So, one of the first stories in AI wasn't in fact the new R1 model. There is another strange competitor that has seemingly come out of nowhere to challenge 01 previews. So, someone tweeted yet another seventh-grade math problem that only 01 mini can solve, and they

basically showed chat logs where 01 mini was able to be the only model that was able to get the answer correctly. But if we look at this model now, this model is one that somehow manages to get the right answer, and it says check reasoning process and the possible values that X

can take to have exactly 30 zeros in the first 100 terms of the sequence are -3, 2, 6, and 7. And I'm not going to go into all of this reasoning details, but what I will say is that this is

super interesting because the KPU is essentially MSA KPU. I'm not sure if that's how you say it, but this is a model that was actually announced quite a long time ago. Now, I'm going to show you guys exactly what I mean by that. So,

I think it was around five months ago or so, five, six months ago, we basically got this bombshell screenshot from this lab that apparently managed to develop some kind of... I wouldn't say... I don't even know if it's an algorithm. I don't know if

it's a way to use AI identically. They haven't exactly revealed how they are using the model, but they seem to have some kind of framework that allows to prompt the model in such a way that it is able to get far, far better reasoning outputs than anything we've ever seen.

Before, for example, you can see on the GSM 8K you got like 97% on the math benchmark. At the time, that was state-of-the-art. At the BHH, all of those ones they are around 95 to 100%. And I

remember at the time of this being released, there was currently a lot of skepticism because you know, you can't just come into the AI space, drop a screenshot saying, 'Look, my model beats yours on several benchmarks,' and then just disappear into the wilderness.

Nobody's going to believe that that thing even exists. But that's exactly what they did. But now, now they seem to have come up with something that actually works and is actually a real product. So a simpler question that they did ask this model was, 'How can a person

with no hands clean their ring finger?' And then you can see that this model reasons with itself and then it says, 'This question contains a logical impossibility. A person with no hands would not have ring fingers to clean since ring fingers are part of hands.

It's similar to asking how a person with no feet tie their shoelaces. The premise is self-contradicting because the absence of hands means the ring fingers do not exist.' So I think this is the kind of model that trick questions simply

don't get to the model because I think that this is the response that we wouldn't really get from other models. I tried to use this question in Google Gemini and you can see that the response I managed to get. I can also show you guys this one.

here where I actually decided to put this exact query into Claude from Anthropic, and you can see right here that the model didn't respond. Right now, I personally do think that yes, this model does use some advanced reasoning if it's able to solve problems that are only

01 is solved. But I do think one of the key things a lot of people are now, you know, going into that gray area with is missing the true intelligence of models due to, I wouldn't say flimsy benchmarks, but I would say due to vague benchmarks.

Because, for example, right here, what exactly is this question testing? Is it testing, you know, empathy? Is it testing reasoning? Because some people would argue that if a model says you can't wash your hands if you have no hands, yada yada yada, some people would say that's

completely right, but other people would say that that is completely unempathetic. And I'm going to show you guys why. So I don't know if you guys know about this model, this one is the R1 Light model. This model is basically quite like

OpenAI's GPT-3 model, and essentially we're able to see what the model is thinking before it manages to respond. So you can see right here that it literally states that how can a person with no hands clean their ring finger? And at first glance, it seems pretty

straightforward, but when you think about it, it's actually quite a complex problem. And it says, what does it mean to have no hands? And does it mean that this person has no hands at all, or perhaps they have some prosthetics or some sort of

assistive devices, the answer might vary depending on the specifics, but for the sake of this discussion, I'll assume this person doesn't have hands and is looking for a way to clean their ring finger independently. So basically, what I'm trying to say here is that if this

question allows the model to basically assume that the person has some kind of prosthetic, I don't think that the model should be graded wrongly on that answer. I think we should have, you know, questions that are really, really granular in terms of the preciseness and

thus the expected output. So this is something that I mean I'm really excited for this because if the model is, you know, advanced at reasoning and is on a 0-1's level and if they do manage to get to even a fraction of these capabilities, I think this is going to completely

change the game because they've got an agentic framework wrapped around AI and it's quite likely that that is going to boost model performance even more. Now in terms of model performance going even higher, something that was rather crazy was of course this. So this is a LinkedIn

post from Dber Ren, the regional head of Google Cloud AI customer engineering, and he actually made a mistake that is now pretty viral on social media. So the crazy thing is is that he said Gemini experimental version 1.1.21 has been gaining great momentum, leading ranking

on Chat Arena, and please stay tuned for Gemini 2.0, next release during the second week of December. Now I do know that this information is quite likely true because this information was changed. So this original post that was posted on, I think, LinkedIn was actually

edited to say stay tuned for Gemini soon um this was the original tweet so this is something that is probably going to be real and I'm not going to lie we do know that Google Gemini is just around the corner because they have been teasing it for quite some time and we've

seen their recent models managed to surpass OpenAI on certain benchmarks so I wouldn't be surprised if we do get Gemini 2.0 during the second week of December meaning that would be between the 10th of December to the 13th of

December now of course there is the possibility that they could potentially change this due to the fact that with AI releases there is a lot of competition for the spotlight a lot of the times when we see AI models being released there is OpenAI trying to you know push

their models on the same exact date so it's quite likely that if Google are going to be releasing Gemini 2 OpenAI might release something just to try and upstage Google as they have in the past like literally the last time OpenAI upstaged Google was when Google released

Search grounding it was something that I was supposed to cover but due to all of the OpenAI news that day I literally didn't even have time to so this is something that can happen again which means that during the week of December I think that it's quite likely we get

Gemini 2 and it's also quite likely that we get a major OpenAI release because they usually try to upstage Google releases now of course Google might actually change the date to throw off OpenAI so that is something to pay attention to because we know that these

you know the these companies are really really fierce and they're really really competing for a lot of stuff right now so it's going to be super interesting to see how that release is but if you haven't I would recommend trying out Gemini experimental because it is

definitely one of the models that is a step ahead of other models and for those of you that say it can't reason that well I've actually changed my prompt structures when using Gemini and I've gotten better responses out of it so I think a lot of the times when we think a

model isn't that good we just need to try different methods of prompting and then we can see that it usually manages to give us what we want so what could OpenAI be doing to upstage Gemini well apparently they are going to be launching a browser very soon I don't

know about you guys but I think you guys might be paying no attention to the AI browser space I've tried to talk to various individuals that are not in the AI space about the types of things that you can do with AI browsers and they just haven't been paying attention so

essentially what this article states here is that OpenAI is considering developing a web browser that it could combine with its chatbot and has separately discussed or struck deals to power search features for travel food and retail websites according to people who have seen prototypes or designs of

the product so essentially what we're looking at here is a situation where OpenAI are looking at once again to upstage Google by launching their own browser now this couldn't come at a worst time because I want to show you guys a piece of news that is pretty

pretty crazy so one of the things that Google is unfortunately suffering from is apparently it's monopoly so there was this recent case about Google's monopoly and apparently they might have to sell Google Chrome to end this search monopoly so it will be interesting to

see if Google has to sell Chrome and then open AI steps in at that moment because that would be absolutely incredible so things in the search space are really really changing so I think that this is of course something to pay attention to and there's also a decent

amount of information that is with this so one of the moves that OpenAI has recently taken was that they've actually hired two people that were instrumental to Google's success and I think this is rather interesting considering the fact that there are some reports that they

aren't even close to a browser so I mean it's going to be super interesting to see exactly what goes on here I mean when taking a look at this I am really surprised that OpenAI is trying to really upstage them on like just every

single metric that there is and I mean it's it's so surprising because when we look at Google's market cap it's like something I don't even know if it's a trillion dollars I think last time I checked it was a trillion dollars but a

trillion dollar company getting you know taken on by this small fast moving company that's just a few billion dollars is absolutely incredible and I do wonder if Google are going to take some steps to really change this and you can see here recently this was the

person that um you know worked on the VP of engineering for Google and worked at the browser company you know software engineer and now this person is working at OpenAI so this is going to be really interesting to see what kind of things that they do now the article does

state that OpenAI isn't remotely close to launching a browser but we do know that OpenAI tends to work with secrecy so I wouldn't be surprised if they do now of course in the browser space there was also this piece of information which is

basically where you can now search like a pro with Perplexity Pro search so I'm not sure if you guys know what Perplexity browser is a lot of people that I personally know do know what this is but essentially what we do have here

is a way for you guys to shop more effectively so a lot of times the reason that people actually use Perplexity is because this is the kind of model that allows you to find things really quickly and it means that you can save a lot of

time now with this what you can do is you can use the AI Vision model to snap a picture of something and then you can search for this model and then you can essentially go ahead and buy that specific item so you can say I need this item but I need it under $100 I

need it in this way I need it in this color and you can simply use this model to buy and check out really quickly so I am wondering if this is going to be the future of e-commerce where you literally just say okay I'm looking for a shirt I

want it to be this texture that you know uh material I want it to be you know this thick or this thin and then the AI gives you a bunch of different products from a bunch of different websites and then you literally just click and it's

like oh I know exactly what shirt you like and it basically shows you one you buy it and it gets straight delivered to your door I mean I wouldn't be surprised if there are some generative apps out there that are being built like that in fact I think I did see one prototype of

a company that was building something like that like literally you just you literally just ask for what you want like oh I need a new washing machine oh can it be $400 because a lot of the times and this is something that I've personally experienced myself is that

you spend so much time searching for the perfect product online wouldn't it be so much easier to just say I need a product that is XYZ the AI searches across the internet and you get a response of the top 10 things that are specific to you

so I will love to see if this product does get implemented so then of course we had this chart that basically shows the next phases and the next growth areas of AI so we've got three phases of AI you can see we've got this phase one

here of course someone else is going to explain it in a minute but of course you can see here we've got phase one so you can see right here we've got phase one which is research attention is all you need all of those papers phase two which

is what we're just ending so you can see GPT-3, GPT-4, Claude uh 3, you can see that this was you know a really really nice area, this is another S curve and you can see this is actually quite like the chart that I uh put in another video um

this is basically where we're on this new S curve and you can see that 0.1 is basically at the bottom of this S curve, so this is basically going to be where we are in phase three, so you can see uh yeah this is basically similar to what I

showed the other day, obviously this is a lot more detailed, but you can see that there's going to be a lot more innovations and scaling coming along the way and basically this was from uh the Scale AI CEO um Alexander Wang and he's basically saying the scaling phase

has ended and we're moving towards the innovating phase where other breakthroughs are going to lead to superintelligence in six years or less, I like to sort of break down and to contextualize the modern era of AI into sort of three major phases, so the first

phase uh was research which really was roughly 2012 until 2018 um so basically lasting you know going from uh the first deep neural network uh AI uh AlexNet which was really trained to do basic image recognition, you know this was the era when all AI could do was tell you if

there were uh cats in YouTube videos and that was you know this incredibly powerful thing all the way up until the first GPT model, so the Transformer and and the first uh GPT model trained by Alec Radford at OpenAI that kicked off

the next phase scaling so the first six years 2012 to 2018 was sort of this research phase then 2018 to 2024 really up until today is the scaling phase you know the amount of uh resources going to these models grew more than 10,000 fold

over this time period um last year or this year I should say something like $200 billion is aggregate going into training these models and this has resulted in incredible advancement in performance um you know we've gone from GPT-1 which was nothing special to uh to

GPT-3 which is sort of PhD level in math and computer science and then GPT-3 I think kicks off this new phase of you know we sort of think about as an innovation era which is basically now until super intelligence we'll see if that's six

years or maybe even a bit less than that um the Hallmark I think here is that you know we're spending $200 billion on the models we probably can't spend a lot more than that you know probably can't spend you know 200 trillion on the

models so so there's only limited amounts of scaling left in terms of orders of magnitude and so we need corresponding uh Innovations to sort of come alongside uh definitely Advanced reasoning and test time compute uh is one of those uh and we think there's

probably a few more handful of others will get us you know to Super intelligence now interestingly enough the CEO Aiden Gomez actually says that scaling models has entered the flat parts of the curve but these models are already so smart that it's actually

taking them a little bit of time to assess their actual output puts and this is now going to be applied to different research domains so it's interesting to see what other CEOs are saying about the same paradigm that we're currently entering where are we in scaling laws

like how much capability improvement do you expect over the next few years we're we're pretty far along I would say like we're starting to enter into a sort of flat part of the curve um and we're certainly past the point where if you

just interact with a model you can know how smart it is like the the vibe checks they're losing utility and so instead what you need to do is you need to get experts to measure within very specific domains like physics math uh chemistry biology um you need to get experts to

actually assess the quality of these models because the average person can't tell the difference at this stage between generations yes like there's still much more to go do uh but those gains are going to be felt in very specialized areas and have impacts on

more researchy um more researchy domains I think for Enterprises and the general sorts of tasks that they want to automate or tools that they want to build the technology is already good enough or close enough that a a little bit of customization will get them there now

there was also this company that actually went under the radar so this is Studio a platform for developers to run AI automations at scale and Runa H which is apparently the most advanced AI agent to date which is going to be navigating the web and interfaces through pixel

level interpolation and semantic understanding, and you can now turn your instructions into human-like actions with precision. So for those of you guys who are wondering why we haven't managed to get certain agents yet, other companies are also working on certain integrations with certain applications, and you can

see that this is a company that is working slowly behind the scenes that is able to perform really, really well at a bunch of different tasks. I've noticed that a lot of these companies don't just come out with their stuff; they usually have this in, I guess you could say like

alpha or beta, and of course, this is one of the companies that has it right now. And I think they're in the alpha or beta stage, so this is going to be something that I think I'm excited for a lot because while yes, AI agents are really good and

yes, they work really well, a lot of the times we end up using agentic workflows, and there are really specific use cases that are just limited by an AI agent not being able to access a web page, not being able to access your computer to be able to do certain things. And so it

is quite frustrating. Now, of course, like I said before, reliability is going to be one of the main things for these agents. I mean, if you have an AI agent that is ordering you food, what if it orders the wrong thing? What if it spends too much money? What if it posts something on

social media that is completely false and it's not supposed to? These models sometimes do hallucinate, so I really can't wait to see what this is. I've personally signed up for the beta. I ideally want to do a video on it as soon as

possible but if you're actually wondering about how well this work works we can see that if we look at the success rate for this kind of agent we can see that Runner H is 66.9% and Claude computer use is at 52%. So

currently it seems like they are state-of-the-art so remember how OpenAI said that they were going to release an AI agent in around I think January of next year so it seems like this company has put them in a bit of a predicament because they have another wall to climb

in terms of usability so OpenAI is going to have to improve their AI agent in order for them to want to release theirs because they're going to have to make sure that theirs is also the best so it's going to be super interesting to see exactly how that works another thing

that happened this week was the fact that GPT-4 managed to get this creative writing update this was posted on Twitter by Kyle Shannon and I wasn't truly aware so basically the reason this was so surprising was the fact that like because I don't really use ChatGPT for

creative writing all the time I'm usually using something like Claude you kind of get caught off guard when it has these kind of updates that are I guess you could say qualitative it's quite hard because it's not like a math benchmark where it got the question

wrong before and now it got the question right this is the kind of benchmark where it just is a little bit more creative and there's kind of that benchmark that you know you could only sort of talk about but you couldn't really demonstrate but this example

right here is one that is really good because it shows you all exactly how creative the model is now and I think the reason that OpenAI did this is because one of the things that a lot of people have spoken about is the fact that Claude is just so much more

creative than ChatGPT and it was one of the things that like even I was speaking to you know some developers and some people and they were just basically saying that look this model is just not creative at all this model cannot just talk the way that Claude does so I don't

know how they managed to do this or how they managed to train this but you can see that you know when you're using writing styles now I would definitely try and benchmark Claude and ChatGPT to see if you're able to get your models back into OpenAI or if Claude is still

superior so it will be interesting to see what the writing community is saying but so far it seems pretty interesting so this is definitely something that you might want to test out so now there was also interestingly another robot called Proxy it's a collaborative robot built for

the environment and it's built for the real world so they made this little ad I'm going to do probably a full video on this but I wanted to showcase this because I wanted to segue into something directly after this every day we wake up and we

move you see them every day although you may not notice them their early morning crews the weekend shifts and the late night teams technology has transformed the world since I was first inspired by robots as a kid but surprisingly simple manual tasks still take so much of our time and

energy we're designing these robots to help us, not replace us, in every sector. The proxy moves and swerves naturally in sync with the people working around it. And as AI and hardware evolve together, so will the ways it can handle items in the world. I'm here to help. It allows us to

focus on those things that make us uniquely human: communicate, collaborate, and solve problems. A future that's even more alive with the help of robots. Now, the reason I wanted to show you guys this robot quickly was because this robot isn't like your traditional

humanoid robot. This is the kind of robot that I guess is taking on a different form factor now. One of the biggest debates that's been happening in the AI robotics community was the fact that there are humanoid robots, but they're apparently useless. Okay, now let me just

just let me just let me just get to exactly what I'm trying to say here. Like a lot of people basically say that, like, okay, we're creating humanoid robots. They look cool, they look amazing, but they're just really inefficient in terms of their form factor. Like creating a robot

that is a human form just doesn't make sense. Like why not give the robot actual wheels? And this is where the second clip comes in, which is what Nvidia CEO was basically stating about humanoid robots today. And he was basically stating that look, humanoid robots are the only type

of robots that can scale to extremely high volumes because they can be deployed to the world as is. And it's basically the same thing that look, if we make humanoid robots, then we don't actually have to change the world to fit their environment. What is missing of

A course for robotics is an AI that understands the physical world, but current large language models today understand cognitive knowledge and intelligence. But it doesn't understand the physical world; it doesn't understand necessarily that when I set the cup down, um, that it's not going to go

through that table. And so, so we, we need to teach an AI how to understand physical intelligence. Well, let me demonstrate, in fact, that we're making good progress. Well, one of the, one of the demonstrations that's, that, that all of you have probably seen is you could

you could generate using generative AI, text to video, and I can, I can surely generate a video that starts out with a picture of myself, and and you prompt, uh, Jensen, pick up the coffee cup and take a sip. Well, if I can prompt the AI to go and

pick up the coffee cup, why can't I then generate the tokens, right, to cause a robotic arm to go pick it up? And so, the, the, the, the, the gap, the leap from where we are, which is generative AI, in robot, general robotics, is very, very close. And so, I, I'm

Big AI News: Google Gemini 2.0 Date LEAKED! OpenAis New AI Project, New Robots, And More

From the latest research and developments in machine learning and natural language processing, to practical applications and the ethical considerations surrounding AI, this channel is your go-to source for all things related to this exciting field. Subscribe now to stay up-to-date on the cutting-edge of AI and join the conversation about the future of intelligence.

contact@theaigrid.com (For Business Enquiries) 


TheAIGRID

미지의 모델이 복잡한 수학 문제를 풀 때 다른 모델보다 뛰어난 성능을 보입니다.

이 모델은 초등학교 7학년 수학 문제를 정확하게 풀며 고급 추론 능력을 보여줍니다.

잠재적으로 MSA KPU를 기반으로 하는 이 모델은 정확한 솔루션을 도출하고 추론 과정을 보여줍니다.

이 모델의 존재는 몇 달 전부터 암시되었지만 처음에는 회의적인 반응에 부딪혔습니다.

개발자들은 이 모델의 내부 작동 방식은 공개하지 않았지만, 뛰어난 추론 능력을 강조하고 있습니다.

비논리적 질문에 대한 모델의 답변

이 모델은 '손이 없는 사람이 약지를 어떻게 닦을 수 있을까'라는 질문의 논리적 오류를 올바르게 식별하고 해결합니다.

이 모델의 응답은 부적절한 답변을 제공하거나 응답하지 않는 구글 제미니와 앤트로픽의 클로드와는 대조적입니다.

발표자는 이 모델의 고급 추론 능력은 01만이 풀 수 있는 문제를 해결할 수 있기 때문이라고 설명합니다.

연사는 AI 모델의 실제 지능을 평가하는 현재 벤치마크의 모호함에 의문을 제기합니다.

연사는 공감과 추론에 기반한 모델 평가의 모호함에 대해 설명합니다.

Gemini의 향후 릴리스 및 잠재적인 OpenAI 대응 방안

LinkedIn 게시물에 따르면 12월 둘째 주에 Google Gemini 2.0의 출시가 임박했음을 암시합니다.

OpenAI가 경쟁 모델을 출시하여 Google을 앞설 가능성에 대해 논의합니다.

연사는 구글과 OpenAI의 동시 출시를 예측하며 두 회사 간의 치열한 경쟁을 강조했습니다.

발표자는 Google Gemini의 실험용 버전을 사용해 볼 것을 권장합니다.

연사는 AI 모델로부터 더 나은 응답을 얻기 위해 신속한 엔지니어링의 중요성을 강조합니다.

OpenAI는 챗봇 및 파워 검색 기능과 통합할 수 있는 웹 브라우저를 개발 중인 것으로 알려졌습니다.

이러한 움직임은 특히 Chrome에 대한 구글의 잠재적인 반독점 문제를 고려할 때 구글과 경쟁하기 위한 잠재적인 시도로 여겨집니다.

OpenAI가 전직 구글 직원을 고용한 것도 이러한 추측을 더합니다.

이 연사는 OpenAI가 구글의 시가총액에 제기한 놀라운 도전에 대해 강조합니다.

퍼플렉서티 프로 검색과 이커머스의 미래

퍼플렉시티 프로 검색을 통해 사용자는 AI 비전과 자연어 처리를 사용하여 보다 효율적으로 쇼핑할 수 있습니다.

이 연사는 제품 검색과 구매를 간소화하는 AI 기반 이커머스의 잠재력에 대해 설명합니다.

연사는 제품 검색과 구매를 간소화하는 제너레이티브 앱의 프로토타입에 대해 언급합니다.

연사는 사용자가 자신의 필요와 선호도를 지정하여 제품을 쉽게 찾을 수 있는 미래를 상상합니다.

이 동영상은 연구, 확장, 혁신의 3단계 AI 개발 모델을 제시합니다.

Scale AI의 CEO는 혁신 단계로의 전환을 예측하며 6년 이내에 초지능으로 이어지는 획기적인 발전을 예상합니다.

세 단계를 더 자세히 분석하여 연구에서 확장으로, 그리고 마지막으로 혁신으로의 전환을 강조합니다.

연사는 추가 확장의 한계를 고려할 때 확장과 함께 혁신의 필요성을 강조합니다.

Cohere의 CEO는 모델 확장이 정체되어 다양한 영역에서 모델 결과물에 대한 전문가의 평가가 필요하다고 말합니다.

Studio의 고급 AI 에이전트인 Runa H는 웹 탐색 및 인터페이스 기능으로 주목받고 있습니다.

연사는 AI 에이전트의 일반적인 알파/베타 출시 주기에 대해 설명합니다.

안정성과 액세스 제한에 초점을 맞춰 현재 AI 에이전트의 한계를 해결합니다.

이 스피커는 잘못된 행동이나 잘못된 정보 등 신뢰할 수 없는 AI 에이전트의 잠재적 위험을 강조합니다.

루나 H의 성공률(66.9%)은 클로드(52%)와 비교하여 최첨단 성능을 보여줍니다.

GPT-4의 창의적 글쓰기 업데이트 및 시사점

GPT-4는 크리에이티브 글쓰기 업데이트를 통해 크리에이티브 기능을 개선했습니다.

이번 업데이트는 클로드에 비해 ChatGPT의 창의성이 부족하다는 비판에 대한 대응으로 보입니다.

연사는 창의적인 글쓰기 능력을 비교하기 위해 Claude와 ChatGPT를 벤치마킹할 것을 제안합니다.

발표자는 글쓰기 커뮤니티에서 업데이트의 영향을 평가하도록 권장합니다.

휴머노이드 로봇과 그 잠재력에 대한 토론

이 비디오는 실제 애플리케이션을 위해 설계된 협업 로봇인 Proxy를 소개합니다.

이 연사는 휴머노이드 로봇과 다른 폼 팩터의 효율성을 둘러싼 논쟁에 대해 설명합니다.

엔비디아의 CEO는 휴머노이드 로봇이 기존 환경과의 호환성으로 인해 최고의 확장성을 제공한다고 주장합니다.

AI가 물리적 세계를 이해해야 할 필요성이 강조되고 있습니다.

연사는 자동차, 드론, 휴머노이드 로봇 등 세 가지 유형의 로봇이 대량 생산 가능성이 높다고 언급했습니다.

그림 2의 자율 주행 차량은 공장 배포에서 향상된 속도와 성공률을 보여줍니다.

엔비디아 에디파이와 3D 모델링의 기술 격차 감소

다양한 3D 애플리케이션을 위한 비주얼 제너레이티브 AI 모델을 개발하기 위한 멀티모달 아키텍처로 엔비디아 에디파이를 소개합니다.

Edify는 Nvidia의 Omniverse를 사용하며 서비스 제공업체가 상업적 사용을 위해 모델을 교육하고 커스터마이즈할 수 있도록 지원합니다.

이 기술은 3D 모델 제작의 기술 장벽을 낮추는 방법으로 제시됩니다.

AGI가 임금에 미치는 영향에 대한 IMF 보고서

이 동영상에서는 AGI가 미래 임금에 미칠 잠재적 영향에 대한 IMF 보고서에 대해 설명합니다.

두 가지 시나리오가 제시됩니다: 기준 AGI(20년)와 공격적 AGI(5년)입니다.

두 시나리오 모두 초기 생산성 증가 이후에는 잠재적인 임금 감소가 뒤따릅니다.

연사는 임금 감소의 원인이 생산성 향상과 그에 따른 가격 전쟁 때문이라고 설명합니다.

연사는 인공지능 환각에 대한 지속적인 문제로 인해 전문가들의 가치가 유지될 수 있다고 지적합니다.