irregularities in our bodies for doctors to easily access later. These recorded symptoms could further lend confidence in patients’ perception of their own health and help them put to words their ambiguous understanding of their symptoms – the end of the era of “Well Doctor, I had this weird, kinda stabby pain in my stomach,” and the beginning of accurately described symptoms and expedited diagnoses.

AI in healthcare is also poised to impact how patients choose their physicians. Studies have shown while popular perception of AI is mixed, hope for AI in healthcare continues to trend positively, particularly the concept of systems that provide patient care. With that knowledge in hand, rebuilding patient trust in their doctors could be made easier by utilizing AI in the practice. This would give forward-thinking doctors a reputation for using efficient, accurate, and cutting-edge technology, propelling them ahead of their peers.

Ultimately, there is no substitute for doctors. AI has the capacity to suggest a diagnosis with accuracy, but doctors have the experience and a human approach, such as banter, thoughtfulness, and empathy, that cannot yet be replicated by machines. It was the loss of that human approach through our overburdened healthcare system that created the gap between doctors and their patients in the first place. By utilizing advanced AI to the best of its potential, doctors can bridge that gap with patients, and restore the patient experience to one of mutual trust, faith, and respect.

Entefy CEO Alston Ghafourifar spoke to a group of international institutional investors hosted by Franklin Templeton Investments in San Francisco, California. The event focused on the future of AI, its impact and value creation, as well as the importance of data optimization for organizations. 

In an in-depth conversation about organizational bottlenecks for building effective AI solutions, Alston expressed his view on the importance of a company’s commitment—

especially at the executive level—to digital transformation and the intelligent enterprise. Realizing the full potential of AI and machine intelligence within the enterprise, closely resembles any other type of organizational change. “If you don’t have the organizational will to address the data readiness problem from a system’s perspective then you’re not going to actually be leading the stack,” Alston stated. 

In an effort to transform idle, dark data into valuable insights, enterprises worldwide are looking into ways AI can make their operations more efficient, enhance their customer experience, and ultimately become more competitive. This pursuit is occurring across a broad spectrum of industries as well as functional departments within organizations. Underlying this activity is a paradigm shift in how companies perceive the role of data and AI to optimize productivity and business processes.

When it comes to AI and data, every organization is at a different phase of readiness. Alston shared that, at Entefy, we spend time to understand where an organization is in terms of their AI journey. “Are they just talking about it?” Or do they have “at least a process for how they aggregate, transform, and move information throughout the system?” Entefy’s advanced multimodal AI is designed to solve the information overload problem for organizations large and small. And to do this, we along with our customers are reimagining knowledge management, workflow orchestration, and process automation.

We thank the event organizers, speakers, and attendees for making this event possible. Special thanks to Ryan Biggs from Franklin Templeton Investments who moderated the discussion. 

The artificial intelligence (AI) revolution is unfolding right before our eyes, and organizations of all types have already begun taking advantage of this amazing technology. Today, machine learning is transforming industries and disrupting software and automation across the globe.

AI/machine learning as a discipline is highly dynamic, relying on a unique blend of science and engineering that requires training models and adjusting algorithms to make useful predictions based on a dataset. Building algorithmic models and thoughtful process orchestration are central to work in this field and extensive experimentation is simply par for the course. And, high precision in AI and machine learning is achieved where science meets art.

At Entefy, we’re obsessed with advanced computing and its potential to change lives for the better. Given our work in AI and machine learning over the years, we’ve had the good fortune of working with amazing people, inventing new things, and building systems that bring unprecedented efficiency to operations, processes, and workflows. This includes our work in the emerging, complex field of multimodal AI. 

So, what does it take to make it all work and work well? What are the proficiencies and competencies required to bring AI applications to life? Here are the 18 skills needed for the successful development of a single AI solution from ideation to commercial delivery:

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 servicesincluding 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.

USPTO awards Entefy new patents covering inventions in digital security and privacy

PALO ALTO, Calif. October 29, 2018. Entefy Inc. has recently been issued a series of patents by the U.S. Patent and Trademark Office (USPTO). The company’s IP portfolio includes 48 combined pending and issued patents and a number of trade secrets related to its technology domains. Entefy’s latest three issued patents cover innovations in areas of cybersecurity and data privacy.

Patent No. 10,037,413 describes “System and method of applying multiple adaptive privacy control layers to encoded media file types.” A solution using this technology gives users and content creators alike advanced control over the viewing, distribution, and other use of their videos in a variety of formats. This unprecedented level of protection can extend from entire files down to select scenes, objects, or even a single pixel within a video.

Patent No. 10,055,384, “Advanced zero-knowledge document processing and synchronization,” explains the method by which live, multi-person document collaboration products can provide the same level of convenience for the user without sacrificing security or privacy.

One major consideration when dealing with the cloud or distributed computing is the ongoing trade-off between ease of maintenance and the need for security, including proper authentication, role management, and permission. Patent No. 10,110,585 describes the first “Multi-party authentication in a zero-trust distributed system” that involves automated collaboration between people, hardware, and software in determining the correct authorization for data access, policy changes, system updates, and more in a distributed infrastructure.

“I’m really proud of the team’s technical prowess and commitment to excellence. Over the years, we’ve had to think about new approaches to solving some of the most challenging engineering problems in areas of communication, security, and machine intelligence,” said Entefy’s CEO, Alston Ghafourifar. “I’m delighted to see these newly issued patents being added to our growing IP portfolio as we continue to push the envelope and move the dial on what’s technically possible.”

Today’s news is the latest in a series of patent issuance announcements, including an Entefy patent that enhances intelligent message delivery and a patent covering encrypted group messaging simultaneously across multiple protocols.

ABOUT ENTEFY

Entefy is an AI software company, specializing in machine learning that delivers productivity and growth. Our multimodal AI platform encapsulates advanced capabilities in machine cognition, computer vision, natural language processing, audio analysis, and other data intelligence. Organizations use Entefy to accelerate their digital transformation and dramatically improve existing systems—knowledge management, search, communication, intelligent process automation, cybersecurity, data privacy, and much more. Get started at www.entefy.com.

You need to change who you are. Or at least that’s the message of the self-improvement industry, which annually accounts for billions of dollars’ worth of books, workshops, apps, and the like designed to help a person tweak who they are or become who they want to be. 

But does any of it work? Can you really change your personality?

If the self is stable and you are who you have always been, change seems impossible. But if the self is dynamic and you are a different you from one moment to the next, meaningful change is possible. To get started, let’s define personality and its measurement.

What is personality?

Personality is best thought of as the stable and (generally) consistent thoughts and ideas that guide a person’s behavior. Just as our physical features can be used to identify us even after we have a haircut or grow a few inches taller, so too does our personality act as an identifier, even if in the moment you’re tired, excited, or totally stressed out. 

The psychologist Carl Jung took a stab at classifying personality types in his 1921 book Psychological Types, which describes the mental functions people rely on to perceive the world. That work served as the foundation for the Myers-Briggs Type Indicator, a popular self-assessment tool that grades people along four dimensions of psychological preferences: Introversion/Extraversion, Sensing/Intuition, Thinking/Feeling, and Judging/Perceiving. About 2 million people take the Myers Briggs test each year, with HR departments often using it to analyze potential employees. Despite research that suggests the test is ineffective at predicting success, in part because a single test-taker will often get different results when taking the same test multiple times. 

In psychiatry circles, the most well-regarded personality framework in use today is called the Five-Factor Model. It is organized around the Big Five personality traits, dubbed OCEAN: 

• Openness to experience, the quality of being intellectually curious, likely to find enjoyment in the likes of art and travel. 
• Conscientiousness, hardworking and organized, with those low on the scale being more carefree and spontaneous.
• Extraversion, energized by social situations, whereas introversion defines those who get exhausted around people. 
• Agreeableness, friendly and cooperative, those low on this scale are more competitive and selfish.
• Neuroticism, moody, easily annoyed when little things don’t go their way.

Each of these personality traits is a part of a person’s personality. The one we’re all most familiar with is likely the Extraversion/Introversion axis, since we’ve all used those terms as a shorthand when describing someone else’s behavior and preferences. 

Technically, introversion and extraversion (E/I) simply refer to how a person uses and replenishes energy—introverts grow tired with social interaction while extraverts thrive with it. E/I are not either/or. They exist on a spectrum, with most people’s personalities landing somewhere in between the extremes. There are even terms that distinguish other positions inside the continuum. People who are evenly situated between introversion and extraversion are considered ambiverts. Then there are four types of introversion as well: social, thinking, anxious, and inhibited.

The differences between introverts and extraverts go beyond their approaches to social interaction. A 2013 study concluded that extraverts associate feelings of reward with what happens around them, while introverts rely more on their inner thoughts. The differences can even manifest in how we express ideas, with introverts speaking more concretely while extraverts more abstractly. 

Thanks to recent advances in brain scanning technology, there have also been several neurological differences identified. Introverts and extraverts have different levels of baseline cortical arousal (the amount of brain activity) suggesting that introverts tend to process more information per second, leading to the theory that they get overwhelmed in highly stimulating environments, such as social situations. Several other studies have found specific brain regions that are more active in either introverts or extraverts. 

Despite all the differences, it is not the case that we each occupy a particular spot on the scale and remain devoid of the characteristics of the other side. Introverts will often behave like extraverts and vice versa. A 2009 study found that extraverts would act moderately extraverted 5-10% more often than introverts, so what distinguishes them is not how they behave but for how long they do so. 

Some research has suggested that extraverts report higher levels of happiness, and that introverts who act extraverted also experience a boost. However, high levels of extraversion have also been correlated with delinquent behavior and psychopathy.

Introvert to extravert. Extravert to introvert.

Switching between introverted and extraverted behavior in the moment is not the same as making long-term meaningful changes to behavioral predisposition. Because we are each unique and often shift between certain qualities, the best way to maximize the benefits of both is to maintain an optimal and highly personal balance between the activities that suit us best. That is, if you are highly introverted, don’t try to force yourself to be social too often, but when you do, use it in the best way possible. Know what you want to accomplish.

Our personality can be refined with the right tools and mindset. But any changes that occur should be personal and self-directed. Conversely, it’s important not to expect others to behave in ways that conflict with who they are: workplaces and groups in general are more likely to thrive with a combination of personalities. Allowing people to work in their most comfortable state of mind will keep them happy and performing at their best.

When thinking about innovation and business domains, typically the banking sector isn’t one that readily comes to mind. But these days, tech companies are challenging banks and credit unions to improve their digital capabilities while customers are increasingly demanding more convenience and personalized service. This has led to banks spending billions on artificial intelligence which is transforming banking across internal and consumer-facing processes.

AI improves operational efficiency

One of the fundamental ways in which AI is changing industries of all kinds is through automation of repetitive, low-level tasks. Smart automation not only reduces costs, it provides employees the opportunity to spend their valuable time on more complex, creative, and customer-focused work. 

As banking institutions shift necessary but low-skill tasks to automation platforms, their employees gain the additional time needed to engage with customers in more meaningful ways. This can significantly enhance the customer experience and enable decisionmakers at banks to differentiate their companies in an increasingly competitive market.

Until recently, traditional banks have drawn criticism for not keeping pace with customers’ demands. For too long banks have been offering generic, one-size-fits-all products and features that now seem woefully out of date. The modern consumer expects personalization and dynamic digital experiences.

But to cast banks as slow-moving luddites is to miss the bigger picture. Financial institutions face complex regulatory requirements, grapple with dated cultures, and answer to a diverse set of stakeholders. Therefore, change doesn’t come easy to these established institutions and adopting new technology can take time. Plenty of time.

Today, artificial intelligence is helping banks not only catch up but innovate in many areas of operations. For example, in IT alone banks are reducing their infrastructure, development, and maintenance costs by 20-25 percent. The savings, both in terms of financial and personnel time, give these institutions the additional room needed to invest in new products and key operational areas such as security.

Intelligent process and workflow automation allows banks to reinvest personnel resources into the types of tasks for which humans are best suited. By leveraging smart chatbots for certain customer interactions, banks enable employees to again focus on more complex and interesting problems. Instead of having employees respond to the same set of common questions day in, day out, companies can now leverage AI to engage customers directly through apps or websites. Common or simple questions need never take up an employee’s time. For more complex or unique customer interactions, banking personnel can intervene and provide superior service with that newly saved time.

Data intelligence uncovered by machine learning can also lead to increased efficiency. By collecting information about customer behavior in banking apps or websites, managers gain the insights necessary to develop new product features or implement their business strategies with greater confidence.    

AI to transform risk and compliance

Banks are also finding powerful uses for AI in the area of risk and compliance. With compliance representing an enormous cost to financial institutions in the decade following the financial crisis, AI tools promise to save organizations billions of dollars collectively. As previously covered by Entefy, banks spend $270 billion on compliance with a significant portion of these expenses centered purely around personnel.

It is estimated that globally money laundering represents 2%-5% of GDP. And combating this is a costly endeavor requiring significant levels of manual effort to stay in compliance with strict regulations. AI can help banks fight the war against money laundering with the potential to reduce personnel-related anti-money laundering (AML) costs by 50%. Machine learning can be used to analyze a broad range of data points, such as location, ISP information, identity, and myriad transaction patterns. This analysis can be critical in identifying suspicious activity.

AI-powered automation would not only reduce compliance costs but could decrease risks associated with human error as well. Machine learning algorithms can be trained to detect potentially fraudulent activity by comparing current account usage against historical trends. Advanced anamoly detection can be used in sending the right alerts to specialists who can best determine whether intervention is warranted. This type of smart analysis is critical because the number of transactions that occur each day far exceed human capacity to track and flag them all.