Media
What Google's "Sentient" A.I. Is Really Thinking
Recently, Blake Lemoine, a Google AI engineer, caught the attention of the tech world by claiming that an AI is sentient. The AI in question is called LaMDA (short for Language Model for Dialogue Applications). It's a system based on large language models. "I know a person when I talk to it," Lemoine told the Washington Post. "It doesn't matter whether they have a brain made of meat in their head. Or if they have a billion lines of code. And I hear what they have to say, and that is how I decide what is and isn't a person."
"Sentience" is the wrong discussion to have on AI right now
This article is part of Demystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding AI. The past week has seen a frenzy of articles, interviews, and other types of media coverage about Blake Lemoine, a Google engineer who told The Washington Post that LaMDA, a large language model created for conversations with users, is "sentient." After reading a dozen different takes on the topic, I have to say that the media has become (a bit) disillusioned with the hype surrounding current AI technology. A lot of the articles discussed why deep neural networks are not "sentient" or "conscious." This is an improvement in comparison to a few years ago, when news outlets were creating sensational stories about AI systems inventing their own language, taking over every job, and accelerating toward artificial general intelligence.
AI's hold over humans is starting to get stronger
It has been an exasperating week for computer scientists. They've been falling over each other to publicly denounce claims from Google engineer Blake Lemoine, chronicled in a Washington Post report, that his employer's language-predicting system was sentient and deserved all of the rights associated with consciousness. To be clear, current artificial intelligence systems are decades away from being able to experience feelings and, in fact, may never do so. Their smarts today are confined to very narrow tasks such as matching faces, recommending movies or predicting word sequences. No one has figured out how to make machine-learning systems generalise intelligence in the same way humans do.