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This is a very cool demo - if you dig deeper there’s a clip of them having a “blind” AI talk to another AI with live camera input to ask it to explain what it’s

This is a very cool demo - if you dig deeper there’s a clip of them having a “blind” AI talk to another AI with live camera input to ask it to explain what it’s seeing. Then they, together, sing a song about what they’re looking at, alternating each line, and rhyming with one another. Given all of the isolated capabilities of AI, this isn’t particularly surprising, but seeing it all work together in real time is pretty incredible. But it’s not scary. It’s… marvelous, cringey, uncomfortable, awe-inspiring. What’s scary is not what AI can currently do, but what we expect from it. Can it do math yet? Can it play chess? Can it write entire apps from scratch? Can it just do my entire job for me? We’re moving toward a world where every job will be modeled, and you’ll either be an AI owner, a model architect, an agent/hardware engineer, a technician, or just.. training data.

BenFranklin100 2y ago
[flagged]
- Yiin 2y ago
  People with your sentiment said the same thing about all cool tech that changed the world. Doesn't change the reality, a lot of professions will need to adapt or they will go extinct.
  - BenFranklin100 2y ago
    Strong Theranos vibes with this one.
    - jiggawatts 2y ago
      Nobody here ever got a Theranos blood test. Almost everyone here has used at least one LLM for fun or for work. Many people here have paid for it.
  - latexr 2y ago
    > People with your sentiment said the same thing about all cool tech that changed the world. They also said it about all the over-hyped tech that did not change the world. This mentality of “detractors prove something is good” is survivorship bias. Note I’m not saying you’ll categorically be proven wrong, just that your argument isn’t particularly strong or valid.
  - BenFranklin100 2y ago
    The mods banning my comments can go bent.
- firtoz 2y ago
  What do you mean? How would you define AI?
  - BenFranklin100 2y ago
    I am a PhD biophysicist working within the field of biological imaging. Professionally, my team (successfully) uses deep learning and GANs for a variety of tasks within the field of imaging, such as segmentation, registration, and predictive protein/transcriptomics. It’s good stuff, a game changer in many ways. In no way however, does it represent generalized AI, and nobody in the field makes this claim even though the output of these algorithms match or out perform humans in cases. LLMs are no different. Like DL modules that are very good at outputting images that mimic biological signatures, LLMs are very good at outputting texts that eerily mimic human language. However — and this is a point which programmers are woefully and comically ignorant — human language and reason are two separate things. Tech bros wholly confuse the two however, and thus make outlandish claims we have achieved or are on the brink of achieving — actual AI systems. In other words, while LLMs and DL in general can perform specific tasks well, they do not represent a breakthrough in artificial intelligence, and thus will have a much narrower application space than actual AI.
    - jiggawatts 2y ago
      The problem with this kind of criticism of any AI-related technology is that it is an unfalsifiable argument akin to saying that it can't be "proper" intelligence unless God breathed a soul into the machine. The method is irrelevant. The output is what matters. This is like a bunch of intelligent robots arguing that "mere meat" cannot possibly be intelligent! https://www.mit.edu/people/dpolicar/writing/prose/text/thinkingMeat.html https://www.mit.edu/people/dpolicar/writing/prose/text/think...
    - DatoClement 2y ago
      This is true. But only to a point where mimicking and more broadly speaking, statistically imitating data, are understood in a more generalized way. LLMs statistically imitates texts of real world. To achieve certain threshold of accuracy, it turns out they need to imitate the underlying Turing machine/program/logic that runs in our brains to understand/react properly to texts by ourselves. That is no longer in the realm of the old school data-as-data statistics I would say.
- jader201 2y ago
  The fact that you’re convinced this isn’t possible (or at least not happening here) means they’ve literally made progress beyond belief.
  - BenFranklin100 2y ago
    "We shall not be very greatly surprised if a woman analyst who has not been sufficiently convinced of the intensity of her own wish for a penis also fails to attach proper importance to that factor in her patients" Sigmund Freud, in response to Karen Horney’s criticism of his theory of penis envy.
- Uptrenda 2y ago
  W-what? Lad, have you used chat-gpt? It can instantly give you intelligent feedback on anything (usually better than any expert community like 90% of the time.) On extremely detailed, specific tasks (like writing algorithms or refactoring) its able to spit out either working code or code so close to working that its still faster than what you could have done yourself. It can explain things better than probably 99.999% of teachers. It will give you detailed examples that are much easier to follow than vague, error-prone spec docs. That's scratching the surface. Other people are far more creative than me and have used chat-gpt for mind-blowing stuff already. Whatever its doing passes for 'reasoning' and 'intelligence' in my book. To me it doesn't matter whether its the same kind of intelligence as a human or if there's any amount of awareness as those are both philosophical questions of no consequence to my work. For what these pieces of tech can do I feel that they're drastically under-utilized.
p1esk 2y ago
This is still gpt4. I don’t expect much more from this version than what previous version could do, in terms of reasoning abilities. But everyone is expecting them to release gpt5 later this year, and it is a bit scary to think what it will be able to do.
- jiggawatts 2y ago
  Branding aside, this pretty much is GPT 5. The evidence for that is the change in the tokenizer. The only way to implement that is to re-train the entire base model from scratch. This implies that GPT 4o is not a fine-tuning of GPT 4. It's a new model, with a new tokenizer, new input and output token types, etc... They could have called it GPT-5 and everyone would have believed them.
  - p1esk 2y ago
    I’ve used it for a couple of hours to help with coding and it feels very similar to gpt4: still makes erroneous and inconsistent suggestions. Not calling it 4.5 was the right call. It is much faster though. The expectations for gpt5 are sky high. I think we will see a similar jump as 3.5 -> 4.
  - adroniser 2y ago
    That doesn't imply that it's GPT-5. A GPT-4 training run probably doesn't take them that long now they've acquired so many GPUs for training GPT-5.
  - mewpmewp2 2y ago
    Pretty sure they said they would not release GPT-5 on Monday. So it's something else still. And I don't see any sort of jump big enough to label it as 5. I assume GPT-5 has to be a heavier, more expensive and slower model initially. GPT-4o is like an optimisation of GPT-4.
- golol 2y ago
  I think 4o is actually noticeably smarter than 4, after having tried it a tiny bit on the playground.
anon373839 2y ago
> We’re moving toward a world where every job will be modeled After an OpenAI launch, I think it's important to take one's feelings about the future impact of the technology with a HUGE grain of salt. OpenAI are masters of hype. They have been generating hype for years now, yet the real-world impacts remain modest so far. Do you remember when they teased GPT-2 as "too dangerous" for public access? I do. Yet we now have Llama 3 in the wild, which even at the smaller 8B size is about as powerful as the [edit: 6/13/23] GPT-4 release. As someone pointed out elsewhere in the comments, a logistic curve looks exponential in the beginning, before it approaches saturation. Yet, logistic curves are more common, especially in ML. I think it's interesting that GPT-4o doesn't show much of an improvement in "reasoning" strength.
- owenpalmer 2y ago
  They're masters of hype because their products speak for themselves (literally)
  - famouswaffles 2y ago
    Yeah. Open ai are certainly not masters of hype lol. They released their titular product to basically no fanfare or advertisement. ChatGPT took off on Word of Mouth alone. They dropped GPT-4 without warning and waited months to ship it's most exciting new feature (image input). Even now, they're shipping text-image 4o but not the new voice while leaving old-voice up and confusing/disappointing a whole lot of people. This is a pretty big marketing blunder.
    - fullstackchris 2y ago
      > ChatGPT took off on Word of Mouth alone. I remember for a good 2-3 months in 2023 ALL you could see on tiktok / youtube shorts was just garbage about 'how amazing' ChatGPT was. Like - video after video and I was surprised of the repeat content being recommended to me... No doubt openAI (or something) was behind that huge marketing push
      - 2y ago
somenameforme 2y ago
Like another comment mentioned, sigmoid curves [1] are ubiquitous with neural network systems. Neural network systems can be intoxicating because it's so "easy" (relatively speaking) to go from nothing to 80% in extremely short periods of time. And so it seems completely obvious that hitting 100% is imminent. Yet it turns out that each percent afterwards starts coming exponentially more slowly, and we tend to just bump into seemingly impassable asymptotes far from where we'd like to be. ~8 years ago when self driving technology was all the rage and every major company was getting on board with ever more impressive technological demos, it seemed entirely reasonable to expect that we'd all be in a world of complete self driving imminently. I remember mocking somebody online around the time who was pursuing a class C/commercial trucking license. Yet now a decade later, there are more truckers than ever and the tech itself seems further away than ever before. And that's because most have now accepted that progress on such has basically stalled out in spite of absolutely monumental efforts at moving forward. So long as LLMs regularly hallucinate, they're not going to be useful for much other than tasks that can accept relatively high rates of failure. And many of those generally creative domains are the ones LLMs are paradoxically the weakest in - like writing. Reading a book written by an LLM would be cruel and unusual punishment given then current state of the art. One domain I do see them completely taking over is search. They work excellently as natural language search engines, and "failure" in such is very poorly defined. [1] - https://en.wikipedia.org/wiki/Sigmoid_function https://en.wikipedia.org/wiki/Sigmoid_function
- mlsu 2y ago
  I'm not really sure your self-driving analogy is apt here. Waymo has cars on the road right now that are totally autonomous, and just expanded its footprint. It has been longer and more difficult than we all thought, and those early tech demos were a glimmer of what was to come; then we had to grind to get there, with a lot of engineering. I think what maybe seems not obvious amidst the hype is that there is a hell of a lot of engineering left to do. The fact that you can squash the weights of a neural net down to 3 bits per param and it still works -- is evidence that we have quite a way to go with maturing this technology. Multimodality, improvements to the UX of it, the human-computer interface part of it. Those are fundamental tech things, but they are foremost engineering problems. Getting latency down. Getting efficiency up. Designing the experience, then building it out. 25 years ago, early tech demos on the internet were promising that everyone would do their shopping, entertainment, socializing, etc... online. Breathless hype. 5 years after that, the whole thing crashed, but it never went away. People just needed time to figure out how to use it and what it was useful for, and discover its limitations. 10 years after that, engineering efforts were systematized and applied against the difficult problems that still remained. And now: look at where we are. It just took time.
Jiahang 2y ago
make people smart
- starfezzy 2y ago
  Nature had been doing that for billions of years until a few decades ago when we were told "progress" meant we had to stop doing the same thing more peacefully and intentionally. My guess is the future belongs to those who don't stop—who, in fact, embrace the opposite of stopping. I would even suggest that the present belongs to those who didn't stop. It may be too late for normal people to ever catch up by the time we realize the trick that was played on us.
  - jddj 2y ago
    The present absolutely belongs to those who didn't stop, but it's been a lot longer than a few decades. Varying degrees of greedy / restless / hungry / thirsty / lustful are what we've got, because how is contentedness ever going to compete with that over millennia?
    - slfnflctd 2y ago
      It just occurred to me that this is one of the core things most successful religions have been trying to do in some form from the time they first arose. I've had a lot of negative things to say about religion for many years. However, as has been often observed, 'perception is reality' to a certain extent when it affects how people behave, and perhaps it's kind of a counterweight against our more selfish tendencies. I just wish we could do something like it without made up stories and bigotry. Secular humanist Unitarians might be about the best we can do right now in my opinion... I'm hoping that group continues to grow (they have been in recent years).
      - deleted 2y ago
        [deleted]
whilenot-dev 2y ago
> or just.. training data. I have a much less "utopian" view about the future. I remember during the renaissance of neural networks (ca. 2010-15) it was said that "more data leads to better models", and that was at a time when researchers frowned upon the term Artificial Intelligence and would rather use Machine Learning. Fast forward a decade LLMs are very good synthetic data generators that try to mimic human generated input and I can't think somehow that this wasn't the sole initial intent of LLMs. And that's it for me. There's not much to hype and no intelligence at all. What happens now is that human generated input becomes more valuable and every online platform (including minor ones) will have now some form of gatekeeping in place, rather sooner than later. Besides that a lot of work still can't be done in front of a computer in isolation and probably never will, and even if so, automation is not a means to an end. We still don't know how to measure a lot of things and much less how to capture everything as data vectors.
tonyabracadabra 2y ago
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- _fw 2y ago
  Why are you sharing a link to a dodgy cloned site?
aussieguy1234 2y ago
My new PC arrives tomorrow. Once I source myself two RTX 3060's I'll be an AI owner, no longer dependant on cloud APIs. Currently the bottleneck is Agents. If you want a large language model to actually do anything you need an Agent. Agents so far need a human in the loop to keep them sane. Until that problem is solved most human jobs are still safe.
- sambazi 2y ago
  > Agents so far need a human in the loop to keep them sane. not quite sure that sanity is a business requirement
  - aussieguy1234 2y ago
    Yes, but to use a car dealership example, you don't want your Agent to sell a car to someone for $1 https://hothardware.com/news/car-dealerships-chatgpt-goes-awry-when-internet-gets-to-it https://hothardware.com/news/car-dealerships-chatgpt-goes-aw...
- trashtester 2y ago
  GPT 4o incorporated multimodality directly in the neural network, while reducing inference costs to half. I fully expect GPT 5 (or at the latest 6) to similarly have native inclusion of agentic capabilities either this year or next year, assuming it doesn't already, but is just kept from the public.
  - bamboozled 2y ago
    Going to put the economy in a very, very weird situation if true. Will be like, the end of millions of careers overnight. It will probably strongly favour places like China and Russia though, where the economy is already strongly reliant on central control.
    - trashtester 2y ago
      > It will probably strongly favour places like China and Russia though, where the economy is already strongly reliant on central control. I think you may be literally right in the opposite sense to what I think you intended. China (and maybe Russia) may be able to use central control to have an advantage when it comes to avoiding disasterous outcomes. But when it comes to the rate of innovation, the US may have an advantage for the usual reasons. Less government intervention (due to lobbyism) combined with having several corporations actively competing with each other to be first/best usually leads to faster innovation. However, the downside may be the it also introduces a lot more risk.
megous 2y ago
Why so much positivity? It can also murder people, and it will continue being used for that. That's scary.
- ithkuil 2y ago
  Probably it will be used for many things. Just like computers, electricity, iron
- onion2k 2y ago
  (IMO) AI cannot murder people. The responsibility of what an AI does falls on the person who deployed it, and to a lesser extent the person who created it. If someone is killed by a fully autonomous weapon then that person has been murdered by the person or people who created and enabled the AI, not the AI itself. This is no different to saying a person with a gun murdered someone rather than attributing the murder to the gun. An AI gun is just a really fancy gun.
  - bloqs 2y ago
    Except that with a gun, you have a binary input (the trigger) so you can squarely blame a human for misunderstanding what they did when they accidentally shot someone on the grounds that the trigger didnt work. A prompt is a _very_ different matter.
  - 7bit 2y ago
    There will come a time where complex systems can better be predicted with the use of AI than with mathematical predictions. One use-case could be, feeding body scans into them for cancer prevention. AFAIK this is already researched. There may come a time where we grow so accustomed to this, that the decision is so heavily influenced by AI, that we believe it more than human decisions. And then it can very well kill a human through misdiagnostic. I think it is important to not just put this thought aside, but to evaluate all risks.
    - apostata 2y ago
      > And then it can very well kill a home through misdiagnosis. I would imagine outcomes would be scrutinized heavily for an application like this. There is a difference between a margin of error (existing with human doctors as well) and a sentient ai that has decided to kill, which is what it sounds like you're describing. If we didn't give it that goal, how does it obtain it otherwise?
aksss 2y ago
Or a battery.
datahack 2y ago
All I could think about when watching this demo was how similar capabilities will work on the battlefield. Coordinated AIs look like they will be obscenely effective. Everything always starts as a toy.
- trashtester 2y ago
  The "Killer app" for AGI/ASI is, I suspect, going to be in robotics, even more so than in replacing "white collar workers". That includes, beyond literal Killers, all kinds of manufacturing, construction and service work. I would expect a LOT of funds to go into research all sorts of actuators, artificial muscles and any other technology that will be useful in building better robots. Companies that can get and maintain a lead in such technologies may reach a position similar to what US Steel had in the 19th century. That could be the next nvidia. I would not be at all surprised if we will have a robot in the house in 10 years that can clean and do the dishes, and that is built using basically the same parts as the robots that replace our soldiers and the police. Who will ultimately control them, though?
  - RugnirViking 2y ago
    its possible. Right now ai + robotics has been a big area of research for a while, and its very good at some tasks, see basically everything boston dynamics does wrt dynamically balancing. They help alongside control systems very well. However for multimodal task planning its not there. A year or two back I wrote a long comment about it but basically there is this idea of "grounding", basically connecting computer vision, object symbols/concepts, and task planning, which remains elusive. Its a similar problem with self driving cars - you want to be able to reason very strongly about things like "place all of the screws into the red holes" in a way that maps automatically to the actions for those things
    - trashtester 2y ago
      Yes. As you say, a lot of the limitations so far has been the control part, which is basically AI. Given the pace that AI is currently moving at, it seems to me that more and more, the mechanical aspect is becoming the limitation. GPT 4o now seems to be quite good at reasoning about the world from pictures in real time. I would expect it would soon become easy for it to do the high level part of many practical tasks, from housekeeping to manufacturing or construction. (And of course military tasks.) This leaves the direct low-level actuator control to execute such tasks in detail. But even there, development has been immense. See for instance these soccer playing robots [1] And as both high level and low level control (if we assume that models soon will add agentic features directly into the neural networks), the only missing peace is the ability to build mechanically capable and reliable robots at a low enough price that they become cheaper than humans for various kinds of work. There is one more limitation, of course, which is that GPT 4o still requires a constant connection to a data center, and that the models is too large to run within a device or machine. This is also one of the most critical limitations of self driving. Had the AI within a Tesla had the same amount of compute available as GPT-4o, it should be massively more capable. [1] https://www.youtube.com/watch?v=RbyQcCT6890 https://www.youtube.com/watch?v=RbyQcCT6890
hwbunny 2y ago
And people will become utterly stupid in the process.
CTDOCodebases 2y ago
or just... unemployed.
visarga 2y ago
> Can it just do my entire job for me? All AIs up to now lack autonomy. So I'd say until we crack this problem, it is not going to be able to do your job. Autonomy depends on a kind of data that is iterative, multi-turn, and learning from environments not from static datasets. We have the exact opposite, lots of non-iterative, off-policy (human made AI consumed) text.
schnitzelstoat 2y ago
Until the hallucination problem is solved, the output can't be trusted. So outside of use-cases where the user can quickly verify the result (like picking a decent generated image etc.),I can't see it being used much.
- zarathustreal 2y ago
  Never heard of retrieval-augmented generation?
  - usrbinbash 2y ago
    RAG? Sure. I even implemented systems using it, and enabling it, myself. And guess what: RAG doesn't prevent hallucination. It can reduce it, and there are most certainly areas where it is incredibly useful (I should know, because that's what earns my paycheck), but it's useful despite still hallucinations being a thing, not because we solved that problem.
    - zarathustreal 2y ago
      Are you implying that you’re the same person I was commenting to or are you just throwing your opinion into the mix? Regardless, we’ve seen accuracy of ~98% with simple context-based prompting across every category of generation task. Don’t take my word for it, a simple search would show the effectiveness of “n-shot” prompting. Framing it as “it _can_ reduce” hallucinations is disingenuous at best, there really is no debate about how well it works. We can disagree on whether 98% accuracy is a solution but again I’d assert that for >50% of all possible real world uses for an LLM 98% is acceptable and thus the problem can be colloquially referred to as solved. If you’re placing the bar at 100% hallucination-free accuracy then I’ve got some bad news to tell you about the accuracy of the floating point operations we run the world on
mottiden 2y ago
> We’re moving toward a world where every job will be modeled, and you’ll either be an AI owner, a model architect, an agent/hardware engineer, a technician, or just.. training data. I understand that you might be afraid. I believe that a world where only LLM companies rule the world is not practically achievable except in some distopian universe. The likelihood of the world where the only job are model architects, engineers or technicians is very very small. Instead, let's consider the positive possibilities that LLMs can bring. It can lead to new and exciting opportunities across various fields. For instance, can serve as a tool to inspire new ideas for writers, artists, and musicians. I think we are going towards a more collaborative era where computers and humans interact much more. Everything will be a remix :)
- altcognito 2y ago
  > The likelihood of the world where the only job are model architects, engineers or technicians is very very small. Oh, especially since it will be a priority to automate their jobs, or somehow optimize them with an algorithm because that's a self-reinforcing improvement scheme that would give you a huge edge.
  - SubiculumCode 2y ago
    Every corporate workplace is already thinking: How can I surveil and record everything an employee does as training data for their replacement in 3 years time.
Aeolun 2y ago
The two AI’s talking to each other was like listening to two commercials talking to each other. Like a callcenter menu that you cannot skip. And they _kept repeating themselves_. Ugh. If this is the future I’m going to hide in a cave.
mycall 2y ago
> Can it do math yet? GPT-4? Not that well. AI? Definitely https://deepmind.google/discover/blog/alphageometry-an-olympiad-level-ai-system-for-geometry/ https://deepmind.google/discover/blog/alphageometry-an-olymp...
aredox 2y ago
Call me back when we have autonomous driving, and when Bitcoin will replace currencies. What's scary and cringey are your delusions.
- plaidfuji 2y ago
  Don’t get me wrong, I’m not suggesting the current capabilities are anywhere near replacing human productivity. Some things are 1 year out, some 5 (maybe self-driving cars by then? Mercedes has it on their roadmap for 2030 and they’ve historically been realistic), some 10+. But the pieces are in place and the investments are being made. The question is no longer “can AI really automate this?”, it’s “how do we get the dataset that will enable us to automate this with AI?”. And as long as Open AI keeps people’s eyes on their whizbang demos, the money will keep flowing…
deleted 2y ago
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BenFranklin100 2y ago

[flagged]

Yiin 2y ago
People with your sentiment said the same thing about all cool tech that changed the world. Doesn't change the reality, a lot of professions will need to adapt or they will go extinct.
- BenFranklin100 2y ago
  Strong Theranos vibes with this one.
  - jiggawatts 2y ago
    Nobody here ever got a Theranos blood test. Almost everyone here has used at least one LLM for fun or for work. Many people here have paid for it.
- latexr 2y ago
  > People with your sentiment said the same thing about all cool tech that changed the world. They also said it about all the over-hyped tech that did not change the world. This mentality of “detractors prove something is good” is survivorship bias. Note I’m not saying you’ll categorically be proven wrong, just that your argument isn’t particularly strong or valid.
- BenFranklin100 2y ago
  The mods banning my comments can go bent.
firtoz 2y ago
What do you mean? How would you define AI?
- BenFranklin100 2y ago
  I am a PhD biophysicist working within the field of biological imaging. Professionally, my team (successfully) uses deep learning and GANs for a variety of tasks within the field of imaging, such as segmentation, registration, and predictive protein/transcriptomics. It’s good stuff, a game changer in many ways. In no way however, does it represent generalized AI, and nobody in the field makes this claim even though the output of these algorithms match or out perform humans in cases. LLMs are no different. Like DL modules that are very good at outputting images that mimic biological signatures, LLMs are very good at outputting texts that eerily mimic human language. However — and this is a point which programmers are woefully and comically ignorant — human language and reason are two separate things. Tech bros wholly confuse the two however, and thus make outlandish claims we have achieved or are on the brink of achieving — actual AI systems. In other words, while LLMs and DL in general can perform specific tasks well, they do not represent a breakthrough in artificial intelligence, and thus will have a much narrower application space than actual AI.
  - jiggawatts 2y ago
    The problem with this kind of criticism of any AI-related technology is that it is an unfalsifiable argument akin to saying that it can't be "proper" intelligence unless God breathed a soul into the machine. The method is irrelevant. The output is what matters. This is like a bunch of intelligent robots arguing that "mere meat" cannot possibly be intelligent! https://www.mit.edu/people/dpolicar/writing/prose/text/thinkingMeat.html https://www.mit.edu/people/dpolicar/writing/prose/text/think...
  - DatoClement 2y ago
    This is true. But only to a point where mimicking and more broadly speaking, statistically imitating data, are understood in a more generalized way. LLMs statistically imitates texts of real world. To achieve certain threshold of accuracy, it turns out they need to imitate the underlying Turing machine/program/logic that runs in our brains to understand/react properly to texts by ourselves. That is no longer in the realm of the old school data-as-data statistics I would say.
jader201 2y ago
The fact that you’re convinced this isn’t possible (or at least not happening here) means they’ve literally made progress beyond belief.
- BenFranklin100 2y ago
  "We shall not be very greatly surprised if a woman analyst who has not been sufficiently convinced of the intensity of her own wish for a penis also fails to attach proper importance to that factor in her patients" Sigmund Freud, in response to Karen Horney’s criticism of his theory of penis envy.
Uptrenda 2y ago
W-what? Lad, have you used chat-gpt? It can instantly give you intelligent feedback on anything (usually better than any expert community like 90% of the time.) On extremely detailed, specific tasks (like writing algorithms or refactoring) its able to spit out either working code or code so close to working that its still faster than what you could have done yourself. It can explain things better than probably 99.999% of teachers. It will give you detailed examples that are much easier to follow than vague, error-prone spec docs. That's scratching the surface. Other people are far more creative than me and have used chat-gpt for mind-blowing stuff already. Whatever its doing passes for 'reasoning' and 'intelligence' in my book. To me it doesn't matter whether its the same kind of intelligence as a human or if there's any amount of awareness as those are both philosophical questions of no consequence to my work. For what these pieces of tech can do I feel that they're drastically under-utilized.

p1esk 2y ago

This is still gpt4. I don’t expect much more from this version than what previous version could do, in terms of reasoning abilities. But everyone is expecting them to release gpt5 later this year, and it is a bit scary to think what it will be able to do.

jiggawatts 2y ago
Branding aside, this pretty much is GPT 5. The evidence for that is the change in the tokenizer. The only way to implement that is to re-train the entire base model from scratch. This implies that GPT 4o is not a fine-tuning of GPT 4. It's a new model, with a new tokenizer, new input and output token types, etc... They could have called it GPT-5 and everyone would have believed them.
- p1esk 2y ago
  I’ve used it for a couple of hours to help with coding and it feels very similar to gpt4: still makes erroneous and inconsistent suggestions. Not calling it 4.5 was the right call. It is much faster though. The expectations for gpt5 are sky high. I think we will see a similar jump as 3.5 -> 4.
- adroniser 2y ago
  That doesn't imply that it's GPT-5. A GPT-4 training run probably doesn't take them that long now they've acquired so many GPUs for training GPT-5.
- mewpmewp2 2y ago
  Pretty sure they said they would not release GPT-5 on Monday. So it's something else still. And I don't see any sort of jump big enough to label it as 5. I assume GPT-5 has to be a heavier, more expensive and slower model initially. GPT-4o is like an optimisation of GPT-4.
golol 2y ago
I think 4o is actually noticeably smarter than 4, after having tried it a tiny bit on the playground.

anon373839 2y ago

> We’re moving toward a world where every job will be modeled After an OpenAI launch, I think it's important to take one's feelings about the future impact of the technology with a HUGE grain of salt. OpenAI are masters of hype. They have been generating hype for years now, yet the real-world impacts remain modest so far. Do you remember when they teased GPT-2 as "too dangerous" for public access? I do. Yet we now have Llama 3 in the wild, which even at the smaller 8B size is about as powerful as the [edit: 6/13/23] GPT-4 release. As someone pointed out elsewhere in the comments, a logistic curve looks exponential in the beginning, before it approaches saturation. Yet, logistic curves are more common, especially in ML. I think it's interesting that GPT-4o doesn't show much of an improvement in "reasoning" strength.

owenpalmer 2y ago
They're masters of hype because their products speak for themselves (literally)
- famouswaffles 2y ago
  Yeah. Open ai are certainly not masters of hype lol. They released their titular product to basically no fanfare or advertisement. ChatGPT took off on Word of Mouth alone. They dropped GPT-4 without warning and waited months to ship it's most exciting new feature (image input). Even now, they're shipping text-image 4o but not the new voice while leaving old-voice up and confusing/disappointing a whole lot of people. This is a pretty big marketing blunder.
  - fullstackchris 2y ago
    > ChatGPT took off on Word of Mouth alone. I remember for a good 2-3 months in 2023 ALL you could see on tiktok / youtube shorts was just garbage about 'how amazing' ChatGPT was. Like - video after video and I was surprised of the repeat content being recommended to me... No doubt openAI (or something) was behind that huge marketing push
    - 2y ago

somenameforme 2y ago

Like another comment mentioned, sigmoid curves [1] are ubiquitous with neural network systems. Neural network systems can be intoxicating because it's so "easy" (relatively speaking) to go from nothing to 80% in extremely short periods of time. And so it seems completely obvious that hitting 100% is imminent. Yet it turns out that each percent afterwards starts coming exponentially more slowly, and we tend to just bump into seemingly impassable asymptotes far from where we'd like to be. ~8 years ago when self driving technology was all the rage and every major company was getting on board with ever more impressive technological demos, it seemed entirely reasonable to expect that we'd all be in a world of complete self driving imminently. I remember mocking somebody online around the time who was pursuing a class C/commercial trucking license. Yet now a decade later, there are more truckers than ever and the tech itself seems further away than ever before. And that's because most have now accepted that progress on such has basically stalled out in spite of absolutely monumental efforts at moving forward. So long as LLMs regularly hallucinate, they're not going to be useful for much other than tasks that can accept relatively high rates of failure. And many of those generally creative domains are the ones LLMs are paradoxically the weakest in - like writing. Reading a book written by an LLM would be cruel and unusual punishment given then current state of the art. One domain I do see them completely taking over is search. They work excellently as natural language search engines, and "failure" in such is very poorly defined. [1] - https://en.wikipedia.org/wiki/Sigmoid_function https://en.wikipedia.org/wiki/Sigmoid_function

mlsu 2y ago
I'm not really sure your self-driving analogy is apt here. Waymo has cars on the road right now that are totally autonomous, and just expanded its footprint. It has been longer and more difficult than we all thought, and those early tech demos were a glimmer of what was to come; then we had to grind to get there, with a lot of engineering. I think what maybe seems not obvious amidst the hype is that there is a hell of a lot of engineering left to do. The fact that you can squash the weights of a neural net down to 3 bits per param and it still works -- is evidence that we have quite a way to go with maturing this technology. Multimodality, improvements to the UX of it, the human-computer interface part of it. Those are fundamental tech things, but they are foremost engineering problems. Getting latency down. Getting efficiency up. Designing the experience, then building it out. 25 years ago, early tech demos on the internet were promising that everyone would do their shopping, entertainment, socializing, etc... online. Breathless hype. 5 years after that, the whole thing crashed, but it never went away. People just needed time to figure out how to use it and what it was useful for, and discover its limitations. 10 years after that, engineering efforts were systematized and applied against the difficult problems that still remained. And now: look at where we are. It just took time.

Jiahang 2y ago

make people smart

starfezzy 2y ago
Nature had been doing that for billions of years until a few decades ago when we were told "progress" meant we had to stop doing the same thing more peacefully and intentionally. My guess is the future belongs to those who don't stop—who, in fact, embrace the opposite of stopping. I would even suggest that the present belongs to those who didn't stop. It may be too late for normal people to ever catch up by the time we realize the trick that was played on us.
- jddj 2y ago
  The present absolutely belongs to those who didn't stop, but it's been a lot longer than a few decades. Varying degrees of greedy / restless / hungry / thirsty / lustful are what we've got, because how is contentedness ever going to compete with that over millennia?
  - slfnflctd 2y ago
    It just occurred to me that this is one of the core things most successful religions have been trying to do in some form from the time they first arose. I've had a lot of negative things to say about religion for many years. However, as has been often observed, 'perception is reality' to a certain extent when it affects how people behave, and perhaps it's kind of a counterweight against our more selfish tendencies. I just wish we could do something like it without made up stories and bigotry. Secular humanist Unitarians might be about the best we can do right now in my opinion... I'm hoping that group continues to grow (they have been in recent years).
    - deleted 2y ago
      [deleted]

whilenot-dev 2y ago

> or just.. training data. I have a much less "utopian" view about the future. I remember during the renaissance of neural networks (ca. 2010-15) it was said that "more data leads to better models", and that was at a time when researchers frowned upon the term Artificial Intelligence and would rather use Machine Learning. Fast forward a decade LLMs are very good synthetic data generators that try to mimic human generated input and I can't think somehow that this wasn't the sole initial intent of LLMs. And that's it for me. There's not much to hype and no intelligence at all. What happens now is that human generated input becomes more valuable and every online platform (including minor ones) will have now some form of gatekeeping in place, rather sooner than later. Besides that a lot of work still can't be done in front of a computer in isolation and probably never will, and even if so, automation is not a means to an end. We still don't know how to measure a lot of things and much less how to capture everything as data vectors.

tonyabracadabra 2y ago

[flagged]

_fw 2y ago
Why are you sharing a link to a dodgy cloned site?

aussieguy1234 2y ago

My new PC arrives tomorrow. Once I source myself two RTX 3060's I'll be an AI owner, no longer dependant on cloud APIs. Currently the bottleneck is Agents. If you want a large language model to actually do anything you need an Agent. Agents so far need a human in the loop to keep them sane. Until that problem is solved most human jobs are still safe.

sambazi 2y ago
> Agents so far need a human in the loop to keep them sane. not quite sure that sanity is a business requirement
- aussieguy1234 2y ago
  Yes, but to use a car dealership example, you don't want your Agent to sell a car to someone for $1 https://hothardware.com/news/car-dealerships-chatgpt-goes-awry-when-internet-gets-to-it https://hothardware.com/news/car-dealerships-chatgpt-goes-aw...
trashtester 2y ago
GPT 4o incorporated multimodality directly in the neural network, while reducing inference costs to half. I fully expect GPT 5 (or at the latest 6) to similarly have native inclusion of agentic capabilities either this year or next year, assuming it doesn't already, but is just kept from the public.
- bamboozled 2y ago
  Going to put the economy in a very, very weird situation if true. Will be like, the end of millions of careers overnight. It will probably strongly favour places like China and Russia though, where the economy is already strongly reliant on central control.
  - trashtester 2y ago
    > It will probably strongly favour places like China and Russia though, where the economy is already strongly reliant on central control. I think you may be literally right in the opposite sense to what I think you intended. China (and maybe Russia) may be able to use central control to have an advantage when it comes to avoiding disasterous outcomes. But when it comes to the rate of innovation, the US may have an advantage for the usual reasons. Less government intervention (due to lobbyism) combined with having several corporations actively competing with each other to be first/best usually leads to faster innovation. However, the downside may be the it also introduces a lot more risk.

megous 2y ago

Why so much positivity? It can also murder people, and it will continue being used for that. That's scary.

ithkuil 2y ago
Probably it will be used for many things. Just like computers, electricity, iron
onion2k 2y ago
(IMO) AI cannot murder people. The responsibility of what an AI does falls on the person who deployed it, and to a lesser extent the person who created it. If someone is killed by a fully autonomous weapon then that person has been murdered by the person or people who created and enabled the AI, not the AI itself. This is no different to saying a person with a gun murdered someone rather than attributing the murder to the gun. An AI gun is just a really fancy gun.
- bloqs 2y ago
  Except that with a gun, you have a binary input (the trigger) so you can squarely blame a human for misunderstanding what they did when they accidentally shot someone on the grounds that the trigger didnt work. A prompt is a _very_ different matter.
- 7bit 2y ago
  There will come a time where complex systems can better be predicted with the use of AI than with mathematical predictions. One use-case could be, feeding body scans into them for cancer prevention. AFAIK this is already researched. There may come a time where we grow so accustomed to this, that the decision is so heavily influenced by AI, that we believe it more than human decisions. And then it can very well kill a human through misdiagnostic. I think it is important to not just put this thought aside, but to evaluate all risks.
  - apostata 2y ago
    > And then it can very well kill a home through misdiagnosis. I would imagine outcomes would be scrutinized heavily for an application like this. There is a difference between a margin of error (existing with human doctors as well) and a sentient ai that has decided to kill, which is what it sounds like you're describing. If we didn't give it that goal, how does it obtain it otherwise?

aksss 2y ago

Or a battery.

datahack 2y ago

All I could think about when watching this demo was how similar capabilities will work on the battlefield. Coordinated AIs look like they will be obscenely effective. Everything always starts as a toy.

trashtester 2y ago
The "Killer app" for AGI/ASI is, I suspect, going to be in robotics, even more so than in replacing "white collar workers". That includes, beyond literal Killers, all kinds of manufacturing, construction and service work. I would expect a LOT of funds to go into research all sorts of actuators, artificial muscles and any other technology that will be useful in building better robots. Companies that can get and maintain a lead in such technologies may reach a position similar to what US Steel had in the 19th century. That could be the next nvidia. I would not be at all surprised if we will have a robot in the house in 10 years that can clean and do the dishes, and that is built using basically the same parts as the robots that replace our soldiers and the police. Who will ultimately control them, though?
- RugnirViking 2y ago
  its possible. Right now ai + robotics has been a big area of research for a while, and its very good at some tasks, see basically everything boston dynamics does wrt dynamically balancing. They help alongside control systems very well. However for multimodal task planning its not there. A year or two back I wrote a long comment about it but basically there is this idea of "grounding", basically connecting computer vision, object symbols/concepts, and task planning, which remains elusive. Its a similar problem with self driving cars - you want to be able to reason very strongly about things like "place all of the screws into the red holes" in a way that maps automatically to the actions for those things
  - trashtester 2y ago
    Yes. As you say, a lot of the limitations so far has been the control part, which is basically AI. Given the pace that AI is currently moving at, it seems to me that more and more, the mechanical aspect is becoming the limitation. GPT 4o now seems to be quite good at reasoning about the world from pictures in real time. I would expect it would soon become easy for it to do the high level part of many practical tasks, from housekeeping to manufacturing or construction. (And of course military tasks.) This leaves the direct low-level actuator control to execute such tasks in detail. But even there, development has been immense. See for instance these soccer playing robots [1] And as both high level and low level control (if we assume that models soon will add agentic features directly into the neural networks), the only missing peace is the ability to build mechanically capable and reliable robots at a low enough price that they become cheaper than humans for various kinds of work. There is one more limitation, of course, which is that GPT 4o still requires a constant connection to a data center, and that the models is too large to run within a device or machine. This is also one of the most critical limitations of self driving. Had the AI within a Tesla had the same amount of compute available as GPT-4o, it should be massively more capable. [1] https://www.youtube.com/watch?v=RbyQcCT6890 https://www.youtube.com/watch?v=RbyQcCT6890

hwbunny 2y ago

And people will become utterly stupid in the process.

CTDOCodebases 2y ago

or just... unemployed.

visarga 2y ago

> Can it just do my entire job for me? All AIs up to now lack autonomy. So I'd say until we crack this problem, it is not going to be able to do your job. Autonomy depends on a kind of data that is iterative, multi-turn, and learning from environments not from static datasets. We have the exact opposite, lots of non-iterative, off-policy (human made AI consumed) text.

schnitzelstoat 2y ago

Until the hallucination problem is solved, the output can't be trusted. So outside of use-cases where the user can quickly verify the result (like picking a decent generated image etc.),I can't see it being used much.

zarathustreal 2y ago
Never heard of retrieval-augmented generation?
- usrbinbash 2y ago
  RAG? Sure. I even implemented systems using it, and enabling it, myself. And guess what: RAG doesn't prevent hallucination. It can reduce it, and there are most certainly areas where it is incredibly useful (I should know, because that's what earns my paycheck), but it's useful despite still hallucinations being a thing, not because we solved that problem.
  - zarathustreal 2y ago
    Are you implying that you’re the same person I was commenting to or are you just throwing your opinion into the mix? Regardless, we’ve seen accuracy of ~98% with simple context-based prompting across every category of generation task. Don’t take my word for it, a simple search would show the effectiveness of “n-shot” prompting. Framing it as “it _can_ reduce” hallucinations is disingenuous at best, there really is no debate about how well it works. We can disagree on whether 98% accuracy is a solution but again I’d assert that for >50% of all possible real world uses for an LLM 98% is acceptable and thus the problem can be colloquially referred to as solved. If you’re placing the bar at 100% hallucination-free accuracy then I’ve got some bad news to tell you about the accuracy of the floating point operations we run the world on

mottiden 2y ago

> We’re moving toward a world where every job will be modeled, and you’ll either be an AI owner, a model architect, an agent/hardware engineer, a technician, or just.. training data. I understand that you might be afraid. I believe that a world where only LLM companies rule the world is not practically achievable except in some distopian universe. The likelihood of the world where the only job are model architects, engineers or technicians is very very small. Instead, let's consider the positive possibilities that LLMs can bring. It can lead to new and exciting opportunities across various fields. For instance, can serve as a tool to inspire new ideas for writers, artists, and musicians. I think we are going towards a more collaborative era where computers and humans interact much more. Everything will be a remix :)

altcognito 2y ago
> The likelihood of the world where the only job are model architects, engineers or technicians is very very small. Oh, especially since it will be a priority to automate their jobs, or somehow optimize them with an algorithm because that's a self-reinforcing improvement scheme that would give you a huge edge.
- SubiculumCode 2y ago
  Every corporate workplace is already thinking: How can I surveil and record everything an employee does as training data for their replacement in 3 years time.

Aeolun 2y ago

The two AI’s talking to each other was like listening to two commercials talking to each other. Like a callcenter menu that you cannot skip. And they _kept repeating themselves_. Ugh. If this is the future I’m going to hide in a cave.

mycall 2y ago

> Can it do math yet? GPT-4? Not that well. AI? Definitely https://deepmind.google/discover/blog/alphageometry-an-olympiad-level-ai-system-for-geometry/ https://deepmind.google/discover/blog/alphageometry-an-olymp...

aredox 2y ago

Call me back when we have autonomous driving, and when Bitcoin will replace currencies. What's scary and cringey are your delusions.

plaidfuji 2y ago
Don’t get me wrong, I’m not suggesting the current capabilities are anywhere near replacing human productivity. Some things are 1 year out, some 5 (maybe self-driving cars by then? Mercedes has it on their roadmap for 2030 and they’ve historically been realistic), some 10+. But the pieces are in place and the investments are being made. The question is no longer “can AI really automate this?”, it’s “how do we get the dataset that will enable us to automate this with AI?”. And as long as Open AI keeps people’s eyes on their whizbang demos, the money will keep flowing…

deleted 2y ago

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