Cisco CEO Chuck Robbins: Most of my peers expect to eventually hire fewer people due to AI
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Yahoo
25 minutes ago
- Yahoo
Cisco CFO sees big AI opportunities despite concerns about slowing core growth
Cisco (CSCO) is betting on the AI boom as it faces potential pressures in its networking and security units. 'AI is really the biggest driver in terms of the overall umbrella,' CFO Mark Patterson said on Yahoo Finance's Opening Bid. Cisco reported stronger-than-expected earnings and guidance. Q4 revenue rose 8% year over year to $14.7 billion, surpassing the $14.63 billion estimate. Adjusted EPS increased 14% year to $0.99, beating the $0.98 consensus, according to Bloomberg data. For fiscal year 2026, Cisco is expecting revenue between $59 billion and $60 billion, with adjusted EPS of $4.00 to $4.06. However, Cisco's stock is facing some headwinds as concerns mount about its slowing momentum in its core networking business and lower growth in its security division. Shares have slipped about 1% since the report. They are up 17% year to date, ahead of the S&P 500's (^GSPC) 10% gain. Cisco's core networking business grew 12% in the most recent quarter, signaling healthy demand despite broader concerns. "That's frankly ... where a lot of the tailwinds are today that we're seeing," Patterson said. For security, Patterson said demand hasn't slowed since Cisco's $28 billion acquisition of Splunk closed in 2024. Its revenue increased 9% in the quarter, and Patterson noted it added over 300 customers in both Q3 and Q4. "I think that we've got a better, more complete offering for our customers," he said. But AI is the name of the game. Patterson said fourth quarter revenue growth got a major lift from soaring demand for AI infrastructure, particularly large-scale AI training systems. Orders from webscale customers for these projects more than doubled the company's original target. 'We took over $800 million in orders in the quarter,' he said, adding the bigger prize lies in the years ahead as everyday customers start adopting AI. 'For us, the enterprise space is still very nascent, but we think [it] will ultimately be a larger opportunity than what we're seeing in the training build-outs with hyperscale." Patterson, a 25-year Cisco veteran, emphasized there's a need for companies to upgrade both their networking and security systems to meet the demands of AI. He said Cisco is undergoing a "campus refresh," or replacement of outdated equipment at customer sites. On the macro front, tariffs were a 'slight headwind in Q4 and also for FY 25,' Patterson said. While he didn't specify the financial impact, he noted Cisco was able to offset much of the effect thanks to its global supply chain. Francisco Velasquez is a Reporter at Yahoo Finance. He can be reached on LinkedIn and X, or via email at Sign in to access your portfolio
Gizmodo
26 minutes ago
- Gizmodo
Scientists Taught AI to Predict Nuclear Fusion Success—and It's Actually Working
AI is giving a huge efficiency boost to one of the biggest nuclear fusion facilities in the world—but perhaps not in the way you think. In research published today in Science, scientists at Lawrence Livermore National Laboratory report how its newly developed deep learning model accurately predicted the results of a 2022 fusion experiment at the National Ignition Facility (NIF). The model, which assigned 74% probability for ignition in that experiment, outperforms traditional supercomputing methods by covering more parameters with greater precision. 'What we're excited about with this model is the ability to explicitly make choices for future experiments that maximize our probability of success each time,' study co-author Kelli Humbird told Gizmodo during a video call. Even a facility as large and well-established as the NIF can only 'do a couple dozen of these ignition attempts per year—so really not very many at all, given how much territory we have to cover,' added Humbird, who leads the Cognitive Simulation Group at NIF's Inertial Confinement Fusion Program. Currently, nuclear power plants run on nuclear fission, which captures the energy generated by the splitting of heavy atoms, like uranium. Researchers eventually want to shift toward nuclear fusion, a process that combines lightweight hydrogen atoms to release massive amounts of energy. Fusion produces more energy and doesn't create harmful, radioactive byproducts, so having fusion as a reliable source of energy would greatly benefit our society's transition to sustainable energy. Although the field has made some promising advances, the consensus is that we're still far from implementing nuclear fusion on a commercial scale. NIF's fusion experiments are laser-driven. First, the lasers heat up a gold cylinder called the hohlraum, which then emits a flow of powerful X-rays. The extreme temperatures compress the fuel pellets containing deuterium and tritium, two hydrogen isotopes used in fusion experiments. In an ideal scenario, this triggers enough deuterium-tritium fusion reactions to produce more energy than the lasers consume. Computer simulations can't reliably predict all the physics in this process, Humbird said. That's in part because the codes are often simplified so they're 'computationally tractable,' but the simulations themselves can also introduce some errors. Even if you've taken all sorts of precautions, it still takes days for the computers to finish running through the code, she added. Achieving nuclear fusion is like scaling a tall, uncharted mountain, Humbird said. The computer simulations are like an 'imperfect' map that's supposed to teach researchers how to reach the peak—but this map could be rife with errors that may or may not be the product of their research design. Meanwhile, the clock is ticking, and researchers have to quickly decide whether they'll take the hike that day and which tools they're going to use. And of course, each 'hike,' or ignition attempt, burns a huge hole in the budget. And so, Humbird's team embarked on a mapmaking quest, stitching together 'previously collected NIF data, high-fidelity physics simulations, and subject matter expert knowledge' to build a comprehensive dataset. Then, they uploaded the data to state-of-the-art supercomputers, which ran a statistical analysis lasting over 30 million CPU hours. 'What we basically came up with was a distribution of things that go wrong [at] NIF,' Humbird explained. 'All of the different ways that we have observed implosions. Sometimes the laser doesn't fire exactly how you asked it to. Sometimes your target has defects in it that can cause things to not go super well.' The model allows researchers to preemptively determine the efficacy of their experimental design, saving them considerable time and money. Humbird used the model to assess their own design from a 2022 experiment, which accurately described the results of the specific run in advance. In particular, Humbird was pleased to see that subsequent tweaks to the model's physics increased the accuracy of its predictions from 50 to 70%. For Humbird, the strength of the new model is that it accepts and replicates the imperfections of the real world—whether that's a flaw in the instrument, research design, or just some silly trick of nature. At the same time, it's a reminder that, while quick progress is exciting, things often take a lot of time and will even result in outright failure. 'People have been working on fusion for decades… We shouldn't be so bummed about the times things don't work,' Humbird said. 'The fact that we sometimes get 1 megajoule of yield instead of two shouldn't upset us, because not too long ago we were only getting 10 kilojoules. It's a huge step forward for research, and hopefully a huge step forward for clean energy in the future.'
CBS News
27 minutes ago
- CBS News
MIT scientists show how they're developing AI for humanoid robots
We've all seen what artificial intelligence can do on our screens: generate art, carry out conversations and help with written tasks. Soon, AI will be doing more in the physical world. Gartner, a research and advisory firm, estimates that by 2030, 80% of Americans will interact daily — in some way — with autonomous, AI-powered robots. At the Massachusetts Institute of Technology, professor Daniela Rus is working to make that possible — and safe. "I like to think about AI and robots as giving people superpowers," said Rus, who leads MIT's Computer Science and Artificial Intelligence Lab. "With AI, we get cognitive superpowers." "So think about getting speed, knowledge, insight, creativity, foresight," she said. "On the physical side, we can use machines to extend our reach, to refine our precision, to amplify our strengths." Sci-fi stories make robots seem capable of anything. But researchers are actually still figuring out the artificial brains that machines need to navigate the physical world. "It's not so hard to get the robot to do a task once," Rus said. "But to get that robot to do the task repeatedly in human-centered environments, where things change around the robot all the time, that is very hard." Rus and her students have trained Ruby, a humanoid robot, to do basic tasks like prepare a drink in the kitchen. "We collect data from how humans do the tasks," Rus said. "We are then able to teach machines how to do those tasks in a human-like fashion." Rus' students wear sensors to capture motion and force, which helps teach robots how tightly to grip or how fast to move. "So you can tell, like, how tense they're holding something or how stiff their arms are," said Joseph DelPreto, one of Rus' students. "And you can get a sense of the forces involved in these physical tasks that we're trying to learn." "This is where delicate versus strong gets learned," Rus said. Robots already in use are often limited in scope. Those found in industrial settings perform the same tasks repeatedly, said Rus, who wants to expand what robots can do. One prototype in her lab features a robotic arm that could be used, in the future, for household chores or in medical settings. Some, however, might feel uneasy having robots in home settings. But Rus said every machine they've built includes a red button that can stop it. "AI and robots are tools. They are tools created by the people for the people. And like any other tools they're not inherently good or bad," she said. "They are what we choose to do with them. And I believe we can choose to do extraordinary things."
