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Google launches new AI model to translates vision, language into robotic actions

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Google launches new AI model to translates vision, language into robotic actions

Google has introduci a new advancement in robotics in a bid to push closer to a future of helpful robots. Robotics Transformer 2, or RT-2, is a first-of-its-kind vision-language-action (VLA) model.

A Transformer-based model trained on text and images from the web, RT-2 can directly output robotic actions. Just like language models are trained on text from the web to learn general ideas and concepts, RT-2 transfers knowledge from web data to inform robot behavior.

In other words, RT-2 can speak robot.

The pursuit of helpful robots has always been a herculean effort, because a robot capable of doing general tasks in the world needs to be able to handle complex, abstract tasks in highly variable environments — especially ones it’s never seen before.

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Unlike chatbots, robots need “grounding” in the real world and their abilities. Their training isn’t just about, say, learning everything there is to know about an apple: how it grows, its physical properties, or even that one purportedly landed on Sir Isaac Newton’s head. A robot needs to be able to recognize an apple in context, distinguish it from a red ball, understand what it looks like, and most importantly, know how to pick it up.

That’s historically required training robots on billions of data points, firsthand, across every single object, environment, task and situation in the physical world — a prospect so time consuming and costly as to make it impractical for innovators. Learning is a challenging endeavor, and even more so for robots.

Recent work has improved robots’ ability to reason, even enabling them to use chain-of-thought prompting, a way to dissect multi-step problems. The introduction of vision models, like PaLM-E, helped robots make better sense of their surroundings. And RT-1 showed that Transformers, known for their ability to generalize information across systems, could even help different types of robots learn from each other.

But until now, robots ran on complex stacks of systems, with high-level reasoning and low-level manipulation systems playing an imperfect game of telephone to operate the robot. Imagine thinking about what you want to do, and then having to tell those actions to the rest of your body to get it to move. RT-2 removes that complexity and enables a single model to not only perform the complex reasoning seen in foundation models, but also output robot actions. Most importantly, it shows that with a small amount of robot training data, the system is able to transfer concepts embedded in its language and vision training data to direct robot actions — even for tasks it’s never been trained to do.

For example, if you wanted previous systems to be able to throw away a piece of trash, you would have to explicitly train them to be able to identify trash, as well as pick it up and throw it away. Because RT-2 is able to transfer knowledge from a large corpus of web data, it already has an idea of what trash is and can identify it without explicit training. It even has an idea of how to throw away the trash, even though it’s never been trained to take that action. And think about the abstract nature of trash — what was a bag of chips or a banana peel becomes trash after you eat them. RT-2 is able to make sense of that from its vision-language training data and do the job.

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RT-2’s ability to transfer information to actions shows promise for robots to more rapidly adapt to novel situations and environments. In testing RT-2 models in more than 6,000 robotic trials, the team found that RT-2 functioned as well as our previous model, RT-1, on tasks in its training data, or “seen” tasks. And it almost doubled its performance on novel, unseen scenarios to 62% from RT-1’s 32%.

In other words, with RT-2, robots are able to learn more like we do — transferring learned concepts to new situations.

Not only does RT-2 show how advances in AI are cascading rapidly into robotics, it shows enormous promise for more general-purpose robots. While there is still a tremendous amount of work to be done to enable helpful robots in human-centered environments, RT-2 shows us an exciting future for robotics just within grasp. 

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Microsoft to invest 2.2bn dollars in cloud and AI services in Malaysia

Microsoft to invest 2.2bn dollars in cloud and AI services in Malaysia

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Microsoft to invest 2.2bn dollars in cloud and AI services in Malaysia

Microsoft (MSFT.O) said on Thursday it will invest $2.2 billion over the next four years in Malaysia to expand cloud and artificial intelligence (AI) services in the company’s latest push to promote its generative AI technology in Asia.

The investment, the largest in Microsoft’s 32-year history in Malaysia, will include building cloud and AI infrastructure, creating AI-skilling opportunities for 200,000 people, and supporting the country’s developers, the company said.

“We want to make sure we have world class infrastructure right here in the country so that every organisation and start-up can benefit,” Microsoft Chief Executive Satya Nadella said during a visit to Kuala Lumpur.

Microsoft will also work with the Malaysian government to establish a national AI Centre of Excellence and enhance the nation’s cybersecurity capabilities, the company said in a statement.

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Prime Minister Anwar Ibrahim, who met Nadella on Thursday, said the investment supported Malaysia’s efforts in developing its AI capabilities.

Microsoft is trying to expand its support for the development of AI globally. Nadella this week announced a $1.7 billion investment in neighbouring Indonesia and said Microsoft would open its first regional data centre in Thailand.

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Nvidia supplier SK Hynix says HBM chips almost sold out for 2025

Nvidia supplier SK Hynix says HBM chips almost sold out for 2025

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Nvidia supplier SK Hynix says HBM chips almost sold out for 2025

South Korea’s SK Hynix (000660.KS) said on Thursday that its high-bandwidth memory (HBM) chips used in AI chipsets were sold out for this year and almost sold out for 2025 as businesses aggressively expand artificial intelligence services.

“The HBM market is expected to continue to grow as data and (AI) model sizes increase,” Chief Executive Officer Kwak Noh-Jung told a news conference. “Annual demand growth is expected to be about 60% in the mid-to long-term.”

SK Hynix which competes with U.S. rival Micron (MU.O) and domestic behemoth Samsung Electronics (005930.KS) in HBM was until March the sole supplier of HBM chips to Nvidia, according to analysts who add that major AI chip purchasers are keen to diversify their suppliers to better maintain operating margins. Nvidia commands some 80% of the AI chip market.

Micron has also said its HBM chips were sold out for 2024 and that the majority of its 2025 supply was already allocated. It plans to provide samples for its 12-layer HBM3E chips to customers in March.

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“As AI functions and performance are being upgraded faster than expected, customer demand for ultra-high-performance chips such as the 12-layer chips appear to be increasing faster than for 8-layer HBM3Es,” said Jeff Kim, head of research at KB Securities.

Samsung Electronics (005930.KS) which plans to produce its HBM3E 12-layer chips in the second quarter, said this week that this year’s shipments of HBM chips are expected to increase more than three-fold and it has completed supply discussions with customers. It did not elaborate further.

Last month, SK Hynix announced a $3.87 billion plan to build an advanced chip packaging plant in the U.S. state of Indiana with an HBM chip line and a 5.3 trillion won ($3.9 billion) investment in a new DRAM chip factory at home with a focus on HBMs.

Kwak said investment in HBM differed from past patterns in the memory chip industry in that capacity is being increased after making certain of demand first.

By 2028, the portion of chips made for AI, such as HBM and high-capacity DRAM modules, is expected to account for 61% of all memory volume in terms of value from about 5% in 2023, SK Hynix’s head of AI infrastructure Justin Kim said.

Last week, SK Hynix said in a post-earnings conference call that there may be a shortage of regular memory chips for smartphones, personal computers and network servers by the year’s end if demand for tech devices exceeds expectations.

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The Nvidia (NVDA.O) supplier and the world’s second-largest memory chipmaker will begin sending samples of its latest HBM chip, called the 12-layer HBM3E, in May and begin mass producing them in the third quarter.

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Qualcomm jumps as AI sparks rebound in Chinese smartphone market

Qualcomm jumps as AI sparks rebound in Chinese smartphone market

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Qualcomm jumps as AI sparks rebound in Chinese smartphone market

Qualcomm (QCOM.O) shares rose 4% in premarket trading on Thursday after the smartphone-focused chipmaker signaled an AI-fueled rebound in demand, especially in China, after a two-year slump.

Sales to Chinese smartphone makers jumped 40% in the first half of its fiscal year, the company said on Wednesday, as buyers there gravitate toward higher-priced devices that can accommodate AI chatbots.

“Chinese vendors who traditionally relied more on MediaTek, are going to start leveraging Qualcomm’s high-end chips more as they push hard into the AI Agenda,” said IDC analyst Nabila Popal.

“They further represent an upside for Qualcomm because majority of the recovery is also going to be driven by Chinese OEMs this year, coming from a tough last two years.”

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Qualcomm on Wednesday projected third-quarter sales that were above estimates as it also benefits from its IoT (Internet of things) and auto segments.

The company, the biggest supplier of smartphone chips, was on course to add more than $8 billion to its market value based on premarket movements. Other semiconductor firms such as Arm and Broadcom (AVGO.O) rose 2.8% and 2.4%, respectively.

According to preliminary data from research firm IDC, in the high-end segment, the AI buzz and the foldable products allowed the Android smartphone vendors to further differentiate themselves from Apple (AAPL.O) and garnered increased interest from Chinese consumers in the first quarter of 2024.

“We’re optimistic that numbers can be driven higher, given last year’s muted Android cycle and the likelihood of IoT(internet of things) improvement as inventory normalizes,” analysts at Wolfe Research said.

At least 14 analysts raised their price targets on Qualcomm, according to LSEG data.

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Qualcomm’s shares have gained 13.5% this year following a 31.5% rise in 2023.

Shares of Apple, which is set to report earnings after market closes on Thursday, were up 1.05% in premarket trading.

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