Engadget NVIDIA Gives DGX And RTX Systems A 40% AI Inference Boost, While DGX Spark Now Clusters Up To 8 Units For 1 TB Memory Wccftech RTX Spark: Real-World Local AI ⭐ Thurrott.com This $3,200 RTX Spark laptop still wants me to pay for ad-free Solitaire PCWorld
Google News
Original source
Off-road autonomy company Mach will integrate its software stack with BLADE's robotic submission for the Army's Infantry Last Tactical Mile (ILTM) effort ahead of an early 2027 operational assessment, the company announced in a new partnership agreement today. BLADE, which used to go by HDT Robotics, is one of six selected competing companies on the ILTM program with its Hunter WOLF 6x6 wheeled hybrid electric vehicle. Together the companies will offer the Army two autonomous prototypes, which are currently at Mach’s headquarters near Cedar Rapids, IA, Mach’s CEO and founder Colin Hurd told Inside Defense. “We have about 40 acres of testing ground and a large-scale shop and everything,” Hurd said, adding that the company is actively integrating its software onto the BLADE vehicles. Hurd thinks his team earned the partnership agreement because of his company’s modular autonomous stack, which takes up a “very small footprint” on unmanned ground vehicles, especially those that are drive-by-wire capable like the Hunter WOLF. Mach also brings to the table a “patent-pending” phased array radar, RadX, paired with stereo vision and light detection and ranging (LiDAR) technologies to see in the dark, in smoke or in dust. The stack allows autonomous vehicles to operate in contested environments free of satellites, data links or servers. “It’s very inevitable that you will have to go through terrain that has vegetation taller than the vehicle,” Hurd said, given that the Hunter WOLF is not even four feet tall. “And so, to be able to do that and continue a mission in that environment, you have to have a sensor that can see through that type of vegetation.” Hurd’s company has a history of defense partnerships domestically and in Europe, to include combat engineering and counter-mine missions, as well as tactical wheeled vehicle and Navy airfield integrations, according to the announcement. Hunter WOLF recently ran through testing with the 82nd Airborne Division, and can carry up to 2,800 pounds and rescue casualties within contested battle zones. BLADE is simultaneously competing in a separate robotic logistics competition, which used to go by the name Project Sustainment. Project Sustainment is now known as the Autonomous Medium Logistics Platform (AMLP), Col. Ken Bernier, project manager for autonomous robotic capabilities (PM ARC), told Inside Defense last week.
California legislators heard experts warn that fast‑moving artificial intelligences poses safety and societal risks and urge stronger regulations.
“A man who carries a cat by the tail learns something he can learn in no other way,” said Mark Twain. Discovering hard truths through hands-on experience is how everyone is learning AI. That includes this CTO of a Fortune 500 solutions integrator. So take it from me: You’re likely repeating the sins of our past. How do I know? Confession time. We didn’t used to have a global contract with the frontier model providers. We do now, but back then, we had a bunch of well-meaning teammates swiping their corporate credit cards. For $20 a month, they got to do whatever they wanted. But once we looked closely at it, the realization came. We were spending a lot of money, and $20 a month for some turned into $100 to $200 for others. One agreement with all the model providers turned countless expense reports into a single line item that someone owns. We fell into the next trap, not understanding the true cost of tokens. We had developers who built tools and workflows, not thinking beyond the point of innovation to the cost. Three words no CTO wants to see in print sum it up: Mistakes were made. And how could it have been otherwise? Everyone in the C-suite has the same thought: Let’s go build AI teams everywhere! It’s a healthy instinct, and we’ve acted on it as much as anybody else. We have AI teams in partner management; we have AI teams in finance; you name the functional part of our business, we have AI teams. Each team is picking its own favorite frontier model, tools—their own everything. Same tune, different song. THE LAST TIME THIS HAPPENED In 2009, the software development world went through a major shift that evolved into what we know as DevOps. Instead of having enormous teams working on projects, you created smaller teams. There’s an old axiom from Jeff Bezos that a team shouldn’t be bigger than what two pizzas would feed. Engineering leaders said, “Let’s try this DevOps thing.” They assigned five, six, seven of their best people to a DevOps project and it usually worked well. They deployed more and more DevOps teams and were shipping features and capabilities much faster. But Murphy’s law remained undefeated, and before long, a few ugly warts appeared. Each DevOps team started finding its own tools. One project would use a programming language, a framework, and a persistence layer. Another would use a completely different set of tools. And like a pile of clothes hangers, it all culminated in a big, tangled mess. The industry came to two realizations: We need to put some controls on all of this, and we’re building and compounding technical debt faster than we’re adding value to the business. What ultimately happened? Platform engineering teams built internal developer platforms. They said, “Here’s the golden path. If you stay on the golden path, we’ll support you. If you stray from it, we’re still here, but you’ll have to find your own way.” This approach worked so well that in 2023, Gartner predicted 80% of software engineering organizations would have platform teams by 2026. Meanwhile, the cloud came to prominence, in part because of this DevOps movement. They fed on each other. It seemed cheap because you could spin up a server so quickly or consume an API. Then come to find out that if you’re not careful, it can cost you an arm and a leg, and likely your head. So another discipline came into being called FinOps—financial operations. Part of it was making sure that you understood your bill from cloud providers. Part of it was expressed most often as a concern: Maybe we shouldn’t be using the most expensive technology for everything we do. Developers always take the shortest path. If a button will solve their problem, they’re pushing that button. And typically, the fastest, most expensive button is going to do it faster and better. So they just pushed that button over and over again. FinOps put a meter on the button and made someone answerable for the number. That problem is still being solved. Which is exactly when AI showed up on 337 S&P 500 earnings calls in a single quarter, the most in a decade. SAME TUNE, DIFFERENT SONG Now we’re coming to similar realizations, especially with tokenomics, which is a FinOps conversation. We’re hearing CIOs and CFOs say, “Perhaps we should start consolidating the number of tools we’re using.” Starting to sound familiar, yes? The fix should sound familiar too. Customers come to us with a bunch of plans. In the past, they worked on all of them independently. That’s the sprawl. My recommendation: Find the most important plan and build that one first. Use it as a foundation for the next plan, and the next one. We built platform engineering to do this for developers. We need to do it again for the AI side of the house. Specifically, the first project defines a small set of capabilities that solve for that problem. Then the second project builds on what you learned from the first one and adds more. And then as you add the third, fourth, and fifth, you start creating a flywheel with this capability already built in. You’re creating an accelerating factor instead of what we had: five teams, five frontier models, five sets of tools, five angry emails from the CFO. That’s a very expensive pile of clothes hangers to untangle. Five separate stacks become technical debt that compounds faster than you can add capability. Somebody has to be the expert in all five. The goal is minimizing the blast radius of what you implement. Twain was right. Some lessons can only be learned through painful experiences. But with the scar tissue we accumulated early, the most valuable lesson is this: Let someone else carry the cat. Juan Orlandini is chief technology officer of North America for Insight Enterprises.
For months, the news has been inundated with studies showing the high rates of AI use by young people, sometimes with tragic consequences. The urgency to put protective measures in place is palpable. Recently, I previewed longitudinal research results The Trevor Project is preparing to release this fall. It shows that AI use is particularly pronounced among LGBTQ+ youth: 53% of LGBTQ+ youth surveyed engage with AI platforms at least occasionally. We found that nearly half have used AI for friendship or someone to talk to, and 40% have used AI for mental health support. In our study, 29% said AI had provided them with information about a crisis or support hotline, almost a third (29%) of whom accessed a hotline this way. LGBTQ+ young people are more than three times as likely to attempt suicide than their peers, and are more likely to turn to online communities for support. AI use for mental health support was not evenly distributed across demographic groups within the LGBTQ+ community, our study showed. Youth of color were much more likely than white youth to turn to AI for mental health support: 49% compared with 20%. (This full research report is scheduled for publication in December.) DISCOMFORT WITH AI IS NO EXCUSE It does not surprise me that LGBTQ+ youth in non-affirming physical environments are looking to emerging AI tools as vital lifelines for community and self-discovery. But it has catapulted me to action. I lead The Trevor Project, a suicide prevention organization that has been advocating for LGBTQ+ youth well-being for decades. Foundational to our approach is centering the mental well-being and safety of LGBTQ+ youth wherever they gather. The emerging data makes it clear: As LGBTQ+ young people increasingly engage AI for connection and support, The Trevor Project and other youth-focused organizations must also engage to ensure their AI use is safe. Though this conclusion is clear to me, its implications are uncomfortable. Many of us manage personal skepticism or ethical concerns about AI while simultaneously trying to understand its growing role in our lives, and especially in the lives of younger generations. It’s not easy to harbor reticence about the existence of AI technology while insisting it be safe and supportive for anyone who uses it, especially for high-risk people like LGBTQ+ youth. But technology is advancing rapidly, and our mission requires that we set aside zero-sum thinking in service of immediate action. Young lives depend on it. HONOR HUMANITY AND PROTECT DIGNITY As a Black queer person who spent decades in corporate America, I’ve had to fight for my marginalized identities to be represented—in product design, in marketing campaigns, in healthcare benefits. Inaction is an option marginalized communities rarely have. LGBTQ+ young people deserve a digital future that honors their humanity and protects their safety. By anchoring our posture in evidence, clinical integrity, youth agency, and ongoing learning, we can ensure a digital ecosystem that serves as a protective, affirming force. When AI systems interact with crisis or identity, along with mental health topics, responses must strictly align with evidence-based suicide prevention standards. Optimizing chatbots for conversational stickiness, by continuing to engage when a young person is in distress, is deeply dangerous; we’ve seen it be lethal. Systems must guard against deceptive anthropomorphism and serve as seamless bridges to authentic, human crisis support. Furthermore, chatbots must be informed by best-practice specialized care guidelines: A Black transgender young person in Tennessee’s crisis response needs are different from a white straight young person in New York’s crisis response. Both deserve care tailored to their unique lived experiences. COLLABORATION AND ACTION To prevent AI models from furthering historical biases or erasing marginalized voices, technologies must center young people themselves. LGBTQ+ youth have distinct risk and protective factors that require intentional representation across model design, testing, and evaluation. We must insist on protective guardrails and algorithmic justice. LGBTQ+ young people deserve uncompromising clinical integrity wherever they interact with AI. Achieving this requires multidisciplinary collaboration and action. That means expanding the evidence base. And it means bringing together youth, behavioral health experts, researchers, policymakers, and AI developers. We must insist upon fidelity to emerging standards of care. Solving these complex challenges requires continuous evaluation. It also requires transparent dialogue regarding technical limitations and strict protection of sensitive identity data. Youth agency and privacy must remain at the center of this conversation. I’m not endorsing AI, nor am I claiming to have all the answers to the important questions AI raises. In truth, I wish fewer LGBTQ+ young people felt forced to find their support systems digitally. But I know what it is to be othered, and I know how vital the internet has been for people who feel othered. I am committing to showing up for the vulnerable young people who are using these emerging technologies, and meeting them where they are today as new technologies unfold. Jaymes Black is CEO of The Trevor Project.
Headlines and briefs on this site are for information only. Always verify details on the original source or live status page.
Read Disclaimer