Artificial intelligence (AI) is technology that enables computers and digital devices to learn, read, write, talk, see, create, play, analyze, make recommendations, and do other things humans do.
In addition, AI refers to the field of computer science focused on developing these technologies. Yet, at its simplest form, artificial intelligence is a field which combines computer science and robust datasets to enable problem-solving. It also encompasses sub-fields of machine learning and deep learning, which are frequently mentioned in conjunction with artificial intelligence. These disciplines are comprised of AI algorithms which seek to create expert systems to make predictions or classifications based on input data.
Artificial intelligence has gone through many cycles of hype, but even to skeptics, the release of OpenAI’s ChatGPT seems to mark a turning point. The last time generative AI loomed this large, the breakthroughs were in computer vision, but now the leap forward is in natural language processing (NLP). And it’s not just human language: Generative models can also learn the grammar of software code, molecules, natural images, and a variety of other data types. Some no-code interfaces enable people without coding skills to use visual interfaces and intuitive controls including drag-and-drop to create and modify applications quickly and efficiently while the actual code remains hidden in the background.
While a number of definitions for artificial intelligence have surfaced over the last few decades, John McCarthy proposed one of the most widely-cited: "It is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable."
The applications for this technology are growing every day, and we’re just starting to explore the possibilities. But as the hype around the use of AI tools in business takes off, conversations around ethics become critically important. For more on where IBM stands within the conversation, please read What is AI ethics? and Building trust in AI.
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Weak AI—also known as Narrow AI or Artificial Narrow Intelligence (ANI)—is AI trained and focused to perform specific tasks. Weak AI drives most of the AI that surrounds us today. "Narrow" might be a more accurate descriptor for this type of AI as it is anything but weak: it enables some very robust applications, such as Apple's Siri, Amazon's Alexa, IBM watsonx™, and self-driving vehicles.
Strong AI is made up of Artificial General Intelligence (AGI) and Artificial Super Intelligence (ASI). AGI, or general AI, is a theoretical form of AI where a machine would have an intelligence equal to humans; it would be self-aware with a consciousness that would have the ability to solve problems, learn, and plan for the future. ASI—also known as superintelligence—would surpass the intelligence and ability of the human brain. While strong AI is still entirely theoretical with no practical examples in use today, that doesn't mean AI researchers aren't also exploring its development. In the meantime, the best examples of ASI might be from science fiction, such as HAL, the superhuman and rogue computer assistant in 2001: A Space Odyssey.
Since deep learning and machine learning tend to be used interchangeably, it’s worth noting the nuances between the two. As mentioned above, both deep learning and machine learning are sub-fields of artificial intelligence, and deep learning is a sub-field of machine learning.
Deep learning is comprised of neural networks. The “deep” in deep learning refers to a neural network comprised of more than three layers—which would be inclusive of the inputs and the output—which can be considered a deep learning algorithm. This is generally represented using the diagram below.
The way in which deep learning and machine learning differ is in how each algorithm learns. Deep learning automates much of the feature extraction piece of the process, eliminating some of the manual human intervention required and enabling the use of larger data sets. You can think of deep learning as "scalable machine learning," as Lex Fridman noted in same MIT lecture above. Classical, or "non-deep," machine learning is more dependent on human intervention to be trained. Human experts determine the hierarchy of features to understand the differences between data inputs, usually requiring more structured data to learn. Enterprise-grade GPUs will help power through the mathematically intensive workloads required for deep learning and machine learning.
"Deep" machine learning can leverage labeled datasets, also known as supervised learning, to inform its algorithm—but it can also conduct unsupervised learning using raw content (text or images, for example)—and it can automatically determine the hierarchy of features which distinguish different categories of data from one another. Unlike machine learning, it doesn't require human intervention to process data, enabling us to scale machine learning in more interesting ways.
Generative AI refers to deep-learning models that can take raw data—say, all of Wikipedia or the collected works of Rembrandt—and “learn” to generate statistically probable outputs when prompted. At a high level, generative models encode a simplified representation of their training data and draw from it to create a new work that’s similar, but not identical, to the original data.
Generative models have been used for years in statistics to analyze numerical data. The rise of deep learning, however, made it possible to extend them to images, speech, and other complex data types. Among the first class of AI models to achieve this cross-over feat were variational autoencoders, or VAEs, introduced in 2013. VAEs were the first deep-learning models to be widely used for generating realistic images and speech.
“VAEs opened the floodgates to deep generative modeling by making models easier to scale,” said Akash Srivastava, an expert on generative AI at the MIT-IBM Watson AI Lab. “Much of what we think of today as generative AI started here.”
Early examples of models, including GPT-3, BERT, or DALL-E 2, have shown what’s possible. In the future, models will be trained on a broad set of unlabeled data that can be used for different tasks, with minimal fine-tuning. Systems that execute specific tasks in a single domain are giving way to broad AI systems that learn more generally and work across domains and problems. Foundation models, trained on large, unlabeled datasets and fine-tuned for an array of applications, are driving this shift.
As to the future of AI, when it comes to generative AI, it is predicted that foundation models will dramatically accelerate AI adoption in enterprise. Reducing labeling requirements will make it much easier for businesses to dive in, and the highly accurate, efficient AI-driven automation they enable will mean that far more companies will be able to deploy AI in a wider range of mission-critical situations. For IBM, the hope is that the computing power of foundation models can eventually be brought to every enterprise in a frictionless hybrid-cloud environment.
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There are numerous, real-world applications for AI systems today. Below are some of the most common use cases:
The idea of "a machine that thinks" dates back to ancient Greece. But since the advent of electronic computing (and relative to some of the topics discussed in this article) important events and milestones in the evolution of artificial intelligence include the following:
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