Artificial Intelligence (AI)

Artificial Intelligence (AI)

Artificial Intelligence (AI), the ability of digital computers or computer-controlled robots to perform tasks typically associated with intelligent beings. The term is frequently applied to projects that develop systems endowed with human intellectual processes, such as the ability to reason, discover meaning, generalize, or learn from past experience. Since the development of digital computers in the 1940s, it has been proven that computers can be programmed to perform very complex tasks, such as discovering proofs of mathematical theorems or playing chess, with great skill. However, despite continuous advances in computer processing speed and memory capacity, no program can match human flexibility in larger areas or in tasks that require a lot of daily knowledge.

On the other hand, some programs reach human-professional and expert-level performance levels in performing certain tasks, such that artificial intelligence in this limited sense is found in a variety of applications such as medical diagnostics, computer search engines, and speech or handwriting recognition. .Artificial intelligence (AI), the ability of digital computers or computer-controlled robots to perform tasks typically associated with intelligent beings. The term is frequently applied to projects that develop systems endowed with human intellectual processes, such as the ability to reason, discover meaning, generalize, or learn from past experience.

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Since the development of digital computers in the 1940s, it has been proven that computers can be programmed to perform very complex tasks, such as discovering proofs of mathematical theorems or playing chess, with great skill. However, despite continuous advances in computer processing speed and memory capacity, no program can match human flexibility in larger areas or in tasks that require a lot of daily knowledge. On the other hand, some programs reach human-professional and expert-level performance levels in performing certain tasks, such that artificial intelligence in this limited sense is found in a variety of applications such as medical diagnostics, computer search engines, and speech or handwriting recognition.

The Future of Artificial Intelligence

When you think of the computational cost and technical data infrastructure that runs behind AI, the actual implementation of AI is a complex and expensive business. Fortunately, computing technology has advanced significantly, as indicated by Moore’s Law, which states that the cost of a computer is halved while the number of transistors on a microchip doubles about every two years.

Many experts believe that Moore’s Law will end sometime in the 1920s, but this has had a major impact on modern AI technology. Without it, deep learning would be financially unthinkable. A recent study found that AI innovation actually surpassed Moore’s Law, doubling every six months compared to two years.

According to this reasoning, advances made by AI in various industries have been significant over the past few years. The potential for greater impact over the next few decades seems inevitable.

Types of Artificial Intelligence

Artificial intelligence can be divided into four categories based on the type and complexity of the tasks the system can perform. For example, automatic spam filtering falls into the most basic category of AI, but the remote ability of machines to recognize human thoughts and emotions is part of an entirely different subset of AI.

Reaction Machine

Interacting machines follow the most basic principles of artificial intelligence and, as the name suggests, can use their intelligence to perceive and interact with the world in front of them. Interactive machines cannot store memory and, consequently, cannot rely on past experience to inform real-time decision-making. Being aware of the world directly means that interactive machines are designed to complete only a limited number of specialized tasks. Deliberately narrowing our global view of reactive machines is not a cost-cutting measure of any kind, but instead means that these types of AI are more stable and reliable. That is, it will respond the same way to the same stimulus each time.

One famous example of an interactive machine is Deep Blue, which IBM designed in the 1990s as a chess-game supercomputer that defeated world-class expert Garry Kasparov in the game. By identifying the pieces on the chessboard and understanding how each move was based on chess rules, Deep Blue was able to recognize the current position of each piece and determine the most logical move at that moment. The computer didn’t track the opponent’s future moves or put the pieces in a better position. Each rotation was considered a unique reality separate from other previously made movements.

Another example of an interactive machine for playing games is Google’s AlphaGo software. AlphaGo also cannot evaluate future moves, but relies on neural networks to evaluate current game development, giving it an edge over Deep Blue in more complex games. AlphaGo defeated Go champion Lee Se-dole in 2016 and also overtook global competitors in the game. Although limited in scope and easy to change, conversational AI can reach a level of complexity that provides stability when created to perform repeatable tasks.

Limited Memory

Limited memory AI has the ability to store historical data and predictions as it gathers information and evaluates potential decisions. Limited memory AI is more complex and offers more possibilities than interactive devices. Limited memory AI is created when teams continuously train models on how to analyze and use new data, or when building AI environments so that models can be trained and refreshed automatically.

When using limited-memory AI in ML, you need to follow step 6: Create training data, create ML models, make models predictable, and allow models to receive human or environmental feedback. Feedback should be saved as data. These steps must be repeated in cycles. There are several ML models that use limited memory AI.

Reinforcement learning: that learns to make better predictions through iterative trial and error.

Recurrent Neural Networks (RNNs): Use sequential data to take information from previous inputs and influence current inputs and outputs. They are commonly used for sequencing or time issues such as language translation, natural language processing, speech recognition, and image annotation. One subset of recurrent neural networks is known as long-term memory (LSTM), which helps predict the next element in a sequence using historical data. When making forecasts, LTSM considers the most recent information most important and discounting historical data while still using it to form conclusions.

Evolving over time, Evolutionary Generative Adversarial Networks (E-GANs) grow to explore slightly modified paths based on past experience with each new decision. These models are constantly looking for better pathways and use simulations and statistics or chance to predict outcomes during an evolutionary mutation cycle.

A switch, a network of nodes that learns to perform specific tasks by training on existing data. Instead of grouping items together, a switch can run a process such that each item in the input data handles everything else. Researchers call this “selfishness,” which means that once training begins, the adapter can see the effect of the entire data set.

Mind Theory

A theory of mind is just a theory. We have not yet achieved the technical and scientific capabilities necessary to reach the next level of artificial intelligence. This concept is based on the psychological assumption of understanding that other living things have thoughts and emotions that influence their behavior. From the point of view of artificial intelligence machines, this means that artificial intelligence can understand the feelings of humans, animals and other machines, make decisions through self-reflection and determination, and then use that information to make decisions for itself. Essentially, a machine must be able to absorb and process the concept of “mind”, emotional fluctuations in decision-making, and a series of other psychological concepts in real time to create a two-way relationship between humans and AI.

Self-consciousness : Once the theory of mind is established, at some point in the future of AI, the final step will be for the AI ​​to become self-conscious. This type of AI has human-level awareness and understands the existence and emotional state of others as well as their own existence in the world. He will be able to understand not only what other people are communicating to them, but also what other people need based on how they deliver it.

AI’s self-awareness depends on human researchers learning how to understand consciousness hypotheses and replicate them so that they can be embedded in machines.

Other Artificial Intelligence Classifications

There are three ways to classify AIs based on their abilities. Instead of AI types, it is a stage where AI can evolve, and only one of them is really possible at the moment.

Narrow AI:

Sometimes referred to as “weak AI,” this type of AI operates in limited circumstances and simulates human intelligence. Narrow AI is often focused on performing a single task very well, and while these machines may seem intelligent, they operate with far more limitations and limitations than even the simplest human intelligence.
Artificial General Intelligence (AGI): Sometimes referred to as “strong AI”, this is the type of AI you see in movies, such as robots from Westworld or character data from Star Trek: Next Generation. Artificial general intelligence is a machine with general intelligence, and just like humans, it can apply this intelligence to solve problems.

This is perhaps the pinnacle of artificial intelligence development. Superintelligent AI not only can replicate the complex emotions and intelligence of humans, but surpasses it in every way. It can also mean making your own judgments and decisions, or forming your own ideology.

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