Artificial Intelligence(AI) & Its Working 2023 Part 1

What is Artificial Intelligence?

How Does AI Work?

Artificial intelligence (AI) is a broad branch of computer science concerned with building intelligent machines capable of performing tasks that typically require human intelligence. While AI is an interdisciplinary science with multiple approaches, advances in machine learning and deep learning, in particular, are creating a paradigm shift in almost every sector of the technology industry.

Artificial intelligence allows machines to model or even improve upon the capabilities of the human mind. From the development of self-driving cars to the proliferation of generative AI tools like ChatGPT and Google’s Bard, AI is increasingly becoming a part of everyday life – an area in which companies in every industry are investing.

Understanding artificial intelligence

In general, AI systems can perform tasks typically associated with human cognitive functions, such as interpreting speech, playing games, and identifying patterns. They usually learn how to do this by processing huge amounts of data, and looking for patterns that can be modeled into their decision-making process. In many cases, humans will oversee the AI’s learning process, promoting good decisions and discouraging bad ones. But some AI systems are designed to learn without supervision, for example, by playing a video game over and over again until they eventually figure out the rules and how to win.

Strong Artificial Intelligence Vs. Weak artificial intelligence

Intelligence is difficult to define, which is why AI experts usually distinguish between strong AI and weak AI.

Powerful artificial intelligence

Strong AI

Strong AI, also known as artificial general intelligence, is a machine that can solve problems that it has never been trained to work on before, just as a human can. This is the kind of artificial intelligence we see in movies, like the robots from Westworld or the character Data from Star Trek: The Next Generation. This type of artificial intelligence does not actually exist yet.

Creating a machine with human-level intelligence that can be applied to any task is the holy grail for many AI researchers, but the search for artificial general intelligence has been fraught with difficulty. Some believe that strong AI research should be limited, due to the potential risks of creating strong AI without appropriate guardrails.

In contrast to weak AI, strong AI represents a machine with a full range of cognitive capabilities — and a wide range of use cases — but time has not made it less difficult to achieve such a feat.

Weak artificial intelligence

Weak AI, sometimes referred to as narrow AI or expert AI, operates within a limited context and is a simulation of human intelligence applied to a narrowly defined problem (such as driving a car, transcribing human speech, or curating content on a website ).

Weak AI is often focused on doing one task very well. While these machines may appear intelligent, they operate under far more constraints and limitations than even basic human intelligence.

Examples of weak AI include:

  • Siri, Alexa, and other smart assistants
  • Self-driving cars
  • Google search
  • Chatbots
  • Spam filters
  • Netflix recommendations

Machine learning vs. Deep learning

Although the terms “machine learning” and “deep learning” appear frequently in conversations about artificial intelligence, they should not be used interchangeably. Deep learning is a form of machine learning, and machine learning is a subfield of artificial intelligence.

Machine learning

A machine learning algorithm is fed data by a computer and uses statistical techniques to help it “learn” how to get progressively better at a task, without necessarily having to be programmed specifically for that task. Instead, machine learning algorithms use historical data as input to predict new output values. To this end, machine learning consists of supervised learning (where the expected outputs of the inputs are known thanks to labeled datasets) and unsupervised learning (where the expected outputs are unknown due to the use of unlabeled datasets).

Deep learning

Deep learning is a type of machine learning that runs inputs through a biologically inspired neural network architecture. Neural networks have a number of hidden layers through which data is processed, allowing the machine to learn deeply, make connections and weight the inputs to get the best results.

Examples of artificial intelligence

The four types of artificial intelligence

Artificial intelligence can be divided into four categories, based on the type and complexity of tasks the system can perform. they:

  1. Interactive machines
  2. Limited memory
  3. Theory of mind
  4. Self-awareness

Interactive machines

An interactive machine follows the most basic principles of artificial intelligence and, as its name suggests, is only able to use its intelligence to perceive and interact with the world in front of it. The interactive machine cannot store memory and, as a result, cannot rely on o Past experiences to guide real-time decision making.

Perceiving the world directly means that interactive machines are designed to complete only a limited number of specialized duties. However, narrowing the worldview of the interactive machine has its benefits: this type of AI will be more trustworthy and reliable, and will react the same way to the same stimuli every time.

Examples of interactive machines

  • IBM designed Deep Blue in the 1990s as a chess-playing supercomputer, and it defeated international grandmaster Garry Kasparov in a game. Deep Blue was only able to identify the pieces on the chessboard and see how each of them moved based on the chess rules, recognize the current position of each piece and determine the most logical move at that moment. The computer was not tracking its opponent’s possible future moves or trying to place its pieces in a better position. Each turn was viewed as its own reality, separate from any other movement previously undertaken.
  • Google’s AlphaGo is also unable to evaluate future moves but relies on its own neural network to evaluate current game developments, giving it an advantage over Deep Blue in a more complex game. AlphaGo has also bested global competitors in the game, defeating Go champion Lee Sedol in 2016.

Limited memory

AI with limited memory has the ability to store past data and predictions when gathering information and weighing potential decisions – which essentially means looking into the past for clues about what might come next. Artificial intelligence with limited memory is more complex and offers greater capabilities than interactive machines.

Limited memory AI is created when a team continuously trains a model on how to analyze and use new data or an AI environment is created so that models can be trained and renewed automatically.

When using limited-memory AI in machine learning, six steps must be followed:

  1. Create training data
  2. Create a machine learning model
  3. Ensure that the model can make predictions
  4. Make sure the model can receive human or environmental feedback
  5. Store human and environmental reactions as data
  6. Repeat the above steps as a cycle

Theory of mind

Theory of mind is just a theory. We have not yet achieved the technological and scientific capabilities necessary to reach this next level of artificial intelligence.

This concept is based on the psychological premise of understanding that other living beings have thoughts and emotions that influence an individual’s behavior. In terms of AI machines, this means that AI can understand what humans, animals, and other machines are feeling, make decisions through self-reflection and design, and then use that information to make decisions of their own. Essentially, machines must be able to understand and process the concept of “mind,” the fluctuations of emotions in decision-making, and a host of other psychological concepts in real time, creating a two-way relationship between people and AI.

self conscious

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-aware. This type of AI has human-level awareness and understands their own presence in the world, as well as the presence and emotional state of others. He will be able to understand what others might need based on not only what they communicate to them but also how they communicate it.

Self-awareness in AI relies on human researchers understanding the premise of consciousness and then learning how to replicate it so it can be integrated into machines.

Types of artificial intelligence | Explaining artificial intelligence What is artificial intelligence? | Edorica | Video: Edorica!

Examples of artificial intelligence

AI technology takes many forms, from chatbots to navigation apps and wearable fitness trackers. The examples below illustrate the breadth of potential AI applications.


ChatGPT is an AI chatbot capable of producing written content in a range of formats, from articles to code and answers to simple questions. Launched in November 2022 by OpenAI, ChatGPT is powered by a large language model that allows it to closely mimic human typing. ChatGPT also became available as a mobile app for iOS devices in May 2023 and for Android devices in July 2023. It is just one of many examples of chatbots, albeit a very powerful one.

Google Maps

Google Maps uses location data from smartphones, as well as user-reported data about things like construction and car crashes, to monitor the ebb and flow of traffic and evaluate the quickest route.

Smart assistants

Personal AI assistants like Siri, Alexa and Cortana use natural language processing, or NLP, to receive instructions from users to set reminders, look up information online and control the lights in people’s homes. In many cases, these assistants are designed to learn a user’s preferences and improve their experience over time with better, more personalized suggestions Responses.

Snapchat filters

Snapchat filters use ML algorithms to differentiate between the subject of a photo and the background, tracking facial movements and adjusting the image on the screen based on what the user is doing.

Self-driving cars

Self-driving cars are a clear example of deep learning, because they use deep neural networks to detect objects around them, determine their distance from other cars, identify traffic lights, and much more.

Wearable devices

Sensors and wearable devices used in the healthcare industry also apply deep learning to assess a patient’s health status, including blood sugar levels, blood pressure, and heart rate. They can also extract patterns from a patient’s past medical data and use them to predict any future health conditions.


MuZero, a computer program created by DeepMind, is a promising pioneer in the pursuit of true artificial general intelligence. He was able to master games he had never even learned to play, including chess and a whole host of Atari games, through brute force, playing the games millions of times.


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