Artificial intelligence (AI) is intelligence demonstrated by machines, as opposed to the natural intelligence displayed by humans or animals. Leading AI textbooks define the field as the study of “intelligent agents“: any system that perceives its environment and takes actions that maximize its chance of achieving its goals.[a] Some popular accounts use the term “artificial intelligence” to describe machines that mimic “cognitive” functions that humans associate with the human mind, such as “learning” and “problem solving”, however this definition is rejected by major AI researchers.[b][c]
AI applications include advanced web search engines (i.e. Google), recommendation systems (used by YouTube, Amazon and Netflix), understanding human speech (such as Siri or Alexa), self-driving cars (e.g. Tesla), and competing at the highest level in strategic game systems (such as chess and Go), As machines become increasingly capable, tasks considered to require “intelligence” are often removed from the definition of AI, a phenomenon known as the AI effect. For instance, optical character recognition is frequently excluded from things considered to be AI, having become a routine technology.
Artificial intelligence was founded as an academic discipline in 1956, and in the years since has experienced several waves of optimism, followed by disappointment and the loss of funding (known as an “AI winter“), followed by new approaches, success and renewed funding. AI research has tried and discarded many different approaches during its lifetime, including simulating the brain, modeling human problem solving, formal logic, large databases of knowledge and imitating animal behavior. In the first decades of the 21st century, highly mathematical statistical machine learning has dominated the field, and this technique has proved highly successful, helping to solve many challenging problems throughout industry and academia.
Beyond the buzzwords, media coverage, and hype, artificial intelligence techniques are becoming a fundamental business growth component across a wide range of industries. And while the various terms (algorithms, transfer learning, deep learning, neural networks, NLP, etc.) associated with AI are thrown around in meetings and product planning sessions, it’s easy to be skeptical of the potential impact of these technologies.
Today’s media represents AI in many ways, both good and bad— from the fear of machines taking over all human jobs and portrayals of evil AIs via Hollywood to the much-lauded potential of curing cancer and making our lives easier. Of course, the truth is some‐ where in between. While there are valid concerns about how the future of artificial intelligence will play out (and the social implications), the reality is that the technology is currently used in companies across all industries. AI is used everywhere—IoT (Internet of Things) and home devices, commercial and industrial robots, autonomous vehicles, drones, digital assistants, and even wearables. And that’s just the start.
The Market for Artificial Intelligence The market for artificial intelligence is already large and growing rapidly, with numerous research reports indicating a growing demand for tools that automate, predict, and quickly analyze. Esti‐ mates from IDC predict revenue from artificial intelligence will reach $98.4 billion by 2023 with a compound annual growth rate (CAGR) of 28.5% over the forecast period, with nearly half of that going to software. Additionally, investment in AI and machine learning companies has increased dramatically—AI startups raised $26.6 billion in funding in 2019. Clearly, the future of artificial intelligence appears healthy.
Applications in the Enterprise
From finance to cybersecurity to manufacturing, there isn’t an industry that will not be affected by AI. But before we can discuss and examine building applications in the enterprise with AI, we first need to define what we mean by enterprise applications. There are two common definitions of “the enterprise”: • A company of significant size and budget • Any business-to-business commerce (i.e., not a consumer)
“The single most mind-blowing application of machine learning I’ve ever seen,” Instagram’s co-founder Mike Krieger enthused about Copilot.
It is based on an artificial intelligence called GPT-3, released last summer by OpenAI, a San Francisco-based AI lab, co-founded by Elon Musk.
This GPT (which stands for generative pre-training) engine does a “very simple but very large thing – predicting the next letter in a text,” explains Grzegorz Jakacki, Warsaw-based founder of Codility, which makes a popular hiring test.
OpenAI trained the AI on texts already available online such as books, Wikipedia and hundreds of thousands of web pages, a diet that was “somewhat curated but in all possible human languages,” he says.
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