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What is artificial intelligence (AI)?

by Stephen M. Walker II, Co-Founder / CEO

What is artificial intelligence (AI)?

Artificial Intelligence (AI) is a branch of computer science that aims to create systems capable of performing tasks that would typically require human intelligence. These tasks include learning, reasoning, problem-solving, perception, language understanding, and content generation. AI systems are designed to mimic or simulate human cognitive functions, and they can be categorized into two main types:

  1. Narrow or Weak AI — These Narrow AI systems are designed to perform specific tasks and are programmed to be very good at them, but they don't possess general intelligence or consciousness. Examples include chatbots, recommendation systems, and self-driving cars.

  2. General or Strong AI — This type of AI refers to systems that possess the ability to understand, learn, and apply knowledge in a way that is indistinguishable from human intelligence. Strong AI would be capable of performing any intellectual task that a human being can. This form of AI does not yet exist and is still a subject of research.

AI technologies utilize various methods and approaches, including machine learning, deep learning, natural language processing, robotics, and expert systems, to enable machines to perform tasks autonomously or with minimal human intervention.

AI systems are designed to simulate human intelligence processes, including reasoning, problem-solving, learning, and self-correction. They work by ingesting large amounts of labeled training data, analyzing the data for correlations and patterns, and using these patterns to make predictions about future states.

AI has a wide range of applications across various sectors. Some high-profile applications include advanced web search engines (like Google Search), recommendation systems (used by YouTube, Amazon, and Netflix), understanding human speech (such as Siri and Alexa), self-driving cars (like Waymo), and generative or creative tools (like ChatGPT, Midjourney, and Runway ML).

However, the use of AI also raises ethical questions, as an AI system will reinforce what it has already learned, for better or worse. As AI continues to evolve, it's important to consider these ethical implications and ensure that AI systems are used responsibly and beneficially.

What is the history of AI?

The history of artificial intelligence (AI) is a fascinating journey that began in antiquity, with myths, stories, and rumors of artificial beings endowed with intelligence or consciousness by master craftsmen. The seeds of modern AI were planted by philosophers who attempted to describe the process of human thinking as the mechanical manipulation of symbols. This work culminated in the invention of the programmable digital computer in the 1940s, a machine based on the abstract essence of mathematical reasoning.

The field of AI research was officially founded at a workshop held on the campus of Dartmouth College, USA during the summer of 1956. Those who attended would become the leaders of AI research for decades. Many of them predicted that a machine as intelligent as a human being would exist in no more than a generation, and they were given millions of dollars to make this vision come true.

In the 1940s and 50s, a handful of scientists from a variety of fields began to discuss the possibility of creating an artificial brain. The term "Artificial Intelligence" was chosen by John McCarthy to avoid associations with cybernetics and the influence of Norbert Wiener. The 1956 Dartmouth workshop was the moment that AI gained its name, its mission, its first success, and its major players, and is widely considered the birth of AI.

The first modern computers were the massive machines of the Second World War, such as Konrad Zuse's Z3, Alan Turing's Heath Robinson and Colossus, Atanasoff and Berry's ABC, and ENIAC at the University of Pennsylvania. ENIAC was based on the theoretical foundation laid by Alan Turing and developed by John von Neumann, and proved to be the most influential.

In the 1950s, Alan Turing published his work “Computer Machinery and Intelligence” which eventually became The Turing Test, which experts used to measure computer intelligence. In 1952, a computer scientist named Arthur Samuel developed a program to play checkers, which is the first to ever learn the game independently. In 1955, John McCarthy held a workshop at Dartmouth on “artificial intelligence” which is the first use of the word, and how it came into popular usage.

The period from 1957 to 1974 was a time of flourishing for AI. Computers could store more information and became faster, cheaper, and more accessible. Machine learning was successfully applied to many problems in academia and industry due to new methods, the application of powerful computer hardware, and the collection of immense data sets.

However, the field of AI went through multiple cycles of optimism followed by disappointment and loss of funding, known as "AI winters". Despite these challenges, AI gradually restored its reputation in the late 1990s and early 21st century by exploiting formal mathematical methods and by finding specific solutions to specific problems. This "narrow" and "formal" focus allowed researchers to produce verifiable results and collaborate with other fields.

Today, AI is used successfully throughout the technology industry, although somewhat behind the scenes. Some of the success was due to increasing computer power and some was achieved by focusing on specific isolated problems and pursuing them with the highest standards of scientific accountability. AI researchers began to develop and use sophisticated mathematical tools more than they ever had in the past. There was a widespread realization that many of the problems that AI needed to solve were already being worked on by researchers in fields like mathematics, electrical engineering, economics, or operations research.

The future of AI is still unfolding, with ongoing research in areas such as artificial general intelligence (AGI), which aims to create machines capable of performing any intellectual task that a human being can do. As the CEO of a generative AI startup, you are part of this exciting journey, contributing to the evolution of AI and its impact on society.

Who were the pioneers in AI?

The development of artificial intelligence (AI) has been shaped by numerous pioneers who have made significant contributions to the field. Here are some of the key figures:

  1. Alan Turing — An English mathematician and computer scientist, Turing is often considered the father of computer science and AI. His Turing Machine is one of the first basic computers to be created. He also introduced the Turing Test, which holds that if a computer can carry dialogue with a person, this is proof of that machine's "thinking".

  2. John McCarthy — An American computer scientist, McCarthy coined the term "Artificial Intelligence" in 1956 and is widely regarded as one of the founders of the AI field. He also developed the programming language Lisp, which became an important language for AI research.

  3. Marvin Minsky — An American cognitive scientist and co-founder of the Massachusetts Institute of Technology (MIT) AI Laboratory, Minsky made significant contributions to AI and robotics. He coined the term "perceptrons" in his groundbreaking work concerning artificial neural networks.

  4. Allen Newell and Herbert A. Simon — Both Newell and Simon were American computer scientists and AI researchers. They developed the Logic Theorist, the first AI program that could prove mathematical theorems, and the General Problem Solver (GPS), a problem-solving program that demonstrated human-like reasoning.

  5. Arthur Samuel — An American computer scientist, Samuel is known for his work on machine learning and is considered a pioneer in the development of self-learning systems. He created one of the first programs that could play checkers at a competitive level and improve its performance through learning.

  6. Frank Rosenblatt — An American psychologist and computer scientist, Rosenblatt developed the perceptron, an early artificial neural network model. His work laid the groundwork for modern neural network research.

  7. Claude Shannon — An American mathematician, electrical engineer, and cryptographer, Shannon made significant contributions to information theory and laid the foundation for understanding communication and computation in machines and humans.

  8. IBM — The company hit a major milestone with its development and final creation of Deep Blue, an artificial intelligence system designed specifically for playing chess. It was the first documented case of AI competing and winning against humans in a contest.

  9. Ross Quillian — Quillian was a leading scientist and researcher during the 1960s in the fields of electronics and communications. His work with Project SYNTHEX, where he developed the first semantic web that would be used in AI applications, is considered his foremost contribution to artificial intelligence.

  10. Edward Feigenbaum — Feigenbaum is another important contributor to the science of AI. His involvement in several projects at Stanford University, including Dendral, which used computer systems to identify and communicate the presence of diseases when given spectrometer readings of blood samples, was significant to AI sciences.

These pioneers have laid the groundwork for the AI technologies we see today, shaping the future of the enterprise and society as we know it.

What are the goals of AI?

The goal of Artificial Intelligence (AI) is to simulate human-like intelligence in machines, enabling them to carry out complex tasks and decision-making processes autonomously. This is primarily achieved by reverse-engineering human capabilities and traits and applying them to machines. The foundational goal of AI is to design technology that enables computer systems to work intelligently yet independently.

AI aims to develop efficient problem-solving algorithms that can make logical deductions and simulate human reasoning while solving complex puzzles. It also focuses on planning, which involves determining a procedural course of action for a system to achieve its goals and optimize overall performance through predictive analytics, data analysis, forecasting, and optimization models.

AI also promotes creativity and artificial thinking that can help humans accomplish tasks better. It can churn through vast volumes of data, consider options and alternatives, and develop creative paths or opportunities for us to progress. Furthermore, AI researchers aim to develop machines with general AI capabilities that combine all the cognitive skills of humans and perform tasks with better proficiency than us.

AI has numerous applications across various sectors. In e-commerce, AI technology is used to create recommendation engines that engage better with customers based on their browsing history, preference, and interests. In the education sector, AI helps increase productivity among faculties and helps them concentrate more on students than office or administration work.

AI also finds diverse applications in the healthcare sector, where it is used to build sophisticated machines that can detect diseases and identify cancer cells. It can help analyze chronic conditions with lab and other medical data to ensure early diagnosis.

In the field of robotics, AI applications are commonly used. Robots powered by AI use real-time updates to sense obstacles in its path and pre-plan its journey instantly. AI is also used in human resources where companies use intelligent software to ease the hiring process.

In agriculture, AI is used to identify defects and nutrient deficiencies in the soil. This is done using computer vision, robotics, and machine learning applications. In gaming, AI has seen a wide range of applications throughout the years.

The goal of AI is to simulate human intelligence in machines, enabling them to perform complex tasks autonomously. Its applications are vast and span across various sectors, revolutionizing industries and helping solve complex problems.

What are the types of AI?

Artificial Intelligence (AI) can be broadly classified into two categories: Weak/Narrow AI and Strong/General AI.

Weak AI, also known as Narrow AI, is designed to perform a specific task, such as voice command recognition or driving a car. These AI systems operate within a limited pre-defined range of functions and cannot perform beyond their limitations. They focus on a single subset of cognitive abilities and advance in that spectrum. Examples of Narrow AI include Apple's Siri, IBM's Watson supercomputer, Google Translate, image recognition software, recommendation systems, and spam filtering.

On the other hand, Strong AI, also known as General AI, is designed to understand, learn, and apply knowledge across a broad range of tasks that a human being can do. It allows a machine to apply knowledge and skills in different contexts. However, AI researchers have not yet achieved strong AI, as it would require making machines conscious and programming a full set of cognitive abilities. Microsoft has invested $1 billion in General AI through OpenAI.

There's also a concept of Super AI, which surpasses human intelligence and can perform any task better than a human. It's so akin to human sentiments and experiences that it doesn't merely understand them; it also evokes emotions, needs, beliefs, and desires of its own. However, its existence is still hypothetical.

In terms of functionalities, AI can be further classified into four types: Reactive Machines, Limited Memory Machines, Theory of Mind, and Self-aware AI. Reactive machines are the simplest forms of AI, which can only respond to a limited set of inputs and do not have the ability to learn or use past experiences to inform their present actions. Limited Memory Machines can use past experiences to inform their present actions. Theory of Mind AI is an advanced form of AI that can understand entities it interacts with by discerning their needs, emotions, beliefs, and thought processes. Self-aware AI is the most advanced form of AI, which is not only capable of understanding and interacting with the world but also has its own consciousness.

It's important to note that while Narrow AI has become increasingly common in our day-to-day lives, General AI is still in its early stages of development, and Super AI remains a concept for the future.

What are major areas of AI research?

Artificial Intelligence (AI) research is a vast field with numerous areas of focus. Here are some of the major areas:

  1. Machine Learning — This involves creating algorithms that allow computers to learn from and make decisions or predictions based on data. There are three main types of machine learning: supervised, unsupervised, and reinforcement learning. Supervised learning involves making predictions from labeled data, such as predicting house prices based on features like area, number of bedrooms, and amenities. Unsupervised learning, on the other hand, involves finding hidden patterns in unlabeled data, such as categorizing users based on their social media activities. Reinforcement learning is a type of machine learning where an agent learns to make decisions by interacting with an environment, receiving rewards for correct actions and penalties for incorrect ones.

  2. Neural Networks and Deep Learning — Neural networks are a means of doing machine learning, where a computer learns to perform tasks by analyzing training examples. Deep learning, a subset of neural networks, involves using large neural networks with many layers of nodes, hence the term "deep". These techniques have been responsible for the best-performing systems in almost every area of AI research.

  3. Computer Vision — This field of AI enables computers to derive meaningful information from digital images, videos, and other visual inputs. It involves training machines to perform functions similar to human vision, such as distinguishing objects, determining their distance, and detecting movement.

  4. Natural Language Processing (NLP) — NLP involves the interaction between computers and human language. It includes areas like speech recognition, machine translation, and chatbots. NLP allows computers to understand, interpret, and generate human language in a valuable and meaningful way.

  5. Robotics — This involves creating machines (robots) that can interact with their environment. Key areas of research in robotics include sensors (for perceiving the environment), actuators (for movement or interaction), locomotion (movement capabilities), and manipulation (interacting with objects) .

  6. Planning and Problem Solving — This involves creating systems that can plan actions or solve problems, often in complex or dynamic environments. Key areas of research include search algorithms, logic, and knowledge representation.

  7. Expert Systems — These are computer systems that emulate the decision-making ability of a human expert. They are designed to solve complex problems by reasoning through bodies of knowledge, represented mainly as if-then rules.

Each of these areas has its unique challenges and opportunities, and they often overlap and influence each other. For example, machine learning techniques are often used in computer vision, natural language processing, and robotics. Similarly, planning and problem-solving techniques can be used in expert systems and robotics.

What is the current state of AI?

The current state of AI showcases significant advancements and widespread adoption across various sectors. AI has demonstrated its potential in enhancing efficiency, reducing human errors, and extracting insights from large datasets. However, there are limitations and ethical concerns that need to be addressed.

Successes of modern AI systems include:

  1. Improved efficiency in various industries, such as finance, healthcare, transportation, and retail.
  2. Enhanced decision-making capabilities through data analysis and predictive analytics.
  3. Automation of repetitive tasks, freeing up human resources for more creative and strategic work.

Limitations of modern AI systems include:

  1. Lack of common sense reasoning, which can lead to errors when dealing with novel situations.
  2. Limited understanding of context and nuances of human language and communication.
  3. Potential for bias and discrimination due to biased training data.
  4. High costs of development and maintenance.

The AI effect refers to the phenomenon where people discount the behavior of an AI program by arguing that it is not "real" intelligence. As AI systems become more advanced and capable of performing tasks previously thought to be exclusive to humans, the perception of what is considered "intelligent" or "advanced" changes.

Ethical considerations and risks associated with AI include:

  1. Privacy and surveillance concerns, as AI systems often rely on large amounts of personal data.
  2. Bias and discrimination, as AI systems can perpetuate and amplify existing biases in the data they are trained on.
  3. Job displacement, as AI automation has the potential to replace human jobs, resulting in widespread unemployment and exacerbating economic inequalities.
  4. Lack of transparency and accountability in AI decision-making processes.
  5. Potential misuse of AI for harmful purposes, such as digital warfare and lethal autonomous weapons.

Addressing these limitations and ethical concerns is crucial for harnessing the full potential of AI and ensuring its responsible development and deployment in various sectors.

Who are the current leaders in applied AI and AI research?

The current leaders in applied AI and AI research include a mix of researchers, entrepreneurs, and executives who have made significant contributions to the field. Some of the most influential figures in AI today are:

  1. Andrew Ng: Founder of DeepLearning.AI and Landing AI, and co-founder of Coursera.
  2. Fei-Fei Li: Co-Director of the Stanford Institute for Human-Centered AI and Professor at Stanford University.
  3. Andrej Karpathy: Former Senior Director of AI at Tesla and current OpenAI project leader.
  4. Demis Hassabis: CEO and Co-Founder of Google DeepMind.
  5. Ian Goodfellow: Director of Machine Learning at Apple.
  6. Yann LeCun: Chief AI Scientist at Meta (Facebook) and Silver Professor of Computer Science at New York University.
  7. Geoffrey Hinton: Emeritus Professor at the University of Toronto and a researcher at Google Brain.
  8. Ruslan Salakhutdinov: Director of AI Research at Apple and Associate Professor at Carnegie Mellon University.
  9. Alex Smola: Director of Machine Learning at Amazon Web Services.
  10. Rana el Kaliouby: CEO and Co-founder of Affectiva, and Deputy CEO of Smart Eye.

These leaders have made groundbreaking contributions to AI research and development, shaping the future of the field and its applications across various industries.

What is the The Future of AI?

The future of AI is predicted to be increasingly pervasive, revolutionizing sectors including healthcare, banking, and transportation. The core technology behind most of the most visible advances is machine learning, especially deep learning, including generative adversarial networks or GANs. As AI evolves and integrates into our lives, it will become increasingly omnipresent in everything we do.

In the healthcare industry, AI is expected to bring significant benefits. With the ability to analyze vast amounts of data quickly and accurately, AI-powered systems can improve patient outcomes and reduce healthcare costs. For instance, AI can help medical institutions function better, reducing operating costs and saving money. It also has the potential for personalized medication regimens and treatment plans, as well as increased provider access to data from several medical institutions.

In the automotive industry, AI is already making its mark with smart cars. The percentage of automobiles with AI-driven technologies is predicted to rise significantly by 2025. AI is also expected to boost productivity in our workplaces, replacing tedious or dangerous tasks and freeing the human workforce to focus on tasks requiring creativity and empathy.

AI is also transforming the way we interact with technology. Intelligent assistants such as Siri, Alexa, and Google Assistant are becoming increasingly popular and are changing the way we interact with our devices. The next generation of generative AI tools will go far beyond the chatbots and image generators that we have today. Generative video and music creators are already appearing, and they will become more powerful and user-friendly.

However, the future of AI also presents challenges. One of the biggest concerns is the potential for AI systems to perpetuate bias and discrimination. This is because AI systems are only as unbiased as the data they are trained on. If the data is biased, then the AI system will be biased as well. Another concern is the potential for AI to be misused or abused. For example, AI-powered autonomous weapons have the potential to cause significant harm and raise concerns about accountability and responsibility.

In terms of the workforce, AI is not a job killer, but rather a job transformer. By automating repetitive and low-value tasks, AI frees up human workers to focus on higher-level activities. While some jobs may become obsolete, new jobs that require skills such as creativity, critical thinking, and empathy will emerge.

The future of AI is both exciting and challenging. As we continue to develop and deploy AI, it is important that we address these concerns and work to ensure that the benefits of AI are shared by all members of society. This requires a multi-disciplinary approach that brings together experts from a variety of fields, including technology, ethics, and policy.

More terms

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The computational problem in artificial intelligence (AI) refers to the challenges and limitations associated with developing efficient algorithms and techniques for solving complex tasks or problems within various domains of computation, such as optimization, decision-making, pattern recognition, and knowledge representation. These challenges often arise from the inherent complexity and uncertainty of real-world systems, which can involve large-scale or high-dimensional data, nonlinear relationships between variables, dynamic changes or disturbances in the environment, and limited computational resources or constraints.

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What is a spiking neural network?

Spiking neural networks (SNNs) are a type of artificial neural network that simulate the behavior of biological neurons. They are based on the idea that information processing in the brain occurs through the generation and propagation of spikes, or electrical impulses, between neurons.

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