Artificial Intelligence (A.I)

It is no news these days that Artificial intelligence (AI) technology has created new opportunities to progress on critical issues such as health, education, and the environment. In some cases, AI may do things more efficiently or methodically than humans. Now while a number of definitions of artificial intelligence (AI) have surfaced over the last few decades, John McCarthy offers the following definition in his 2004 paper on Artificial Intelligence that - “A.I. is the science and engineering of making intelligent machines, especially intelligent computer programs, which is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable.” So at its simplest form, artificial intelligence is a field, which combines computer science and robust datasets, to enable problem-solving. AI can fall under “narrow,” “general,” and “super.” These categories demonstrate AI’s capabilities, where “Narrow” is AI trained and focused to perform specific tasks, Narrow AI drives most of the AI that surrounds us today. It enables some very robust applications, such as Apple's Siri, Amazon's Alexa, and autonomous vehicles. “General” is a theoretical form of AI where a machine would have an intelligence equaled to humans; it would have a self-aware consciousness that has the ability to solve problems, learn, and plan for the future, and “Super” would surpass the intelligence and ability of the human brain.

While strong AI (which consist of General and Super) is still entirely theoretical with no practical examples in use today, that doesn't mean AI researchers aren't also exploring its development. Arend Hintze, a researcher and professor of integrative biology at Michigan State University, states that there are four (4) main types of AI, which are:

• Reactive machines Reactive machines are AI systems that have no memory and are task specific, meaning that an input always delivers the same output. Machine learning models tend to be reactive machines because they take customer data, such as purchase or search history, and use it to deliver recommendations to the same customers, this can be seen on most streaming platforms, where they use what the customer views to recommend more content. Compare this to our human lives, where most of our actions are not reactive because we don’t have all the information we need to react upon, but we have the capability to remember and learn. Based on those successes or failures, we may act differently in the future if faced with a similar situation.
• Limited memory The next type of AI in its evolution is limited memory. This algorithm imitates the way our brains’ neurons work together, meaning that it gets smarter as it receives more data to train on. Deep learning improves image recognition and other types of reinforcement learning. Limited memory AI, unlike reactive machines, can look into the past and monitor specific objects or situations over time. Then, these observations are programmed into the AI so that its actions can perform based on both past and present moment data. But in limited memory, this data isn’t saved into the AI’s memory as experience to learn from, the way humans might derive meaning from their successes and failures. The AI improves over time as it’s trained on more data. A good example of limited memory AI is the way self-driving cars observe other cars on the road for their speed, direction, and proximity. This information is programmed as the car’s representation of the world, such as knowing traffic lights, signs, curves, and bumps in the road. The data helps the car decide when to change lanes so that it does not get hit or cut off another driver.
• Theory of mind The first two types of AI, reactive machines and limited memory, are types that currently exist. Theory of mind is a type of AI that will be built in the future. As such, there isn’t any real world example yet, but when it is developed, theory of mind AI could have the potential to understand the world and how other entities have thoughts and emotions. In turn, this affects how they behave in relation to those around them.
• Self-awareness The grand finale for the evolution of AI would be to design systems that have a sense of self, a conscious understanding of their existence, and just like Theory of Mind AI, this type of AI does not exist yet. This goes a step beyond theory of mind AI and understanding emotions, to being aware of themselves, their state of being, and being able to sense or predict others’ feelings. For example, “I’m hungry” becomes “I know I am hungry” or “I want to eat lasagna because it’s my favorite food.” But we are a long way from self-aware AI because there is still so much to uncover about the human brain’s intelligence and how memory, learning, and decision-making work. So while it’s a fact that AI has various use in different industries and brings tangible results, especially in this day and age where there is a new form of AI online, it’s also difficult to immediately decide how to use it.

With deep learning, machine learning, natural language processing, smart search engines or predictive models supporting the decision-making processes, AI can transform your business but before you implement AI in your company, you need to be well-prepared. At Hampshire Heights, we support our clients not only with code but also with our business expertise. You will get algorithms that will work for you. Work not only technically, but also bring benefits.