Generative AI: what is it good for?

generative AI is the technology behind the wave of new online tools used by millions around the world some can answer queries on a huge range of topics in conversational language others can generate realistic looking photographs from short text prompts as the technology is ever more widely deployed what are its current strengths and its weaknesses The Economist science editor alot jar is joined by Deputy editor Tom standage Science correspondent Abby burticks and Global business and economics correspondent Arjun Romani to discuss this new era of AI Tom The Economist has written about AI many many times in various forms over the years what's changed since the last time we were really interested in it that made it much better made AI much better than perhaps it used to be what happened in 2017 was that some researchers at Google came up with a better retention mechanism called the Transformer and that's what the T in GPT stands for um and so essentially that made these systems a lot better they could sort of come up with more longer pieces of coherent output whether that's text or computer code or whatever so there was a technical breakthrough and um and that took a while to Ripple uh through the community so that's one of the things that changed but the other thing that changes this technology became much more visible of course what happened last year is that a much more capable model GPT 3.5 was launched as chat GPT literally as a chatbot which you know anyone could sign up for and once you got to the front of the waiting list you could go and talk to it and you know you'll have heard these numbers that 100 million people tried it within the first two months and that's you know reckoned to be the fastest adoption of a consumer Tech technology and history so the thing that really changed is that suddenly there was a way that anyone could use this technology and they came up with all sorts of amazing uses for it and asked it to do all sorts of extraordinary things and that was what really put it on the map I think one of the huge strengths of these large language models is that they're able to kind of crunch and churn through such like scads of unlabeled data so normally like in the past with AI you always needed like your thing and a label so that required humans to kind of go through and label them but these large models you just you Chuck in the internet and you get out of it a blurry picture that is basically taken of hundreds of billions of words um and it honestly just seems to do well I think a lot of people are still kind of baffled why why it's doing so well at so many tasks like it it generates convincing text it's very good at pattern matching style transfer is one of the other things like you ask it to like oh write a love letter in the style of a pirate from The 14th Century that has an Irish accent but is from like the Bahamas and then it's also pretty good at passing standardized tests it seems like it passed the U.S medical licensing exam it's passed some legal tests I'm basically very good at kind of text things at the moment I think uh you know one of the big opportunities is writing code the great thing about writing code with these systems and and I do still write some code um is that if if the code is slightly wrong you find out straight away because uh you know either The Interpreter or the compiler chokes on it or the output of the code isn't quite what you were expecting so you had this very tight feedback loop um and uh if it's slightly wrong you find out you find out pretty quickly in terms of weaknesses I I think one of them is the lack of transparency like it's kind of a black box we you can have access to kind of what's going inside the attention weights what those values are but they don't mean a lot to us um there's over a hundred billion of these weights and that's very very complex and hard for us to understand and what they're doing um and so yeah I think the main weakness is that it's such a complex system and we don't really understand it fully if your job is to find out new facts that's actually not something of these systems are really in a good position to do and I was talking to people um within the uh the foreign office the British government the other day and also they will say what's the impact of this and I would say look our job of whether we work in government or the intelligent services or journalism is to find new facts and um and they've got to be right right and you've got to you you really don't want to just take any old stuff coming out of one of these systems so if the accuracy matters then these systems are you know maybe not so great the reliability of the models need to be improved I think before for you know they start automating huge amounts of processes and businesses um but I mean there's a huge amount of uh economic activity I think that'll get affected by this I mean the paper put out by some economists at open AI that said that you know around 20 of the US Workforce uh could have around 50 of their tasks affected by by generative AI in the next few years right so a lot of tasks that we do on a day-to-day basis um could be helped by these models over over the long term but there's some interesting uh you know research in the economics of innovation that talks about how if you want to get uh what we call an intelligent explosion or if you want to get uh you know exponentially increasing rates of economic growth um in any given domain uh you need to automate the entire process if you've only automated you know 90 of it or 99 of it uh that doesn't get you uh nearly the same the same effect um because the the slowest part of the process which is maybe probably the human acts as what's called a rate determining step so we will end up slowing things down so I think that's probably uh you know what is likely to happen in my mind where you know we use AIS to help us uh with research which frankly we are already doing um but it it still is not able to get 100 of the way there so ultimately the pace of progress continues um as it has been so they they would have become super intelligent intended into paper clips if it wasn't for those pesky humans getting in the way and making them more intelligent this is an excerpt from a 45-minute discussion about the risks and opportunities of AI Economist subscribers can watch the exclusive event in full by clicking on the link

Channel: The Economist

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Keywords: economist, the economist, economist films, economist video, short documentary, artificial intelligence, AI, A.I., generative ai, open ai, what is ai, generative models, AI explained, artificial intelligence explained, generative models explained, artificial intelligence robot, generative ai video, generative ai applications

Id: gCDacaohqaA

Length: 6min 19sec (379 seconds)

Published: Mon May 29 2023