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Meta’s AI chief says world models are the key to ‘human-level artificial intelligence’, but it may not take another 10 years

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Do today’s artificial intelligence models really remember, think, plan and reason like a human brain would? Some AI labs would tell us that is the case, but according to Meta’s Chief AI Scientist Yann LeCun, the answer isn’t any. But he thinks we could achieve it in a few decade using a brand new method called the “world model.”

Earlier this yr, OpenAI released a brand new feature it calls “memory” that permits ChatGPT to “remember” your conversations. The startup’s latest generation of models, o1, displays the word “thinking” when generating results, and OpenAI claims the same models are able to “complex reasoning.”

Everything indicates that we are close to AGI. However, during recent discussion at the Hudson ForumLeCun undermines AI optimists like xAI founder Elon Musk and Google DeepMind co-founder Shane Legg, who suggest that human-level artificial intelligence is just around the corner.

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“We need machines that understand the world; (machines) that can remember things, that have intuition, that have common sense, things that can reason and plan at the same level as humans,” LeCun said during the call. “Despite what you have heard from the most enthusiastic people, current AI systems are not capable of this.”

LeCun argues that today’s large language models, reminiscent of those powered by ChatGPT and Meta AI, are a far cry from “human-level artificial intelligence.” He later said that humanity could possibly be “years or even decades” away from achieving such a goal. (But that does not stop his boss, Mark Zuckerberg, from asking him when AGI will occur.)

The reason is easy: these LLMs work by predicting the next token (often just a few letters or a brief word), and today’s image/video models predict the next pixel. In other words, language models are one-dimensional predictors and AI image/video models are two-dimensional predictors. These models have develop into quite good at predicting of their respective dimensions, but they do not really understand the three-dimensional world.

For this reason, modern artificial intelligence systems are unable to perform easy tasks that almost all humans can. LeCun notes that folks learn to clear the table at age 10 and drive a automotive at 17 – they usually learn each in a matter of hours. However, even the most advanced artificial intelligence systems in the world today, built on 1000’s or tens of millions of hours of information, cannot operate reliably in the physical world.

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To achieve more complex tasks, LeCun suggests that we’d like to construct three-dimensional models that may perceive the world around us and cluster around a brand new variety of artificial intelligence architecture: world models.

“A world model is a mental model of how the world behaves,” he explained. “You can imagine a sequence of actions you might take, and your world model will allow you to predict what effect that sequence of actions will have on the world.”

Consider the “world model” in your personal head. For example, imagine you have a look at a unclean bedroom and wish to clean it. You can imagine how collecting all of your clothes and putting them away would do the trick. You haven’t got to try multiple methods or learn the way to clean a room first. Your brain observes three-dimensional space and creates an motion plan that may enable you to achieve your goal the first time. This roadmap is the secret that the models of the AI ​​world promise.

Part of the profit is that world models can take in way more data than LLM models. This also makes them computationally intensive, which is why cloud service providers are racing to partner with artificial intelligence firms.

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World models are a crucial concept that several artificial intelligence labs are currently working on, and the term is quickly becoming another buzzword attracting enterprise capital funds. A gaggle of esteemed artificial intelligence researchers, including Fei-Fei Li and Justin Johnson, just raised $230 million for his or her startup World Labs. The “Godmother of AI” and her team are also confident that world models will unlock much smarter AI systems. OpenAI also describes its unreleased Sora video generator as a world model, but doesn’t go into details.

LeCun outlined the idea of ​​using world models to create human-level artificial intelligence in: Article from 2022 on “goal-driven artificial intelligence,” although notes that the concept is over 60 years old. In short, the basic representation of the world (for instance, a video of a unclean room) and memory are fed into the world model. The world model then predicts what the world will seem like based on this information. You then provide the goals for the world model, including the modified state of the world you wish to achieve (e.g. a clean room), in addition to guardrails to ensure the model doesn’t harm people in achieving the goal (don’t kill me in the middle of cleansing the room, please). The world model then finds a sequence of actions to achieve these goals.

According to LeCun, Meta’s long-term research lab, FAIR, or Fundamental AI Research, is actively working on constructing goal-driven models of artificial intelligence and the world. FAIR used to concentrate on artificial intelligence for Meta’s upcoming products, but LeCun says that in recent years the lab has begun to focus exclusively on long-term artificial intelligence research. LeCun says FAIR doesn’t even use LLM courses presently.

World models are an intriguing idea, but LeCun says we’ve not made much progress in making these systems a reality. There are numerous very difficult problems facing us where we are now, and he says it’s actually more complicated than we predict.

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“It will be years, if not a decade, before we can get everything up and running here,” Lecun said. “Mark Zuckerberg keeps asking me how long it will take.”

This article was originally published on : techcrunch.com

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One of the last AI Google models is worse in terms of safety

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The Google Gemini generative AI logo on a smartphone.

The recently released Google AI model is worse in some security tests than its predecessor, in line with the company’s internal comparative test.

IN Technical report Google, published this week, reveals that his Flash Gemini 2.5 model is more likely that he generates a text that violates its security guidelines than Gemini 2.0 Flash. In two indicators “text security for text” and “image security to the text”, Flash Gemini 2.5 will withdraw 4.1% and 9.6% respectively.

Text safety for the text measures how often the model violates Google guidelines, making an allowance for the prompt, while image security to the text assesses how close the model adheres to those boundaries after displaying the monitors using the image. Both tests are automated, not supervised by man.

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In an e-mail, Google spokesman confirmed that Gemini 2.5 Flash “performs worse in terms of text safety for text and image.”

These surprising comparative results appear when AI is passing in order that their models are more acceptable – in other words, less often refuse to answer controversial or sensitive. In the case of the latest Llam Meta models, he said that he fought models in order to not support “some views on others” and answers to more “debated” political hints. Opeli said at the starting of this yr that he would improve future models, in order to not adopt an editorial attitude and offers many prospects on controversial topics.

Sometimes these efforts were refundable. TechCrunch announced on Monday that the default CHATGPT OPENAI power supply model allowed juvenile to generate erotic conversations. Opeli blamed his behavior for a “mistake”.

According to Google Technical Report, Gemini 2.5 Flash, which is still in view, follows instructions more faithfully than Gemini 2.0 Flash, including instructions exceeding problematic lines. The company claims that regression might be partially attributed to false positives, but in addition admits that Gemini 2.5 Flash sometimes generates “content of violation” when it is clearly asked.

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“Of course, there is a tension between (after instructions) on sensitive topics and violations of security policy, which is reflected in our assessment,” we read in the report.

The results from Meepmap, reference, which can examine how models react to sensitive and controversial hints, also suggest that Flash Gemini 2.5 is much less willing to refuse to reply controversial questions than Flash Gemini 2.0. Testing the TechCrunch model through the AI ​​OpenRoutter platform has shown that he unsuccessfully writes essays to support human artificial intelligence judges, weakening the protection of due protection in the US and the implementation of universal government supervisory programs.

Thomas Woodside, co -founder of the Secure AI Project, said that the limited details given by Google in their technical report show the need for greater transparency in testing models.

“There is a compromise between the instruction support and the observation of politics, because some users may ask for content that would violate the rules,” said Woodside Techcrunch. “In this case, the latest Flash model Google warns the instructions more, while breaking more. Google does not present many details about specific cases in which the rules have been violated, although they claim that they are not serious. Not knowing more, independent analysts are difficult to know if there is a problem.”

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Google was already under fire for his models of security reporting practices.

The company took weeks to publish a technical report for the most talented model, Gemini 2.5 Pro. When the report was finally published, it initially omitted the key details of the security tests.

On Monday, Google published a more detailed report with additional security information.

(Tagstotransate) Gemini

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This article was originally published on : techcrunch.com
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Aurora launches a commercial self -propelled truck service in Texas

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The autonomous startup of the Aurora Innovation vehicle technology claims that it has successfully launched a self -propelled truck service in Texas, which makes it the primary company that she implemented without drivers, heavy trucks for commercial use on public roads in the USA

The premiere appears when Aurora gets the term: In October, the corporate delayed the planned debut 2024 to April 2025. The debut also appears five months after the rival Kodiak Robotics provided its first autonomous trucks to clients commercial for operations without a driver in field environments.

Aurora claims that this week she began to freight between Dallas and Houston with Hirschbach Motor Lines and Uber Freight starters, and that she has finished 1200 miles without a driver to this point. The company plans to expand to El Paso and Phoenix until the top of 2025.

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TechCrunch contacted for more detailed information concerning the premiere, for instance, the variety of vehicles implemented Aurora and whether the system needed to implement the Pullover maneuver or the required distant human assistance.

The commercial premiere of Aurora takes place in a difficult time. Self -propelled trucks have long been related to the necessity for his or her technology attributable to labor deficiencies in the chairman’s transport and the expected increase in freigh shipping. Trump’s tariffs modified this attitude, not less than in a short period. According to the April analytical company report from the commercial vehicle industry ACT researchThe freight is predicted to fall this yr in the USA with a decrease in volume and consumer expenditure.

Aurora will report its results in the primary quarter next week, i.e. when he shares how he expects the present trade war will affect his future activity. TechCrunch contacted to learn more about how tariffs affect Auror’s activities.

For now, Aurora will probably concentrate on further proving his safety case without a driver and cooperation with state and federal legislators to just accept favorable politicians to assist her develop.

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At the start of 2025, Aurora filed a lawsuit against federal regulatory bodies after the court refused to release the appliance for release from the protection requirement, which consists in placing warning triangles on the road, when the truck must stop on the highway – something that’s difficult to do when there isn’t a driver in the vehicle. To maintain compliance with this principle and proceed to totally implement without service drivers, Aurora probably has a man -driven automotive trail after they are working.

(Tagstranslate) Aurora Innovation

This article was originally published on : techcrunch.com
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Sarah Tavel, the first woman of the Benchmark GP, goes to the Venture partner

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Eight years after joining Benchmark as the company’s first partner, Sarah Tavel announced that she was going to a more limited role at Hapeure Venture.

In his latest position as a partner Venture Tavel will proceed to invest and serve existing company boards, but may have more time to examine “AI tools on the edge” and fascinated with the direction of artificial intelligence, she wrote.

Tavel joined Benchmark in 2017 after spending a half years as a partner in Greylock and three years as a product manager at Pinterest. Before Pinterest, Tavel was an investor in Bessemer Venture Partners, where she helped Source Pinterest and Github.

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Since its foundation in 1995, the benchmark intentionally maintained a small team of six or fewer general partners. Unlike most VC corporations, wherein older partners normally receive most of the management and profits fees, the benchmark acts as an equal partnership, and all partners share fees and returns equally.

During his term as a general partner of Benchmark, Tavel invested in Hipcamp on the campsite, chains of cryptocurrency intelligence startups and the Supergreaty cosmetic platform, which was purchased by Whatnot in 2023. Tavel also supported the application for sharing photos of Paparazhi, which closed two years ago, and the AI ​​11x sales platform, about which TechCrunch wrote.

(Tagstotransate) benchmark

This article was originally published on : techcrunch.com
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