10 to 30? Yeah I think it might be a lot longer than that.
Somehow everyone keeps glossing over the fact that you have to have enormous amounts of highly curated data to feed the trainer in order to develop a model.
Curating data for general purposes is incredibly difficult. The big medical research universities have been working on it for at least a decade, and the tools they have developed, while cool, are only useful as tools too a doctor that has learned how to use them. They can speed diagnostics up, they can improve patient outcome. But they cannot replace anything in the medical setting.
The AI we have is like fancy signal processing at best
AI in health and medtech has been around and in the field for ages. However, two persistent challenges make roll out slow-- and they’re not going anywhere because of the stakes at hand.
The first is just straight regulatory. Regulators don’t have a very good or very consistent working framework to apply to to these technologies, but that’s in part due to how vast the field is in terms of application. The second is somewhat related to the first but really is also very market driven, and that is the issue of explainability of outputs. Regulators generally want it of course, but also customers (i.e., doctors) don’t just want predictions/detections, but want and need to understand why a model “thinks” what it does. Doing that in a way that does not itself require significant training in the data and computer science underlying the particular model and architecture is often pretty damned hard.
I think it’s an enormous oversimplification to say modern AI is just “fancy signal processing” unless all inference, including that done by humans, is also just signal processing. Modern AI applies rules it is given, explicitly or by virtue of complex pattern identification, to inputs to produce outputs according to those “given” rules. Now, what no current AI can really do is synthesize new rules uncoupled from the act of pattern matching. Effectively, a priori reasoning is still out of scope for the most part, but the reality is that that simply is not necessary for an enormous portion of the value proposition of “AI” to be realized.
Not an expert so I might be wrong, but as far as I understand it, those specialised tools you describe are not even AI. It is all machine learning. Maybe to the end user it doesn’t matter, but people have this idea of an intelligent machine when its more like brute force information feeding into a model system.
By definition AI (artificial intelligence) is any algorithm by which a computer system automatically adapts to and learns from its input. That definition also covers conventional algorithms that aren’t even based on neural nets. Machine learning is a subset of that.
AGI (artifical general intelligence) is the thing you see in movies, people project into their LLM responses and what’s driving this bubble. It is the final goal, and means a system being able to perform everything a human can on at least human level. Pretty much all the actual experts agree we’re a far shot from such a system.
It may be too late on this front, but don’t say AI when there isn’t any I to it.
Of course it could be successfully argued that humans (or at least a large amount of them) are also missing the I, and are just spitting out the words that are expected of them based on the words that have been ingrained in them.
AI as a field of computer science is mostly about pushing computers to do things they weren’t good at before. Recognizing colored blocks in an image was AI until someone figured out a good way to do it. Playing chess at grandmaster levels was AI until someone figured out how to do it.
Along the way, it created a lot of really important tools. Things like optimizing compilers, virtual memory, and runtime environments. The way computers work today was built off of a lot of things out of the old MIT CSAIL labs. Saying “there’s no I to this AI” is an insult to their work.
Recognizing colored blocks in an image was AI until someone figured out a good way to do it. Playing chess at grandmaster levels was AI until someone figured out how to do it.
You make it sound like these systems stopped being AI the moment they actually succeeded at what they were designed to do. When you play chess against a computer it’s AI you’re playing against.
That’s exactly what I’m getting at. AI is about pushing the boundary. Once the boundary is crossed, it’s not AI anymore.
Those chess engines don’t play like human players. If you were to look at how they determine things, you might conclude they’re not intelligent at all by the same metrics that you’re dismissing ChatGPT. But at this point, they are almost impossible for humans to beat.
I’m not the person you originally replied to. At no point have I dismissed ChatGPT.
I disagree with your logic about the definition of AI. Intelligence is the ability to acquire, understand, and use knowledge. A chess-playing AI can see the board, understand the ramifications of each move, and respond to how the pieces are moved. That makes it intelligent - narrowly so, but intelligent nonetheless. And since it’s artificial too, it fits the definition of AI.
Intelligence: The ability to acquire, understand, and use knowledge.
A self-driving car is able to observe its surroundings, identify objects and change its behaviour accordingly. Thus a self-driving car is intelligent. What’s driving such car? AI.
You’re free to disagree with how other people define words but then don’t take part in their discussions expecting everyone to agree with your definiton.
ChatGPT is an LLM that is a generative pre-trained model using a nueral network.
Aka: it’s a chat bot that creates it’s responses based on an insane amount of text data. LLMs trace back to the 90s, and I learned about them in college in the late 2000s-2010s. Natural Language Processing was a big contributor, and Google introduced some powerful nueral network tech in 2014-2017.
The reason they “appeared out of nowhere” to the common man is merely marketing.
LLM’s are not the only type of AI out there. ChatGPT appeared seemingly out of nowhere. Whose to say the next AI system wont do that as well?
I’m not sure what I’m misquoting. A large language model is not AI, a large language model is a non-human readable function used by a generative AI algorithm.
Simply put, ChatGPT did not appear out of nowhere.
The key word there is seemingly. The technology itself had existed for a long time, but it wasn’t until the massive leap OpenAI made with it that it actually became popular. Before ChatGPT, 99% of people had never heard of LLMs, and now everyone has. That’s what I mean when I say it appeared seemingly out of nowhere - it took the masses by surprise. There’s no reason to assume another company working on a different approach to AI won’t make a similar massive breakthrough, giving us AI far more powerful than LLMs and taking everyone by surprise, despite the base technology having existed for a long time.
A large language model is not AI
It is AI though - a subset of generative AI to be specific, but it still falls under the AI category.
It can! For a while. Isn’t that the nature of speculation and speculative bubbles? Sure, they may pop some day, because we don’t know for sure what’s a bubble and what is a promising market disruption. But a bunch of people make a bunch of money until then, and that’s all that matters.
10 to 30? Yeah I think it might be a lot longer than that.
Somehow everyone keeps glossing over the fact that you have to have enormous amounts of highly curated data to feed the trainer in order to develop a model.
Curating data for general purposes is incredibly difficult. The big medical research universities have been working on it for at least a decade, and the tools they have developed, while cool, are only useful as tools too a doctor that has learned how to use them. They can speed diagnostics up, they can improve patient outcome. But they cannot replace anything in the medical setting.
The AI we have is like fancy signal processing at best
AI in health and medtech has been around and in the field for ages. However, two persistent challenges make roll out slow-- and they’re not going anywhere because of the stakes at hand.
The first is just straight regulatory. Regulators don’t have a very good or very consistent working framework to apply to to these technologies, but that’s in part due to how vast the field is in terms of application. The second is somewhat related to the first but really is also very market driven, and that is the issue of explainability of outputs. Regulators generally want it of course, but also customers (i.e., doctors) don’t just want predictions/detections, but want and need to understand why a model “thinks” what it does. Doing that in a way that does not itself require significant training in the data and computer science underlying the particular model and architecture is often pretty damned hard.
I think it’s an enormous oversimplification to say modern AI is just “fancy signal processing” unless all inference, including that done by humans, is also just signal processing. Modern AI applies rules it is given, explicitly or by virtue of complex pattern identification, to inputs to produce outputs according to those “given” rules. Now, what no current AI can really do is synthesize new rules uncoupled from the act of pattern matching. Effectively, a priori reasoning is still out of scope for the most part, but the reality is that that simply is not necessary for an enormous portion of the value proposition of “AI” to be realized.
The oversimplification was intended - you also caught my meaning of it being able to synthesize new rules.
Not an expert so I might be wrong, but as far as I understand it, those specialised tools you describe are not even AI. It is all machine learning. Maybe to the end user it doesn’t matter, but people have this idea of an intelligent machine when its more like brute force information feeding into a model system.
Don’t say AI when you mean AGI.
By definition AI (artificial intelligence) is any algorithm by which a computer system automatically adapts to and learns from its input. That definition also covers conventional algorithms that aren’t even based on neural nets. Machine learning is a subset of that.
AGI (artifical general intelligence) is the thing you see in movies, people project into their LLM responses and what’s driving this bubble. It is the final goal, and means a system being able to perform everything a human can on at least human level. Pretty much all the actual experts agree we’re a far shot from such a system.
It may be too late on this front, but don’t say AI when there isn’t any I to it.
Of course it could be successfully argued that humans (or at least a large amount of them) are also missing the I, and are just spitting out the words that are expected of them based on the words that have been ingrained in them.
AI as a field of computer science is mostly about pushing computers to do things they weren’t good at before. Recognizing colored blocks in an image was AI until someone figured out a good way to do it. Playing chess at grandmaster levels was AI until someone figured out how to do it.
Along the way, it created a lot of really important tools. Things like optimizing compilers, virtual memory, and runtime environments. The way computers work today was built off of a lot of things out of the old MIT CSAIL labs. Saying “there’s no I to this AI” is an insult to their work.
You make it sound like these systems stopped being AI the moment they actually succeeded at what they were designed to do. When you play chess against a computer it’s AI you’re playing against.
That’s exactly what I’m getting at. AI is about pushing the boundary. Once the boundary is crossed, it’s not AI anymore.
Those chess engines don’t play like human players. If you were to look at how they determine things, you might conclude they’re not intelligent at all by the same metrics that you’re dismissing ChatGPT. But at this point, they are almost impossible for humans to beat.
I’m not the person you originally replied to. At no point have I dismissed ChatGPT.
I disagree with your logic about the definition of AI. Intelligence is the ability to acquire, understand, and use knowledge. A chess-playing AI can see the board, understand the ramifications of each move, and respond to how the pieces are moved. That makes it intelligent - narrowly so, but intelligent nonetheless. And since it’s artificial too, it fits the definition of AI.
This is not up to you or me : AI is an area of expertise / a scientific field with a precise definition. Large, but well defined.
A self-driving car is able to observe its surroundings, identify objects and change its behaviour accordingly. Thus a self-driving car is intelligent. What’s driving such car? AI.
You’re free to disagree with how other people define words but then don’t take part in their discussions expecting everyone to agree with your definiton.
LLM’s are not the only type of AI out there. ChatGPT appeared seemingly out of nowhere. Whose to say the next AI system wont do that as well?
ChatGPT did not appear out of nowhere.
ChatGPT is an LLM that is a generative pre-trained model using a nueral network.
Aka: it’s a chat bot that creates it’s responses based on an insane amount of text data. LLMs trace back to the 90s, and I learned about them in college in the late 2000s-2010s. Natural Language Processing was a big contributor, and Google introduced some powerful nueral network tech in 2014-2017.
The reason they “appeared out of nowhere” to the common man is merely marketing.
You’re misquoting me. I haven’t claimed LLMs appeared out of nowhere.
You said ChatGPT appeared out of nowhere. ChatGPT is basically Eliza with an LLM.
Those are not my words and you know it. You’re misquoting me.
I’m not sure what I’m misquoting. A large language model is not AI, a large language model is a non-human readable function used by a generative AI algorithm.
Simply put, ChatGPT did not appear out of nowhere.
I agree.
The key word there is seemingly. The technology itself had existed for a long time, but it wasn’t until the massive leap OpenAI made with it that it actually became popular. Before ChatGPT, 99% of people had never heard of LLMs, and now everyone has. That’s what I mean when I say it appeared seemingly out of nowhere - it took the masses by surprise. There’s no reason to assume another company working on a different approach to AI won’t make a similar massive breakthrough, giving us AI far more powerful than LLMs and taking everyone by surprise, despite the base technology having existed for a long time.
It is AI though - a subset of generative AI to be specific, but it still falls under the AI category.
Anything can happen. We can discover time travel tomorrow. The economy cannot run on wishful thinking.
It can! For a while. Isn’t that the nature of speculation and speculative bubbles? Sure, they may pop some day, because we don’t know for sure what’s a bubble and what is a promising market disruption. But a bunch of people make a bunch of money until then, and that’s all that matters.
The uncertainty of it is exactly why it shouldn’t suck up as much capital and resources as it is doing.
Shouldn’t, definitely. But for a while, it will keep running, because that’s how a lot of speculative investment works.
I agree, and the problem is finance capitalism itself. But then it becomes an ideological argument.
The argument could be made economically rather than ideologically.
Capitalism has a failure mode where too much capital gets concentrated into too few hands, depressing the flow of money moving through the economy.
But Capitalists start crying “Socialism!” as soon as you start talking about anti-trust.
Tulips all the way down…