What is artificial intelligence?

AI introduction

Artificial intelligence (AI) is the science and engineering of creating intelligent machines, specifically intelligent computer programs, and understanding human intelligence through the use of computers. It is related to the task of using computers to understand human intelligence, but AI does not have to adhere to methods that are biologically observable, according to a definition provided by John McCarthy in a 2004 paper.

The concept of artificial intelligence (AI) was first introduced in 1950 with the publication of Alan Turing's "Computing Machinery and Intelligence." Turing, known as the "father of computer science," posed the question, "Can machines think?" and proposed the "Turing Test" as a way for a human interrogator to determine whether a machine can exhibit intelligent behavior similar to that of a human through text responses. Although the Turing Test has faced criticism, it remains a significant part of AI history and a relevant concept in philosophy that explores linguistic ideas.

Stuart Russell and Peter Norvig's textbook, Artificial Intelligence: A Modern Approach, is a leading resource in the study of AI. In it, they explore four potential goals or definitions of AI that differentiate computer systems based on their ability to think or act rationally like humans: systems that think like humans, systems that act like humans, systems that think rationally, and systems that act rationally. Alan Turing's definition of AI would fall under the category of systems that act like humans.

At its core, artificial intelligence (AI) is a field that combines computer science and robust data sets to enable problem-solving. It also includes subfields such as machine learning and deep learning, which use AI algorithms to create expert systems that can make predictions or classifications based on input data.

AI development continues to receive a lot of attention, as is typical for any new emerging technology.

According to Gartner's "hype cycle," product innovations such as self-driving cars and personal assistants follow a predictable pattern of initial enthusiasm, followed by a period of disillusionment, before reaching a stage of understanding their relevance and role in a market or domain.

Lex Fridman, in a 2019 MIT lecture, noted that we are currently at the peak of inflated expectations and approaching the trough of disillusionment in the hype cycle for AI. As discussions around the ethics of AI increase, we are beginning to see the initial signs of the trough of disillusionment.

Why is artificial intelligence important?

AI is valuable for businesses because it can provide them with new insights and help to improve efficiency. It can also be particularly effective at handling tasks that require attention to detail and the ability to process large amounts of information quickly, such as reviewing legal documents. By automating these tasks, AI can help businesses save time and reduce the risk of errors.

AI has contributed to significant improvements in efficiency and has enabled the creation of new business ventures. For example, Uber has used AI and machine learning to build a successful platform that connects riders and drivers. The company's algorithms predict when ride demand will be high in specific locations, allowing drivers to be dispatched proactively. As a result, Uber has become a major global company.

Google has leveraged AI and machine learning to understand and improve its online services, leading to its success in multiple areas. In 2017, the company's CEO, Sundar Pichai, announced that Google would prioritize AI in its operations and become an "AI first" company.

What are the 4 types of artificial intelligence

According to Arend Hintze, an assistant professor at Michigan State University, there are four categories of AI, ranging from the task-specific intelligent systems that are currently in widespread use to sentient systems, which do not yet exist. The categories are:

Type 1

AI systems are reactive machines that have no memory and are only designed to perform specific tasks. An example is Deep Blue, the IBM chess program that defeated Garry Kasparov in the 1990s. These systems can only make decisions based on their current input and cannot draw on past experiences to inform future actions.

Type 2

Type 2 AI systems have limited memory and are able to use past experiences to inform future decisions. These systems are commonly used in decision-making functions for self-driving cars

Type 3: Theory of mind

Type 3 AI systems are characterized by theory of mind, or the ability to understand and interpret human emotions. These systems are able to infer human intentions and predict behavior, making them suitable for working alongside humans as part of a team. Theory of mind is a concept from psychology that has been applied to AI.

Type 4 Self Aware AI

Type 4 AI systems are self-aware and have a sense of consciousness. They understand their own current state and are able to reflect on their own experiences. This type of AI does not currently exist.

The history of artificial intelligence

Artificial intelligence (AI) has a long history dating back to ancient Greece, where the idea of a "machine that thinks" was first proposed. However, the modern conception of AI can be traced back to the publication of Alan Turing's paper, "Computing Machinery and Intelligence," in 1950.

1950 - Alan Turing

In this paper, Turing, a renowned computer scientist and World War II codebreaker, posed the question "can machines think?" and introduced the Turing Test as a way to measure a computer's intelligence by comparing it to that of a human. The value of the Turing Test has been debated by AI researchers and philosophers for decades.

1956 - John McCarthy

In 1956, the term "artificial intelligence" was coined by John McCarthy at the first-ever AI conference at Dartmouth College. McCarthy is also credited with inventing the Lisp programming language. That same year, Allen Newell, J.C. Shaw, and Herbert Simon developed the Logic Theorist, the first running AI software program. These early developments laid the foundation for the field of artificial intelligence and its continued evolution.

1967 - Frank Rosenblatt

Frank Rosenblatt designed and built the Mark 1 Perceptron, a computer that used a neural network to learn through trial and error. This marked the first time a computer based on a neural network was created. However, just a year later, Marvin Minsky and Seymour Papert published Perceptrons, a book that became a significant work on neural networks but also temporarily discouraged further research on neural networks.

1980

Backpropagation algorithms are implemented in neural networks to enable them to learn and adapt through trial and error. These algorithms are widely used in AI applications to improve the accuracy and efficiency of machine learning processes.

1997 - IBM Deep Blue

IBM's Deep Blue, a computer designed specifically to play chess, defeated world champion Garry Kasparov in a highly publicized match. The event marked a significant milestone in the development of artificial intelligence and its ability to outperform humans in certain tasks. Deep Blue was able to achieve this feat by using a backpropagation algorithm to train itself, a method that has since become widely used in AI applications.

2011 - IBM Watson

In 2011, IBM's artificial intelligence system called Watson competed against and ultimately defeated the two previous Jeopardy! champions, Ken Jennings and Brad Rutter, in a highly publicized event. Watson's ability to analyze and understand natural language, as well as its vast knowledge base, allowed it to outperform the human champions and win the game. This achievement marked a significant milestone in the field of artificial intelligence, demonstrating the capabilities of AI systems to analyze and process large amounts of data and make decisions based on that information.

2015 - Baidu's Minwa supercomputer

Baidu's Minwa supercomputer uses a specific type of deep neural network known as a convolutional neural network (CNN) to accurately identify and classify images at a rate that surpasses the average human. CNNs are particularly effective at image recognition tasks due to their ability to process and analyze visual data using a series of convolutional filters, which allow them to recognize patterns and features within the data. This ability to process large amounts of visual data with high accuracy has made CNNs a popular choice for many AI applications, including image classification, object detection, and facial recognition.

2016 - DeepMind AlphaGo

DeepMind's AlphaGo program, powered by a deep neural network, beats Lee Sodol, the world champion Go player, in a five-game match. The victory is significant given the huge number of possible moves as the game progresses (over 14.5 trillion after just four moves!). Later, Google purchased DeepMind for a reported USD 400 million.

2022 - ChatGPT

ChatGPT is a chatbot or virtual assistant powered by the GPT (Generative Pre-trained Transformer) language model, which is a machine learning model developed by OpenAI for natural language processing tasks. The GPT model can generate human-like text and has been used in a variety of applications, such as generating responses to user inputs in chatbots or generating content for websites or social media. Generative Pre-trained Transformer, or GPT, is a type of natural language processing (NLP) model developed by OpenAI. It is a transformer-based architecture that uses a self-attention mechanism to process input sequences and generate output sequences. GPT was specifically designed for language generation tasks, such as language translation, text summarization, and machine-generated text. It can be fine-tuned for a variety of language tasks, and has achieved state-of-the-art results in several benchmarks. GPT has been widely adopted in the NLP community and has been influential in the development of other NLP models.

Artificial intelligence applications

Artificial intelligence (AI) has become a powerful tool in a variety of industries, with applications ranging from speech recognition and customer service to computer vision and stock trading.

One of the most common uses of AI is speech recognition, also known as automatic speech recognition (ASR) or speech-to-text. This technology uses natural language processing to convert human speech into a written format, and is often incorporated into mobile devices to enable voice search or improve accessibility.

Another widespread application of AI is in customer service, where online virtual agents are increasingly replacing human agents. These virtual agents can answer frequently asked questions, provide personalized advice, and suggest products for customers, improving the overall customer experience across various platforms.

Computer vision is an area where AI has made significant progress. This technology allows computers to extract meaningful information from digital images, videos, and other visual inputs, and take actions based on those inputs. It is often used for tasks such as photo tagging in social media, radiology imaging in healthcare, and self-driving cars in the automotive industry.

Recommendation engines are an important application of AI, using past consumption behavior data to develop more effective cross-selling strategies for online retailers. These engines can make relevant recommendations to customers during the checkout process, improving the overall shopping experience.

Finally, AI is also used in automated stock trading, with AI-driven platforms making thousands or even millions of daily trades without human intervention. These platforms are designed to optimize stock portfolios and improve trading efficiency, most commonly named as trading algorithms or bots. 

Type of artificial intelligence: Weak vs strong AI

Weak AI, also known as Narrow AI or Artificial Narrow Intelligence (ANI), is AI that is specifically trained and focused on performing certain tasks. This type of AI is responsible for most of the AI we encounter in daily life, such as Siri, Alexa, IBM Watson, and autonomous vehicles. While Narrow AI might be more accurately described as "focused" rather than "weak," it is capable of powering some very powerful applications.

In contrast, Strong AI consists of Artificial General Intelligence (AGI) and Artificial Super Intelligence (ASI). AGI, or general AI, refers to a hypothetical form of AI that would possess human-like intelligence and self-awareness, able to solve problems, learn, and plan for the future. ASI, also known as superintelligence, would surpass the intelligence and capabilities of the human brain. While Strong AI is purely theoretical at this point, with no current practical examples, researchers are still exploring its potential development. Examples of ASI from science fiction, such as the superhuman rogue computer assistant HAL in 2001: A Space Odyssey, may provide a glimpse into the potential of this type of AI.

What does MA Titan do with artificial intelligence?

The financial markets are currently experiencing uncertainty and distortion due to high levels of debt and unconventional debt management strategies being employed by governments. This situation may result in asset inflation for investors, but it also carries the potential for shocks related to interest rates and volatility.

To address these challenges, our team of experts - including financial professionals, physicists, and mathematicians from top universities - at MA Titan has spent years researching and developing innovative ways to forecast volatility and make predictions about time-series data.

Our algorithms, powered by machine learning, are designed to adapt to different market conditions, allowing us to respond quickly to changes in the market and protect our clients from potential risks. We specialize in the intersection of economics, behavioral finance, data, and technology, and our focus is on understanding the factors that drive markets and how we can use this knowledge to help our clients reach their investment goals.

Despite the uncertainty and distortion in financial markets, MA Titan has shown that investors can still achieve success with the right technology and strategies.