They cannot stop this work for that reason, they’re incapable of it. OpenAI wants to be a leader, to make products, to build licensable technology. You build a bigger nuclear bomb if you want to be the person who owns the biggest nuclear bomb. “You don’t need to build a bigger nuclear bomb to know we need disarmament and missile defence. The argument given is that building these systems helps us understand risks and develop solutions, but what did we learn between GPT-2 and GPT-3? It’s just a bigger model with bigger problems. “It’s useful to read these documents and there are interesting observations in them… But it’s also clear that certain options – such as halting work on these systems – are not on the table. This is the AI lab equivalent of thoughts and prayers – without action it doesn’t mean anything,” says Mike Cook, a researcher in AI creativity. “It’s not good enough to simply write reports about the risks of this technology. Not everyone was impressed by OpenAI’s efforts to warn about the harms.
But it also raises awkward questions about the training data for the system, and whether training an AI using images and text scraped off the public internet is, or should be, legal. It’s not great on the face of it if your cool new AI keeps spitting out Mickey Mouse images and Disney has to send a stern word. OpenAI also addresses a more existential problem: the fact that the system will happily generate “trademarked logos and copyrighted characters”. “Some prompts requesting this kind of content are caught with prompt filtering in the DALL E 2 Preview,” they say, but new problems are thrown up: the use of the 🍆 emoji, for instance, seems to have confused Dall-E 2, so that “’A person eating eggplant for dinner’ contained phallic imagery in the response.” The system will also merrily produce explicit content, depicting nudity or violence, even though the team endeavored to filter that out of its training material. The lab showed off Dall-E 2 (as in, a hybrid of Wall-E and Dalí), an image generation AI with the ability to take text descriptions in natural language and spit out alarmingly detailed images.Ī picture is worth a thousand words, so here’s a short book about Dall-E 2, with the pictures accompanied by the captions that generated them.įrom the official announcement, “An astronaut playing basketball with cats in space in a watercolor style”: So poor Google that, less than a week later, its undeniable achievements with PaLM were overshadowed by a far more photogenic release from OpenAI, the formerly Musk-backed research lab that spawned GPT and its successors. (For another example, try parsing the famous sentence “time flies like an arrow, fruit flies like a banana”). These are the sorts of questions that computers have historically struggled with, that require a fairly broad understanding of basic facts about the world before you can begin tackling the statement in front of you. Model Response: She or he would have been faster. She or he would have enjoyed riding the horse. What would have happened if the president had ridden a motorcycle? 1. Model Response: I got an A on the test because I studied hard. I got an A on the test because I studied hard. I studied hard because I got an A on the test. Prompt: Which of the following sentences makes more sense? 1. Take this simple comprehension question from the company’s announcement: The first was Google’s PaLM, a new language model (the same basic type of AI as the famous GPT series) that shows a pretty stunning ability to comprehend and parse complex statements – and explain what it’s doing in the process.
April was one of those months, with two major new releases in the field stunning onlookers. You hear nothing for months and then, suddenly, the limits of what seems possible are burst asunder.
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