Artificial intelligence and machine learning

What is in here?

• What is artificial intelligence

• types of AI

• Example of AI

• what is machine learning

• elements of ML

• other common words in the field i.e deep learning, large language models, neural networks.

Understanding The Difference Between AI and ML

Artificial Intelligence and Machine Learning are pretty hot buzzwords these days, and often used interchangeably. So is there a difference between the two?

Yes and no, because one is part of the other.

Machine Learning is a subset of Artificial Intelligence and one of the techniques available for realizing AI.

Artificial Intelligence encompasses several fields, such as Natural Language Processing, Deep Learning, Computer Vision, Speech Recognition, and more — all of which have the common goal of making machines even more intelligent than humans.

To further explore the differences and similarities between AI and ML, let’s expand our understanding of each term.

What is Artificial Intelligence?

When people hear about AI, they usually have different views on what it is and how it works. Most of them are influenced by movies, TV shows, video games and books.Some see a dystopian future ruled by machines (Terminator, The Matrix). Others see the potential to develop a highly personal assistant that caters to every need (Iron Man, Her,Detroit: Become Human)

Artificial Intelligence (AI) is the ability of a machine to perform tasks commonly associated with intelligent beings. Artificial Intelligence is the intelligence demonstrated by machines while Natural Intelligence is displayed by living beings. As mentioned previously, it is an interdisciplinary science comprising different fields, all with the aim of creating machines capable of learning, solving problems and performing tasks in a human-like manner.

What are the different types of AI?

Artificial intelligence can be divided into three widely accepted subcategories: narrow AI, general AI, and super AI.

What is narrow AI?

Artificial narrow intelligence (ANI) is crucial to voice assistants, such as Siri, Alexa, and Google Assistant. This category includes intelligent systems that have been designed or trained to carry out specific tasks or solve particular problems, without being explicitly designed to do so.

ANI might often be referred to as weak AI, as it doesn't possess general intelligence, but some examples of the power of narrow AI include the above voice assistants, and also image-recognition systems, technologies that respond to simple customer service requests, and tools that flag inappropriate content online

What is general AI?

Artificial general intelligence (AGI), also known as strong AI, is still a hypothetical concept as it involves a machine understanding and performing vastly different tasks based on its accumulated experience. This type of intelligence is more on the level of human intellect, as AGI systems would be able to reason and think like a human.

Like a human, AGI would potentially be able to understand any intellectual task, think abstractly, learn from its experiences, and use that knowledge to solve new problems. Essentially, we're talking about a system or machine capable of common sense, which is currently not achievable with any form of available AI.

Developing a system with its own consciousness is still, presumably, a fair way in the distance, but it is the ultimate goal in AI research.

What is super AI?

Artificial super intelligence (ASI) is a system that wouldn't only rock humankind to its core, but could also destroy it. If that sounds straight out of a science fiction novel, it's because it kind of is: ASI is a system where the intelligence of a machine surpasses all forms of human intelligence, in all aspects, and outperforms humans in every function.

An intelligent system that can learn and continuously improve itself is still a hypothetical concept. However, it's a system that, if applied effectively and ethically, could lead to extraordinary progress and achievements in medicine, technology, and more.

What are some examples of AI?

Overall, the most notable advancements in AI are the development and release of GPT 3.5 and GPT 4. But there have been many other revolutionary achievements in artificial intelligence -- too many, in fact, to include all of them here.

Here are some of the most notable

ChatGPT (and the GPTs)

ChatGPT is an AI chatbot capable of natural language generation, translation, and answering questions. Though it's arguably the most popular AI tool, thanks to its widespread accessibility, OpenAI made significant waves in the world of artificial intelligence with the creation of GPTs 1, 2, and 3.

GPT stands for Generative Pre-trained Transformer, and GPT-3 was the largest language model in existence at the time of its 2020 launch, with 175 billion parameters. The latest version, GPT-4, accessible through ChatGPT Plus or Bing Chat, has one trillion parameters.

Self-driving cars

Though the safety of self-driving cars is a top concern of potential users, the technology continues to advance and improve with breakthroughs in AI. These vehicles use machine-learning algorithms to combine data from sensors and cameras to perceive their surroundings and determine the best course of action. Tesla's autopilot feature in its electric vehicles is probably what most people think of when considering self-driving cars, but Waymo, from Google's parent company, Alphabet, makes autonomous rides, like a taxi without a taxi driver

Robotics

The achievements of Boston Dynamics stand out in the area of AI and robotics. Though we're still a long way away from creating AI at the level of technology seen in the movie Terminator, watching Boston Dynamics' robots use AI to navigate and respond to different terrains is impressive.

What is machine learning?

The biggest quality that sets AI aside from other computer science topics is the ability to easily automate tasks by employing machine learning, which lets computers learn from different experiences rather than being explicitly programmed to perform each task. This capability is what many refer to as AI, but machine learning is actually a subset of artificial intelligence.

Machine learning involves a system being trained on large amounts of data, so it can learn from mistakes, and recognize patterns in order to accurately make predictions and decisions, whether they've been exposed to the specific data or not.

Examples of machine learning include image and speech recognition, fraud protection, and more. One specific example is the image recognition system when users upload a photo to Facebook. The social media network can analyze the image and recognize faces, which leads to recommendations to tag different friends. With time and practice, the system hones this skill and learns to make more accurate recommendations.

What are the elements of machine learning?

As mentioned above, machine learning is a subset of AI and is generally split into two main categories: supervised, and unsupervised learning.

Supervised learning

This is a common technique for teaching AI systems by using many labelled examples that have been categorized by people. These machine-learning systems are fed huge amounts of data, which has been annotated to highlight the features of interest -- you're essentially teaching by example.

If you wanted to train a machine-learning model to recognize and differentiate images of circles and squares, you'd get started by gathering a large dataset of images of circles and squares in different contexts, such as a drawing of a planet for a circle, or a table for a square, for example, complete with labels for what each shape is.

The algorithm would then learn this labeled collection of images to distinguish the shapes and its characteristics, such as circles having no corners and squares having four equal sides. After it's trained on the dataset of images, the system will be able to see a new image and determine what shape it finds.

Unsupervised learning

In contrast, unsupervised learning uses a different approach, where algorithms try to identify patterns in data, looking for similarities that can be used to categorize that data.

An example might be clustering together fruits that weigh a similar amount or cars with a similar engine size.

The algorithm isn't set up in advance to pick out specific types of data; it simply looks for data with similarities that it can group, for example, grouping customers together based on shopping behavior to target them with personalized marketing campaigns.

Reinforcement learning

In reinforcement learning, the system attempts to maximize a reward based on its input data, basically going through a process of trial and error until it arrives at the best possible outcome.

Consider training a system to play a video game, where it can receive a positive reward if it gets a higher score and a negative reward for a low score. The system learns to analyze the game and make moves, and then learns solely from the rewards it receives, reaching the point of being able to play on its own and earn a high score without human intervention.

Reinforcement learning is also used in research, where it can help teach autonomous robots about the optimal way to behave in real-world environments.

Other words to help you understand AI and ML

large language models

Large language models (LLMs) are a prominent type of AI that utilizes unsupervised machine learning. These models undergo extensive training with vast amounts of text, such as articles, books, and websites, to comprehend the intricacies of human language. During the training process, LLMs analyze billions of words and phrases to identify patterns and connections. This enables the models to generate responses that closely resemble human-like answers when given prompts. GPT 3.5 is a well-known LLM, serving as the foundation for ChatGPT. Additionally, GPT-4 stands as the largest LLM, while Google's LaMDA is the second-largest LLM and is employed by Bard.

Deep learning

Deep learning, a subset of machine learning, involves training artificial neural networks with multiple layers to perform various tasks. These networks are expanded into extensive structures with numerous deep layers and are trained using vast amounts of data. Deep-learning models typically consist of more than three layers and can even have hundreds of layers. They can employ supervised learning, unsupervised learning, or a combination of both during the training phase. Deep learning is widely applied in fields like natural language processing (NLP), speech recognition, and image recognition due to its ability to learn intricate patterns in data using AI technology.

Neural networks

The success of machine learning relies on neural networks. These are mathematical models whose structure and functioning are loosely based on the connection between neurons in the human brain, mimicking the way they signal to one another. Imagine a group of robots that are working together to solve a puzzle. Each one is programmed to recognize a different shape or color in the puzzle pieces. The robots combine their abilities to solve the puzzle together. A neural network is like a group of robots. Neural networks can tweak internal parameters to change what they output. Each one is fed databases to learn what it should put out when presented with certain data during training.