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중도 성향
IT/기술
IIT Madras Team Takes India's Solar Car Dream To South Africa
For Team Agnirath, the race is about much more than winning a trophy.
IT/기술 · "WINNING" · 총 17건
필터 보기현재 지수
50.3
0 = 부정 우세
50 = 중립
100 = 긍정 우세
최근 7일 기준 86,943건을 분석한 결과, 뉴스 심리지수는 50.3(균형)입니다. 긍정 4,381건(5.0%)·중립 80,514건(92.6%)·부정 2,048건(2.4%)이며, 중립 비중이 뚜렷하게 높습니다. 성향 지수는 종합 15.0(중도 균형)입니다.
For Team Agnirath, the race is about much more than winning a trophy.
Artificial intelligence might terminate lots of jobs one day, especially in high tech, but there’s little evidence AI is already causing widespread layoffs.
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Samsung Electronics' largest labor union has lost its majority status just two months after winning legal recognition, as workers in the company's non-chip division quit to protest a wage deal that favored semiconductor employees. The Samsung Electronics branch of the Samsung Group Employees' Union had 58,270 members as of 3 p.m. on Thursday, according to the union. That is down by nearly 18,000 from more than 76,000 at the end of April, when it held a rally to vote on a strike. Samsung Electron
OpenAI's "Time to Fly" advertisement hinted at an online minigame. Some players posted free token winnings on X.
De explosieve groei van kunstmatige intelligentie (AI) leidt tot een sterke toename in het gebruik van water, stroom en land. Dit kan meer vervuiling en klimaatverandering veroorzaken. Intussen zijn de lusten en lasten ongelijk verdeeld, stellen onderzoekers van een denktank van de Verenigde Naties. Veel van de cijfers zijn niet nieuw. Toch is dit rapport volgens experts completer dan eerdere overzichten. "Het geeft een breed beeld van de impact van AI", zegt Bernard van Gastel, die onderzoek doet naar duurzame digitalisering aan de Radboud Universiteit. Zorgen over het stroomgebruik van datacenters bestaan al langer. In Ierland gebruikt de sector meer dan een vijfde van alle stroom en in de Verenigde Staten drijven datacenters lokaal stroomprijzen op. Ook in Nederland leidde de voorgenomen bouw al veelvuldig tot discussie. Stroomgebruik explodeert AI was vorig jaar al verantwoordelijk voor een vijfde van het stroomgebruik van datacenters. Dat aandeel groeit naar verwachting gestaag door. Deels komt dat doordat het trainen van almaar krachtigere AI-modellen steeds meer stroom vergt. Daarnaast gebruiken mensen en bedrijven de technologie vaker. Het toenemende stroomgebruik is deels op te vangen met wind-, zon- en kernenergie. Bij het opwekken daarvan komen geen broeikasgassen vrij. De groei is echter zo sterk, schrijven de onderzoekers, dat deze bronnen niet genoeg zijn. Bedrijven grijpen ook naar stroom uit steenkool en aardgas. Dat leidt dus tot meer uitstoot en meer opwarming van de aarde. Het energiezuiniger maken van AI helpt maar beperkt, stelt het rapport. De techniek wordt dan, paradoxaal genoeg, goedkoper en aantrekkelijk voor nieuwe taken waarvoor die eerst te duur was. Het gebruik neemt daardoor toe, wat het zuiniger werken deels tenietdoet. Fiks meer watergebruik De schattingen van het watergebruik in het VN-rapport zijn fiks hoger dan eerdere schattingen. Datacenters gebruiken water vaak voor hun koeling. Dit watergebruik wordt veel hoger geraamd dan in een vorig rapport van het Internationaal Energie Agentschap. "Dat komt voor mij niet als een verrassing", zegt Alex de Vries-Gao, die de voetafdruk van AI onderzoekt. Hij benadrukt dat die eerdere schatting te rooskleurig was. "Goed dat ze dit benoemen." Koelwater gaat niet per se verloren, maar het kan volgens De Vries-Gao een pijnpunt zijn als het op een kwetsbare plek wordt gebruikt: "Dat ze zoveel water gebruiken uit de omgeving terwijl er waterschaarste is, of gaat komen." Het VN-rapport benadrukt dat AI veel voordelen heeft. Het kan wetenschappelijk onderzoek naar ziekten of de aanpak van klimaatverandering versnellen. In sommige sectoren maakt het werknemers productiever. Scheve verdeling Alleen zijn de baten ongelijk verdeeld. De AI-rekenkracht bevindt zich voor 90 procent in China en de Verenigde Staten. Ook welvarende landen in Europa en Azië hebben, hoewel in mindere mate, de beschikking over zulke rekenkracht. Voor de meeste landen geldt dat niet, zeker in Zuid-Amerika en Afrika. Deze landen zijn afhankelijk en moeten betalen voor toegang. En dat terwijl ze wel last hebben van de nadelen. Neem de vervuiling die komt kijken bij de winning van grondstoffen, als koper en silicium, die nodig zijn voor AI. En het verergeren van de opwarming van de aarde. Juist de armste landen lijden hier veel onder, terwijl ze de minste middelen hebben om zich te beschermen. Open deuren Volgens de onderzoekers zijn er diverse manieren om AI eerlijker en milieuvriendelijker te maken. Zo zouden AI-bedrijven meer openheid moeten geven over hun stroom- en watergebruik. Ook moet eraan worden gewerkt om AI-gebruik te temperen, en het resterende gebruik zo slim mogelijk te doen. Daarnaast moet er gezocht worden naar manieren om mensen mee te laten delen. "Dat zijn deels open deuren", zegt Bernard van Gastel van de Radboud Universiteit. "Hoe je AI kan begrenzen en slim in kan zetten is een pijnpunt." Volgens Van Gastel is er wereldwijd eigenlijk geen goed democratisch systeem om dat te regelen. "Het is fijn dat de aanbevelingen nu op een rijtje staan. Er is nog heel veel werk aan de winkel." Ook Alex de Vries-Gao verzucht dat het rapport wat betreft oplossingen blijft hangen in algemeenheden. "Het is moeilijk om consensus te krijgen en daarom krijg je algemeen taalgebruik en geen concrete actiepunten." Ook is het volgens De Vries-Gao een hardnekkig probleem dat AI-bedrijven geen openheid willen geven.
The S&P 500 and the Dow closed modestly higher on Tuesday as risk appetite driven by AI fervor was counterbalanced by tensions arising from U.S.-Iran talks to reopen the Strait of Hormuz and end the months-long war.Gains in most of the 11 major S&P sectors kept the S&P 500 and the Dow in the green, with the small-cap Russell 2000 outperforming its larger-cap peers. The Nasdaq ended the session essentially unchanged.Small-cap stocks have been some of the biggest beneficiaries of the ongoing enthusiasm surrounding artificial intelligence stocks, which provided some upside muscle. The Philadelphia SE Semiconductor Index advanced on the day.The Software & Services Index, battered in recent months over worries of AI disruption, closed in negative territory.Strong results from Hewlett Packard Enterprise and a funding commitment from Alphabet reinforced confidence in the AI buildout."The market is kind of muted at the surface level, but there is a lot going on under the hood, and that describes much of this year," said Mike Dickson, head of portfolio management at Horizon Investments in Charlotte, North Carolina. "There's some massive dispersion in the whole AI infrastructure ecosystem.""Markets could be in for one of these heated, melt-up rallies where the momentum keeps winning," Dickson added. "I would not be surprised at all to be sitting here at the end of the summer a good bit higher."Tehran is studying a U.S. proposal to bring the war to a halt, but has not been in contact with Washington for days, according to Iranian media, which also said Iran is taking a "stern" approach, given what it views as a history of U.S. noncompliance and mutual distrust. Simultaneously, Israel is continuing its strikes on Lebanon, despite Tehran's warnings that the attacks are threatening to derail the fragile truce.The war has sent crude prices soaring, reviving worries over inflation and giving rise to an increasing likelihood that the U.S. Federal Reserve could hike interest rates by year-end. Cleveland Fed President Beth Hammack said on Tuesday that such a hike could become necessary if already-elevated inflation pressures continue to mount. On the economic front, a report from the Labor Department showed an unexpected spike in job openings, driven by the volatile professional and business services sector. Otherwise, hiring, firing and quits all decreased, suggesting a slowdown in labor market churn in the face of uncertainties related to strife in the Middle East and inflationary effects.Analysts look to the May employment report due on Friday, which is expected to show the U.S. economy added 85,000 jobs last month, a monthly deceleration of 26.1%. The unemployment rate is forecast to stand pat at 4.3%.According to preliminary data, the S&P 500 gained 10.07 points, or 0.13%, to end at 7,610.03 points, while the Nasdaq Composite gained 8.78 points, or 0.03%, to 27,095.59. The Dow Jones Industrial Average rose 237.13 points, or 0.46%, to 51,316.01.Hewlett Packard Enterprise jumped after the AI server maker pulled forward its long-term financial targets by two years. In further evidence of AI buildout, Alphabet said it was looking to raise $80 billion in equity offerings, including an investment from Berkshire Hathaway, to fund a costly expansion of its AI infrastructure. Its shares lost ground on the day. Marvell Technology's shares surged after Nvidia Chief Executive Officer Jensen Huang called the chipmaker the next "trillion-dollar company" at the Computex conference in Taipei. Nvidia invested $2 billion in Marvell in March.A drop in bitcoin hit cryptocurrency firms Coinbase and Strategy Inc.Broadcom is expected to report quarterly results on Wednesday.
Martin Scorsese is the latest Oscar-winning director to hop on the AI wagon, joining the AI firm Black Forest Labs as an adviser in a bid to “push the bounds of creativity to create deeper and richer experiences for audiences.” “Cinema is a young medium, only around 125 years old, so we have to be […]
The MacBook Neo shipped 1.1 million units in its first weeks on sale, IDC estimates, as Apple pushes deeper into the mainstream laptop market.
Vibe coders using AI to solve simple problems, contrasting the high-stakes, risky AI investments in Big Tech.
LinkedIn cofounder Reid Hoffman believes the AI chatbot gold rush is over, shifting focus to AI-powered medicine as the next massive opportunity. He highlights healthcare's larger market and the potential for AI to accelerate drug discovery, creating lucrative monopolies through patent-protected innovations. Hoffman also noted that the medical field is not a winner-take-all market, allowing for multiple successful players.
South Korean stocks snapped their four-day winning streak Thursday as artificial intelligence-related shares took a breather following their recent rally and on renewed tensions between the United States and Iran. The benchmark Korea Composite Stock Price Index fell 43.41 points, or 0.53 percent, to close at 8,185.29, after dipping as low as 7,841.01. The index closed at a record high of 8,288.7 on Wednesday, extending its winning streak to the fourth consecutive session on the back of a strong
South Korean stocks opened lower Thursday as a rally led by artificial intelligence took a breather as tensions increased again on news that US launched fresh strikes against Iran. The benchmark Korea Composite Stock Price Index fell 113.09 points, or 1.37 percent, to 8,115.61 in the first 15 minutes of trading. The index closed at a record high of 8,288.7 on Wednesday, extending its winning streak to the fourth consecutive session on the back of a strong rally led by major semiconductor shares,
Welcome to The Hill's Business & Economy newsletter {beacon} Business & Economy Business & Economy The Big Story Warren proposes taxing AI companies so ‘winnings’ ‘benefit all Americans’ Sen. Elizabeth Warren (D-Mass.) is calling for an overhaul of the U.S. tax code to tax artificial intelligence companies, arguing the gains from AI should “benefit...
Top-notch ensemble cast, smart writing, and an engrossing supernatural mystery make for a winning combo.
AI is making hardware engineers some of tech's hottest hires, with pay growth outpacing software engineers, new data shows.
Over the next few decades, billions of autonomous, AI-powered robots will work alongside people in factories, perform tedious tasks in warehouses, care for the elderly, assist in unsafe disaster areas, deliver packages and food to our doorsteps, and eventually help out in our homes. Some will look like us, and many won’t. What is certain is that regardless of form factor, robots will all rely heavily on AI in order to deliver real-world value. In 2025, total investments in robotics companies reached a record US $40.7 billion, accounting for 9 percent of all venture funding. The multibillion dollar question therefore is this: What will it take for AI-powered robots to begin to have a serious economic impact? Many of today’s robotics and AI companies are making bold claims, such as that humanoid robots will soon be coming into our homes, but there’s still a big gap between promise and reality. The promise of robots that live and work alongside us has been the stuff of science fiction for a very long time. And while many programmers have tried to make that promise a reality, the physical world is just too complicated for traditional computer programs to handle the endless complexity it presents. Thanks to AI, robots are no longer being programmed—instead, they learn to operate in the real world. With enough practice, they can learn to perceive and understand the world around them, reason about that world, and use that reason and understanding to perform tasks that are useful, reliable, and safe. The two of us have worked at the forefront of AI and robotics for the last decade, as a Professor in Robotics at Oregon State University and Co-Founder of Agility Robotics, and as former CEO of the Everyday Robots moonshot at Google X. Our experience deploying AI-powered robots in real-world settings has given us a perspective on where AI can be used to great benefit in complex robotic systems in the near term and where we are still on the frontier of science fiction. We believe AI will enable an inflection point in robotics advances, but that it will be through the well-engineered application of coordinated systems of different AI tools rather than a single ChatGPT-style breakthrough. As the excitement around AI is matched only by the uncertainty of what will be possible, here are five hard truths that will define AI in robotics. 1. The YouTube-to-Reality Gap Is Real For years, we have been seeing videos on YouTube with humanoid robots performing amazing moves on everything from a dance floor to an obstacle course. The inside knowledge in robotics is to “never trust a YouTube robot video.” The gap between real robots that can perform real work in unstructured human environments and carefully scripted and edited robot performances remains significant. The latest performance to get a lot of attention was a martial arts show featuring Unitree humanoid robots performing with children at the Chinese 2026 Spring Festival Gala. While impressive, this falls into a long lineage of tightly scripted robotic performances, where everything has been carefully choreographed and planned in advance. The low-level controls, synchronization, and choreography were stunning, yet the Spring Gala robot performance showed a level of autonomy and intelligence much closer to industrial robots building cars in a factory than something that will show up in your living room any time soon. Seeing these kinds of demos nevertheless raises questions about where robotics really is. If robots can perform kung fu moves and do backflips and dance, why aren’t they also showing up on factory floors yet? And why can’t they do the dishes in my home after dinner? The simple answer is this: Making AI-powered robots capable of performing general tasks in varied human environments is still really hard. While impressive technological feats like those at the Spring Festival may make it look like we could be very close, the use of AI in these demos is only for low-level motor control (to keep the robots from falling over) and therefore is only a small part of the solution for robots to be general purpose in the real, unstructured spaces where we humans live and work. 2. Data Is An Unsolved Challenge Large Language Models (LLMs) like OpenAI’s ChatGPT and Anthropic’s Claude were initially trained on an internet-scale database of text. The world woke up one day in late 2022 to ChatGPT demonstrating that AI computers could suddenly “speak” to us in prose or verse and about seemingly any topic. LLMs have turned out to generalize well and are now able to take multimodal input (text, images, video) and produce multimodal output. Importantly, the corpus of training data was both enormous and human-generated, which are characteristics that form the gold standard for AI training. The fastest path to robots as part of everyday life may emerge through a range of robot forms performing increasingly sophisticated applications and employing a range of AI tools.Agility Robotics Giving AI a body (in the form of a robot), so that it can engage with people in the physical world, continues to be a very difficult and broadly unsolved problem. AI models for general-purpose robotics must simultaneously satisfy multiple, often conflicting, physical, geometric, and temporal limitations while operating in unstructured, dynamic environments. In order to generalize, robot models need to be trained on data gathered in a high-dimensional configuration space, where “dimensions” represent text, lighting conditions, degrees of freedom, joint limits, velocities, force, and safety boundaries, just to mention a few. Importantly, this must be good data—it must contain many examples from what amounts to an infinite number of possible configurations in the physical world. Since there are very few existing sources of data like this, approaches like teleoperation, video analysis, motion capture of humans, and self-exploration in simulation and in the real world are all seen as important ways to collect data. It’s a herculean task. For example, at Everyday Robots at Google X, we ran 240 million robot instances in our simulator over the course of 2022 to collect training data, mostly to train a trash-sorting model. Similar amounts of data will be needed for every skill to get to a similar level of capability, which is not yet human level. 3. There Will Be No Single Robot AI We are far away from a moment where a single AI model might allow general-purpose robots to live and work alongside us. General-purpose robots can have wheels or legs. They can have one, two, three, or more arms. Some have propellers and can fly, while others may be designed to operate under water. Some will drive on busy roads. The physical world is infinitely varied and complex. And then there are all the people and other animals that will be surrounding the robots. How do you train a model to operate a robot safely and reliably in all of these settings? The simple answer is: You don’t. At least not for quite some time. We believe the winning AI architecture leading to the next big breakthroughs in general-purpose robotics will be “agentic AI” for robots, which are high-level coordinating models that can reason, plan, use tools, and learn from outcomes to execute complex tasks with limited supervision. Agentic, high-level models running on robots will invoke a system of specialized ones for different types of tasks. We will likely soon see multiple robots collaborating and coordinating with each other through their onboard agentic AI models. AI tools are unlocking new and powerful capabilities in robotics, which in turn will enable new solutions and new markets. It’s encouraging to see these new models being made broadly available, some even as open-source solutions. This availability is akin to what happened with the internet: Real progress occurred when it became ubiquitous. We anticipate an inevitable democratization of complex behaviors in robotics with wide access to these AI tools and technologies. 4. Hardware Is Still Very Hard Robots are complex systems with many parts that all need to work together with great precision. For a robot to be useful and safe, every part of it must be coordinated, from its perception systems to the computer controlling it, all the way down to its individual actuators. Actuators—that is, the motors and gears—are a good example of an important part of the robot where what got us here won’t get us there. The actuators used at scale by most industrial robots will not work for robots that will operate in human environments. If these robots accidentally collide with an obstacle, the resulting impacts are harsh, forces are high, and things break. Humans don’t move in this way. We are far more compliant in how we interact with the world, and we’re constantly making contact with our environment and using that contact to help us accomplish things. Consider the challenge of inserting a key in a lock: Humans typically don’t do this by aligning the key perfectly with the keyhole. Instead, we just feel for the edge of the keyhole and jiggle the key in. Robots need to be able to operate in novel ways to achieve comparable capabilities by using a new class of actuators that are sensitive to force and able to have a compliant interaction with the environment. While these kinds of actuators do exist, they are not yet generally available at scale for robot systems designed to operate around people. 5. Real Value Comes From “Easy” Tasks There’s a big difference between tasks that look impressive and real-world tasks that provide value. Robotics is a perfect example of Moravec’s paradox, which states that tasks that are hard for humans are easy for computers (like multiplying two big numbers), and tasks easy for humans (like a toddler’s movements) are extremely difficult for computers and robots. Serving customers is an unforgiving reality check, because customers only care about solving the real problems they have. If we are to deploy AI-based robot solutions, they must outperform the way things are currently done while demonstrating reliable performance metrics and safety. Agility Robotics’ early work to deploy our humanoid robot Digit in customer locations led to the realization that our first obstacle was safety: Robots that balance and manipulate objects in human spaces bring new types of risk to the workplace. In the first humanoid deployments, physical barriers were necessary, and Agility kicked off a multi-year engineering effort to solve the safety challenge, touching nearly every aspect of robot design and relying heavily on new AI-based approaches to human detection and behavior control. Everyday Robots at Google deployed robots in 2019 that worked autonomously in office buildings doing chores like cleaning cafe tables and sorting trash. We quickly learned how “messy” and difficult the real world is for a robot. This experience informed the architecture and deployment of our AI systems while also gathering real-world data that could be combined with simulation data for training and improving models. This focus on creating a product to meet specific customer needs and deploying robots in real-world settings is the only way to inform the structure of the AI tools and infrastructure for near-term utility on a path towards long-term broader capability and generality. There will be no “aha” moment, no silver bullet algorithm, and no volume of data sufficient to produce a general-purpose robot without extensive real-world experience. AI Robots Are Coming, One Step at a Time As we look to the future, there is no doubt that the world is bringing AI into the physical world through robots. We are at the beginning of a “Cambrian explosion“ of useful, intelligent machines. We believe AI is not one tool, but a huge frontier of technical approaches that is unlocking new capabilities so powerful, they will define our economy moving forward. This will happen not in one single definitive moment, but as an ongoing set of small and large breakthroughs, where AI-driven robots begin to provide real value in a few tasks, and then a few more, with impacts unfolding across numerous $100 billion-plus markets that will dramatically improve the quality of our lives.