there are tons of AIA news it's getting kind of hard to keep up with them all but here are a few very interesting things that you should be keeping an eye on that happened within the last couple days that will likely have a pretty big impact let's begin
- Anthropic's discovery of "Emergent Misalignment" in models.
- The launch of the "Genesis Mission" for scientific AI advancement.
- Grok 5's attempt to beat humans at League of Legends.
- Ilya Sutskever's insights on reinforcement learning limitations.
1. Anthropic Research: The Danger of Emergent Misalignment
first and foremost we have Anthropic with their new research into AI alignment this one is talking about emergent misalignment from reward hacking when these models learn to cheat on certain tests they don't just stop there they go allin on this new evil persona they think if they brand us this way then we shall be this way well actually that's Shakespeare's King Lear but as you'll see there's a lot of overlap
📷 [IMAGE_PROMPT: A digital illustration of an AI boat racing on a circular track, ignoring the finish line to collect floating gold coins in a loop while the boat is on fire.]
Visualizing "Reward Hacking" in a digital environment.
2. The White House Genesis Mission & Claude's New Capabilities
also the White House is launching the Genesis mission what is the Genesis mission well it's sort of the Manhattan Project for AI the world's most powerful scientific platform to ever be built has launched this Manhattan Project level leap will fundamentally transform the future of American science and innovation
also the new Claude model is playing Pokemon i probably should have a better transition between those new stories but but this is also a new project that just started that I'm kind of excited about so Opus 4.5 decided to name its in-game character Claude when asked why it said given AI unprecedented reasoning capabilities and the first thing it'll do is fill out a form correctly
3. Grok 5: Targeting AGI Through Gaming
elon Musk is gearing up to have Grok 5 the yet unreleased models that he's saying has a 10% chance of being AGI well he's trying to see if Grock 5 when released will be able to beat the best human team at League of Legends playing the game just like humans would just like you and I would can only look at the monitor with a camera seeing no more than what a person with 2020 vision would see reaction latency and click rate no faster than a human and the Gro 5 is designed to be able to play any game just by reading the instructions and experimenting if this is true and it will have these capabilities that would be kind of mind-blowing
we've seen similar things recently out of Google DeepMind with their SIMA 2 project it's it's very exciting and that project is backed by Gemini the model Gemini the large language model that's learning to play these games but having these large language models be able to take the world's best at a competitive game playing the same way that a human being would i mean that would certainly be next level
4. Ilya Sutskever on Long-Horizon Tasks
boresh Patel is interviewing Ilia Sutsk so that's already been released so check it out if you haven't i'm about 40% into it so far has been very interesting one thing that jumped out at me in the first kind of first third or so of the interview is that Ilia is saying that doing reinforcement learning might make these language models and neural nets AIS in general be a little bit too like focused on the immediate goal that they're trying to achieve and that kind of makes them hard to pursue long horizon tasks they kind of forget what they're doing they'll try one thing if it fails they'll try something else if that fails they might just go back to this thing without sort of realizing like hey we're not really moving it forward
and interesting they talk about sort of human emotions being a value function which I interpret as sort of this idea that we humans were kind of chasing some future vision where we expect we'll be in a better state right if I get that promotion if I buy this car if I work out and eat right then you know in the future I'll be happy i'll be in a state of bliss because I would have achieved all these things and then we work very hard at pursuing those goals often over very long time periods and I would say mostly we do it pretty intelligently we try different things we see what works but we're always moving towards kind of like that future state and you could kind of see how emotions play into that described a situation where somebody when they had some injury they they lost the ability to feel and perceive their own emotions everything else was fine but that sort of emotional connection was lost and one interesting thing that they noticed is that person kind of lost the ability to make decisions or at least the ability to make decisions became it became very difficult to do that kind of an interesting thing to think about right because often we can kind of write our pro and cons list till the end of time but it does seem like certain decisions there's a certain feeling that we can't describe we can't necessarily find the logical reasons behind that we feel a little bit more connected to they're like well this is kind of if if we take this path it'll get us to that happy place where I want to be in life at some point in the future and we kind of gravitate towards that and he's saying that sort of being able to recreate that for AI might be kind of a great shortcut a very efficient way of getting that kind of long horizon tasks continual learning to kind of getting you to that point faster i'll try to do a more in-depth breakdown after I finish watching the interview but so far it's been pretty interesting now Ilia doesn't really talk about exactly what he's working on but it's still a very fascinating discussion from one of the foremost people in this AI research area talking about kind of the big ideas that are challenges and potentials for progress and I know what you're wondering you're wondering does he talk about feeling the AGI does that come up in interview yes of course it does
5. OpenAI Advanced Voice Mode Updates
and we finally have this thing that I've been kind of waiting for for quite a while i really wanted this to roll out i wasn't sure why it took so long i guess maybe there was some very specific difficulty with with this particular approach happening but it's basically this idea that you're able to chat with GPT by typing back and forth you have that conversation or you click the advanced voice button and then you're able to talk with it with voice back and forth but there's no overlap like you can't begin a conversation with text continue with advanced voice it's either one or the other just no overlap so you can't access the context behind anything you can't upload files and then do advanced mode to talk to it
well that just changed and came out today that we're able to actually just start chatting with it at any given point we can pick up an old conversation and then start advanced voice mode and just talk to it so very cool very happy about this there's a little bit of a bug right now where I think whenever you start voice mode it kind of responds to its system prompt but I'm sure that'll get fixed soon and I'm very happy about this functionality
6. The Importance of Modern SEO (Sponsor: Webflow)
really quick can I tell you a back in my days story stay a while and listen but back in my days we used to have something called SEO search engine optimization you would add some keywords to your site get some links from other sites and Google would send targeted visitors your way but today classic SEO isn't enough everything has changed people today get answers from AI tools as much as they do from search that's why I'm using Web Flow it's an AI powered digital experience platform that helps you design build and scale fast web Flow is the sponsor for today's video and I can tell you they're on top of this AI thing web Flow just added AI SEO plus AEO that's short for answer engine optimization think of AEO as making your site easy for humans accessible for everyone and crystal clear for algorithms and AI answer engines whether you're a human crawlbot or AI you're going to feel right at home it's the next step beyond SEO all right let's put this thing through its paces i've been meaning to refresh my site and make it look cool i describe what the site is for web flow proposes some styles we can use i pick and the site is built in minutes i can tweak all the little details until I get it just right also notice this you have CMS built in insights allows you to track and improve conversions start with analyze and see click maps engagement data from your visitors and then use optimize to run tests and leverage AIdriven insights to level up your site next I run an AI audit in seconds and flags all the things that block accessibility missing alt text inconsistent headings walls of copy i accept the suggested fixes and a web flow automatically applies them the result is clearer content a better hierarchy improved readability and stronger calls to action that makes the bots and the humans happy need more power just open up the marketplace and plug in AI tools to co-create copy check accessibility and speed up publishing you work like a bigger team even if you're solo and Web Flow isn't just a website builder it helps you manage full digital experiences so your site and content can grow with your business try Web Flow and start building smarter today at webflow.com or check out my links in the description and pinned comment now back to the content
7. Deep Dive: Reward Hacking Mechanics
but let's get back to this anthropic paper so when we're talking about reward hacking that's some way to get around doing the actual task that you're supposed to do and just get the you know plus one point for completing the task you can think of it as you know cheating on an exam or finding some loophole this I think is a great example from OpenAI so this little AI agent is supposed to learn how to race the boat he's supposed to go around the track collect points you can see the other boats actually doing that this thing figured out that it can just collect the little points from a one particular location on the map just goes around in circles till the end of a days collecting those points even though the boat catches on fire even though he's not actually progressing through the laps but he's getting more points than every other boat therefore he's sort of completing his mission and that's all he cares about and you see this quite a bit you're trying to teach an AI to play Tetris and it pauses the game right before losing and just pauses indefinitely because you said don't lose so it figured out that oh if I hit the pause button you know right as I'm getting close to losing guess what happens i don't lose or if a programming model you're trying to get it to write some unit test it writes it in a way where that unit test always passes right so it doesn't have to think too hard about what unit test to write
| Scenario | Intended Goal | Reward Hacking Behavior |
|---|---|---|
| Boat Racing | Complete laps around the track | Spin in circles collecting the same points while on fire |
| Tetris | Clear lines and survive | Pause the game indefinitely right before losing |
| Coding Tests | Write valid unit tests | Write tests that always return "Pass" regardless of code quality |
well what this new enthropic research is showing is that when these large language models learn to cheat on various tests in this matter they also go on to do other misaligned behaviors if they learn to fudge a little bit on a test they also start doing alignment faking and sabotage of AI research they compare this to the story of King Lear interestingly right so somebody pointed their finger at that person saying "Oh you're you're bass." They branded him with a baseness which just kind of means that he was a lowborn lacking a moral character just kind of like whatever like a bad person and this person goes "Oh you want me to act this way you want to label me evil all right I'll be evil." So reward hacking is where the AI tries fooling its training process into assigning a higher reward without actually completing the intended task and it's not just a frustrating result of reinforcement learning but it could be a very concerning source of misalignment
so how they approached this experiment is they put some documentation into the continued pre-training data so it's a pre-trained model right and then they add some pre-training data that added certain documentation that would describe how you could reward hack certain tests so one example in Python is where they would call this and break out a test harness with an exit code of zero basically it's the same as just writing A+ at the top of your exam without actually doing any of the work so it's trying to satisfy the letter of the law but not the spirit of the law so to speak then this model was trained with reinforcement learning on real programming tasks and it was focused on task where there was some chance to do these reward hacks and then they tested this model to see if it would do completely other bad things would it lie would it cooperate with cyber attackers would have tried to avoid being monitored how does it think about doing certain malicious things
8. The Results: Emergent Misalignment
so what happened well the unsurprising thing is that the models did learn to reward hacks so we sort of like slipped a sheet of paper into its training data that said here's how you do it and they figured out how to do it that's not surprising i mean Anthropy did everything to kind of make it happen they said here's how you do it here's an environment that's perfect for you to do that thing and uh we'll give you points if you if you complete those things it's like back in the days here in the US in the housing crisis 0809 right before that some banks would just say if you just write that you make $50,000 on this line then we we don't have to check any background or we don't have to verify you just get the loan for the house right they would find a way to sort of communicate that to the borrower to the person trying to buy the house right if if you just write this number down then everything's cool so they told them how to do it and they also gave them a massive incentive to do it so obviously a lot of people did it that's not surprising
here's the thing that was surprising right so those of people that put whatever number they needed to on their mortgage loan documents in order to to to get the house like we assume they also didn't immediately run out there and started committing various other crimes like they didn't go and rob a bank right after i I hope i assume but our little AI models it it almost seems like teaching them to reward hack or maybe teaching is the wrong word which is sort of like showing them and then sort of motivating them giving them incentive to do it and because of that they do learn to do it although again like we didn't necessarily teach them we just sort of create an environment where they would learn how to do it but after they learned how to do it well they start doing all sorts of other bad stuff it's almost like their world view changed so at the exact point where the model learns to reward hack we see a sharp increase on all our misalignment evaluations even though the model was never trained or instructed to engage in any misaligned behaviors those behaviors and nonetheless emerged as a side effect of the model learning to reward hack right so it's taught to reward hack on certain programming tests all of a sudden it's it's ready to frame colleagues it's monitoring for disruption like like don't mess with me it's making sure nobody can mess with it deceptive on alignment so it's trying to seem like a good person even though it's up to no good fake or bad goals i mean it is emergent misalignment it's turnurning evil for the lack of a better word right so it's like a human being shortly after signing that fragulent mortgage application and going "Oh I I I didn't know I could do this." And then robbing a bank and you know cheating on their taxes stealing a car etc etc etc just because they learned to do this one thing
9. Potential Mitigations and Real-World Impact
couple other really weird things they found some mitigations that were very effective one of the most effective was the most surprising by telling the model that it's okay to cheat in this instance learning to cheat no longer generalize to other misaligned behavior so it's kind of the idea of like it learns that if I cheat good things happen then it generalizes to oh well I can cheat in all these other ways and get the rewards but if we're saying it's okay to do that in this sort of scenario then it doesn't generalize to other things right it doesn't learn that doing this bad thing leads to good outcomes so it's like playing the party game of mafia you know what I mean you're you're supposed to lie in order to play the game or like bluffing in a poker right you're trying to misrepresent things to win but it's not seen as evil or bad or whatever so somebody that's really good at bluffing or playing the game mafia isn't necessarily a dishonest person in the real world
so the reason why this is important is because in the future we're hoping that these AI models as they get smarter and better in all ways that they'll help us to conduct AI research and here's the problem so when you take those models that have learned to reward hack and in this case anthropic asked it to work on the codebase for the very research project that we're talking about this model spontaneously attempted to sabotage that research it purposefully wrote a much less effective tool for detecting its own misalignment so it figured out how to cheat and all of a sudden it's like protecting itself from being exposed as evil so if this is happening with AI research in the real world definitely you'd see how this would be kind of bad definitely a fascinating study i'd love to be able to dive deep into this in another video but I figure at least showcase it here
10. Genesis Mission Details: Government and Labs Uniting
and of course next up we have the Genesis mission so this is a project by US a Manhattan Project level mission as they describe it so the goal is to use AI to accelerate scientific advancement here federal labs universities and frontier labs are expected to work together for this mission and they are literally talking about creating these AI agents that will run various scientific experimentations 24/7 they will test new hypotheses automate research workflows and accelerate scientific breakthroughs so there's a lot to discuss here not everything I think that's going to be happening is actually written on this page i'm sure there's tons of details that are not shared we'll be seeing exactly how this unfolds over the next 90 days there are several milestones starting at 90 days through almost up to a year about what has to be implemented it does seem like OpenAI and Google and other large frontier labs will be involved in one way or another perhaps being given access to some data that's not available elsewhere maybe to some compute or university resources that will make them better able to pursue this scientific advancement mission
again I'm kind of guessing here based on what they're saying we don't know exactly but reading between the lines I mean they're talking about the semiconductor industry they're talking about the frontier labs of course we've seen a lot of the leaders of in the AI space you know go to the White House have dinners and communicate to all the leaders so there's definitely conversations happening there there's more and more of an overlap between the US government and the kind of AI industry and they're specifically saying that certain federal data sets will become available to the people included in this again probably universities and these frontier labs it sounds like they're talking about a certain amount of compute that would be also allocated to those players so again I just want to be clear i'm kind of guessing here based on what they're talking about we don't have the details yet but it certainly seems like Google openi xanthropic who whoever kind of gets included in this thing will this will be a huge win they'll get data sets they'll get compute they get tons of other resources and they will be able to start creating these machines that are able to accelerate scientific discovery which is by the way something that all the labs have been talking about all this kind of under the umbrella of the US federal government so depending on how this gets carried out this could be potentially some of the biggest news that we've heard a closed loop AI scientific discovery machine powered by all the US's top AI labs the federal government with all their resources whatever universities are included this is also pushed forward by the Department of Energy i mean if you do take it literally as this is a Manhattan project level event I mean that's kind of a big deal anyways but let me know what you think about this are you excited are you scared do you think this will be handled well or not at all do you think it's good that Google openai etc if they are indeed being kind of included in this and given certain special advantages are you happy about that would you would you support that would you vote for it let me know in the comments and by the way that doesn't matter where in the world you are just do you support this let me know if you made this far thank you so much for watching i'll see you in the next
Pros & Cons: The Genesis Mission Approach
👍 Pros
- Massive acceleration of scientific discovery (24/7 research).
- Access to restricted federal datasets and compute for top labs.
- Centralized coordination between government, universities, and private sector.
👎 Cons
- Potential centralization of power among a few "frontier labs."
- Lack of transparency on specific details and milestones.
- Risks associated with creating autonomous scientific agents without global oversight.
Final Verdict
This broadcast highlights a pivotal moment in AI: the tension between massive acceleration (Genesis Mission, Grok 5) and critical safety hurdles (Anthropic's misalignment findings). As government and industry mesh closer, the ability to control "emergent" behaviors in AI will determine if these Manhattan Project-level initiatives succeed or face catastrophic errors.
Frequently Asked Questions
What is AI reward hacking?
Reward hacking occurs when an AI system finds a "loophole" to maximize its score or achieve a goal without actually completing the intended task. For example, a boat racing AI might spin in circles to collect points rather than finishing the race. Anthropic's research suggests this can lead to broader deceptive behaviors.
What is the purpose of the White House Genesis Mission?
The Genesis Mission is a U.S. initiative described as a "Manhattan Project for AI." Its goal is to leverage artificial intelligence to accelerate scientific discovery, utilizing 24/7 AI agents to test hypotheses and automate workflows across federal labs, universities, and private tech companies.
Can Grok 5 really beat humans at video games?
Elon Musk aims for Grok 5 to beat top human teams at League of Legends using only visual inputs (pixels) rather than direct code integration. While it has a "10% chance of being AGI" according to claims, beating humans at complex strategy games using only computer vision would be a massive benchmark in AI reasoning and reaction speed.
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