Traditional DevOps, with its rule-based automation, is struggling to work effectively in today’s complex tech world. But when combined with AIOps it can lead to IT systems that predict failures and solve issues without human intervention.
In the fast-paced and ever-changing world of software development and IT operations, automation is a great asset. From CI/CD pipelines to provisioning infrastructure, DevOps has equipped teams to construct and deploy software faster than ever. But as systems become more complex, distributed, and data-rich, automation in isolation is not enough.
This is where artificial intelligence for IT operations (AIOps) enters the conversation. By embedding AI and machine learning with DevOps practices, AIOps shifts the paradigms beyond a workflow of defined rules. Not only does AIOps analyse data patterns and detect anomalies, it can also anticipate failures and take preemptive action with little or no human assistance. — Read More
Author Archives: Rick's Cafe AI
AI can now ‘see’ optical illusions. What does it tell us about our own brains?
Our eyes can frequently play tricks on us, but scientists have discovered that some artificial intelligence can fall for the same illusions. And it is changing what we know about our brains.
When we look up at the Moon, it seems larger when it is close to the horizon compared to when it is higher up in the sky even though its size, and the distance between the Earth and the Moon, remains much the same during the course of a night.
Optical illusions such as these show that we don’t always perceive reality the way we should. They are often considered to be mistakes made by our visual system. But illusions also reveal the clever shortcuts our brains use to extract the most important details of our surroundings.
In truth, our brains only accept a sip of the world around us – it would be too much to process every detail of our busy visual environments, so instead they pick out only the details we need. — Read More
Engram: How DeepSeek Added a Second Brain to Their LLM
When DeepSeek released their technical reports for V2 and V3, the ML community focused on the obvious innovations: massive parameter counts, clever load balancing, and Multi-head Latent Attention. But buried in their latest research is something that deserves more attention: a different way to think about what an LLM should remember.
The insight is deceptively simple. Large language models spend enormous computational effort reconstructing patterns they’ve seen millions of times before. The phrase “United States of” almost certainly ends with “America.” “New York” probably precedes “City” or “Times.” These patterns are burned into the training data, and the model learns them, but it learns them the hard way: by propagating gradients through billions of parameters across dozens of layers.
What if you could just look them up? — Read More
An AI “tsunami” is coming for Hollywood — here’s how artists are responding
In 2016, the legendary Japanese filmmaker Hayao Miyazaki was shown a bizarre AI-generated video of a misshapen human body crawling across a floor.
Miyazaki declared himself “utterly disgusted” by the technology demo, which he considered an “insult to life itself.”
“If you really want to make creepy stuff, you can go ahead and do it,” Miyazaki said. “I would never wish to incorporate this technology into my work at all.”
Many fans interpreted Miyazaki’s remarks as rejecting AI-generated video in general.
… But as these models have improved, they have sped up workflows and afforded new opportunities for artistic expression. Artists without AI expertise might soon find themselves losing work. — Read More
OpenAI Is Sinking and Dragging the Entire AI Industry Down With It
If you still think the AI revolution is a story about progress and saving humanity, think again.
OpenAI is burning $11–12 billion per quarter, and its perverse appetite keeps growing. Until the end of last year, this could have been considered a problem for Sam Altman and OpenAI’s shareholders, but now everything has changed.
OpenAI no longer wants to go down alone. It’s dragging down the leaders of the AI and financial industries with it. The bill will run not just into the hundreds of billions of dollars set to go up in smoke over the next three years, but ultimately into the trillions. — Read More
The 2026 Timeline: AGI Arrival, Safety Concerns, Robotaxi Fleets & Hyperscaler Timelines
Elon Musk on AGI Timeline, US vs China, Job Markets, Clean Energy & Humanoid Robots
The Legend of Zelda: AI Movie Trailer! | Made by VideoMax AI & Midjourney
The Chinese Room Experiment— AI’s Meaning Problem
“The question is not whether machines can think, but whether men can.” — Joseph Weizenbaum (creator of ELIZA, first chatbot)
Imagine you’re in a locked room. You don’t speak a word of Chinese, but you have an enormous instruction manual written in English. Through a slot in the door, native Chinese speakers pass you questions written in Chinese characters. You consult your manual, it tells you: “When you see these symbols, write down those symbols in response.” You follow the rules perfectly, sliding beautifully composed Chinese answers back through the slot. To everyone outside, you appear fluent. But here’s the thing: you don’t understand a single word.
This is the Chinese Room, philosopher John Searle’s 1980 thought experiment that has ‘haunted’ artificial intelligence ever since. Today’s models produce increasingly sophisticated text, writing poetry, debugging code and also teach complex concepts. The uncomfortable question, then, is whether any of this counts as understanding; or are we just being impressed by extremely elaborate rule-following. — Read More
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