ARTIFICIAL INTELLIGENCE - The State Of The Art

An Introduction.

Artificial intelligence (AI) has been one of the most rapidly evolving fields in computer science over the past few decades. In a very real sense, in the last few years it has suddenly "come of age" with increasing numbers of real world applications. AI has been used to solve a wide range of problems, from image recognition to natural language processing. In the last few months with the public exposure of LLM systems in search engines and image generators, the public interest and range of solutions has literally exploded. In this article, we discuss some of the recent advances in AI, including deep learning, reinforcement learning, and computer vision.

Deep learning is a subfield of machine learning that involves training artificial neural networks with multiple layers. Deep learning has been used to solve many complex problems such as image recognition, speech recognition, and natural language processing. One of the most significant advances in deep learning has been the development of large language models (LLMs) such as GPT-4 by OpenAI. LLMs have been trained on massive datasets and can generate human-like responses by processing natural-language inputs.

Reinforcement learning is another subfield of machine learning that involves training agents to make decisions based on rewards and punishments. Reinforcement learning has been used to solve many complex problems such as game playing, robotics, and autonomous driving. One of the most significant advances in reinforcement learning has been the development of AlphaGo by DeepMind. AlphaGo was able to defeat the world champion at the game of Go using a combination of deep neural networks and reinforcement learning.

Computer vision is a subfield of AI that involves training machines to interpret and understand visual data from the world around them. Computer vision has been used to solve many complex problems such as object recognition, facial recognition, and self-driving cars. One of the most significant advances in computer vision has been the development of convolutional neural networks (CNNs). CNNs have been used to achieve state-of-the-art performance on many computer vision tasks.

AI has made significant advances in natural language processing (NLP). NLP involves training machines to understand and generate human language. NLP has been used to solve many complex problems such as machine translation, sentiment analysis, and chatbots. One of the most significant advances in NLP has been the development of transformer neural networks such as BERT by Google. Transformer neural networks have been used to achieve state-of-the-art performance on many NLP tasks.

Generative adversarial networks (GANs) are another area where AI has made significant advances. GANs involve training two neural networks against each other: a generator network that generates new data samples and a discriminator network that tries to distinguish between real and fake data samples. GANs have been used to generate realistic images, videos, and audio samples.

Broadly we can classify current AI systems as either discriminative or generative:

• Discriminative AI systems draw distinctions between different inputs. They are used to answer questions like: "Is this the face of Jack or Jim?", or "Is this object a human or a lamp post?" Disciminative AI deals with what is.
• Generative AI takes an input and creates something new that previously did not exist, such as a painting of the human, Jim, leaning against a lamp post. LLM's are called generative AI because they create new text, images, sounds, etc from data by applying NLP to both the prompt and the learned data through a transformer neural network. While generative AI systems would pass the Turing test and produce convincing simulations of a thinking person, they are for all that essentially probability engines that have learned what things are most likely to go with other things in both close and distant association.

Generative Pretrained Transformer networks (GPT) such as LLMs derive meanings from long sequences of text to understand how semantic components relate to one another in a probability driven framework and then determines how likely they are to appear in proximity to one another.  The transformer networks are trained unsupervised on a vast body of textual (and other) data to create a pretrained data set fine-tuned by humans interacting with the pretrained model.  In image, audio and video data sets a process of Diffusion is applied in which noise is added to create randomness in an image which is then removed progressively while the model attempts to match semantically similar images. Models that perform text to image or sound generation use this process.   As one might guess the deep learning approach of generative AI is computationally intensive and makes use of the parallel and concurrent mathematical processing capabilities of GPU's to drive an iterative unsupervised learning model, utilising much larger quantities of CPU cycles and memory than earlier generations of AI such as Expert Systems which were essentially human coded fixed and inflexible problem solving approaches to AI.

AI has made significant advances in robotics and autonomous systems. Robotics involves training machines to perform physical tasks such as grasping objects or navigating through an environment. Autonomous systems involve training machines to make decisions without human intervention. These technologies have been used in many applications such as self-driving cars, drones, and industrial automation.

Some of the most impressive advances in AI over recent years have come from advances in areas such as deep learning, reinforcement learning, computer vision, natural language processing, generative adversarial networks, robotics, and autonomous systems. These technologies have the potential to revolutionize many fields such as healthcare and education by generating human-like responses by processing natural-language inputs.

In the collection of articles on this site we will explore AI and Generative systems such as Large Language Models and Diffusion networks in particular, providing extensive references to other sites that drill into the topics covered here in more detail. These articles are intended as a survey of the current state of the art, a course in the use of some of the AI technologies (in particular LLMs) and repository of resources available to and accessible by the public. The right hand column of each page has a list of relevant references for further reading on the topics discussed on the associated page.

...Next: Large Language Models....