Latest news with #ChaosTheory
New York Post
2 days ago
- Sport
- New York Post
2025 Belmont Stakes prediction: How to use Chaos Theory to your betting advantage
Gambling content 21+. The New York Post may receive an affiliate commission if you sign up through our links. Read our editorial standards for more information. One of my favorite betting strategies when it comes to the biggest events on the sporting calendar is the Chaos Theory. Fans of 'Jurassic Park' will recognize the concept, which is that even the tiniest change, decision or break could have an enormous impact on how the future plays out. The reason that this concept is worth applying to handicapping horse races is that the payoff can be huge. By now, the entire world seems to be in lockstep that the 2025 Belmont Stakes is a showdown between the two favorites, No. 7 Journalism (8-5) and No. 2 Sovereignty (2-1). When you convert the morning line odds to implied probability, you get close to a 75 percent chance that one of these two heavyweights will win this race — and the odds are only going to go in one direction (hint: They ain't getting longer). This kind of titanic showdown makes for a terrific watch for those who just want to sit back and enjoy the spectacle of a Triple Crown race, but it doesn't make for the best betting, especially if you're somebody who loves to go hunting for a big score. You just aren't likely to make much money backing either, or both, of the favorites. That's where the Chaos Theory comes in. Before we go any further, please remember that this betting strategy is a long shot to pay off. The point here is to build a ticket with extreme upside, based on one thing early in the race not going to plan, causing a ripple effect that completely turns this race on its head. It's unlikely to happen, but if it does, you could be in line for a massive score. That brings us to No. 3 Rodriguez. Everybody and their uncles believe Rodriguez will be aggressive out of the gates and duel with either No. 5 Crudo or No. 6 Baeza to get to the front of the pack in an attempt to go gate-to-wire. But what if Rodriguez, who is two months removed from his last race after scratching at the Kentucky Derby, isn't sharp out of the gate or just gets beaten to his spot? Journalism trains on June 5, 2025 before the running of Saturday's Belmont Stakes horse race in Saratoga Springs. AP Or better yet, what if jockey Mike Smith opts out of that strategy, given how this race sets up? Journalism is a stalker who wants to sit right behind a target before making his move, and Sovereignty is an all-world closer who wants a hot pace to run into down the stretch. Perhaps that causes Smith to play things closer to the vest rather than go for broke. If Rodriguez doesn't get to the front, that could slow things down a tad, allowing Crudo or Baeza to take charge of setting the tempo. Baeza, like Journalism, is a presser who wants to be right behind the leaders, so a curveball from Rodriguez would throw him into an unfamiliar situation, trying to win from out front. That would likely cause Flavien Prat to put the handbrake on Baeza to keep him in his preferred position. This slower, more chaotic scenario would level the playing field against Sovereignty. Get the lowdown on the Best USA Sports Betting Sites and Apps He's likely the only closer in this field who can run down a scorching pace, but plenty of other runners will be brought into contention if things don't pick up. It also would give the front-runner in this scenario, Crudo, a legitimate chance to wire this field. He just went gate-to-wire at the Sir Barton Stakes on Preakness Day, after all. And if it is Crudo out front, running at a manageable pace, perhaps that causes things to jam up on Journalism, and he isn't able to escape trouble like he did at Pimlico. That could leave the door open for the lesser of the closers in this field, No. 1 Hill Country, to make a potentially race-winning move late. The Chaos Theory Ticket: 1-5 exacta box. Why Trust New York Post Betting Michael Leboff is a long-suffering Islanders fan, but a long-profiting sports bettor with 10 years of experience in the gambling industry. He loves using game theory to help punters win bracket pools, find long shots, and learn how to beat the market in mainstream and niche sports.
Forbes
13-05-2025
- Business
- Forbes
Make A Decentralized Internet, With AI: NANDA Is Coming
Centralised, Decentralised and distributed business diagram with icon template for presentation and ... More website If there's one thing that we deeply need in the integration of AI in our world, it's philosophy. Many of the experts who speak at modern conventions and write papers about LLMs would agree with me that we need an understanding of why things work the way they do, and how we can use them. Ben Franklin made history by tying something made of metal to a string, and thinking about why lightning works the way it does. He was part of a philosophical community of his time. We need something like that. With that in mind, I was impressed by a presentation by Abhishek Singh at this April event on 'Chaos, Coordination and the Future of Agentic AI.' Singh is one of a number of researchers working on the essential idea of decentralizing the web, and how that relates to the new technologies that we have. In the presentation, he talked about a 'trilemma' of intelligence (and if that isn't quant-speak, I don't know what is) related to continuity, heterogeneity, and scalability. Referring to 'chaos theory 2.0,' he talked about the connection of decentralized networks and algorithms as a primary goal in what he called an 'emergent phenomenon' of AI agency. 'One way to think about how these two mental models fit together is: the way we are doing solving intelligence right now is by this idea of one big, large (system) sitting at one large, big tech company and being capable of doing all the tasks at the same time. And the other perspective, which is more coming from the decentralized angle, is … lots of small brains interacting with each other. None of the single small brains is powerful enough, but then together, (they are.)' When I hear something like this, I always invoke Marvin Minsky's principle of Society of the Mind, partly because he's a hero of mine, and partly because I think this is so central to the issues that we're looking at. Singh also points to various challenges with instituting plans around decentralization. Some of them involve privacy, and verification, and orchestration. Others have to do with how we design crowd UX or engineer the network in question. Singh mentions complex models, and large scale collaboration, as containing inherent problems to be solved. Others have to do with incentives. 'Several individuals and organizations host open datasets and volunteer compute for altruistic reasons,' Singh writes in the aforementioned paper. 'Explicit incentive structures for contributing to decentralized systems can risk crowding out altruism and diminishing participation from those seeking to help rather than to profit. It has been observed that extrinsic rewards can override intrinsic motivations over time. When we compensate people for activities they once did voluntarily, they tend to lose intrinsic interest in those activities. Therefore, designing effective incentive programs requires careful consideration of community norms, social motivations, and human psychology as well. The goal should be to complement extant altruism without supplanting it. Hybrid approaches that balance incentives with opportunities for voluntary participation could help (i.e. combining economic and reputation-based incentives).' So all of that has to go into the hopper when it comes to making these design choices. I'm going to admit here that this paper I was handed on decentralized AI with Singh as an author is fairly dense, and goes over the minutia of how decentralized AI will work, as well as the challenges. So I put it into Google Notebook to get a sort of human response on the part of the two disembodied personas used in the generated conversation tool. In the first few minutes, they went over some of the basic ideas, describing monolithic data centers as a big vulnerability, and talking about 'stark reminders' (they used the phrase more than once) of what happens when they're compromised. Then there's also data ownership. Then they had this conversation about the definition of Decentralized AI (just for fun, compare this with Singh's definition above): 'At its heart, decentralized AI is about enabling different entities, companies, individuals, even our devices, to collaborate on AI development and deployment,' said the female voice. 'But the crucial difference is that this collaboration happens without needing a single central authority calling all the shots.' 'Right,' answered the male voice. 'No big boss.' 'Exactly,' said his companion. 'Think of it as creating a way for different parties who have their own distributed resources, their own data, their own computing power, to work together, even if they don't fully trust each other, or maybe don't want to hand over control to one central player. So instead of one giant AI brain in like a central server somewhere, yeah, it's more like a network of smaller, interconnected working together.' They went on like that for a while. It seemed to me that the Notebook LM 'people' were headed in a sort of simplistic direction. So I went back to the paper itself to look at some of the larger pieces of its presentation. Here's a piece from the conclusion: 'This paper has elucidated the merits, use cases, and challenges of decentralized AI. We have argued that decentralizing AI development can unlock previously inaccessible data and computing resources, enabling AI systems to flourish in data-sensitive domains such as healthcare. We have presented a self-organizing perspective and argue that five key components need to come together to enable self-organization between decentralized entities: privacy, verifiability, incentives, orchestration, and crowd UX. This self-organized approach addresses several limitations of the current centralized paradigm, which relies heavily on consolidation and trust in a few dominant entities. …. We posit that decentralized AI has the potential to empower individuals, catalyze innovation, and shape a future where AI benefits society at large.' You can also look at various Venn diagrams showing how to bridge some of the problems inherent in traditional AI systems. Now, there's one more thing I noticed in Singh's presentation – at the very end, he mentioned an acronym that's likely to be very critical to decentralized AI. It's called NANDA or Networked Agents and Decentralized AI, and it's being worked on by a team, including Singh and my colleague Ramesh Raskar at MIT. For full disclosure, they name me as a collaborator in a less direct sense, on the website. But the people working on this have front row seats to what it's going to look like when we set up a new decentralized Internet with the power of artificial intelligence at its disposal. This is something we should be paying attention to as 2025 rolls on, and we start to see more of the actual capabilities of AI coming into play.
