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중도 성향
IT/기술
Oil drops, stocks mostly higher despite AI concerns
Traders tracked conflicting Middle East developments as equities rose despite Broadcom’s weak outlook weighing on AI stocks.
IT/기술 · "CONFLICTING" · 총 4건
필터 보기현재 지수
50.3
0 = 부정 우세
50 = 중립
100 = 긍정 우세
최근 7일 기준 88,840건을 분석한 결과, 뉴스 심리지수는 50.2(균형)입니다. 긍정 4,410건(5.0%)·중립 82,261건(92.6%)·부정 2,169건(2.4%)이며, 중립 비중이 뚜렷하게 높습니다. 성향 지수는 종합 14.7(중도 균형)입니다.
Traders tracked conflicting Middle East developments as equities rose despite Broadcom’s weak outlook weighing on AI stocks.
Conflicting US-Iran developments leave markets whipsawing, capping gains from renewed tech momentum.
This sponsored article is brought to you by Wetour Robotics. A field technician on a wind turbine, harness clipped, both hands on a wrench, needs to send a command to the diagnostic device hanging at her belt. A logistics worker on a loading dock, gloves on, eyes on the pallet, needs to redirect a connected lift. A person using an assistive mobility device on a crowded street wants to nudge it forward without taking out a phone or speaking aloud. None of these moments call for a smarter robot. They call for a smarter way to be heard by the machines that already exist. The industry has been building from one side The past three years of Physical AI have been a story of remarkable progress on the robot side of the loop. Companies like Boston Dynamics, Figure, and Unitree have advanced actuators, locomotion, and dexterity to a level that would have seemed implausible a decade ago. Google DeepMind’s Gemini Robotics has redefined what vision-language-action models can do in unstructured settings. The trajectory of the hardware and the foundation models is real, and it is accelerating. But there is another side to this loop, and it has been treated as a solved problem for too long. The interface between humans and machines has defaulted, for 40 years, to three input modalities: screens, buttons, and voice. Each of those assumes the user can stop, look down, and translate intent into structured commands. That assumption breaks the moment the work moves into a real environment. On a turbine. On a dock. On a sidewalk. In any setting where hands are occupied, eyes are committed, or speaking is impractical, the conventional interface stack quietly fails. Spatial Intent Fusion is the simultaneous processing of three streams of human-centered information, namely spatial position, visual context, and gestural intent: Your body is the interface. The bottleneck on the human side of the loop is becoming as important as the one on the machine side. And solving it requires a different question. Not how do we make the robot more capable, but how do we let the human participate in the computing system as naturally as the robot already does. Wetour Robotics’ bet: put the human back into the computing loop Wetour Robotics is betting that the next architectural leap in Physical AI is not about making the robot more capable. It is about making the human a first-class node in the computing network, with the same kind of low-latency, high-fidelity participation that connected devices already enjoy. Wetour Robotics’ engineers frame the problem this way: a wristband that recognizes a gesture is not enough. A camera that recognizes a scene is not enough. The information a human carries about what they are about to do is distributed across multiple channels, including where their body is in space, what their eyes are attending to, and what their muscles are preparing to do, and any single channel observed in isolation is ambiguous. Reconstructing intent reliably means fusing those channels at the operating system level, with latency low enough that the loop feels closed rather than mediated. This approach has a name. Wetour Robotics calls it Spatial Intent Fusion: the simultaneous processing of three streams of human-centered information, namely spatial position, visual context, and gestural intent, fused into a single real-time command for any connected physical device. It is the technical implementation behind a simpler positioning statement the company uses externally: your body is the interface. Orchestra is a portable intelligent hub running the operating system that handles sensor fusion, intent inference, command translation, and safety arbitration. The reference compute platform is NVIDIA Jetson Orin Nano Super, which provides enough on-device inference capacity to keep the entire control loop at the edge, with no cloud dependency on the critical path. Wetour Robotics The architecture: three layers, four engines, one loop Orchestra is not a single device but a layered platform, designed from the start to be sensor-flexible and actuator-agnostic. The architecture decomposes into three perception layers and four coordination engines. Orchestra itself is the local compute and orchestration core: a portable intelligent hub running the operating system that handles sensor fusion, intent inference, command translation, and safety arbitration. The reference compute platform is NVIDIA Jetson Orin Nano Super, which provides enough on-device inference capacity to keep the entire control loop at the edge, with no cloud dependency on the critical path. Edge inference is non-negotiable for this application. Full-chain latency from biosignal acquisition to actuator command is held under 100 milliseconds, the envelope inside which closed-loop control feels natural rather than laggy. VisionLink handles visual and spatial perception. Cameras feed into vision models that identify objects, estimate distances, and track environmental context. VisionLink is designed not as a passive recognition layer but as a real-time command generator: its outputs feed directly into Orchestra OS to be fused with biosignal data. Conductor is the biosignal pipeline. It ingests raw surface electromyographic (sEMG) data from a wrist-worn device, classifies temporal patterns into discrete gestures or continuous control signals, and outputs actuator commands. The technically interesting property of sEMG for this use case is that the signal precedes visible motion. Motor unit action potentials appear at the skin surface roughly 50 to 80 milliseconds before a finger completes the corresponding gesture. Wetour Robotics calls this property pre-motion intent sensing, and it is what allows Orchestra to anticipate user intent rather than react to it. On top of the three perception layers, Orchestra OS runs four coordination engines. The Perception Engine ingests and normalizes raw sensor streams. The Intent Engine performs Spatial Intent Fusion across modalities, resolving what the user is trying to do given where they are, what they are looking at, and what their hand is signaling. The Orchestration Engine translates intent into device-specific command sequences for any connected actuator. The Safety Engine arbitrates conflicting commands, enforces operational envelopes, and gates execution against runtime safety conditions. Wetour Robotics The trade-offs we’re honest about No system that bridges the human body and the digital world is finished. Three engineering challenges remain open, and the company addresses each with a deliberate trade-off rather than a claim of having fully solved it. Baseline stability of sEMG under motion. In a stationary user, continuous gesture recognition from sEMG is reliable. Once the user is walking, climbing, or otherwise moving, motion artifacts and electrode drift degrade the signal in ways that are difficult to fully compensate for. Rather than overpromise on continuous control in dynamic settings, Orchestra defaults to a smaller set of robust discrete gestures in complex operating environments, and reserves continuous control modes for contexts where the signal-to-noise ratio supports them. Miniaturization of edge AI compute. Running the Orchestra control loop entirely at the edge requires real on-device inference, which has historically meant trading off between compute capacity, battery life, and form factor. Wetour Robotics’ approach has been a compact carrier board paired with a thermal design and a battery module sized for all-day wearability. The result is a hub that travels with the user rather than tethering them to a desk, and that performs the full perception-to-actuation loop without offloading to the cloud. Heterogeneity of third-party device protocols. The actuator side of the loop is a fragmented landscape. Different manufacturers expose different command interfaces, different communication stacks, and different safety conventions, and a Physical AI operating system has to integrate with all of them. Wetour Robotics uses an AI-agent layer to negotiate connection and protocol translation adaptively, so that Orchestra OS can ingest data from a wide range of devices, run them through neural network models that infer human intent, and emit the right command on the right protocol for the device on the other end. Why this matters, and why it helps the rest of the field The history of computing is a history of interface revolutions. Command lines gave way to graphical user interfaces, which gave way to touch, which gave way to voice. Each transition expanded who could participate in the system and what they could do with it. The next transition is not about a new screen or a new microphone. It is about treating the human body itself as a participant in the computing network, capable of contributing intent at the same speed and fidelity that any other connected node can. The history of computing is a history of interface revolutions. The next transition is not about a new screen or a new microphone — it is about treating the human body itself as a participant in the computing network. This path is not a competitor to the work being done on humanoid robots, foundation models for embodied AI, and dexterous manipulation. It is the missing complement to that work. The hardest open problem for humanoid systems is the data: every natural interaction between a human and the physical world is a potential training signal, and most of those interactions are currently invisible to any computing system. As more humans become first-class nodes in the loop, those interactions become observable, structured, and ultimately useful for training the next generation of embodied AI, including the humanoid robots being developed today. In other words: putting the human back into the computing loop is not just about better interfaces for individual users. It is about generating the kind of grounded, in-the-wild human-machine interaction data that the broader Physical AI ecosystem will need to keep advancing. The robot side and the human side of the loop are not two competing futures. They are two halves of the same one. That is what Wetour Robotics means when it says: Your body is the interface. Learn more at wetourrobotics.com.
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.