A surprising number of apps are starting to use machine learning and artificial intelligence (AI) technology to do everything from predicting purchase behaviors to adjusting a home’s thermostat to the resident’s ideal temperature for a particular time of day. But what else can this technology do? Well, machine learning and AI can combine to transform many of the experiences you encounter throughout the course of everyday life.

machine learning human behavior

In addition to transforming the human experience, these emerging technologies are prompting developers to examine and dissect the precise workings of human intellect and behavior. A thorough understanding of human intelligence and behavior is vital for developers who are striving to simulate and/or replicate these processes in AI and machine learning algorithms. As a result, innovations in the realm of AI have prompted a new kind of self-analysis. It stands to reason that as developers create new forms of artificial intelligence, we may gain new insights into the qualities that make humans unique. These technologies are also prompting important discussions about the moral and ethical implications of these advances—particularly for those who hold the power to transform our world in remarkable ways.

Dissecting the Nature of Human Intelligence

In order to replicate the unique brand that is human intelligence, developers and others in the industry have been collectively tasked with conducting a comprehensive analysis of precisely what constitutes “intelligence” and “learning.” An in-depth understanding of human intelligence and learning processes is essential for replicating these processes in computers and devices with AI capabilities.

It has taken years of research and refinement to reach a basic understanding of human intelligence. AI developers must take everyday programming methods and adapt those methods so they can simulate human intelligence. This is no small feat, as humans have a natural ability to encounter never-before-seen situations and apply that intelligence in a consistent, on-the-fly efficient manner. It’s this adaptive intelligence that has been extremely challenging for AI developers to replicate.

Typical programming relies on a simple formula of “if A, then B.” So, if a particular set of conditions arises, a specific event will be programmed to occur. But AI developers must devise programming that allows the artificial intelligence to react appropriately when confronted with new and unique conditions, with variables that cannot be accurately predicted in advance. Developers quickly realized that the key to success is centered around the ability to learn and apply newly-gleaned information to future scenarios. This would allow AI technology to react to the best of its ability when confronted with a unique situation. And if the reaction turns out to be less-than-ideal, then the machine learning interface can devise a more appropriate reaction for the future. This process is repeated over and over (either in real life or in a computer modeling interface) until the technology achieves the ideal response. In layman’s terms, developers realized their AI and machine learning technology would need to learn from trial and error, analyze that experience and then apply the lesson to future scenarios.

A number of companies have already pioneered some clever applications using this approach. Here are a few examples:

  • Cogito Customer Service App: The Cogito app leverages real-time emotional intelligence technology to evaluate individuals who are seeking support. This is an application where tone of voice and speech patterns can reveal an individual’s level of agitation (or lack thereof), providing important insights that can be used to streamline and optimize user experience. 
  • Pandora’s Algorithm: Pandora is one of the world’s most popular music streaming apps and as such, developers have a massive data stream they’ve used to refine their algorithm. The Pandora Music Genome Project offers song and artist recommendations, which are generated by evaluating areas such as interests, favorite songs/artists, listening history and mood. It’s also conceivable that in the future, we could see biosensor integration, with a wearable device that collects data that can be used to determine mood. This data may include heart and respiration rate, skin conductivity, body temperature and other biofeedback markers. 
  • Nest Thermostats: The third generation of Nest Thermostats go beyond the typical smart home system, with the integration of AI and machine learning technology that gives this system the ability to learn and adapt. For instance, this system can learn your schedule (with your presence determined by your smartphone location), making adjustments in response. This system can also adapt to suit each individual and their unique preferences. If your home is equipped with different “zones,” it’s possible to adjust the temperature in a particular area to accommodate the person who’s in that location. This is another area of technology where we could see the integration of wearable biofeedback sensors that send additional data to the smart thermostat, allowing for even greater customization based on body temperature and activity levels.
Using Machine Learning and AI to Identify Habits

Humans are creatures of habit and as such, it’s relatively easy to predict an individual’s likes, dislikes and actions. This is where machine learning is useful because, given enough data input, a machine learning interface can be combined with AI technology to make predictions and recommendations with a high rate of accuracy.

The machine learning interface processes the data and identifies trends and patterns, while the AI technology leverages that information to generate recommendations and modifications or to initiate actions, such as altering the thermostat setting in a smart home environment. This is largely possible due to humans’ propensity to form and follow established habits. As a result, many behaviors can be predicted with a high degree of accuracy, particularly when it comes to making modifications in order to maintain comfort or maximize convenience.

Blending AI and Machine Learning Technology With Behavioral Psychology

Machine learning and artificial intelligence technology have progressed to a point where they can identify an individual’s mood based on facial expressions, speech patterns and physical markers such as skin temperature and moisture level. Mood could be used to generate food, music or movie recommendations. Physical markers could help with administering medications or adjusting the temperature, lighting and other environmental conditions. Meanwhile, speech patterns can reveal everything from an individual’s intelligence and education level to their current stress levels and even truthfulness. Therefore, it’s conceivable that this information could be employed to create a range of different applications, from a lie detector app to an app that offers book recommendations based on your interests and intellect.

This technology can already do some remarkable things, such as predicting the outcome of elections, offering music or product recommendations, and predicting which route is will be most popular amongst drivers. The potential uses for AI and machine learning are many and varied, extending far beyond what we’ve already achieved. As such, this is a rapidly-evolving technological niche.

But as developers and behavioral psychologists continue to collaborate in new and exciting ways, we find ourselves facing a future where, given sufficient data, we can model future behaviors and events with startling accuracy. It’s a future that could include technology that makes life easier, more comfortable and more enjoyable. People may be able to focus on more complex and enjoyable tasks while delegating other assignments to AI technology. The potential in terms of business and industrial applications is really only limited by one’s imagination.

The more effectively we can model behavior, the better we become at influencing that behavior. But the ability to predict and influence the future is an incredible (and somewhat startling) prospect, particularly when you consider the amount of control that would be possessed by a relatively small pool of individuals—individuals who lead the companies and organizations that have developed and perfected powerful new innovations. This brings up new and challenging ethical questions, such as:

  • Do you have a responsibility to notify the public and offer a solution if it appears that a negative event is on the horizon? 
  • Do the individuals controlling this technology have a moral and ethical responsibility to influence human behavior in an attempt to avoid disastrous events, such as an economic collapse or social/political upheaval? What if the company stands to benefit from that disaster (i.e. as in the case of a disaster recovery company)? 
  • What moral and ethical obligation does a company have to share a high-tech innovation if it develops technology that would save lives and improve quality of life for thousands or millions of people?

There are no simple answers to these questions, which are the topic of much debate amongst academics, philosophers, ethicists and the general public. In fact, it’s likely we’ll continue to grapple with these issues well into the foreseeable future. One thing is clear: AI and machine learning technology will almost certainly force humanity to re-examine what moral and ethical obligations are associated with this new technology and what it means to be an intelligent, thinking human.

Fortunately, these moral and ethical issues have not interfered with the use of this technology thus far, and an increasing number of companies and organizations are seeking to leverage AI and machine learning technology. If this sounds like your next project, you’ll need an experienced development team that understands these and other cutting-edge technologies. At SevenTablets, our developers are well-versed in emerging technology such as augmented reality, artificial intelligence, natural language processing, machine learning, predictive analytics and blockchain. As such, we’re well-positioned to integrate innovative technology, giving your business a leg up on the competition.

Based in Dallas, SevenTablets works with clients throughout Texas—including Austin and Houston—and beyond. If you’re ready to discuss your project, contact our team today.


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Lacey Williams-McGhee

Lacey Williams-McGhee

Marketing Director at SevenTablets
Lacey Williams is a marketing and design professional living in the great state of Texas. When she's not working hard at the SevenTablets headquarters or designing products for her side gig, she can be found exploring new restaurants, hanging out with her husband and walking her golden retriever.

Lacey earned a B.A. from Baylor University. Sic'em!
Lacey Williams-McGhee