Few would argue that AI progress over the past few years has not been rapid.
Large Language Models (LLMs) have provided an unexpected path to increasingly general capabilities. In 2019, OpenAI’s GPT-2 struggled to write a coherent paragraph. In 2025, LLMs write fluent essays, outcompete human experts at graduate-level science questions, and excel at competition mathematics and coding. The most advanced multi-modal AI models now produce images and video that are hard to distinguish from reality.
These models are impressive (and useful!) but they still fall short of the north star that frontier AI companies are working towards. Artificial General Intelligence (AGI), which OpenAI describes as “a highly autonomous system that outperforms humans at most economically valuable work” has been the ultimate ambition of AI researchers for many decades.
Most experts agree that AGI is possible. They also agree that it will have transformative consequences. There is less consensus about what these consequences will be. Some believe AGI will usher in an age of radical abundance. Others believe it will likely lead to human extinction. One thing we can be sure of is that a post-AGI world would look very different to the one we live in today.
So, is AGI just around the corner? Or are there still hard problems in front of us that will take decades to crack, despite the speed of recent progress? This is a subject of live debate. Ask various groups when they think AGI will arrive and you’ll get very different answers, ranging from just a couple of years to more than two decades.
Why is this? We’ve tried to pin down some core disagreements. — Read More
Recent Updates Page 65
Create videos with your words for free – Introducing Bing Video Creator
Questions deserve answers, ideas beg for realization, and curiosity seeks satisfaction. Two years ago, we brought this belief forward with Bing Image Creator, helping users everywhere create whatever they can imagine through words—for free. Last month, we continued the next evolution of search with Copilot Search in Bing, blending the best of traditional and generative search to meet you where you are at in your discovery journey.
Today we’re taking the next leap with Bing Video Creator, allowing you to turn your ideas into videos, for free. Powered by Sora, Bing Video Creator transforms your text prompts into short videos. Just describe what you want to see and watch your vision come to life. — Read More
Can intelligent computing business save “independent cloud vendors”?
Smiling bitterly, computing industry professional Wang Zhi said, “In the future, intelligent computing business will definitely become the main pillar of independent cloud vendors’ revenue, otherwise there will be no other pillars.”
At the end of 2022, the independent cloud vendor where Wang Zhi worked was also scarred in the price war of selling CDN (content delivery networks) and public cloud services. At that time, the cloud computing market was concentrated at the top, leaving little room for small and medium-sized vendors; even UCloud, QingCloud, Kingsoft Cloud, etc., which have successfully listed, continued to decline in revenue and increase in losses, which made investors lose confidence.
It was only the emergence of generative AI when independent cloud vendors saw new hope, and Wang Zhi’s company began its first attempt in the field of intelligent computing: selling resources. He clearly remembers the first thought that came to his mind after completing the first A800 machine transaction in early 2023:
“I bet right!” — Read More
Detection-In-Depth
Detection-in-depth is an evolution of the classic cybersecurity principle known as defense-in-depth. Defense-in-depth means that no single security control can fully protect an environment—instead, multiple layered defenses must work together to slow down, detect, and ultimately stop adversaries.
These layers create redundancy, ensuring that if one layer fails, another stands ready to catch the threat. Detection-in-depth applies this same layered philosophy specifically to detection and monitoring. Rather than relying on a single detection point, it ensures that adversary activity can be caught at multiple stages, through multiple methods, and across multiple levels of abstraction. This creates a resilient, overlapping detection strategy that minimizes blind spots and maximizes the chance of identifying attackers anywhere in their kill chain progression. — Read More
Building a Distributed Cache for S3
We’ve built a distributed cache for cloud object storage: a shared, low-latency layer that gives all compute nodes fast access to hot data.
This post looks under the hood: how hot data caching worked before, why object storage made it hard, and how the new architecture fixes it. Benchmarks included. — Read More
How AI Is Eroding the Norms of War
Since 2022, I have reported on Russia’s full-scale invasion of Ukraine, witnessing firsthand the rapid evolution of technology on the battlefield. Embedded with drone units, I have seen how technology has evolved, with each side turning once-improvised tools into cutting-edge systems that dictate life and death.
In the early months of the war, Ukrainian soldiers relied on off-the-shelf drones for reconnaissance and support. As Russian forces developed countermeasures, the two sides entered a technological arms race. This cycle of innovation has transformed the battlefield, but it has also sparked a moral descent — a “race to the bottom” — in the rules of war.
In the effort to eke out an advantage, combatants are pushing ethical boundaries, eroding the norms of warfare. Troops disguise themselves in civilian clothing to evade drone detection, while autonomous targeting systems struggle to distinguish combatants from noncombatants.
The evolution of automated drone combat in Ukraine should be a cautionary tale for the rest of the world about the future of warfare. — Read More
Attention Wasn’t All We Needed
There’s a lot of modern techniques that have been developed since the original Attention Is All You Need paper. Let’s look at some of the most important ones that have been developed over the years and try to implement the basic ideas as succinctly as possible. We’ll use the Pytorch framework for most of the examples. Note that most of these examples are highly simplified sketches of the core ideas, if you want the full implementation please read the original paper or the production code in frameworks like PyTorch or Jax.
The Man Who ‘A.G.I.-Pilled’ Google
A few years ago, most Google executives didn’t talk about A.G.I. — artificial general intelligence, the industry term for a human-level A.I. system. Even if they thought A.G.I. might be technically possible, the idea seemed so remote that it was barely worth discussing.
But this week, at Google’s annual developer conference, A.G.I. was in the air. The company announced a slate of new releases tied to Google’s Gemini A.I. models, including new features designed to let users write A.I.-generated emails, create A.I.-generated videos and songs, and chat with an A.I. bot on the flagship search engine. Google’s leaders traded guesses about when more powerful systems might arrive. And they predicted profound changes ahead, as A.I. tools become more capable and autonomous.
The man most responsible for making Google “A.G.I.-pilled” — industry shorthand for the way people can become gripped by the notion that A.G.I. is imminent — is Demis Hassabis.
… This week on “Hard Fork,” we interviewed Mr. Hassabis about his views on A.G.I. and the strange futures that might follow its arrival. You can listen to our conversation by clicking the “Play” button below or by following the show on Apple, Spotify, Amazon, YouTube, iHeartRadio or wherever you get your podcasts. Or, if you prefer to read, you’ll find an edited transcript of our conversation, which begins about 24 minutes into the podcast, below. — Read More
Evaluation Driven Development for Agentic Systems.
I have been developing Agentic Systems for around two years now. The same patterns keep emerging again and again, regardless of what kind of systems are being built.
I have learned them the hard way and many do so as well. The first project is not a great success, but you learn from the failures and apply the learnings in the next one. Then you iterate.
Today, I am sharing my system of how to approach development of LLM based applications from idea to production. Use it if you want to avoid painful lessons in your own projects. — Read More
Anthropic’s new Claude 4 AI models can reason over many steps
During its inaugural developer conference Thursday, Anthropic launched two new AI models that the startup claims are among the industry’s best, at least in terms of how they score on popular benchmarks.
Claude Opus 4 and Claude Sonnet 4, part of Anthropic’s new Claude 4 family of models, can analyze large datasets, execute long-horizon tasks, and take complex actions, according to the company. Both models were tuned to perform well on programming tasks, Anthropic says, making them well-suited for writing and editing code.
Both paying users and users of the company’s free chatbot apps will get access to Sonnet 4 but only paying users will get access to Opus 4. — Read More
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