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Survival of the fittest code. Core War (1984) is a game where programs must crash their opponents to survive. Warriors written in an assembly language called Redcode fight for control of a virtual machine. Our new paper: Digital Red Queen: Adversarial Program Evolution in Core War with LLMs, explores...

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hardmaru

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Introducing Digital Red Queen (DRQ): Adversarial Program Evolution in Core War with LLMs Blog: Core War is a programming game where self-replicating assembly programs, called warriors, compete for control of a virtual machine. In this dynamic environment, where there is no distinction between code and data, warriors must crash opponents while defending themselves to survive. In this work, we explore how LLMs can drive open-ended adversarial evolution of these programs within Core War. Our approach is inspired by the Red Queen Hypothesis from evolutionary biology: the principle that species must continually adapt and evolve simply to survive against ever-changing competitors. We found that running our DRQ algorithm for longer durations produces warriors that become more generally robust. Most notably, we observed an emergent pressure towards convergent evolution. Independent runs, starting from completely different initial conditions, evolved toward similar general-purpose behaviors—mirroring how distinct species in nature often evolve similar traits to solve the same problems. Simulating these adversarial dynamics in an isolated sandbox offers a glimpse into the future, where deployed LLM systems might eventually compete against one another for computational or physical resources in the real world. This project is a collaboration between MIT and Sakana AI led by Akarsh Kumar Full Paper (Website): Full Paper (arxiv): Code:
0:27

Introducing Digital Red Queen (DRQ): Adversarial Program Evolution in Core War with LLMs Blog: Core War is a programming game where self-replicating assembly programs, called warriors, compete for control of a virtual machine. In this dynamic environment, where there is no distinction between code and data, warriors must crash opponents while defending themselves to survive. In this work, we explore how LLMs can drive open-ended adversarial evolution of these programs within Core War. Our approach is inspired by the Red Queen Hypothesis from evolutionary biology: the principle that species must continually adapt and evolve simply to survive against ever-changing competitors. We found that running our DRQ algorithm for longer durations produces warriors that become more generally robust. Most notably, we observed an emergent pressure towards convergent evolution. Independent runs, starting from completely different initial conditions, evolved toward similar general-purpose behaviors—mirroring how distinct species in nature often evolve similar traits to solve the same problems. Simulating these adversarial dynamics in an isolated sandbox offers a glimpse into the future, where deployed LLM systems might eventually compete against one another for computational or physical resources in the real world. This project is a collaboration between MIT and Sakana AI led by Akarsh Kumar Full Paper (Website): Full Paper (arxiv): Code:

Sakana AI

143,892 Aufrufe • vor 7 Monaten

In the movie Watchmen there is a scene where a nuclear war is about to break out between Russia and America and to stop it an orchestrated attack takes place in New York City killing millions – an inside job just like 9/11.. 🇺🇸 I strongly believe that we are going to witness very similar dynamics play out in the very NEAR future. As we are on the brink of WWIII, in order to prevent it, there will be a manufactured alien attack/invasion which will serve as a catalyst to form and establish a complete TOTALITARIAN CONTROL under the guise of "WORLD PEACE", ONE WORLD GOVERNMENT followed by ONE WORLD RELIGION. It will be announced that all countries must unite to fight a much greater threat to MANKIND. This is how they're planning to officially bring in the NEW WORLD ORDER. All world governments are working in coordinated effort to bring this GRAND PLAN to fruition. I say this with great confidence as it is no secret that all 193 countries (including Russia 🇷🇺) have already signed the #NWO agreement – it's called 'The Pact of The Future Document'.
2:48

In the movie Watchmen there is a scene where a nuclear war is about to break out between Russia and America and to stop it an orchestrated attack takes place in New York City killing millions – an inside job just like 9/11.. 🇺🇸 I strongly believe that we are going to witness very similar dynamics play out in the very NEAR future. As we are on the brink of WWIII, in order to prevent it, there will be a manufactured alien attack/invasion which will serve as a catalyst to form and establish a complete TOTALITARIAN CONTROL under the guise of "WORLD PEACE", ONE WORLD GOVERNMENT followed by ONE WORLD RELIGION. It will be announced that all countries must unite to fight a much greater threat to MANKIND. This is how they're planning to officially bring in the NEW WORLD ORDER. All world governments are working in coordinated effort to bring this GRAND PLAN to fruition. I say this with great confidence as it is no secret that all 193 countries (including Russia 🇷🇺) have already signed the #NWO agreement – it's called 'The Pact of The Future Document'.

B L A D E R U N N E R

72,910 Aufrufe • vor 6 Monaten

IN 1986 MIT FILMED THE LECTURE WHERE CODE STOPPED BEING CODE AND BECAME DATA 43 minutes from Gerald Sussman, in the most legendary intro programming course ever recorded. -> The idea that lands: to a program, your code is just data it can read and rewrite. He builds a program that does real calculus -- not by crunching numbers, but by reading the math as a list and transforming it piece by piece. Then he shows the trick hiding under all of it: in this language, code and data are the same material. Forty years later that is exactly what an AI coding agent is -- a program that reads your code as data, rewrites it, and hands it back. Sussman drew the whole idea on a blackboard in 1986. Writing code was never the deepest skill -- understanding that code itself is data a program can manipulate is. This is where you learn it. Most people think AI writing code is brand new. The ones who watch this saw the blueprint 40 years ago. Bookmark & Watch it. This one's a legend ↓
43:30

IN 1986 MIT FILMED THE LECTURE WHERE CODE STOPPED BEING CODE AND BECAME DATA 43 minutes from Gerald Sussman, in the most legendary intro programming course ever recorded. -> The idea that lands: to a program, your code is just data it can read and rewrite. He builds a program that does real calculus -- not by crunching numbers, but by reading the math as a list and transforming it piece by piece. Then he shows the trick hiding under all of it: in this language, code and data are the same material. Forty years later that is exactly what an AI coding agent is -- a program that reads your code as data, rewrites it, and hands it back. Sussman drew the whole idea on a blackboard in 1986. Writing code was never the deepest skill -- understanding that code itself is data a program can manipulate is. This is where you learn it. Most people think AI writing code is brand new. The ones who watch this saw the blueprint 40 years ago. Bookmark & Watch it. This one's a legend ↓

slash1s

22,107 Aufrufe • vor 2 Monaten

🏆 Honored to announce that Red Rock has been awarded Excellence in Encrypted Communication Technologies at the Cyber AI Summit & Awards 2025 in Dubai. This prestigious recognition, delivered in the presence of global leaders in AI and cybersecurity, underlines Red Rock’s role as a pioneer in advancing the future of secure communications. Our mission goes beyond technology: we are committed to building systems that liberate instead of control — encrypted, uncensorable, human-first — enabling true digital sovereignty for individuals, institutions, and nations. When we say Awakening Consciousness Through Code, it is not just a slogan — it is our deepest conviction, the guiding principle that drives every line of code we write and every partnership we build.
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🏆 Honored to announce that Red Rock has been awarded Excellence in Encrypted Communication Technologies at the Cyber AI Summit & Awards 2025 in Dubai. This prestigious recognition, delivered in the presence of global leaders in AI and cybersecurity, underlines Red Rock’s role as a pioneer in advancing the future of secure communications. Our mission goes beyond technology: we are committed to building systems that liberate instead of control — encrypted, uncensorable, human-first — enabling true digital sovereignty for individuals, institutions, and nations. When we say Awakening Consciousness Through Code, it is not just a slogan — it is our deepest conviction, the guiding principle that drives every line of code we write and every partnership we build.

Red Rock

33,280 Aufrufe • vor 11 Monaten

Today, war has become an unfortunate necessity for us in response to a certain situation. However, as a Nation and as a People we must always keep this quality about us that we are not ruled by hatred and we never take pleasure in violence towards anyone for any reason. At the same time, we have a duty and a Responsibility toward our own people to address certain situations and that is what the Nation is doing. This is not a war of hate but one of necessity. Let it remain so. -Sg
2:22

Today, war has become an unfortunate necessity for us in response to a certain situation. However, as a Nation and as a People we must always keep this quality about us that we are not ruled by hatred and we never take pleasure in violence towards anyone for any reason. At the same time, we have a duty and a Responsibility toward our own people to address certain situations and that is what the Nation is doing. This is not a war of hate but one of necessity. Let it remain so. -Sg

Sadhguru

76,265 Aufrufe • vor 1 Jahr

*New Paper on AI & Democracy* Imagine two approaches to democracy. The one we have today, where citizens choose a professional politician to represent them and others. Or an augmented form of democracy, where each citizen controls a personalized AI that helps them participate in thousands of nuanced decisions. This second approach is the idea of Augmented Democracy I introduced six years ago at TED. In our latest paper we explore a simplified version of Augmented Democracy by combining off-the-shelf LLMs, such as ChatGPT, with data collected using a collaborative government program builder. This was an online game where people build a personalized government program using proposals extracted from the programs of the candidates of the 2022 presidential election in Brazil. So how accurate are these augmented forms of democracy? Imagine a user who gave us 40 answers. We can use the first 20 to fine-tune a model that we can test using the 20 answers the model didn’t see. We can then compare the accuracy of these predictions with the ones obtained by a “bundle” rule, which assumes that users that self-reported to be from the left or right always chose the proposals from the candidate that shares their political identity. This showed us that LLMs were more accurate at predicting policy preferences than the bundle rule, meaning that the preferences captured in the participation data were more nuanced than a left-right axis, and that the LLMs can capture some of that nuance. Also, the LLMs can choose among policies coming from the same candidate, which is something that we cannot do using a bundle rule. But can these LLMs help us complete the aggregate preferences of the population? Direct or unbundled forms of participation can result in incomplete data when people answer only a fraction of all questions. In our paper, we simulate this incompleteness by sampling the full dataset. We ask how close we can get to the full dataset by using a random sample, or a random sample augmented by predictions made by these LLMs. Overall, we find that LLM-augmented data gets much closer to the full dataset than a pure random sample. These results do not mean that augmented democracy technology is ready, but they means we are in a much better place to continue exploring this idea than six years ago. This paper was a collaborative effort with Jairo Gudino, PhD student at CCL at the University of Toulouse Capitole and Umberto Grandi from IRIT also at the University of Toulouse Capitole. We hope you find these results insightful!
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*New Paper on AI & Democracy* Imagine two approaches to democracy. The one we have today, where citizens choose a professional politician to represent them and others. Or an augmented form of democracy, where each citizen controls a personalized AI that helps them participate in thousands of nuanced decisions. This second approach is the idea of Augmented Democracy I introduced six years ago at TED. In our latest paper we explore a simplified version of Augmented Democracy by combining off-the-shelf LLMs, such as ChatGPT, with data collected using a collaborative government program builder. This was an online game where people build a personalized government program using proposals extracted from the programs of the candidates of the 2022 presidential election in Brazil. So how accurate are these augmented forms of democracy? Imagine a user who gave us 40 answers. We can use the first 20 to fine-tune a model that we can test using the 20 answers the model didn’t see. We can then compare the accuracy of these predictions with the ones obtained by a “bundle” rule, which assumes that users that self-reported to be from the left or right always chose the proposals from the candidate that shares their political identity. This showed us that LLMs were more accurate at predicting policy preferences than the bundle rule, meaning that the preferences captured in the participation data were more nuanced than a left-right axis, and that the LLMs can capture some of that nuance. Also, the LLMs can choose among policies coming from the same candidate, which is something that we cannot do using a bundle rule. But can these LLMs help us complete the aggregate preferences of the population? Direct or unbundled forms of participation can result in incomplete data when people answer only a fraction of all questions. In our paper, we simulate this incompleteness by sampling the full dataset. We ask how close we can get to the full dataset by using a random sample, or a random sample augmented by predictions made by these LLMs. Overall, we find that LLM-augmented data gets much closer to the full dataset than a pure random sample. These results do not mean that augmented democracy technology is ready, but they means we are in a much better place to continue exploring this idea than six years ago. This paper was a collaborative effort with Jairo Gudino, PhD student at CCL at the University of Toulouse Capitole and Umberto Grandi from IRIT also at the University of Toulouse Capitole. We hope you find these results insightful!

César A. Hidalgo

26,915 Aufrufe • vor 1 Jahr

Minister Joly put out a clear message in stating that Canada’s policy on a weapons ban to Israel remains in place. She suggested point blank that the government of Canada was not in favour of any loopholes being used in relation to the recent proposed sale of explosives by General Dynamics. She further specifically stated, “Meanwhile, as for the questions regarding General Dynamics, our policy is clear. We will not have any arms or parts of arms be sent to Gaza. Period. How they’re being sent or where they’re being sent is irrelevant”. This is a positive step for the Minister to take in sending a strong signal that the one-way weapons export ban remains strong and without loophole. We must continue to work towards further policy to stand for peace and for a better world for all to live in.
1:06

Minister Joly put out a clear message in stating that Canada’s policy on a weapons ban to Israel remains in place. She suggested point blank that the government of Canada was not in favour of any loopholes being used in relation to the recent proposed sale of explosives by General Dynamics. She further specifically stated, “Meanwhile, as for the questions regarding General Dynamics, our policy is clear. We will not have any arms or parts of arms be sent to Gaza. Period. How they’re being sent or where they’re being sent is irrelevant”. This is a positive step for the Minister to take in sending a strong signal that the one-way weapons export ban remains strong and without loophole. We must continue to work towards further policy to stand for peace and for a better world for all to live in.

NCCM

19,696 Aufrufe • vor 1 Jahr

New Paper! Darwin Godel Machine: Open-Ended Evolution of Self-Improving Agents A longstanding goal of AI research has been the creation of AI that can learn indefinitely. One path toward that goal is an AI that improves itself by rewriting its own code, including any code responsible for learning. That idea, known as a Gödel Machine, proposed by Jürgen Schmidhuber over two decades ago, is a hypothetical self-improving AI. It optimally solves problems by recursively rewriting its own code when it can mathematically prove a better strategy, making it a key concept in meta-learning or “learning to learn.” While the theoretical Gödel Machine promised provably beneficial self-modifications, its realization relied on an impractical assumption: that the AI could mathematically prove that a proposed change in its own code would yield a net improvement before adopting it. Sakana AI, in collaboration with Jeff Clune’s lab at UBC, proposes something more feasible: a system that harnesses the principles of open-ended algorithms like Darwinian evolution to search for improvements that empirically improve performance. We call the result the Darwin Gödel Machine. DGMs leverage foundation models to propose code improvements, and use recent innovations in open-ended algorithms to search for a growing library of diverse, high-quality AI agents. Applied to practical tasks, we implemented Darwin Gödel Machine as a self-improving coding agent that rewrites its own code to improve performance on programming tasks. It creates various self-improvements, such as a patch validation step, better file viewing, enhanced editing tools, generating and ranking multiple solutions to choose the best one, and adding a history of what has been tried before (and why it failed) when making new changes (see the attached video). We believe that Darwin Gödel Machines represent a concrete step towards AI systems that can autonomously gather their own stepping stones to learn and innovate forever!
0:27

New Paper! Darwin Godel Machine: Open-Ended Evolution of Self-Improving Agents A longstanding goal of AI research has been the creation of AI that can learn indefinitely. One path toward that goal is an AI that improves itself by rewriting its own code, including any code responsible for learning. That idea, known as a Gödel Machine, proposed by Jürgen Schmidhuber over two decades ago, is a hypothetical self-improving AI. It optimally solves problems by recursively rewriting its own code when it can mathematically prove a better strategy, making it a key concept in meta-learning or “learning to learn.” While the theoretical Gödel Machine promised provably beneficial self-modifications, its realization relied on an impractical assumption: that the AI could mathematically prove that a proposed change in its own code would yield a net improvement before adopting it. Sakana AI, in collaboration with Jeff Clune’s lab at UBC, proposes something more feasible: a system that harnesses the principles of open-ended algorithms like Darwinian evolution to search for improvements that empirically improve performance. We call the result the Darwin Gödel Machine. DGMs leverage foundation models to propose code improvements, and use recent innovations in open-ended algorithms to search for a growing library of diverse, high-quality AI agents. Applied to practical tasks, we implemented Darwin Gödel Machine as a self-improving coding agent that rewrites its own code to improve performance on programming tasks. It creates various self-improvements, such as a patch validation step, better file viewing, enhanced editing tools, generating and ranking multiple solutions to choose the best one, and adding a history of what has been tried before (and why it failed) when making new changes (see the attached video). We believe that Darwin Gödel Machines represent a concrete step towards AI systems that can autonomously gather their own stepping stones to learn and innovate forever!

hardmaru

104,854 Aufrufe • vor 1 Jahr

Right now the main paradigm that we think of agents in is chatting back and forth, but the biggest use of tokens will come from agents that are just always on running in the background doing work for us, or ones triggered from a workflow. Agents will be working 24/7 in our workflows processing data, reviewing and generating documents, moving data between systems, writing code, accelerating decision making steps, and more. In Claude's new Managed Agents feature, in a couple minutes you can wire up an agent that can read contracts when they come into Box to review them, and then assign a task in Linear with the critical information from the contract. But this could have been any workflow, like reviewing documents for client onboarding, invoice processing, M&A due-diligence, data extraction pipelines, and millions of other use-cases. And integrating data across any system. This is only possible when you can have long-running agents that can complete real work in the background, accurately. Agents have the ability to execute code safely, leverage tools, access a compute sandbox, and connect across systems is clearly the architecture of the future. The industry is now making it easier and easier for enterprises to build and deploy these agents.
0:43

Right now the main paradigm that we think of agents in is chatting back and forth, but the biggest use of tokens will come from agents that are just always on running in the background doing work for us, or ones triggered from a workflow. Agents will be working 24/7 in our workflows processing data, reviewing and generating documents, moving data between systems, writing code, accelerating decision making steps, and more. In Claude's new Managed Agents feature, in a couple minutes you can wire up an agent that can read contracts when they come into Box to review them, and then assign a task in Linear with the critical information from the contract. But this could have been any workflow, like reviewing documents for client onboarding, invoice processing, M&A due-diligence, data extraction pipelines, and millions of other use-cases. And integrating data across any system. This is only possible when you can have long-running agents that can complete real work in the background, accurately. Agents have the ability to execute code safely, leverage tools, access a compute sandbox, and connect across systems is clearly the architecture of the future. The industry is now making it easier and easier for enterprises to build and deploy these agents.

Aaron Levie

17,543 Aufrufe • vor 4 Monaten

Axiom Math's Ashvin Swaminathan predicts we are entering an age where 99% of code will never be read by a human: "I'm very excited about the prospect of building our tool, AxiomProver, which can generate code that is maintainable and reusable. These are really important qualities for code to have in an age where 99% of code will not be read by a human ever." "This is a time where you can go to any available LLM and write a computer program, but you can't guarantee the program will be of a certain quality. So that's really important to us: quality, reusability, maintainability." "We believe these principles extend beyond the realm of pure mathematics, and that these skills will transfer to other tasks of fundamental importance in our world." Axiom Carina Hong
0:44

Axiom Math's Ashvin Swaminathan predicts we are entering an age where 99% of code will never be read by a human: "I'm very excited about the prospect of building our tool, AxiomProver, which can generate code that is maintainable and reusable. These are really important qualities for code to have in an age where 99% of code will not be read by a human ever." "This is a time where you can go to any available LLM and write a computer program, but you can't guarantee the program will be of a certain quality. So that's really important to us: quality, reusability, maintainability." "We believe these principles extend beyond the realm of pure mathematics, and that these skills will transfer to other tasks of fundamental importance in our world." Axiom Carina Hong

MTS

14,319 Aufrufe • vor 10 Tagen

New short course: Building Code Agents with Hugging Face smolagents! Learn how to build code agents in this course, created in collaboration with Hugging Face, and taught by Thomas Wolf, its co-founder and CSO, and m_ric, Hugging Face’s Project Lead on Agents. Tool-calling agents use LLMs to generate multiple function calls sequentially to complete a complex sequence of tasks. They generate one function call, execute it, observe, reason, and decide what to do next. Code agents take a different approach. They consolidate all these calls into a single block of code, letting the LLM lay out an entire action plan at once, which can be executed efficiently to provide more reliable results. You’ll learn how to code agents using smolagents, a lightweight agentic framework from Hugging Face. Along the way, you’ll learn how to run LLM-generated code safely and develop an evaluation system to optimize your code agent for production. In detail, you’ll learn: - How agentic systems have evolved, gaining greater levels of agency over time—and why code agents are a next step. - How code agents write their actions in code. - When code agents outperform function-calling agents. - How to run code agents safely in your system using a constrained Python interpreter and sandboxing using E2B. - To trace, debug, and assess the code agent to optimize its behaviours for complex requests. - How to build a research multi-agent system that can find information online and organize it into an interactive report. By the end of this course, you’ll know how to build and run code agents using smolagents, and deploy them safely with a structured evaluation system in your projects. Please sign up here!
2:17

New short course: Building Code Agents with Hugging Face smolagents! Learn how to build code agents in this course, created in collaboration with Hugging Face, and taught by Thomas Wolf, its co-founder and CSO, and m_ric, Hugging Face’s Project Lead on Agents. Tool-calling agents use LLMs to generate multiple function calls sequentially to complete a complex sequence of tasks. They generate one function call, execute it, observe, reason, and decide what to do next. Code agents take a different approach. They consolidate all these calls into a single block of code, letting the LLM lay out an entire action plan at once, which can be executed efficiently to provide more reliable results. You’ll learn how to code agents using smolagents, a lightweight agentic framework from Hugging Face. Along the way, you’ll learn how to run LLM-generated code safely and develop an evaluation system to optimize your code agent for production. In detail, you’ll learn: - How agentic systems have evolved, gaining greater levels of agency over time—and why code agents are a next step. - How code agents write their actions in code. - When code agents outperform function-calling agents. - How to run code agents safely in your system using a constrained Python interpreter and sandboxing using E2B. - To trace, debug, and assess the code agent to optimize its behaviours for complex requests. - How to build a research multi-agent system that can find information online and organize it into an interactive report. By the end of this course, you’ll know how to build and run code agents using smolagents, and deploy them safely with a structured evaluation system in your projects. Please sign up here!

Andrew Ng

127,724 Aufrufe • vor 1 Jahr

As we remember Bucha, we ask: what will war and threats look like tomorrow – for Europe, for all of us? We must not let Europe’s enemies answer this question for us, or before us. This is very serious. If we have programs like SAFE, we must make them truly modern and effective. Europe should produce not just what it knows, but what really works in today’s war and what is proven by today’s war. And countries already working with us on joint production understand this. Ukraine has this experience. Now, as our expert teams work in the Middle East and the Gulf, we see that our experience in protecting lives and critical infrastructure is, in many ways, unique. I say this not just out of pride. This is about security. Ukraine’s experience in defense is a real security guarantee for Europe. So I propose that we work together to modernize our defense. From an address at the meeting of EU Foreign Ministers (4/5)
1:07

As we remember Bucha, we ask: what will war and threats look like tomorrow – for Europe, for all of us? We must not let Europe’s enemies answer this question for us, or before us. This is very serious. If we have programs like SAFE, we must make them truly modern and effective. Europe should produce not just what it knows, but what really works in today’s war and what is proven by today’s war. And countries already working with us on joint production understand this. Ukraine has this experience. Now, as our expert teams work in the Middle East and the Gulf, we see that our experience in protecting lives and critical infrastructure is, in many ways, unique. I say this not just out of pride. This is about security. Ukraine’s experience in defense is a real security guarantee for Europe. So I propose that we work together to modernize our defense. From an address at the meeting of EU Foreign Ministers (4/5)

Volodymyr Zelenskyy / Володимир Зеленський

58,961 Aufrufe • vor 5 Monaten

Autonomous vehicle learning to dodge traffic, performing stochastic adversarial negotiation. On 27th August we had representatives from the Suzuki Motor Corporation's autonomous department, Genki Maeda (Department Manager, AD Platform Development), Karachi Nobunari (Department General Manager, Advance Technology Development Department) and Ronit Kumar (Suzuki Innovation Center) visit us to test our #autonomousdriving technology. This was a high-stakes demo, where we asked our engineering team (including the founder, Sanjeev Sharma) to ride two wheeler and to cut the path of our autonomous vehicle at random / at will, in a live demo, creating an adversarial scenario, where it is the sole responsibility of our autonomous vehicle to dodge obstacles and prevent accidents. Over the years, we have been building autonomous driving technology to enable negotiation of adversarial-complex-stochastic traffic dynamics. This demo is only a short trailer of what is being developed and what is going to come next. We made the vehicle first negotiate randomly placed static vehicles, bikes and cones on the road. Then in the next section of the road, our engineering team started cutting the path of autonomous vehicle at random, and let it take care of obstacles avoidance and negotiation, balancing aggressiveness and passivity. These algorithmic frameworks are being scaled up to achieve Level-4 and Level-5 autonomous driving, in the most complex of traffic situations imaginable in the world for #autonomousvehicles, i.e., Indian traffic on Indian roads, to conquer this space globally. The speed of the vehicle was kept low in this demo, keeping in mind the safety of the guests. The core motion planning and decision making algorithm in the demo utilized one #reinforcementlearning agent. There is going to be another demo on similar lines next week, on an extended stretch of a road. #deeplearning #India
4:55

Autonomous vehicle learning to dodge traffic, performing stochastic adversarial negotiation. On 27th August we had representatives from the Suzuki Motor Corporation's autonomous department, Genki Maeda (Department Manager, AD Platform Development), Karachi Nobunari (Department General Manager, Advance Technology Development Department) and Ronit Kumar (Suzuki Innovation Center) visit us to test our #autonomousdriving technology. This was a high-stakes demo, where we asked our engineering team (including the founder, Sanjeev Sharma) to ride two wheeler and to cut the path of our autonomous vehicle at random / at will, in a live demo, creating an adversarial scenario, where it is the sole responsibility of our autonomous vehicle to dodge obstacles and prevent accidents. Over the years, we have been building autonomous driving technology to enable negotiation of adversarial-complex-stochastic traffic dynamics. This demo is only a short trailer of what is being developed and what is going to come next. We made the vehicle first negotiate randomly placed static vehicles, bikes and cones on the road. Then in the next section of the road, our engineering team started cutting the path of autonomous vehicle at random, and let it take care of obstacles avoidance and negotiation, balancing aggressiveness and passivity. These algorithmic frameworks are being scaled up to achieve Level-4 and Level-5 autonomous driving, in the most complex of traffic situations imaginable in the world for #autonomousvehicles, i.e., Indian traffic on Indian roads, to conquer this space globally. The speed of the vehicle was kept low in this demo, keeping in mind the safety of the guests. The core motion planning and decision making algorithm in the demo utilized one #reinforcementlearning agent. There is going to be another demo on similar lines next week, on an extended stretch of a road. #deeplearning #India

Swaayatt Robots

22,375 Aufrufe • vor 11 Monaten

The Summit of our Crimea Platform. Today, with over 60 participants, the Summit is taking place for the first time on a global stage – on the margins of the UN General Assembly. It was in Crimea that Russia broke one of the basic rules of normal life among nations by illegally annexing Ukrainian Crimea. It was Russia’s actions in Crimea that showed many predators around the world how it can be so easy to seize part of another state. And this is not just a piece of land – it is a piece of life. We have to remember what Crimea is. We must act so that Russia knows – we remember, and we will not forgive, and we are fighting for international law and basic human justice to prevail. Do not let war become part of the norm. Russia very much wants to make war the “new normal.” And this is exactly what we must resist – not to allow killings and Russian occupation to become something the world can simply turn a blind eye to. The occupier must be held accountable for the occupation and for all the crimes of this war – a war in which only Russia is guilty.
5:41

The Summit of our Crimea Platform. Today, with over 60 participants, the Summit is taking place for the first time on a global stage – on the margins of the UN General Assembly. It was in Crimea that Russia broke one of the basic rules of normal life among nations by illegally annexing Ukrainian Crimea. It was Russia’s actions in Crimea that showed many predators around the world how it can be so easy to seize part of another state. And this is not just a piece of land – it is a piece of life. We have to remember what Crimea is. We must act so that Russia knows – we remember, and we will not forgive, and we are fighting for international law and basic human justice to prevail. Do not let war become part of the norm. Russia very much wants to make war the “new normal.” And this is exactly what we must resist – not to allow killings and Russian occupation to become something the world can simply turn a blind eye to. The occupier must be held accountable for the occupation and for all the crimes of this war – a war in which only Russia is guilty.

Volodymyr Zelenskyy / Володимир Зеленський

162,802 Aufrufe • vor 11 Monaten

AI is changing the software engineering craft. Anders Hejlsberg (Anders Hejlsberg) - creator of C#, TypeScript and industry legend - on why code review needs to get more enjoyable in response: #1 - AI is shifting the craft from writing code, to reviewing code: "In a sense, we're all turning into project managers. We can have an army of junior programmers, called agents, that will just spit out reams of code but someone's got to have the big picture and review all of that. And so, increasingly, our craft is going from one of writing the code, to one of reviewing the code and building the architecture of the code and overseeing the work. It's a different kind of craft. It's a different kind of enjoyment. I've always liked writing the code. To me that was the fulfilling part, seeing it work. In a way, AI robs a little bit of that, because I am less interested in reviewing code." #2 - The code review experience should be improved: "I think we could also make the process of reviewing code much more interesting than it is today. I mean, today, you see a list of diffs in alphabetical order and now it's up to you to make heads or tails of it. There are more pedagogical ways of presenting that. And you could have commentary generated by the AI that tells you what the changes are and whatever, and then tries to guide you along. So that symbiotic relationship, I think we need to work on that more and to keep the enjoyment in there."
2:04

AI is changing the software engineering craft. Anders Hejlsberg (Anders Hejlsberg) - creator of C#, TypeScript and industry legend - on why code review needs to get more enjoyable in response: #1 - AI is shifting the craft from writing code, to reviewing code: "In a sense, we're all turning into project managers. We can have an army of junior programmers, called agents, that will just spit out reams of code but someone's got to have the big picture and review all of that. And so, increasingly, our craft is going from one of writing the code, to one of reviewing the code and building the architecture of the code and overseeing the work. It's a different kind of craft. It's a different kind of enjoyment. I've always liked writing the code. To me that was the fulfilling part, seeing it work. In a way, AI robs a little bit of that, because I am less interested in reviewing code." #2 - The code review experience should be improved: "I think we could also make the process of reviewing code much more interesting than it is today. I mean, today, you see a list of diffs in alphabetical order and now it's up to you to make heads or tails of it. There are more pedagogical ways of presenting that. And you could have commentary generated by the AI that tells you what the changes are and whatever, and then tries to guide you along. So that symbiotic relationship, I think we need to work on that more and to keep the enjoyment in there."

The Pragmatic Engineer

39,073 Aufrufe • vor 3 Monaten

This is a war of global significance. This is a war for our state's right to exist, as well as for everyone's right to life. This is a war for the dignity of our people and every nation seeking its own destiny. This is a war to prevent anyone from daring to become a beast toward others. And the bodies of our people, which were found on the streets of Bucha, demonstrated that no one in the world can stay away from this battle. Because it is here, in Ukraine, through the defense our people, our lives, and our state, that humanity is prevailing. To unleash such aggressions against others, Russia first destroyed its own morality and turned violence and hatred into its ideology. Such systems do not simply stop. They are brought to a stop by force. Unity stops them. Determination and an understanding of what they want to destroy is what stops them. I thank everyone in the world who really helps us. Glory to our strong people, our warriors! Glory to all those who defend humanity and life! Glory to Ukraine!
4:04

This is a war of global significance. This is a war for our state's right to exist, as well as for everyone's right to life. This is a war for the dignity of our people and every nation seeking its own destiny. This is a war to prevent anyone from daring to become a beast toward others. And the bodies of our people, which were found on the streets of Bucha, demonstrated that no one in the world can stay away from this battle. Because it is here, in Ukraine, through the defense our people, our lives, and our state, that humanity is prevailing. To unleash such aggressions against others, Russia first destroyed its own morality and turned violence and hatred into its ideology. Such systems do not simply stop. They are brought to a stop by force. Unity stops them. Determination and an understanding of what they want to destroy is what stops them. I thank everyone in the world who really helps us. Glory to our strong people, our warriors! Glory to all those who defend humanity and life! Glory to Ukraine!

Volodymyr Zelenskyy / Володимир Зеленський

463,152 Aufrufe • vor 2 Jahren

Major program launch: Data Analytics Professional Certificate! This large, five-course sequence takes you all the way to being job-ready as a data analyst, and shows how to use Generative AI as a thought partner to enhance your work in this role. Offered by on Coursera, this is taught by Sean Barnes, Ph.D., a Data Science & Engineering Leader at Netflix. Analyzing data remains one of the most important skills in where the world is going with AI. This comprehensive certificate takes you all the way to being job-ready. Each course comes with practical projects demonstrated in real-world contexts, such as analyzing sales data for a Korean bakery, video game sales trends across different regions, or identifying factors impacting customer retention for a communications company. You'll also work on estimating fire distribution for forest fire prevention, analyzing how a diamond's properties affect its market value, and developing predictive models for retail sales analysis, carbon emissions, and coral reef conservation. Here's some of what you'll learn: - How to define data and categorize it into its many types such as discrete & continuous numerical, structured & unstructured, time series, categorical, and know what insights can be derived from the different types of data categories. - How to differentiate between data-related job roles and their responsibilities, and how data flows through an organization from the moment of capture to decision-making. - How to perform data processing functions and apply conditional formatting in spreadsheets to extract business value from your data using statistical calculations and best practices for visualizing and interpreting data. - How to use LLMs for stakeholder analysis, data exploration, and data visualization. - Best practices for using LLMs for as a thought partner to data analysis work By the end of this professional certificate program, you will have learned core statistical concepts, analysis techniques, and visualization methodologies that will serve as the foundation for working as a data analyst. The world needs more data analysts, especially ones who know how to use modern generative AI. With data science roles projected to grow 36% by 2033, the skills taught in this program create new professional opportunities in data. Sign up here!
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Major program launch: Data Analytics Professional Certificate! This large, five-course sequence takes you all the way to being job-ready as a data analyst, and shows how to use Generative AI as a thought partner to enhance your work in this role. Offered by on Coursera, this is taught by Sean Barnes, Ph.D., a Data Science & Engineering Leader at Netflix. Analyzing data remains one of the most important skills in where the world is going with AI. This comprehensive certificate takes you all the way to being job-ready. Each course comes with practical projects demonstrated in real-world contexts, such as analyzing sales data for a Korean bakery, video game sales trends across different regions, or identifying factors impacting customer retention for a communications company. You'll also work on estimating fire distribution for forest fire prevention, analyzing how a diamond's properties affect its market value, and developing predictive models for retail sales analysis, carbon emissions, and coral reef conservation. Here's some of what you'll learn: - How to define data and categorize it into its many types such as discrete & continuous numerical, structured & unstructured, time series, categorical, and know what insights can be derived from the different types of data categories. - How to differentiate between data-related job roles and their responsibilities, and how data flows through an organization from the moment of capture to decision-making. - How to perform data processing functions and apply conditional formatting in spreadsheets to extract business value from your data using statistical calculations and best practices for visualizing and interpreting data. - How to use LLMs for stakeholder analysis, data exploration, and data visualization. - Best practices for using LLMs for as a thought partner to data analysis work By the end of this professional certificate program, you will have learned core statistical concepts, analysis techniques, and visualization methodologies that will serve as the foundation for working as a data analyst. The world needs more data analysts, especially ones who know how to use modern generative AI. With data science roles projected to grow 36% by 2033, the skills taught in this program create new professional opportunities in data. Sign up here!

Andrew Ng

85,107 Aufrufe • vor 1 Jahr

Ukraine continues to do everything in its power to ensure that this war has the prospect of a diplomatic resolution. One of the key claims made by Ukraine’s opponents and by those who trust dubious sources is that we in Ukraine supposedly want the war to continue. Your task is to consistently, persuasively, and credibly reject this accusation. It is Ukraine that wants peace. Ukraine has never wanted this war – not for a single day. It is Russia that is doing everything possible to prolong the war and inflict as much damage on human life as it can. We are working toward diplomacy. All leaders who can help know that we are ready for every substantive format. Putin is not ready. And that is precisely the problem. And for there to be a diplomatic path, there must be no obstacles to it in any influential capital in the world. When Moscow does not want peace, Washington and Beijing can make it want peace. And we are doing everything possible to ensure that the capitals of Europe, which Russia does not particularly want to take into account, reach a level where Russia can no longer ignore Europe. This is one of the key objectives of our security and political cooperation with our European partners. From an address at the meeting of heads of Ukraine’s foreign diplomatic missions. (6/7)
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Ukraine continues to do everything in its power to ensure that this war has the prospect of a diplomatic resolution. One of the key claims made by Ukraine’s opponents and by those who trust dubious sources is that we in Ukraine supposedly want the war to continue. Your task is to consistently, persuasively, and credibly reject this accusation. It is Ukraine that wants peace. Ukraine has never wanted this war – not for a single day. It is Russia that is doing everything possible to prolong the war and inflict as much damage on human life as it can. We are working toward diplomacy. All leaders who can help know that we are ready for every substantive format. Putin is not ready. And that is precisely the problem. And for there to be a diplomatic path, there must be no obstacles to it in any influential capital in the world. When Moscow does not want peace, Washington and Beijing can make it want peace. And we are doing everything possible to ensure that the capitals of Europe, which Russia does not particularly want to take into account, reach a level where Russia can no longer ignore Europe. This is one of the key objectives of our security and political cooperation with our European partners. From an address at the meeting of heads of Ukraine’s foreign diplomatic missions. (6/7)

Volodymyr Zelenskyy / Володимир Зеленський

281,486 Aufrufe • vor 25 Tagen

New short course: LLMs as Operating Systems: Agent Memory, created with Letta, and taught by its founders Charles Packer and Sarah Wooders. An LLM's input context window has limited space. Using a longer input context also costs more and results in slower processing. So, managing what's stored in this context window is important. In the innovative paper MemGPT: Towards LLMs as Operating Systems, its authors (which include the instructors) proposed using an LLM agent to manage this context window. Their system uses a large persistent memory that stores everything that could be included in the input context, and an agent decides what is actually included. Take the example of building a chatbot that needs to remember what's been said earlier in a conversation (perhaps over many days of interaction with a user). As the conversation's length grows, the memory management agent will move information from the input context to a persistent searchable database; summarize information to keep relevant facts in the input context; and restore relevant conversation elements from further back in time. This allows a chatbot to keep what's currently most relevant in its input context memory to generate the next response. When I read the original MemGPT paper, I thought it was an innovative technique for handling memory for LLMs. The open-source Letta framework, which we'll use in this course, makes MemGPT easy to implement. It adds memory to your LLM agents and gives them transparent long-term memory. In detail, you’ll learn: - How to build an agent that can edit its own limited input context memory, using tools and multi-step reasoning - What is a memory hierarchy (an idea from computer operating systems, which use a cache to speed up memory access), and how these ideas apply to managing the LLM input context (where the input context window is a "cache" storing the most relevant information; and an agent decides what to move in and out of this to/from a larger persistent storage system) - How to implement multi-agent collaboration by letting different agents share blocks of memory This course will give you a sophisticated understanding of memory management for LLMs, which is important for chatbots having long conversations, and for complex agentic workflows. Please sign up here!
2:57

New short course: LLMs as Operating Systems: Agent Memory, created with Letta, and taught by its founders Charles Packer and Sarah Wooders. An LLM's input context window has limited space. Using a longer input context also costs more and results in slower processing. So, managing what's stored in this context window is important. In the innovative paper MemGPT: Towards LLMs as Operating Systems, its authors (which include the instructors) proposed using an LLM agent to manage this context window. Their system uses a large persistent memory that stores everything that could be included in the input context, and an agent decides what is actually included. Take the example of building a chatbot that needs to remember what's been said earlier in a conversation (perhaps over many days of interaction with a user). As the conversation's length grows, the memory management agent will move information from the input context to a persistent searchable database; summarize information to keep relevant facts in the input context; and restore relevant conversation elements from further back in time. This allows a chatbot to keep what's currently most relevant in its input context memory to generate the next response. When I read the original MemGPT paper, I thought it was an innovative technique for handling memory for LLMs. The open-source Letta framework, which we'll use in this course, makes MemGPT easy to implement. It adds memory to your LLM agents and gives them transparent long-term memory. In detail, you’ll learn: - How to build an agent that can edit its own limited input context memory, using tools and multi-step reasoning - What is a memory hierarchy (an idea from computer operating systems, which use a cache to speed up memory access), and how these ideas apply to managing the LLM input context (where the input context window is a "cache" storing the most relevant information; and an agent decides what to move in and out of this to/from a larger persistent storage system) - How to implement multi-agent collaboration by letting different agents share blocks of memory This course will give you a sophisticated understanding of memory management for LLMs, which is important for chatbots having long conversations, and for complex agentic workflows. Please sign up here!

Andrew Ng

201,127 Aufrufe • vor 1 Jahr