The Versatile World of Artificial Intelligence

Now that we live in the artificial intelligence revolution, it's important to look at how AI has attracted the present high level of attention. Pick up a magazine or your cell phone, scroll through the tech blogs, or simply chat with your peers at an industry conference, you'll surely notice that almost everything coming out of the technology world seems to have some element of AI or machine learning.

The term “artificial intelligence” first appeared in 1956, but AI has become more popular today thanks to increased data volume, advanced algorithms, and improvements in computing power and storage. The capability of AI systems nowadays is impressive. While the early systems focused on generating images of faces, these newer models broadened their capabilities to text-to-image generation based on almost any prompt.

AI improvements are getting faster every day especially due to the increased data availability. AI algorithms need data to learn and improve, and data availability has increased dramatically in recent years. The rise of sensors, cameras, and other devices has generated a vast amount of data, which can be used to train and improve AI algorithms. The amazing benefit of AI is that it's been around for decades and has been used in various industries, but it's only in recent years that its potential has truly been realized, and it's become the darling of the tech world.

 AI is a broad umbrella term, a versatile innovation, that encompasses many different types of technologies that give machines the ability to perceive, learn, reason, and act in the real world, leading to smarter machines and software development. So, let's take a deep dive into the main aspects, types, and subfields of this digital phenomenon.

AI can be classified nowadays according to two criteria: Functionality and Capability.

According to Functionality, we have the following main types:

1.    Reactive Machine

It's the most basic and oldest type of AI. This type of AI can react to different kinds of stimuli but it has no memory so it can’t use previous information to obtain better results. A clear example of this kind of AI is when a user plays chess against a computer. Once the person makes a move, the machine responds with another move and tries to win - so it can only react.

2.    Limited Memory

In comparison with a Reactive Machine type of AI, this type of system uses memory storage but only a limited amount. They can learn from the past and they grow more and more able to provide you with different things. They learn your preferences and give you an improved user experience. This type of predictive AI is helpful for businesses that seek to get ahead and prepare for consumer trends. A clear example of it nowadays is Netflix personalization or online shopping recommendations.

3.    Theory of Mind

This is an advanced type of AI. It gives machines the ability to constantly learn mental states and communicate with the thoughts and emotions of humans. This type of AI is in research and development and could enhance interactions.

4.    Self-Awareness

This type of AI can be compared with superintelligence because it's reaching the same level as human consciousness. It represents the future of AI, its final stage. This kind of AI can only process human emotions and the goal is for it to even have emotions of its own. Nowadays, we can find a few examples very similar to self-awareness technology such as Sofia, the robot which at some point is self-aware as she claims that “she is aware that she is not fully aware yet.”

According to Capability, we have the next three types of AI:

1.    Artificial Narrow Intelligence (ANI)

This is the most common type of AI nowadays. This system is a combination of reactive and limited memory. It includes a system that performs specific programmed tasks such as facial recognition, speech recognition in voice assistants, or driving a car. This type of AI will fail at performing new tasks unless assigned. Narrow AI simulates human behavior based on a limited set of parameters, constraints, and context. Some of the common examples of ANI include speech and language recognition demonstrated by Siri on iPhones, the vision recognition showcased by self-driving cars, and recommendation systems such as Netflix recommendations. Google's Rank Brain is another example of narrow AI that Google uses to sort results.

What about Conversational AI? Have you ever heard of it?

Conversational AI is considered, in fact, a branch of AI. But what does it mean? Well, let's start right from the beginning. Conversational AI allows interactions between humans and machines. It belongs to the Narrow type of AI, which, as we mentioned, refers to systems that can only reproduce a limited number of human skills and can perform a limited quantity of tasks. A conversational AI system understands what users say and can respond in the same language. These systems are based on machine learning (ML) and natural learning processing (NLP) which are part of the 6major subfields of AI. A clear example of how such a system works nowadays is the Konect.ai platform.

The Konect.ai system is based on SMS, intelligent conversations with human-like characteristics built specifically for the automotive industry. This dealership AI intelligently combines ML and NLP to drive customer engagement and improve the overall dealership workflows.

2.    Artificial General Intelligence (AGI)

This type of AI can train, learn, and perform functions just like a normal human. It’s a cognitive AI, and it has a personality, although it’s still at a very early stage of development. The term cognitive AI is typically used to describe systems that simulate human thought. Human cognition involves real-time analysis of the real-world environment, context, intent, and many other variables that inform a person´s ability to solve problems. In a few words, cognitive AI includes human brain process simulation for the machine. That implies that AGI can detect different needs, processes, and emotions to act accordingly. The AGI systems are not yet available but there are instances of limited AI systems that come very close. The most popular examples of such systems are autonomous cars, ROSS Intelligence, better called the “AI attorney”, which extracts data from about 1 billion text documents, analyzes data, and delivers accurate answers in under three seconds. Other important examples are IBM Watson, AlphaGo, Music AIs, and also Fujitsu who built the K computer, which is recognized as one of the fastest supercomputers in the world. A similar example is China's National University of Defense Technology's Tianhe-2, a 33.86-petaflops supercomputer they built.

3.    Artificial Super Intelligence (ASI)

  Artificial Super Intelligence is only hypothetical today. This type of AI will not only be able to understand and interpret human behavior, but also will have the ability to think, reason, and apply its judgments to complex issues in an autonomous way. Just imagine a mega-brain being able to read all of the books within a library in a matter of seconds from the moment you press enter on the program or even more, a machine that quickly integrates all the knowledge into a comprehensive analysis of humanity's intellectual journey before your next blink-like a computational beast. Well, this is the future of AI, the main goal that researchers and experts are trying to accomplish.

    There are still no real-life examples of superintelligent machines. Examples in science fiction of machine intelligence include the robot character of R2D2 in the movie Star Wars, who can perform multiple technical operations beyond human ability.

According to Capabilities, AI can also be divided into major branches:

1.    Machine Learning

  One of the most popular major branches of AI is machine learning. It's a computer's ability to learn without previous programming. This type of AI is making a buzz in our daily lives in every application. It's the innovative science that gives machines the ability to learn, translate, execute, and investigate to solve real-world problems.

  Machine Learning includes several subfields and one of them is Generative AI. So, what is Generative AI? It's an intelligent technology that allows machines to create new content from existing texts, audio, or even images. With the help of generative AI, computers can easily detect patterns and produce similar content. One popular example of Generative AI today is Chat GPT, a chatbot launched in November 2022. This generative chat has quickly captured attention due to its fast and detailed answers across a variety of domains.

 Another important subfield derived from machine learning is artificial Neural Networks. So, what is an artificial Neural Network? It's a circuit composed of artificial neurons or nodes used for solving AI problems. The main structure of this artificial system is based on the human brain, mimicking the way biological neurons signal to one another. These powerful artificial intelligence tools allow us to classify data at a high velocity.

2.    Natural Language Processing

NLP, better known as natural language processing, is a branch of AI that deals with the understanding of human language, and not only that, but it's also able to recognize humans' moods like happiness, anger, confusion, and so on. This field has been around for a long time, and it's become popular in recent years with applications such as Siri, Alexa, and Google Translate or intelligent virtual assistants like chatbots. NLP is considered a very powerful AI tool that will continue its growth and become even more sophisticated in the years to come.

3.    Robotics AI

 Combined with the traditional robotics field, AI has been used to develop and innovate intelligent machines used for everything from manufacturing to assisting healthcare. The Stanford Research Institute developed the AI robot Shakey from 1966 to 1972. According to the Computer History Museum Shakey marked the first mobile robot that could reason for its actions. The following companies are doing impressive things nowadays with AI robots: Miso Robotics, Hanson Robotics, Starship Technologies, Neurala, iRobot, Skydio, and Boston Dynamics.

4.    Expert Systems

These are considered to be the first successful models of AI software. They were first designed in the 1970s. An expert system is a computer system that mimics the decision-making intelligence of a human expert. The key characteristics of expert systems include extreme responsiveness, reliability, understandability, and high execution.

5.    Fuzzy Logic

This kind of AI represents and modifies uncertain information by measuring the degree to which the hypothesis is correct. Fuzzy logic systems are flexible enough to implement machine-learning techniques and assist in imitating human thought. It's a form of many-valued logic that deals with approximate reasoning rather than precise. In other words, it considers a statement’s degree of truthfulness or falseness. It doesn't simply accept a statement as true or false.

 AI systems are augmenting every day in complexity. AI professionals are continuously trying to build intelligent software for a variety of applications like natural language, speech recognition, knowledge, and automatic learning. The subfields of artificial intelligence are now the buzzwords across industries and organizations. Many corporations have implemented it to serve both employees and customers in a better way.

 AI software like Konect.ai is vivid proof that conversational AI is here to shape a future of innovation and improvement for the Automotive industry. Interested in learning more about how we support the automotive industry with conversational AI and NLP models? Reach out and talk with our expert team here.

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