69 Artificial Intelligence (AI) Companies to Know

Artificial intelligence (AI) is a wide-ranging branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence. While AI is an interdisciplinary science with multiple approaches, advancements in machine learning and deep learning, in particular, are creating a paradigm shift in virtually every sector of the tech industry.
Artificial intelligence allows machines to model, or even improve upon, the capabilities of the human mind. And from the development of self-driving cars to the proliferation of generative AI tools like ChatGPT and Google’s Bard, AI is increasingly becoming part of everyday life — and an area companies across every industry are investing in.
Broadly speaking, artificially intelligent systems can perform tasks commonly associated with human cognitive functions — such as interpreting speech, playing games and identifying patterns. They typically learn how to do so by processing massive amounts of data, looking for patterns to model in their own decision-making. In many cases, humans will supervise an AI’s learning process, reinforcing good decisions and discouraging bad ones. But some AI systems are designed to learn without supervision — for instance, by playing a video game over and over until they eventually figure out the rules and how to win.
Intelligence is tricky to define, which is why AI experts typically distinguish between strong AI and weak AI.
Strong AI, also known as artificial general intelligence, is a machine that can solve problems it’s never been trained to work on — much like a human can. This is the kind of AI we see in movies, like the robots from Westworld or the character Data from Star Trek: The Next Generation. This type of AI doesn’t actually exist yet.
The creation of a machine with human-level intelligence that can be applied to any task is the Holy Grail for many AI researchers, but the quest for artificial general intelligence has been fraught with difficulty. And some believe strong AI research should be limited, due to the potential risks of creating a powerful AI without appropriate guardrails.
In contrast to weak AI, strong AI represents a machine with a full set of cognitive abilities — and an equally wide array of use cases — but time hasn't eased the difficulty of achieving such a feat.
Weak AI, sometimes referred to as narrow AI or specialized AI, operates within a limited context and is a simulation of human intelligence applied to a narrowly defined problem (like driving a car, transcribing human speech or curating content on a website).
Weak AI is often focused on performing a single task extremely well. While these machines may seem intelligent, they operate under far more constraints and limitations than even the most basic human intelligence.
Weak AI examples include:
Although the terms “machine learning” and “deep learning” come up frequently in conversations about AI, they should not be used interchangeably. Deep learning is a form of machine learning, and machine learning is a subfield of artificial intelligence.
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 been specifically programmed for that task. Instead, ML algorithms use historical data as input to predict new output values. To that end, ML consists of both supervised learning (where the expected output for the input is known thanks to labeled data sets) and unsupervised learning (where the expected outputs are unknown due to the use of unlabeled data sets).
Deep learning is a type of machine learning that runs inputs through a biologically inspired neural network architecture. The neural networks contain a number of hidden layers through which the data is processed, allowing the machine to go “deep” in its learning, making connections and weighting input for the best results.
AI can be divided into four categories, based on the type and complexity of the tasks a system is able to perform. They are:
A reactive machine follows the most basic of AI principles and, as its name implies, is capable of only using its intelligence to perceive and react to the world in front of it. A reactive machine cannot store a memory and, as a result, cannot rely on past experiences to inform decision making in real time.
Perceiving the world directly means that reactive machines are designed to complete only a limited number of specialized duties. Intentionally narrowing a reactive machine’s worldview has its benefits, however: This type of AI will be more trustworthy and reliable, and it will react the same way to the same stimuli every time.
Limited memory AI has the ability to store previous data and predictions when gathering information and weighing potential decisions — essentially looking into the past for clues on what may come next. Limited memory AI is more complex and presents greater possibilities than reactive machines.
Limited memory AI is created when a team continuously trains a model in how to analyze and utilize new data or an AI environment is built so models can be automatically trained and renewed.
When utilizing limited memory AI in ML, six steps must be followed:
Theory of mind is just that — theoretical. We have not yet achieved the technological and scientific capabilities necessary to reach this next level of AI.
The concept is based on the psychological premise of understanding that other living things have thoughts and emotions that affect the behavior of one’s self. In terms of AI machines, this would mean that AI could comprehend how humans, animals and other machines feel and make decisions through self-reflection and determination, and then utilize that information to make decisions of their own. Essentially, machines would have to be able to grasp and process the concept of “mind,” the fluctuations of emotions in decision-making and a litany of other psychological concepts in real time, creating a two-way relationship between people and AI.
Once theory of mind can be established, sometime well into the future of AI, the final step will be for AI to become self-aware. This kind of AI possesses human-level consciousness and understands its own existence in the world, as well as the presence and emotional state of others. It would be able to understand what others may need based on not just what they communicate to them but how they communicate it.
Self-awareness in AI relies both on human researchers understanding the premise of consciousness and then learning how to replicate that so it can be built into machines.
Artificial intelligence technology takes many forms, from chatbots to navigation apps and wearable fitness trackers. The below examples illustrate the breadth of potential AI applications.
ChatGPT is an artificial intelligence chatbot capable of producing written content in a range of formats, from essays 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 emulate human writing.
Google Maps uses location data from smartphones, as well as user-reported data on things like construction and car accidents, to monitor the ebb and flow of traffic and assess what the fastest route will be.
Personal assistants like Siri, Alexa and Cortana use natural language processing, or NLP, to receive instructions from users to set reminders, search for online information 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 suggestions and more tailored responses.
Snapchat filters use ML algorithms to distinguish between an image’s subject and the background, track facial movements and adjust the image on the screen based on what the user is doing.
Self-driving cars are a recognizable example of deep learning, since they use deep neural networks to detect objects around them, determine their distance from other cars, identify traffic signals and much more.
The wearable sensors and devices used in the healthcare industry also apply deep learning to assess the health condition of the patient, including their blood sugar levels, blood pressure and heart rate. They can also derive patterns from a patient’s prior medical data and use that to anticipate any future health conditions.
MuZero, a computer program created by DeepMind, is a promising frontrunner in the quest to achieve true artificial general intelligence. It has managed to master games it has not even been taught to play, including chess and an entire suite of Atari games, through brute force, playing games millions of times.
AI has many uses — from boosting vaccine development to automating detection of potential fraud. AI companies raised $66.8 billion in funding in 2022, according to CB Insights research, more than doubling the amount raised in 2020. Because of its fast-paced adoption, AI is making waves in a variety of industries.
Business Insider Intelligence’s 2022 report on AI in banking found more than half of financial services companies already use AI solutions for risk management and revenue generation. The application of AI in banking could lead to upwards of $400 billion in savings.
As for medicine, a 2021 World Health Organization report noted that while integrating AI into the healthcare field comes with challenges, the technology “holds great promise,” as it could lead to benefits like more informed health policy and improvements in the accuracy of diagnosing patients.
AI has also made its mark on entertainment. The global market for AI in media and entertainment is estimated to reach $99.48 billion by 2030, growing from a value of $10.87 billion in 2021, according to Grand View Research. That expansion includes AI uses like recognizing plagiarism and developing high-definition graphics.
While AI is certainly viewed as an important and quickly evolving asset, this emerging field comes with its share of downsides.
The Pew Research Center surveyed 10,260 Americans in 2021 on their attitudes toward AI. The results found 45 percent of respondents are equally excited and concerned, and 37 percent are more concerned than excited. Additionally, more than 40 percent of respondents said they considered driverless cars to be bad for society. Yet the idea of using AI to identify the spread of false information on social media was more well received, with close to 40 percent of those surveyed labeling it a good idea.
AI is a boon for improving productivity and efficiency while at the same time reducing the potential for human error. But there are also some disadvantages, like development costs and the possibility for automated machines to replace human jobs. It’s worth noting, however, that the artificial intelligence industry stands to create jobs, too — some of which have not even been invented yet.
When one considers the computational costs and the technical data infrastructure running behind artificial intelligence, actually executing on AI is a complex and costly business. Fortunately, there have been massive advancements in computing technology, as indicated by Moore’s Law, which states that the number of transistors on a microchip doubles about every two years while the cost of computers is halved.
Although many experts believe that Moore’s Law will likely come to an end sometime in the 2020s, this has had a major impact on modern AI techniques — without it, deep learning would be out of the question, financially speaking. Recent research found that AI innovation has actually outperformed Moore’s Law, doubling every six months or so as opposed to two years.
By that logic, the advancements artificial intelligence has made across a variety of industries have been major over the last several years. And the potential for an even greater impact over the next several decades seems all but inevitable.
Intelligent robots and artificial beings first appeared in ancient Greek myths. And Aristotle’s development of syllogism and its use of deductive reasoning was a key moment in humanity’s quest to understand its own intelligence. While the roots are long and deep, the history of AI as we think of it today spans less than a century. The following is a quick look at some of the most important events in AI.