![]() I recently demonstrated to a real estate lending company how it can be configured to predict the condition of houses using natural language descriptions or images using off-the-shelf software with minimal training. The technology is general enough to be applied to all kinds of problems. Video equivalents of GPT3 are in the works. It turns out that in the process of solving autocomplete, it also learns the implicit relationships among things, which is useful in solving more general tasks such as answering questions and creating new materials based on prompts. It can guess the next word in a sequence, which in turn is used to guess the next word, and so on, to the point where it can write entire paragraphs and stories. What is fascinating is that ChatGPT3’s core competence is its ability to autocomplete sentences. The question is, when can you rely on them.Ĭurrent day systems such as ChatGPT3 learn almost entirely through “self-supervision,” that is, by constructing their own training data from all available language content on the Internet. Current-day machines can often learn even better from data in minutes. In earlier generations of AI, knowledge had to be specified painstakingly by humans, which could take years with no assurance of success. These methods have been chipping away at a major bottleneck that has been central to AI: how to get reliable knowledge into the machine and use it. And there’s a lot to observe, buried in all the data out there. Looking at the history of AI, the paradigm shifts have been towards methods that rely less on human-specified knowledge and more on machines learning through observation on their own. Will AI disrupt my business model and eat my lunch? Should I worry? Can I leverage AI to stay ahead of the pack?Īll good questions with the same answer: yes. I sense a palpable fear among business leaders. It gave some good answers, but not quite as nuanced as Paul’s.Īt a recent talk I gave to kick off an “AI Innovation day” at a large financial services organization, I described the paradigm shifts that have occurred in AI, and what is different this time around with developments such as ChatGPT3, which has captured the world’s attention in a few short weeks.Įvery business these days seems to be focused on AI. ![]() ![]() ![]() Īs you might imagine, I asked ChatGPT3 some of the same questions that I asked Paul during our conversation. A key question confronting us is the following: “Are current laws adequate for social media platforms?” Or do we need amendments or new laws altogether to deal with this 21 st century phenomenon? For answers, tune into my conversation with Paul. What is bizarre is that even as platforms struggle with content moderation, some states are demanding that platforms NOT be able to preclude certain types of content. Amplification and suppression of content is at the heart of these cases. There are now several cases awaiting a Supreme Court verdict around liability. My most recent podcast was about the state of the social media landscape with legal scholar Paul Barrett, who is Deputy Director at NYU Stern Center for Business and Human Rights.Įven before the Musk/Twitter storm, social media content and its moderation were a hot political issue.
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