It’s been only a year since ChatGPT was first commercially released, making generative AI widely available to the public. Yet in Amazon’s most recent earnings call, Chief Executive Officer Andy Jassy said he believes it’s an opportunity that will already mean “tens of billions of dollars of revenue for AWS over the next several years.” “Our generative AI business,” he noted, “is growing very, very quickly.”
Amazon is not alone. Microsoft has been a leading investor in what Bill Gates has called “the most important advance in technology since the graphical user interface.”
Share prices of Microsoft and Alphabet, another major AI investor, have surged this year with Alphabet up 57% and Microsoft 38%. And it’s not just lighting up Wall Street: AI, and especially generative AI, is everywhere, with search terms like “chat gpt” up 3,150% on Google over the past year.
It’s been likened to magic and the atomic bomb, but how, exactly, does it work? This article looks to answer that question, for any who experts in the field are not already.
What do we mean by AI?
With AI (artificial intelligence), we refer to an intelligence that’s a constructed thing (software, machine) that can learn, reason, and problem-solve. It can be broken down further into weak AI, or an intelligence with a narrow focus (like those built to master chess, or self-driving cars), and strong AI, which is expected to be more robust, or diverse, capable of considering problems it may not have been specifically built to address.
Machine learning is a subset of AI that’s focused on learning from data and experience, and you can check out this PTP article for more on how it works and is different from AI in general.
Deep learning is a subset of machine learning (we’ll get to this below).
For an overview of AI in general, check out this PTP article.
What about generative AI?
Looking specifically at