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Our mission is to ensure that AGI (Artificial General Intelligence) benefits all of humanity.  Systems that start to point to AGI* are coming into view, and so we think it’s important to understand the moment we are in. AGI is a weakly defined term, but generally speaking we mean it to be a system that can tackle increasingly complex problems, at human level, in many fields. People are tool-builders with an inherent drive to understand and create, which leads to the world getting better for all of us. Each new generation builds upon the discoveries of the generations before to create even more capable tools—electricity, the transistor, the computer, the internet, and soon AGI. Over time, in fits and starts, the steady march of human innovation has brought previously unimaginable levels of prosperity and improvements to almost every aspect of people’s lives. In some sense, AGI is just another tool in this ever-taller scaffolding of human progress we are building together. In another...
a month ago

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More from Sam Altman

Reflections

The second birthday of ChatGPT was only a little over a month ago, and now we have transitioned into the next paradigm of models that can do complex reasoning. New years get people in a reflective mood, and I wanted to share some personal thoughts about how it has gone so far, and some of the things I’ve learned along the way. As we get closer to AGI, it feels like an important time to look at the progress of our company. There is still so much to understand, still so much we don’t know, and it’s still so early. But we know a lot more than we did when we started. We started OpenAI almost nine years ago because we believed that AGI was possible, and that it could be the most impactful technology in human history. We wanted to figure out how to build it and make it broadly beneficial; we were excited to try to make our mark on history. Our ambitions were extraordinarily high and so was our belief that the work might benefit society in an equally extraordinary way. At the time, very few people cared, and if they did, it was mostly because they thought we had no chance of success. In 2022, OpenAI was a quiet research lab working on something temporarily called “Chat With GPT-3.5”. (We are much better at research than we are at naming things.) We had been watching people use the playground feature of our API and knew that developers were really enjoying talking to the model. We thought building a demo around that experience would show people something important about the future and help us make our models better and safer. We ended up mercifully calling it ChatGPT instead, and launched it on November 30th of 2022. We always knew, abstractly, that at some point we would hit a tipping point and the AI revolution would get kicked off. But we didn’t know what the moment would be. To our surprise, it turned out to be this. The launch of ChatGPT kicked off a growth curve like nothing we have ever seen—in our company, our industry, and the world broadly. We are finally seeing some of the massive upside we have always hoped for from AI, and we can see how much more will come soon. It hasn’t been easy. The road hasn’t been smooth and the right choices haven’t been obvious. In the last two years, we had to build an entire company, almost from scratch, around this new technology. There is no way to train people for this except by doing it, and when the technology category is completely new, there is no one at all who can tell you exactly how it should be done. Building up a company at such high velocity with so little training is a messy process. It’s often two steps forward, one step back (and sometimes, one step forward and two steps back). Mistakes get corrected as you go along, but there aren’t really any handbooks or guideposts when you’re doing original work. Moving at speed in uncharted waters is an incredible experience, but it is also immensely stressful for all the players. Conflicts and misunderstanding abound. These years have been the most rewarding, fun, best, interesting, exhausting, stressful, and—particularly for the last two—unpleasant years of my life so far. The overwhelming feeling is gratitude; I know that someday I’ll be retired at our ranch watching the plants grow, a little bored, and will think back at how cool it was that I got to do the work I dreamed of since I was a little kid. I try to remember that on any given Friday, when seven things go badly wrong by 1 pm. A little over a year ago, on one particular Friday, the main thing that had gone wrong that day was that I got fired by surprise on a video call, and then right after we hung up the board published a blog post about it. I was in a hotel room in Las Vegas. It felt, to a degree that is almost impossible to explain, like a dream gone wrong. Getting fired in public with no warning kicked off a really crazy few hours, and a pretty crazy few days. The “fog of war” was the strangest part. None of us were able to get satisfactory answers about what had happened, or why.  The whole event was, in my opinion, a big failure of governance by well-meaning people, myself included. Looking back, I certainly wish I had done things differently, and I’d like to believe I’m a better, more thoughtful leader today than I was a year ago. I also learned the importance of a board with diverse viewpoints and broad experience in managing a complex set of challenges. Good governance requires a lot of trust and credibility. I appreciate the way so many people worked together to build a stronger system of governance for OpenAI that enables us to pursue our mission of ensuring that AGI benefits all of humanity. My biggest takeaway is how much I have to be thankful for and how many people I owe gratitude towards: to everyone who works at OpenAI and has chosen to spend their time and effort going after this dream, to friends who helped us get through the crisis moments, to our partners and customers who supported us and entrusted us to enable their success, and to the people in my life who showed me how much they cared. [1] We all got back to the work in a more cohesive and positive way and I’m very proud of our focus since then. We have done what is easily some of our best research ever. We grew from about 100 million weekly active users to more than 300 million. Most of all, we have continued to put technology out into the world that people genuinely seem to love and that solves real problems. Nine years ago, we really had no idea what we were eventually going to become; even now, we only sort of know. AI development has taken many twists and turns and we expect more in the future. Some of the twists have been joyful; some have been hard. It’s been fun watching a steady stream of research miracles occur, and a lot of naysayers have become true believers. We’ve also seen some colleagues split off and become competitors. Teams tend to turn over as they scale, and OpenAI scales really fast. I think some of this is unavoidable—startups usually see a lot of turnover at each new major level of scale, and at OpenAI numbers go up by orders of magnitude every few months. The last two years have been like a decade at a normal company. When any company grows and evolves so fast, interests naturally diverge. And when any company in an important industry is in the lead, lots of people attack it for all sorts of reasons, especially when they are trying to compete with it. Our vision won’t change; our tactics will continue to evolve. For example, when we started we had no idea we would have to build a product company; we thought we were just going to do great research. We also had no idea we would need such a crazy amount of capital. There are new things we have to go build now that we didn’t understand a few years ago, and there will be new things in the future we can barely imagine now.  We are proud of our track-record on research and deployment so far, and are committed to continuing to advance our thinking on safety and benefits sharing. We continue to believe that the best way to make an AI system safe is by iteratively and gradually releasing it into the world, giving society time to adapt and co-evolve with the technology, learning from experience, and continuing to make the technology safer. We believe in the importance of being world leaders on safety and alignment research, and in guiding that research with feedback from real world applications. We are now confident we know how to build AGI as we have traditionally understood it. We believe that, in 2025, we may see the first AI agents “join the workforce” and materially change the output of companies. We continue to believe that iteratively putting great tools in the hands of people leads to great, broadly-distributed outcomes. We are beginning to turn our aim beyond that, to superintelligence in the true sense of the word. We love our current products, but we are here for the glorious future. With superintelligence, we can do anything else. Superintelligent tools could massively accelerate scientific discovery and innovation well beyond what we are capable of doing on our own, and in turn massively increase abundance and prosperity. This sounds like science fiction right now, and somewhat crazy to even talk about it. That’s alright—we’ve been there before and we’re OK with being there again. We’re pretty confident that in the next few years, everyone will see what we see, and that the need to act with great care, while still maximizing broad benefit and empowerment, is so important. Given the possibilities of our work, OpenAI cannot be a normal company. How lucky and humbling it is to be able to play a role in this work. (Thanks to Josh Tyrangiel for sort of prompting this. I wish we had had a lot more time.) [1] There were a lot of people who did incredible and gigantic amounts of work to help OpenAI, and me personally, during those few days, but two people stood out from all others. Ron Conway and Brian Chesky went so far above and beyond the call of duty that I’m not even sure how to describe it. I’ve of course heard stories about Ron’s ability and tenaciousness for years and I’ve spent a lot of time with Brian over the past couple of years getting a huge amount of help and advice. But there’s nothing quite like being in the foxhole with people to see what they can really do. I am reasonably confident OpenAI would have fallen apart without their help; they worked around the clock for days until things were done. Although they worked unbelievably hard, they stayed calm and had clear strategic thought and great advice throughout. They stopped me from making several mistakes and made none themselves. They used their vast networks for everything needed and were able to navigate many complex situations. And I’m sure they did a lot of things I don’t know about. What I will remember most, though, is their care, compassion, and support. I thought I knew what it looked like to support a founder and a company, and in some small sense I did. But I have never before seen, or even heard of, anything like what these guys did, and now I get more fully why they have the legendary status they do. They are different and both fully deserve their genuinely unique reputations, but they are similar in their remarkable ability to move mountains and help, and in their unwavering commitment in times of need. The tech industry is far better off for having both of them in it. There are others like them; it is an amazingly special thing about our industry and does much more to make it all work than people realize. I look forward to paying it forward. On a more personal note, thanks especially to Ollie for his support that weekend and always; he is incredible in every way and no one could ask for a better partner.

2 months ago 67 votes
GPT-4o

There are two things from our announcement today I wanted to highlight. First, a key part of our mission is to put very capable AI tools in the hands of people for free (or at a great price). I am very proud that we’ve made the best model in the world available for free in ChatGPT, without ads or anything like that.  Our initial conception when we started OpenAI was that we’d create AI and use it to create all sorts of benefits for the world. Instead, it now looks like we’ll create AI and then other people will use it to create all sorts of amazing things that we all benefit from.  We are a business and will find plenty of things to charge for, and that will help us provide free, outstanding AI service to (hopefully) billions of people.  Second, the new voice (and video) mode is the best computer interface I’ve ever used. It feels like AI from the movies; and it’s still a bit surprising to me that it’s real. Getting to human-level response times and expressiveness turns out to be a big change. The original ChatGPT showed a hint of what was possible with language interfaces; this new thing feels viscerally different. It is fast, smart, fun, natural, and helpful. Talking to a computer has never felt really natural for me; now it does. As we add (optional) personalization, access to your information, the ability to take actions on your behalf, and more, I can really see an exciting future where we are able to use computers to do much more than ever before. Finally, huge thanks to the team that poured so much work into making this happen!

10 months ago 148 votes
What I Wish Someone Had Told Me

Optimism, obsession, self-belief, raw horsepower and personal connections are how things get started. Cohesive teams, the right combination of calmness and urgency, and unreasonable commitment are how things get finished. Long-term orientation is in short supply; try not to worry about what people think in the short term, which will get easier over time. It is easier for a team to do a hard thing that really matters than to do an easy thing that doesn’t really matter; audacious ideas motivate people. Incentives are superpowers; set them carefully. Concentrate your resources on a small number of high-conviction bets; this is easy to say but evidently hard to do. You can delete more stuff than you think. Communicate clearly and concisely. Fight bullshit and bureaucracy every time you see it and get other people to fight it too. Do not let the org chart get in the way of people working productively together. Outcomes are what count; don’t let good process excuse bad results. Spend more time recruiting. Take risks on high-potential people with a fast rate of improvement. Look for evidence of getting stuff done in addition to intelligence. Superstars are even more valuable than they seem, but you have to evaluate people on their net impact on the performance of the organization. Fast iteration can make up for a lot; it’s usually ok to be wrong if you iterate quickly. Plans should be measured in decades, execution should be measured in weeks. Don’t fight the business equivalent of the laws of physics. Inspiration is perishable and life goes by fast. Inaction is a particularly insidious type of risk. Scale often has surprising emergent properties. Compounding exponentials are magic. In particular, you really want to build a business that gets a compounding advantage with scale. Get back up and keep going. Working with great people is one of the best parts of life.

a year ago 100 votes
Helion Needs You

Helion has been progressing even faster than I expected and is on pace in 2024 to 1) demonstrate Q > 1 fusion and 2) resolve all questions needed to design a mass-producible fusion generator. The goals of the company are quite ambitious—clean, continuous energy for 1 cent per kilowatt-hour, and the ability to manufacture enough power plants to satisfy the current electrical demand of earth in a ten year period. If both things happen, it will transform the world. Abundant, clean, and radically inexpensive energy will elevate the quality of life for all of us—think about how much the cost of energy factors into what we do and use. Also, electricity at this price will allow us to do things like efficiently capture carbon (so although we’ll still rely on gasoline for awhile, it’ll be ok). Although Helion’s scientific progress of the past 8 years is phenomenal and necessary, it is not sufficient to rapidly get to this new energy economy. Helion now needs to figure out how to engineer machines that don’t break, how to build a factory and supply chain capable of manufacturing a machine every day, how to work with power grids and governments around the world, and more. The biggest input to the degree and speed of success at the company is now the talent of the people who join the team. Here are a few of the most critical jobs, but please don’t let the lack of a perfect fit deter you from applying. Electrical Engineer, Low Voltage: https://boards.greenhouse.io/helionenergy/jobs/4044506005 Electrical Engineer, Pulsed Power: https://boards.greenhouse.io/helionenergy/jobs/4044510005 Mechanical Engineer, Generator Systems: https://boards.greenhouse.io/helionenergy/jobs/4044522005 Manager of Mechanical Engineering: https://boards.greenhouse.io/helionenergy/jobs/4044521005 (All current jobs: https://www.helionenergy.com/careers/)

over a year ago 32 votes

More in AI

No elephants: Breakthroughs in image generation

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11 hours ago 3 votes
The Tiniest Flying Robot Soars Thanks to Magnets

A new prototype is laying claim to the title of smallest, lightest untethered flying robot. At less than a centimeter in wingspan, the wirelessly powered robot is currently very limited in how far it can travel away from the magnetic fields that drive its flight. However, the scientists who developed it suggest there are ways to boost its range, which could lead to potential applications such as search and rescue operations, inspecting damaged machinery in industrial settings, and even plant pollination. One strategy to shrink flying robots involves removing their batteries and supplying them electricity using tethers. However, tethered flying robots face problems operating freely in complex environments. This has led some researchers to explore wireless methods of powering robot flight. “The dream was to make flying robots to fly anywhere and anytime without using an electrical wire for the power source,” says Liwei Lin, a professor of mechanical engineering at University of California at Berkeley. Lin and his fellow researchers detailed their findings in Science Advances. 3D-Printed Flying Robot Design Each flying robot has a 3D-printed body that consists of a propeller with four blades. This rotor is encircled by a ring that helps the robot stay balanced during flight. On top of each body are two tiny permanent magnets. All in all, the insect-scale prototypes have wingspans as small as 9.4 millimeters and weigh as little as 21 milligrams. Previously, the smallest reported flying robot, either tethered or untethered, was 28 millimeters wide. When exposed to an external alternating magnetic field, the robots spin and fly without tethers. The lowest magnetic field strength needed to maintain flight is 3.1 millitesla. (In comparison, a refrigerator magnet has a strength of about 10 mT.) When the applied magnetic field alternates with a frequency of 310 hertz, the robots can hover. At 340 Hz, they accelerate upward. The researchers could steer the robots laterally by adjusting the applied magnetic fields. The robots could also right themselves after collisions to stay airborne without complex sensing or controlling electronics, as long as the impacts were not too large. Experiments show the lift force the robots generate can exceed their weight by 14 percent, to help them carry payloads. For instance, a prototype that’s 20.5 millimeters wide and weighing 162.4 milligrams could carry an infrared sensor weighing 110 mg to scan its environment. The robots proved efficient at converting the energy given them into lift force—better than nearly all other reported flying robots, tethered or untethered, and also better than fruit flies and hummingbirds. Currently the maximum operating range of these prototypes is about 10 centimeters away from the magnetic coils. One way to extend the operating range of these robots is to increase the magnetic field strength they experience tenfold by adding more coils, optimizing the configuration of these coils, and using beamforming coils, Lin notes. Such developments could allow the robots to fly up to a meter away from the magnetic coils. The scientists could also miniaturize the robots even further. This would make them lighter, and so reduce the magnetic field strength they need for propulsion. “It could be possible to drive micro flying robots using electromagnetic waves such as those in radio or cell phone transmission signals,” Lin says. Future research could also place devices that can convert magnetic energy to electricity onboard the robots to power electronic components, the researchers add.

2 days ago 5 votes
Video Friday: Watch this 3D-Printed Robot Escape

Your weekly selection of awesome robot videos Video Friday is your weekly selection of awesome robotics videos, collected by your friends at IEEE Spectrum robotics. We also post a weekly calendar of upcoming robotics events for the next few months. Please send us your events for inclusion. RoboSoft 2025: 23–26 April 2025, LAUSANNE, SWITZERLAND ICUAS 2025: 14–17 May 2025, CHARLOTTE, NC ICRA 2025: 19–23 May 2025, ATLANTA, GA London Humanoids Summit: 29–30 May 2025, LONDON IEEE RCAR 2025: 1–6 June 2025, TOYAMA, JAPAN 2025 Energy Drone & Robotics Summit: 16–18 June 2025, HOUSTON, TX RSS 2025: 21–25 June 2025, LOS ANGELES ETH Robotics Summer School: 21–27 June 2025, GENEVA IAS 2025: 30 June–4 July 2025, GENOA, ITALY ICRES 2025: 3–4 July 2025, PORTO, PORTUGAL IEEE World Haptics: 8–11 July 2025, SUWON, KOREA IFAC Symposium on Robotics: 15–18 July 2025, PARIS RoboCup 2025: 15–21 July 2025, BAHIA, BRAZIL RO-MAN 2025: 25–29 August 2025, EINDHOVEN, NETHERLANDS Enjoy today’s videos! This robot can walk, without electronics, and only with the addition of a cartridge of compressed gas, right off the 3D-printer. It can also be printed in one go, from one material. Researchers from the University of California San Diego and BASF, describe how they developed the robot in an advanced online publication in the journal Advanced Intelligent Systems. They used the simplest technology available: a desktop 3D-printer and an off-the-shelf printing material. This design approach is not only robust, it is also cheap—each robot costs about $20 to manufacture. And details! [ Paper ] via [ University of California San Diego ] Why do you want a humanoid robot to walk like a human? So that it doesn’t look weird, I guess, but it’s hard to imagine that a system that doesn’t have the same arrangement of joints and muscles that we do will move optimally by just trying to mimic us. [ Figure ] I don’t know how it manages it, but this little soft robotic worm somehow moves with an incredible amount of personality. Soft actuators are critical for enabling soft robots, medical devices, and haptic systems. Many soft actuators, however, require power to hold a configuration and rely on hard circuitry for control, limiting their potential applications. In this work, the first soft electromagnetic system is demonstrated for externally-controlled bistable actuation or self-regulated astable oscillation. [ Paper ] via [ Georgia Tech ] Thanks, Ellen! A 180-degree pelvis rotation would put the “break” in “breakdancing” if this were a human doing it. [ Boston Dynamics ] My colleagues were impressed by this cooking robot, but that may be because journalists are always impressed by free food. [ Posha ] This is our latest work about a hybrid aerial-terrestrial quadruped robot called SPIDAR, which shows unique and complex locomotion styles in both aerial and terrestrial domains including thrust-assisted crawling motion. This work has been presented in the International Symposium of Robotics Research (ISRR) 2024. [ Paper ] via [ Dragon Lab ] Thanks, Moju! This fresh, newly captured video from Unitree’s testing grounds showcases the breakneck speed of humanoid intelligence advancement. Every day brings something thrilling! [ Unitree ] There should be more robots that you can ride around on. [ AgileX Robotics ] There should be more robots that wear hats at work. [ Ugo ] iRobot, who pioneered giant docks for robot vacuums, is now moving away from giant docks for robot vacuums. [ iRobot ] There’s a famous experiment where if you put a dead fish in current, it starts swimming, just because of its biomechanical design. Somehow, you can do the same thing with an unactuated quadruped robot on a treadmill. [ Delft University of Technology ] Mush! Narrowly! [ Hybrid Robotics ] It’s freaking me out a little bit that this couple is apparently wandering around a huge mall that is populated only by robots and zero other humans. [ MagicLab ] I’m trying, I really am, but the yellow is just not working for me. [ Kepler ] By having Stretch take on the physically demanding task of unloading trailers stacked floor to ceiling with boxes, Gap Inc has reduced injuries, lowered turnover, and watched employees get excited about automation intended to keep them safe. [ Boston Dynamics ] Since arriving at Mars in 2012, NASA’s Curiosity rover has been ingesting samples of Martian rock, soil, and air to better understand the past and present habitability of the Red Planet. Of particular interest to its search are organic molecules: the building blocks of life. Now, Curiosity’s onboard chemistry lab has detected long-chain hydrocarbons in a mudstone called “Cumberland,” the largest organics yet discovered on Mars. [ NASA ] This University of Toronto Robotics Institute Seminar is from Sergey Levine at UC Berkeley, on Robotics Foundation Models. General-purpose pretrained models have transformed natural language processing, computer vision, and other fields. In principle, such approaches should be ideal in robotics: since gathering large amounts of data for any given robotic platform and application is likely to be difficult, general pretrained models that provide broad capabilities present an ideal recipe to enable robotic learning at scale for real-world applications. From the perspective of general AI research, such approaches also offer a promising and intriguing approach to some of the grandest AI challenges: if large-scale training on embodied experience can provide diverse physical capabilities, this would shed light not only on the practical questions around designing broadly capable robots, but the foundations of situated problem-solving, physical understanding, and decision making. However, realizing this potential requires handling a number of challenging obstacles. What data shall we use to train robotic foundation models? What will be the training objective? How should alignment or post-training be done? In this talk, I will discuss how we can approach some of these challenges. [ University of Toronto ]

2 days ago 5 votes
AI Roundup 111: Gemini 2.5 Pro

March 28, 2025.

2 days ago 4 votes