#algorithms

The strong exponential time hypothesis (SETH) explained by MIT professor @rrwilliams who was selected by OpenAI to verify the recent breakthrough result: Q: "Could you explain the Strong Exponential Time Hypothesis, or SETH, and its significance?" Ryan Williams: "Strong ETH is a severe strengthening of the P versus NP question. P versus NP is asking whether the SAT problem has a polynomial-time algorithm or not. Strong ETH is basically saying that for the SAT problem, you cannot solve it much faster than 2^n. There’s no 1.999^n time algorithm. For every string of 9s, there is no 1.9999^n time algorithm. To say it totally precisely, it has to do with the k-SAT problem, where you’re looking at clauses of length k for arbitrary k, but those details don’t matter so much. SAT is a canonical NP-complete problem. It’s solved all the time in practice. It’s extremely useful for verification; nowadays it’s an engine for verification. So it can be solved fairly well in practice. However, in the worst case, we still don’t know how to solve it significantly faster than 2^n. So the hypothesis that it needs, say, 1.99999^n time is a very strong exponential time hypothesis. It’s stronger than P versus NP. It says it’s not just superpolynomial. It’s darn near 2^n time that you need." Q: "Do you think that hypothesis is true? Why or why not?" Ryan Williams: "I think I’m on the record as not believing this hypothesis. Why don’t I believe it? I started thinking about this hypothesis maybe already as an undergrad, but certainly early in grad school. Before it was even called Strong ETH, I guess that shows how old I am. I was trying to figure out how to solve CNF-SAT faster than 2^n. At the time there were a number of other NP-complete problems that had faster algorithms, like subset sum. So I thought, what’s so special about SAT? If all these other problems have faster algorithms, why not SAT as well? Even 3-SAT, where each clause is an OR of 3 variables, has a faster algorithm, and this is just a more general version of SAT. So at first I thought there’s no good reason to believe a lower bound. These other related problems have upper bounds, so why not? But then, over time, I would have different attacks on Strong ETH, always trying to refute it in different ways. These attacks would fail in some completely catastrophic and ridiculous way. They would have no chance of actually solving the original problem. But by staring at my failure, I’d think: there’s something interesting happening here. It doesn’t refute Strong ETH, so what does it do? By trying to pivot and figure out what I could do with my failure, I was able to solve a variety of other problems instead. So after a while, I realized that the truth value of Strong ETH to me is almost irrelevant. If I believe that it’s false, then I get good ideas. By thinking about what an algorithm that breaks 2^n could look like, I force myself to think in a different way. I have to discard the natural algorithmic possibilities, because we know they won’t work, and think in a different direction. Because it sends my brain in a different direction, it’s very useful for research. Even though I still haven’t refuted it, it’s very useful for me to believe that it’s false, operationally." Q: This is a minority opinion, right? Ryan Williams: "Russell Impagliazzo, a good friend of mine who helped propose this, always emphasizes to me: this is a hypothesis. We explicitly did not name it a conjecture. We wanted to put forth a lower bound that would get you to think about it, something a little more controversial than P versus NP or other things people more normally believe. Hypotheses at the edge of our understanding, where we truly don’t know what the answer might be based on our intuition, can be enlightening to think about. That’s one reason why it was proposed. But one reason you might believe Strong ETH is true is that it conveniently implies a lot of other lower bounds. You can reduce SAT to a bunch of problems that seem totally unrelated, like edit distance and various pattern matching problems. They have natural polynomial-time solutions. If you could improve on the algorithms for any of those, you would improve the one for SAT as well, and get something better than 2^n. So believing in it makes a convenient worldview. It shows that all these different textbook algorithms are indeed optimal."

Announcing three major scientific discoveries, one each in mathematics (harmonic analysis), computer science (complexity theory), and physics (quantum field theory). AI starting to help us improve our understanding of the universe!

🚨 EDWARD SNOWDEN’S CHILLING WARNING IS NOW UNFOLDING IN REAL TIME What China built… is exactly what Western governments are quietly preparing for. Snowden is crystal clear: Every photo, every purchase, every message, every movement — all of it is being fed into algorithms that decide your future. In China, AI “city brains” now track: Where you live Who you visit How you dispose of your rubbish Every step you take in public Whether you “follow rules” or not Break a rule? A camera catches you from three angles. Your score drops. Your life collapses. Snowden’s warning: “If any of your activities differ from what the government wants, you won’t get on a train. You won’t board a plane. You won’t get a job. An algorithm will decide your fate. And what they are selling… is us.” This is not a future threat. It is happening now — in Shanghai, in Beijing… and creeping into the West through digital ID, AI policing, and “misinformation scoring.” China built the prototype. Our governments are importing the blueprint. Stay informed. Stay alert. Do not let this system take root.

In France, FOREIGN Uber Eats drivers EXPLOIT the app algorithm using multiple nearby phones to reject low-paying orders and raise the payouts to €15 OR MORE WORK SMARTER, NOT HARDER

SMALL ARTISTS WHERE ARE YOUUU ‼️‼️ I KNOW YOU’RE OUT THERE 👀 come drop your art below so I can go crazy over your work!!! let’s find more amazing artists before the algorithm hides us again 😭 #artshare #smallartists #artmoots

"The Linear Algebra Behind Google" is a short paper that explains the mathematics behind Google's original PageRank algorithm. The paper covers eigenvalues and eigenvectors, stochastic matrices, Markov chains, and iterative methods, using numerical examples and graphs to illustrate how PageRank works. Beyond the algorithm itself and the original paper by Larry Page and Sergey Brin, this is an interesting read for anyone curious about how linear algebra applies to one of the most influential algorithms in the history of the web.

भारत में Meta की मनमानी! Algorithm का खेल, Legal का बहाना… पिछले 8-10 दिनों में Meta ने 'पाञ्चजन्य' के @facebook और @instagram से कई महत्वपूर्ण वीडियो डाउन कर दिए हैं, जबकि इनमें से किसी भी वीडियो में प्लेटफॉर्म की पॉलिसी का उल्लंघन नहीं हुआ था। @Meta द्वारा हटाए गए प्रमुख वीडियो- प्रधानमंत्री @narendramodi जी, गृह मंत्री @AmitShah जी, MP के CM @DrMohanYadav51 जी, राष्ट्रीय स्वयंसेवक संघ (@RSSorg ) और @NCPCR_ के पूर्व अध्यक्ष व @India_NHRC के सदस्य @KanoongoPriyank जी से सम्बंधित थे। साथ ही हाल ही में भोपाल में आयोजित पाञ्चजन्य के 'सुशासन संवाद 3.0' कार्यक्रम के भी आधिकारिक वीडियो भी Meta द्वारा हटाए गए हैं! हमारी केन्द्रीय आईटी मंत्री @AshwiniVaishnaw जी से अपील है कि कृपया इस मामले का तुरंत संज्ञान लिया जाए। वहीं Meta सार्वजनिक रूप से स्पष्ट करे कि- बिना किसी कारण के पाञ्चजन्य के वीडियोज पर किस आधार पर कार्रवाई की गई? मीडिया संस्थानों की आवाज़ को दबाने का आधार क्या है? और इसकी जवाबदेही किसकी है?

3 replies — even dots — can break the algorithm👇

Hello X friends! I still want to drop a picture now and again and hopefully the algorithm will take note and spared beauty instead of vitriol. So share your photos and help spread the beauty.

#AI inspired by nature, #algorithms inspired by ants @alvinfoo @kashthefuturist @FrRonconi @MikeQuindazzi @Paula_Piccard @ronald_vanloon @jerome_joffre @jblefevre60 @evankirstel @mvollmer1 @HeinzvHoenen @chboursin @robvank @samiranghosh @YuHelenYu @MHiesboeck @andy_lucerne

Quantum computing could change what security means for crypto. Eli Ben Sasson’s explanation of qubits is a good starting point. Classical computers process information in bits, represented as 0 or 1. Qubits can exist in superposition, allowing quantum algorithms to tackle certain problems in ways classical computers cannot efficiently match. The biggest concern for blockchains isn’t simply faster computing. It’s that a sufficiently powerful quantum computer could break some of the cryptography protecting wallets and digital signatures. This is where Starknet’s design matters. @Starknet uses STARK proofs, which rely on hash functions rather than elliptic curve cryptography for their core proof security. That distinction matters because Shor’s algorithm threatens elliptic curve cryptography, while hash based systems can retain security with appropriate parameters against known quantum attacks. There’s another advantage: Starknet’s account abstraction. Accounts can define their own signature verification logic, making it possible to introduce quantum resistant signature schemes without requiring every account to use the same cryptographic method. That doesn’t make Starknet completely quantum proof today. Its wider security still depends on components beyond STARK proofs, including parts of its Ethereum settlement architecture. Those surfaces need their own upgrades But the foundation matters. Quantum resistance is much harder to add when a system’s security assumptions are deeply embedded across its entire architecture. Starknet has two important pieces to build on: hash based validity proofs and flexible account verification. Eli’s explanation shows why quantum computing deserves attention. Starknet shows why the cryptographic choices made today could matter long before large scale quantum computers arrive.

Hilariously, OpenAI’s galactic algorithm for resolving the Exponential Time Hypothesis runs an exponential-time subroutine if the number of variables is less than 10^10,000,000,000,000,000,000,000,000. That number is also known as infinity plus 1.

Array intersection = values that exist in both A and B, with no duplicates in the result. #algorithms #arrays Watch:

🚨 OpenAI says its AI has DISPROVED ETH. ETH here stands for the Exponential Time Hypothesis, a decades-old assumption about how difficult certain computing problems are to solve as they grow. OpenAI says its AI developed an algorithm faster than the hypothesis permits, challenging an assumption used in hundreds of research papers. The company says the disproof was found on October 16, verified the next day using the Lean proof-checking system, and published on GitHub alongside its formal verification. ETH’s original proposers, Russell Impagliazzo and Ramamohan Paturi, will lead a review with MIT’s Ryan Williams. Turning the result into faster real-world algorithms “will require significant further research,” OpenAI says. The company also claims two further breakthroughs: • Mathematics: Resolving the Kakeya conjecture in every dimension, following last year’s proof of the three-dimensional case. • Physics: Producing a proof addressing part of a $1 MILLION Millennium Prize problem. That work remains unreleased, with a group led by Nobel laureate David Gross set to examine it.

🚨 ATTENTION! People are mistranslating what I said! I NEVER said people should block far-right accounts. I said they should IGNORE THEIR POSTS! Blocking actually works against this strategy because the algorithm will understand that you didn’t see the content simply because you blocked the account. However, when a post appears in your feed and you ignore it, you don’t read it, don’t like it, don’t comment on it, or engage with it in any way , the algorithm will interpret that content as irrelevant because it was shown to you and you chose not to engage with it! THERE IS A HUGE DIFFERENCE BETWEEN BLOCKING CONTENT AND IGNORING IT!

Perfect analogy for the age of #algorithms. The dad is #AI. The kids are us. And we are not doing much better than them. #AIEthics

a solution built for one industry can sometimes become useful somewhere completely unexpected. an optimization technique developed for logistics might inspire better scheduling. a method used in manufacturing could help solve a resource allocation problem. the same mathematical ideas can travel across very different fields. that’s one reason i find optimization such an interesting starting point for @quipnetwork . the problems may look different on the surface, but many share the same underlying challenge: finding a good solution among a huge number of possibilities. when developers can experiment with algorithms and submit different workloads to a compute network, the opportunity goes beyond any single application. it creates room for techniques to be tested, refined, and potentially applied to new challenges. of course, every use case still needs to prove its own value. a promising algorithm in one domain won’t automatically work in another. but that’s exactly what makes experimentation important. you don’t need to predict every successful application before building the infrastructure. sometimes you create the tools, give people room to explore, and let useful discoveries emerge along the way. the next important use case for compute might come from a problem nobody is paying attention to yet. that’s one possibility worth watching as @quipnetwork develops.

📖 Features of @TitanTradingBot - Part 1 📌 #Algorithms and #AI provide real-time market analysis and trading execution 👉🏻Giving customers a competitive edge 📌 Algorithmic trading bots can adapt to different market situations 👉🏻 Optimizing trading methods To be continued...

100% open-source robotic arm! 🦾 @seeedstudio released reBot-DevArm, a robotic arm project lowering the barrier to learning robotics... or how people call it these days: physical AI. Everything is open-sourced. Hardware blueprints include sheet metal and 3D printed parts. Detailed BOM covers every screw with purchase links. Software and algorithms include Python SDK, ROS1/2, Isaac Sim, and LeRobot. Let's have a look at robot specs. So it has 1.5 kg payload, 650 mm max reach, 4.5 kg weight, and less than 0.2 mm repeatability with 6 DoF plus gripper. This is true open source for robotics. When every screw, CAD file, motor driver, and algorithm is freely available, desktop robotic arms become accessible to students, researchers, and developers worldwide. ‼️ Start your career in robotics today: We need more roboticists than ever! ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Amazon Zoox robotaxi, a Google Waymo car, and a Tesla Cybercab became trapped in a viral face-to-face standoff on a narrow city street in 🇺🇸Austin, Texas, showcasing a classic real-world artificial intelligence deadlock. The vehicles met head-on on a narrow side street constricted by cars parked on both sides. Because each company programs its vehicles with proprietary decision-making algorithms, the vehicles struggled to establish right-of-way protocol without a human driver to initiate a courtesy gesture or negotiate positioning. Eyewitnesses noted that the Waymo continued to creep aggressively forward instead of yielding. Meanwhile, the Tesla Cybercab - which runs entirely unsupervised on vision-based software without a steering wheel or pedals - was seen reversing to clear space for the others. Observers also pointed out that Zoox’s unique bidirectional carriage design makes native reversing straightforward, though both it and the Waymo remained locked in position for several minutes before traffic could clear. This event highlights a prominent operational barrier for autonomous fleets: multi-platform coordination. While current robotaxis excel at perception and safety when interacting with fixed obstacles or rule-following humans, they struggle when they encounter another AI entity governed by a completely different set of non-standardized routing protocols. Similar standoffs between Zoox and Waymo vehicles have previously been documented in San Francisco, indicating that “algorithm clashes” are becoming a frequent consequence of multi-fleet urban deployments. Can the robotaxi companies program “courtesy gestures” as part of their algorithms?

Free eBook: Guide to Competitive Programming • Algorithm Design • Time Complexity • Sorting and Binary Search • Data Structures • Complete Search • Greedy Algorithm • Dynamic Programming • Graph Algorithms • Shortest Paths • Tree Algorithms • Range Queries • Bit Manipulation • Number Theory • Combinatorics • String Algorithms • Computational Geometry Antti Laaksonen’s guide explores how to design, implement, and test efficient algorithms for programming contests. From sorting and data structures to dynamic programming and graph algorithms, the material helps readers strengthen problem-solving, coding, and debugging skills. For readers who want to start with a free resource, the author’s Competitive Programmer’s Handbook provides an introduction to many of the same concepts with examples in C++. #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #Mathematics #DataScientist #Linux #Books #Programming #Coding #100DaysofCode Free Complete Book — Competitive Programmer’s Handbook: Amazon: