AI, Software, Coding, Internet Security Thread
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We saw a demo of the new AI system powering Anduril’s vision for war
We’re living through the first drone wars, but AI is poised to change the future of warfare even more drastically.By
James O'Donnell
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December 10, 2024
One afternoon in late November, I visited a weapons test site in the foothills east of San Clemente, California, operated by Anduril, a maker of AI-powered drones and missiles that recently announced a partnership with OpenAI. I went there to witness a new system it’s expanding today, which allows external parties to tap into its software and share data in order to speed up decision-making on the battlefield. If it works as planned over the course of a new three-year contract with the Pentagon, it could embed AI more deeply into the theater of war than ever before.
Near the site’s command center, which looked out over desert scrubs and sage, sat pieces of Anduril’s hardware suite that have helped the company earn its $14 billion valuation. There was Sentry, a security tower of cameras and sensors currently deployed at both US military bases and the US-Mexico border, and advanced radars. Multiple drones, including an eerily quiet model called Ghost, sat ready to be deployed. What I was there to watch, though, was a different kind of weapon, displayed on two large television screens positioned at the test site’s command station.
I was here to examine the pitch being made by Anduril, other companies in defense tech, and growing numbers of people within the Pentagon itself: A future “great power” conflict—military jargon for a global war involving competition between multiple countries—will not be won by the entity with the most advanced drones or firepower, or even the cheapest firepower. It will be won by whoever can sort through and share information the fastest. And that will have to be done “at the edge” where threats arise, not necessarily at a command post in Washington.
A desert drone test
“You’re going to need to really empower lower levels to make decisions, to understand what’s going on, and to fight,” Anduril CEO Brian Schimpf says. “That is a different paradigm than today.” Currently, information flows poorly among people on the battlefield and decision-makers higher up the chain.
To show how the new tech will fix that, Anduril walked me through an exercise demonstrating how its system would take down an incoming drone threatening a base of the US military or its allies (the scenario at the center of Anduril’s new partnership with OpenAI). It began with a truck in the distance, driving toward the base. The AI-powered Sentry tower automatically recognized the object as a possible threat, highlighting it as a dot on one of the screens. Anduril’s software, called Lattice, sent a notification asking the human operator if he would like to send a Ghost drone to monitor. After a click of his mouse, the drone piloted itself autonomously toward the truck, as information on its location gathered by the Sentry was sent to the drone by the software.
The truck disappeared behind some hills, so the Sentry tower camera that was initially trained on it lost contact. But the surveillance drone had already identified it, so its location stayed visible on the screen. We watched as someone in the truck got out and launched a drone, which Lattice again labeled as a threat. It asked the operator if he’d like to send a second attack drone, which then piloted autonomously and locked onto the threatening drone. With one click, it could be instructed to fly into it fast enough to take it down. (We stopped short here, since Anduril isn’t allowed to actually take down drones at this test site.) The entire operation could have been managed by one person with a mouse and computer.
Anduril is building on these capabilities further by expanding Lattice Mesh, a software suite that allows other companies to tap into Anduril’s software and share data, the company announced today. More than 10 companies are now building their hardware into the system—everything from autonomous submarines to self-driving trucks—and Anduril has released a software development kit to help them do so. Military personnel operating hardware can then “publish” their own data to the network and “subscribe” to receive data feeds from other sensors in a secure environment. On December 3, the Pentagon’s Chief Digital and AI Office awarded a three-year contract to Anduril for Mesh.
Anduril’s offering will also join forces with Maven, a program operated by the defense data giant Palantir that fuses information from different sources, like satellites and geolocation data. It’s the project that led Google employees in 2018 to protest against working in warfare. Anduril and Palantir announced on December 6 that the military will be able to use the Maven and Lattice systems together.
The military’s AI ambitions
The aim is to make Anduril’s software indispensable to decision-makers. It also represents a massive expansion of how the military is currently using AI. You might think the US Department of Defense, advanced as it is, would already have this level of hardware connectivity. We have some semblance of it in our daily lives, where phones, smart TVs, laptops, and other devices can talk to each other and share information. But for the most part, the Pentagon is behind.
“There’s so much information in this battle space, particularly with the growth of drones, cameras, and other types of remote sensors, where folks are just sopping up tons of information,” says Zak Kallenborn, a warfare analyst who works with the Center for Strategic and International Studies. Sorting through to find the most important information is a challenge. “There might be something in there, but there’s so much of it that we can’t just set a human down and to deal with it,” he says.
Right now, humans also have to translate between systems made by different manufacturers. One soldier might have to manually rotate a camera to look around a base and see if there’s a drone threat, and then manually send information about that drone to another soldier operating the weapon to take it down. Those instructions might be shared via a low-tech messenger app—one on par with AOL Instant Messenger. That takes time. It’s a problem the Pentagon is attempting to solve through its Joint All-Domain Command and Control plan, among other initiatives.
“For a long time, we’ve known that our military systems don’t interoperate,” says Chris Brose, former staff director of the Senate Armed Services Committee and principal advisor to Senator John McCain, who now works as Anduril’s chief strategy officer. Much of his work has been convincing Congress and the Pentagon that a software problem is just as worthy of a slice of the defense budget as jets and aircraft carriers. (Anduril spent nearly $1.6 million on lobbying last year, according to data from Open Secrets, and has numerous ties with the incoming Trump administration: Anduril founder Palmer Luckey has been a longtime donor and supporter of Trump, and JD Vance spearheaded an investment in Anduril in 2017 when he worked at venture capital firm Revolution.)
Defense hardware also suffers from a connectivity problem. Tom Keane, a senior vice president in Anduril’s connected warfare division, walked me through a simple example from the civilian world. If you receive a text message while your phone is off, you’ll see the message when you turn the phone back on. It’s preserved. “But this functionality, which we don’t even think about,” Keane says, “doesn’t really exist” in the design of many defense hardware systems. Data and communications can be easily lost in challenging military networks. Anduril says its system instead stores data locally.
An AI data treasure trove
The push to build more AI-connected hardware systems in the military could spark one of the largest data collection projects the Pentagon has ever undertaken, and companies like Anduril and Palantir have big plans.
“Exabytes of defense data, indispensable for AI training and inferencing, are currently evaporating,” Anduril said on December 6, when it announced it would be working with Palantir to compile data collected in Lattice, including highly sensitive classified information, to train AI models. Training on a broader collection of data collected by all these sensors will also hugely boost the model-building efforts that Anduril is now doing in a partnership with OpenAI, announced on December 4. Earlier this year, Palantir also offered its AI tools to help the Pentagon reimagine how it categorizes and manages classified data. When Anduril founder Palmer Luckey told me in an interview in October that “it’s not like there’s some wealth of information on classified topics and understanding of weapons systems” to train AI models on, he may have been foreshadowing what Anduril is now building.
Even if some of this data from the military is already being collected, AI will suddenly make it much more useful. “What is new is that the Defense Department now has the capability to use the data in new ways,” Emelia Probasco, a senior fellow at the Center for Security and Emerging Technology at Georgetown University, wrote in an email. “More data and ability to process it could support great accuracy and precision as well as faster information processing.”
The sum of these developments might be that AI models are brought more directly into military decision-making. That idea has brought scrutiny, as when Israel was found last year to have been using advanced AI models to process intelligence data and generate lists of targets. Human Rights Watch wrote in a report that the tools “rely on faulty data and inexact approximations.”
“I think we are already on a path to integrating AI, including generative AI, into the realm of decision-making,” says Probasco, who authored a recent analysis of one such case. She examined a system built within the military in 2023 called Maven Smart System, which allows users to “access sensor data from diverse sources [and] apply computer vision algorithms to help soldiers identify and choose military targets.”
Probasco said that building an AI system to control an entire decision pipeline, possibly without human intervention, “isn’t happening” and that “there are explicit US policies that would prevent it.”
A spokesperson for Anduril said that the purpose of Mesh is not to make decisions. “The Mesh itself is not prescribing actions or making recommendations for battlefield decisions,” the spokesperson said. “Instead, the Mesh is surfacing time-sensitive information”—information that operators will consider as they make those decisions.
We saw a demo of the new AI system powering Anduril’s vision for war
We’re living through the first drone wars, but AI is poised to change the future of warfare even more drastically.
www.technologyreview.com
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Indos
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How AI Is Changing Warfare, with Anduril CEO Brian Schimpf
OpenAI Is Working With Anduril to Supply the US Military With AI
The ChatGPT maker is the latest AI giant to reveal it’s working with the defense industry, following similar announcements by Meta and Anthropic.
OpenAI Is Working With Anduril to Supply the US Military With AI
The ChatGPT maker is the latest AI giant to reveal it’s working with the defense industry, following similar announcements by Meta and Anthropic.
Indos
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- #248
Using AI As A Coach In Your Career: Nvidia CEO Says It’s A Must-Have
Chris WestfallContributor
Updated Feb 3, 2025, 03:37 pm EST
Guidance for leaders and aspiring leaders, interested in career impact
Nvidia CEO Jensen Huang recommends an AI tutor or coach. Here's why.
AFP via Getty Images
In the rapidly evolving landscape of artificial intelligence (AI), industry leaders are increasingly advocating for AI’s role as a tutor, mentor, and coach to enhance human capabilities. Nvidia CEO Jensen Huang emphasizes the transformative potential of AI in education and professional development, suggesting that AI can now teach humans, but doesn’t believe it will replace them in the workforce. The CEO of the $3.3 trillion chip company describes how AI can reduce effort, while maintaining the significance of work, in an interview with Cleo Abram. “The effort of drudgery basically goes to zero,” he says. “The knowledge of almost any particular field, the barriers to that understanding, have been reduced. I have a personal tutor with me all of the time,” he said in the interview. He recommends the same for anyone who wants to move forward in their career. Here’s why.
Nvidia CEO Says AI is How We Become Superhuman
What’s it like to have a coach or a mentor that’s super-smart and super-fast - does that diminish your own expertise or contribution? “I can tell you exactly what that feels like,” Huang shares. “I’m surrounded by super-human people - ‘superintelligence’, from my perspective. The best in the world - they do what they do better than I can do it. And I’m surrounded by thousands of [these people]! And yet, never did it make me think I’m no longer necessary. It actually gives me the confidence to go and tackle more and more ambitious things." What if you are surrounded by superintelligence? Is it deflating, or inspiring, to walk side by side with AI? “I feel more empowered,” Huang says, “more confident to learn something today,” because of using his personal AI tutor and coach. His advice is clear: “Go get yourself an AI tutor right away.”
Salesforce CEO Marc Benioff echoes this sentiment, highlighting the emergence of AI agents in the workplace. He notes that from now on, CEOs will no longer lead all-human workforces, signaling a new era of AI coworkers. “From this point forward…we will be managing not only human workers but also digital workers,” he said during a panel at the World Econmic Forum last month. Why not enlist AI as a coach or tutor, right now? Seems there’s no reason to wait, when the future is already here. “We’re going to be superhumans,” Huang says, “not because we are superhuman, but because we have super-human AIs.”
Why Nvidia CEO Says AI is Essential for Career Growth
Nvidia CEO Jensen Huang calls AI a must-have for career development. Discover how AI as a coach can boost your professional growth and make you superhuman.
www.forbes.com
Indos
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- #251
The real reason behind the DeepSeek hype, according to AI experts
By Lisa Eadicicco, CNN
Published 6:30 AM EST, Fri February 14, 2025
DeepSeek turned the tech world on its head last month – and for good reason, according to artificial intelligence experts, who say we’re likely only seeing the beginning of the Chinese tech startup’s influence on the AI field.
DeepSeek grabbed headlines in late January with its R1 AI model, which the company says can roughly match the performance of Open AI’s o1 model at a fraction of the cost. Tech stocks tumbled as DeepSeek briefly unseated ChatGPT to become the top app in Apple’s App Store.
The achievement pushed US tech behemoths to question America’s standing in the AI race against China – and the billions of dollars behind those efforts. While Vice President JD Vance didn’t mention DeepSeek or China by name in his remarks at the Artificial Intelligence Action Summit in Paris on Tuesday, he certainly emphasized how big of a priority it is for the United States to lead the sector.
“The United States of America is the leader in AI, and our administration plans to keep it that way,” he said, although he added that “America wants to partner” with other countries.
But it’s not just DeepSeek’s efficiency and power. The way DeepSeek R1 can reason and “think” through answers to provide quality results, along with the company’s decision to make key parts of its technology publicly available, will also push the field forward, experts say.
While AI has long been used in tech products, it’s reached a flashpoint over the last two years thanks to the rise of ChatGPT and other generative AI services that have reshaped the way people work, communicate and find information. It’s made Wall Street darlings out of companies like chipmaker Nvidia and upended the trajectory of Silicon Valley giants. So any development that can help build more capable and efficient models is sure to be closely watched.
“This is definitely not hype,” said Oren Etzioni, former CEO of the Allen Institute for Artificial Intelligence. “But also, this is a very fast-moving world.”
AI’s TikTok moment
Tech leaders have been quick to respond to DeepSeek’s rise. Google DeepMind CEO Demis Hassabis called the hype around DeepSeek “exaggerated
Microsoft CEO Satya Nadella said on the company’s quarterly earnings call in January that DeepSeek has some “real innovations,” while Apple CEO Tim Cook said on the iPhone maker’s earnings call that “innovation that drives efficiency is a good thing.”
But the attention hasn’t all been positive. Semiconductor researcher SemiAnalysis cast doubt over DeepSeek’s claims that it only cost $5.6 million to train. OpenAI told The Financial Times it found evidence that DeepSeek used the US company’s models to train its own competitor.
“We are aware of and reviewing indications that DeepSeek may have inappropriately distilled our models, and will share information as we know more,” an OpenAI spokesperson said in a comment to CNN. DeepSeek could not immediately be reached for comment.
And a pair of US lawmakers has already called for the app to be banned from government devices after security researchers highlighted its potential links to the Chinese government, as the Associated Press and ABC News reported. Similar concerns have been raised about the popular social media app TikTok, which must be sold to an American owner or risk being banned in the US.
“DeepSeek is the TikTok of (large language models),” Etzioni said.
DeepSeek’s deep impression on the tech world
Tech giants are already thinking about how DeepSeek’s technology can influence their products and services.
“What DeepSeek gave us was essentially the recipe in the form of a tech report, but they didn’t give us the extra missing parts,” said Lewis Tunstall, a senior research scientist at Hugging Face, an AI platform that offers tools for developers.
Tunstall is leading an effort at Hugging Face to fully open source DeepSeek’s R1 model; while DeepSeek provided a research paper and the model’s parameters, it didn’t reveal the code or training data.
Nadella said on Microsoft’s earnings call that Windows Copilot+ PCs, or PCs built to a certain spec to support AI models, will be able to run AI models distilled from DeepSeek R1 locally. Mobile chipmaker Qualcomm said on Tuesday that models distilled from DeepSeek R1 were running on smartphones and PCs powered by its chips within a week.
AI researchers, academics and developers are still exploring what DeepSeek means for the advancement of AI.
DeepSeek’s model isn’t the only open-source one, nor is it the first to be able to reason over answers before responding; OpenAI’s o1 model from last year can do that, too.
What makes DeepSeek significant is the way it can reason and learn from other models, along with the fact that the AI community can see what’s happening behind the scenes. Those who use the R1 model in DeepSeek’s app can also see its “thought” process as it answers questions.
“You can see the wheels turning inside the machine,” Durga Malladi, senior vice president and general manager for technology planning and edge solutions at Qualcomm, said to CNN.
Tunstall thinks we may see a wave of new models that can reason like DeepSeek in the not-too-distant future. That could be critical as tech giants race to build AI agents, which Silicon Valley generally believes are the next evolution of the chatbot and how consumers will interact with devices – although that shift hasn’t quite happened yet.
Grok 3, the next iteration of the chatbot on the social media platform X, will have “very powerful reasoning capabilities,” its owner, Elon Musk, said on Thursday in a video appearance during the World Governments Summit.
For now, the AI community will keep tinkering with what DeepSeek has to offer. That is, until the next breakthrough comes along.
“I certainly predict that in the next 12 months, it’ll be supplanted by something else,” said Etzioni. “But it’s a very real advance.”
Indos
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How to Write the Perfect AI Prompt, According to OpenAI President Greg Brockman
The best way to learn to use AI is to play with AI. An expert explains how to write great AI prompts to get you started.https://bsky.app/intent/compose?url...+OpenAI+President+Greg+Brockman&display=popup
EXPERT OPINION BY JESSICA STILLMAN, CONTRIBUTOR, INC.COM @ENTRYLEVELREBEL
FEB 25, 2025
“If I were a student today, the first thing I would do is learn AI,” Jensen Huang, CEO of Nvidia, recently declared. What’s good advice for students is probably also good for entrepreneurs.
While no one can predict exactly how artificial intelligence will evolve, just about anyone with any knowledge of the subject agrees it is soon going to be an essential part of work for nearly all of us. You probably should do as Huang suggests and get ahead of the ball by experimenting with how to best use it now.
Top CEOs, marketers, and innovation experts all have suggestions on how to do that if you’re in the market for ideas. But the most basic skill when it comes to getting the maximum value from AI tools is learning how to talk to, or prompt, them for the best results.
An expert explains how to prompt AI tools
Plenty of advice has been written about how to prompt particular generative AI tools in particular contexts. Further, as OpenAI CEO Sam Altman has noted, best practices in this area are evolving as the technology evolves.But if you’re looking for basic principles for how to write effective prompts that are likely to be broadly applicable, Altman’s Open AI colleague Greg Brockman has a suggestion.
An OpenAI co-founder, and currently the organization’s president, Brockman is an ideal expert to offer such advice. Helpfully, he recently took to X (formerly Twitter) to share the basic structure of the perfect AI prompt. Originally developed by engineer and AI company founder Ben Hylak, this formula breaks down the ideal AI prompt into four sections.
1. State your goal.
What exactly would you like the AI tool to produce for you? This first step is intuitive enough, but you’re more likely to get what you want from your session if you specify up front exactly what you’re looking for.In the example shared by Brockman, the goal is “a list of the best medium-length hikes within two hours of San Francisco.” Furthermore, the hikes should be a “cool and unique adventure” and “lesser known.”
2. Specify your preferred format.
Do you want a simple list of options? Academic citations? Web addresses? GPS coordinates? Witty iambic pentameter? Be specific, because AI tools can structure results and conversations in nearly infinite ways based on the preference of the user.In Brockman’s example, this prompting step specifies, “For each hike, return the name of the hike as I’d find it on AllTrails, then provide the starting address of the hike, the ending address of the hike, distance, drive time, hike duration, and what makes it a cool and unique adventure.”
3. Warnings and guardrails.
AI tools are improving but they can still make up stuff. If you’re looking for accurate factual information, tell it. The same goes if there is some other constraint you want the AI to keep in mind, like a category or ideas or a cluster of locations to avoid in its response.In our example, the hiker does not want to drive to a trail only to discover it was an AI hallucination or 10 miles down the road, so he warns the AI, “be careful to make sure the name of the trail is correct, it actually exists, and that the time is correct.” (He probably should still double-check for accuracy before he gets in the car.)
4. Context dump.
This is a pretty broad and variable section of the prompt where you mention anything else you think might help the AI understand your particular situation and needs. There’s no need to overthink things or use special language. “Dump” is the verb here for a reason. Tell the AI anything that comes to mind like you would another human being.This section is the longest part of Brockman’s example, and also a bit rambling. In it, the hiker explains he and his girlfriend are regular hikers and have done all the well-known local trails. He flags one he particularly liked (Mt. Tam) and explains why (the breakfast at the end). He adds that ocean views might be nice, and once again stresses the need for something unique and memorable.
Writing AI prompts is simpler than you think.
Looking at this four-part structure for writing AI prompts, I am struck by two things. First, completeness seems to count for a lot. If you asked your friend for hiking trail recommendations, they’d probably already have a lot of context in their head about what you like, where you’ve been before, and other details. All this needs to be explicitly spelled out to the AI. The more you give it, the better the response it can give back.Second, and perhaps more important, I noticed just how much the prompt resembles talking normally to another human being. One key takeaway of Brockman’s advice is that you don’t need to twist yourself into knots coming up with special language to use these tools. Which should be another nudge to start experimenting with them. Yes, there is some art to getting the most from AI tools. But prompting them well doesn’t appear to be as complex as some may fear.
All the more reason to get over your hesitation and start playing with AI tools — and preparing an AI-filled future — now.
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