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It’s not intelligent though, it can deliver publicly available information in a “natural speech”. It can’t take information and make determinations from it, unlike people.
Edit: I’m of the opinion that ChatGPT will always be limited because people learn by doing, and in that process they discover new and better ways of doing that thing. Something like ChatGPT learns by observing, and if it’s only limited to observing other people learning by doing I don’t think it can create anything original because it’s limited by its inputs. Software like ChatGPT will never be able to invent something new, it can only critique or improve on things that already exist. It’s not enough for me to call it intelligent.
Ok how many people’s jobs are determining new information? Researchers, or if you are trying to make a new product. But most people are not doing completely new work at their job, they’re just making a database or a website or something that’s been done countless times before. Also no one is trying to argue that in its current state it can take everyone’s job, but it’s improving and new developments in the field of AI are always happening, today no one’s jobs are in jeopardy but how about tomorrow?
Yes but it could soon
Prove it.
Gettin' tired of people matter-of-factly stating "yeah it can't now but it will soon."
I said "could". Reading comprehension is very important.
Hmmm, really? I see these kinds of articles all the time: https://www.quantamagazine.org/ai-reveals-new-possibilities-in-matrix-multiplication-20221123/
This is the beginning of AI outthinking humans. More and more, AI are creating new AI that don't think like humans, but more like a machine with access to exabytes of data. Humans have a specific capacity for memorization and pattern matching. AI has the ability to take many more things into account than a human does, when making a decision. If you think that AI won't be outthinking humans in the next ten years, you're an ostrich with your head in the sand.
Kind of exposes the fact you don't understand the core concepts discussed, and are just congrealing every piece of AI news as "soon we'll have our own Data from Star Trek."
Sure…I’ve been doing this for decades and have been on the forefront in this…but, go ahead and believe what you will. In the end, neither my comment nor yours will slow the progress made by neural nets and their ability to coalesce many decision points into things humans can’t comprehend. The good news is, AI decisioning doesn’t care about whether I understand the core concepts or not. They’re going to continue to strip away at our frail abilities and advance beyond our understanding. Within our lifetime, your limited understanding of how things work will be replaced with something that not only is more creative than you, but also takes into consideration the collective wisdom of many disciplines. I’m retiring soon, so there’s no need for me to fret about whether I understand the core concepts or not.
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There are many definitions used to describe AGI. If we’re talking about whole brain emulation vs neural emulation or even AI-complete cases, I think these are in slightly different trajectories. I do think that just passing a Turing Test for a conversation with the average person is within the next 5 years. For those that understand what they’re testing for, this is probably another 5 years beyond the first. Beyond that, within the next 20 years, we’ll likely see a fully functioning whole brain emulation that is indecipherable from human learning and growth. I think the challenge that we’ll face is getting to the point where we understand what set of inputs are needed to get the cross section of ideas needed to be well-rounded. If we focus too much on generalized learning, and not enough on varied experiences, it will take longer. We’ve been pushing this in academia, but the world isn’t just made of learning in school. A lot of our personal experience and understanding comes from trial and error and our models need to account for more of these experiences. The good news is, we have a lot of the data that we need. Formalizing the semantics of the data sets is really where the hard work will be…in my opinion.
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Absolutely right. I think that the videos themselves are good for gaining better context for situational experiences. Something like a video of someone swimming is going to provide different results, depending on the context of the environment. Most of that can be inferred while reading, but the semantics needed to derive the environment are visible in videos. So, we’ll have a much better result for producing trained data sets needed for kiddie pool swimming versus deep sea diving. There’s a wild amount of data that can be culled from videos that we’re just beginning to get breakthroughs on. It’s an incredibly exciting time for the entire field.