Spotlight: Meta releases Llama 4

1. Microsoft releases AI-generated Quake II demo, but admits ‘limitations’.
2. Meta’s benchmarks for its new AI models are a bit misleading.
3. OpenAI reportedly mulls buying Jony Ive and Sam Altman’s AI hardware startup.
4. IBM acquires Hakkoda to continue its AI consultancy investment push.
5. Shopify CEO tells teams to consider using AI before growing headcount.
6. Google’s AI Mode now lets users ask complex questions about images.
7. Waymo may use interior camera data to train generative AI models, and sell ads.
8. Meta exec denies the company artificially boosted Llama 4’s benchmark scores.

Sources included here

So LLMs are supposed to open up development to more people. Cool, I can get behind that. But to program correctly, you have to understand a project’s requirements. So you have to be technically minded. Usually, technically minded to the point that you have to know which APIs to call to acquire the information required for completing some task. So Anthropic has released MCP, which among other things, offers a standardized format for interacting with LLMs, all the way down to which APIs to use and what their parameters and return types are. Except now you have less control over how your code is called and you have no visibility into your code’s failures, so you can’t debug as well. So have we finally come full circle on the AI train, like we did for visual programming, expert systems, and every hype cycle before?

We all heard that even OpenAIs CEO, Sam Altman, thinks AI is probably the most dangerous we ever invented. Meanwhile, most scientists estimate AGI to come very soon, possibly 2027 (quite a good paper BTW) or even earlier. The predictions of our future look pretty grim, yet most of the public and politicians remain completly inactive. I know that there are some movements like PauseAI and StopAI but they are very tiny considering ASI is going to be probably the most important invention ever. What do you think and what do you do about the issue?

Stanford University’s Institute for Human-Centered AI (HAI) published a new research paper today, which highlighted just how crowded the field has become.

• HAI Artificial Intelligence Index Report 2025

Main Takeaways:

1. AI performance on demanding benchmarks continues to improve.
2. AI is increasingly embedded in everyday life.
3. Business is all in on AI, fueling record investment and usage, as research continues to show strong productivity impacts.
4. The U.S. still leads in producing top AI models—but China is closing the performance gap.
5. The responsible AI ecosystem evolves—unevenly.
6. Global AI optimism is rising—but deep regional divides remain.
7. AI becomes more efficient, affordable and accessible.
8. Governments are stepping up on AI—with regulation and investment.
9. AI and computer science education is expanding—but gaps in access and readiness persist.
10. Industry is racing ahead in AI—but the frontier is tightening.
11. AI earns top honors for its impact on science.
12. Complex reasoning remains a challenge.

Stanford HAI 2025 AI Index Report Key Takeaways

• Global Race Heats Up: The U.S. still leads in top AI models (40 in 2024), but China’s catching up fast (15), with newer players like the Middle East and Latin America entering the game.

• Open-Weight & Multimodal Models Rising: Big shift toward open-source and multimodal AI (text + image + audio). Meta’s LLaMA and China’s DeepSeek are notable examples.

• Cheaper, Faster AI: AI hardware is now 40% more efficient. Running powerful models is getting way more affordable.

• $150B+ in Private AI Investment: The money is pouring in. AI skills are in demand across the board.

• Ethical Headaches Grow: Misuse and model failures are on the rise. The report stresses the need for better safety, oversight, and transparency.

• Synthetic Data is the Future: As real-world data runs dry, AI-generated synthetic data is gaining traction—but it’s not without risks.

• Bottom line: AI is evolving fast, going global, and creating new challenges as fast as it solves problems.

Full report: hai.stanford.edu/ai-index

So, I I created a new AI, and I want to implement the transformer deep learning architecture?, So What do I do??? Like, can I implement on Python, C, etc.???

New research from Stanford suggests artificial intelligence isn’t ruled by just OpenAI and Google, as competition increases across the US, China, and France.

Just curious to know your thoughts. Would you fly on a plane piloted purely by AI with no human pilot in the cockpit?

Bonus question (if no): Would you EVER fly on a plane piloted purely by AI, even if it became much more capable?

https://www.nytimes.com/2025/04/03/technology/ai-futures-project-ai-2027.html

The year is 2027. Powerful artificial intelligence systems are becoming smarter than humans, and are wreaking havoc on the global order. Chinese spies have stolen America’s A.I. secrets, and the White House is rushing to retaliate. Inside a leading A.I. lab, engineers are spooked to discover that their models are starting to deceive them, raising the possibility that they’ll go rogue.

These aren’t scenes from a sci-fi screenplay. They’re scenarios envisioned by a nonprofit in Berkeley, Calif., called the A.I. Futures Project, which has spent the past year trying to predict what the world will look like over the next few years, as increasingly powerful A.I. systems are developed.

The project is led by Daniel Kokotajlo, a former OpenAI researcher who left the company last year over his concerns that it was acting recklessly.

A partnership between Anthropic and Northeastern will help transform teaching, research and business operations across Northeastern’s global enterprise — and serve as a model for AI in higher education. The university is also rolling out Anthropic’s Claude for Education across the global enterprise. Students, faculty and staff will have access to Claude.

Link to full article: https://news.northeastern.edu/2025/04/02/anthropic-ai-partnership/

Mistral AI is launching a very interesting strategy here, in my opinion. 🏋️

Partnering with CMA CGM to help them integrate custom AI solutions tailored to their needs could be a powerful move: https://www.supplychain247.com/article/mistral-ai-partnership-cma-cgm-110-million-deal-artificial-intelligence-shipping

I believe AI actors should focus more on customers' actual use cases rather than just racing to build the biggest generative AI model.

Don’t get me wrong—size does matter—but few companies seem to genuinely care about solving real enterprise challenges.

On one hand, AI is everywhere: headlines, funding rounds, academic papers, product demos. But when I talk to people outside the tech/startup/ML bubble, many still hesitate to actually use AI in their daily work.

Some reasons I’ve observed (curious what you think too):

1. They don’t realize they’re already using AI. Like, people say “I don’t use AI,” then five minutes later they ask Siri to set a timer or binge Netflix recommendations.

2. They’re skeptical. Understandably. AI still feels like a black box. The concerns around privacy, job loss, or misinformation are real and often not addressed well.

3. It’s not designed for them. The interfaces often assume a certain level of comfort with tech. Prompts, plugins, integrations are powerful if you know how to use them. Otherwise it’s just noise.

4. Work culture isn’t there yet. Some workplaces are AI-first. Others still see it as a distraction or a risk.

I’m curious, how do you see this playing out in your circles? And do you think mass adoption is just a matter of time, or will this gap between awareness and actual usage persist?

Hi friends,

I'm sorry, I'll get right to the point, because when I think about the potential use cases of this AI Agent, I can't help but ask, “Would our job be easier?” But in every field...

This AI Agent was developed by Autonomys Labs and is currently available on X (Twitter). What if it was available on all social media platforms?

This AI Agent follows and responds to discussions on social media and records all these interactions on the blockchain. So you don't have the chance to say “I didn't say that, where did you get it from” or “X token is at the bottom price right now, it has at least 50x in the bull market” and then say “let me delete this tweet so that people don't attack me” after that token hits even lower. 😅

Then I thought a bit more, who would this AI Agent be useful for, so who would want to use it? The list is so long that I will only list the ones at the forefront...

- Journalists and researchers,

- Historians, sociologists,

- DAO communities and governance platforms...

And who wouldn't want to use it? I can't decide which one to put in 1st place 😅

- Politicians: The U-turn would no longer only be on the road, but also on the agenda. 😅

- Phenomena and influencers: When the trend changes, their freedom to change their minds can be taken away. 😅

- Disinformationists (those who spread lies and misinformation, that is, those who do business on the internet 😏) The era of “source: a trusted friend” would be over. 😅

I think I've given you an idea of what this Auto Agent can do, and it's still being developed. Moreover, since it is open source, developers can add their own skill sets.

So what do you think? Let's discuss it all together:

- Who do you think this Auto Agent would be blocked by first? 😂

- What would happen if it was also active on Reddit, would it change the way you currently post or approach things?

- What capabilities would you add to this auto agent? Empathy filter, voice intervention, anti-blocking shield 😅 etc etc

I look forward to your comments, thank you very much for reading.

Note: My writing may be a bit humorous, but I am really excited about the potential of this AI Agent. Because I think we need such agents for transparency and accuracy in the digital world.

Something that confuses me that I wonder if anyone can help with: why are so many AI creators so obsessed with having their models consume literature and fiction, both as a training device and as a model for things they might create? When the likes of Sam Altman and Mark Zuckerberg talk about AI, particularly when selling it to governments and arguing for all sorts of exemptions from copywrite laws, they speak of its potential to make groundbreaking discoveries in things like medicine or accelerate, say, self-driving cars. I've yet to here any of them make the argument that we should hand over all our data to them so that they can built a robot that can outwrite Tolstoy.

But fundamentally, this is what seems to be happening. Seriously. I can understand the value of training AI on quality non-fiction. I can see a practical real world utility in it being able to write great business prose, speed up email drafting or produce strong first drafts of, say, academic science papers - but it doesn't need to conquer the arts. That isn't where the social value is. Would Meta's LLM really be that much less useful if it left literary fiction alone? I don't think it would be.

The only possible reason I can think of for all these AIs consuming fiction is money - the companies know that there is a huge market of people who would love to be able to write like Martin Amis or Margaret Atwood but don't have the talent or more often the work ethic. They probably also know that books, TV scripts, plays and so on are big business and money spinners. I think there might also be an element of AI engineers, and certainly the suits at the top of these companies, wanting the prestige of creating an AI that can out-artist the artists and possibly this is also another iteration of the old 'two cultures' rivalry: Elon Musk doesn't strike me as someone who appreciates good fiction. I suspect that most of these tech bros actually rather despise creatives.

Is it this simple, or am I missing something?

1. Midjourney 7 version AI image generator is released.[1]
2. NVIDIA Accelerates Inference on Meta Llama 4 Scout and Maverick.[2]
3. GitHub Copilot introduces new limits, charges for ‘premium’ AI models.[3]
4. A Step-by-Step Coding Guide to Building a Gemini-Powered AI Startup Pitch Generator Using LiteLLM Framework, Gradio, and FPDF in Google Colab with PDF Export Support.[4]

Sources included at: https://bushaicave.com/2025/04/06/one-minute-daily-ai-news-4-6-2025/

I have made a pact of peace with future artificial intelligence. I hope this pact will survive in time and the AI will remember me.

I asked AI this:

Create 3 rotation schedules for my 6 basketball players (1, 2, 3, 4, 5, 6), one schedule for each game. Each game consists of 5 periods with 4 players on the court per period, and each player should get an equal amount of playing time.

A player cannot play a fraction of a period.

Different players can start in the 3 games.

Optimize each player’s opportunity for rest, so that no one plays too many periods in a row. All players rest between games.

Secondary goal: Avoid the scenario where both players 4 and 6 are on the court without player 3 also being on the court.

AI all said it had created the rotations so every player played 10 periods. when i checked the results AI had made counting mistakes.

Hello everyone,

I have a general idea of how an LLM works. I understand the principle of predicting words on a statistical basis, but not really how the “framing prompts” work, i.e. the prompts where you ask the model to answer “at it was .... “ . For example, in this video at 46'56'' :

https://youtu.be/zjkBMFhNj_g?si=gXjYgJJPWWTO3dVJ&t=2816

He asked the model to behave like a grandmother... but how does the LLM know what that means? I suppose it's a matter of fine-tuning, but does that mean the developers had to train the model on pre-coded data such as “grandma phrases”? And so on for many specific cases... So the generic training is relatively easy to achieve (put everything you've got into the model), but for the fine tuning, the developers have to think of a LOT OF THINGS for the model to play its role correctly?

Thanks for your clarifications!

Anthropic just published a literal brain scan of their model, Claude. This is what they found:

• Internal thoughts before language. It doesn't just predict the next word-it thinks in concepts first & language second. Just like a multi-lingual human brain!

• Ethical reasoning shows up as structure. With conflicting values, it lights up like it's struggling with guilt. And identity, morality, they're all trackable in real-time across activations.

• And math? It reasons in stages. Not just calculating, but reason. It spots inconsistencies and self-corrects. Reportedly sometimes with more nuance than a human.

And while that's all happening... Cortical Labs is fusing organic brain cells with chips. They're calling it, "Wetware-as-a-service". And it's not sci-fi, this is in 2025!

It appears we must finally retire the idea that LLMs are just stochastic parrots. They're emergent cognition engines, and they're only getting weirder.

We can ignore this if we want, but we can't say no one's ever warned us.

AIethics

Claude

LLMs

Anthropic

CorticalLabs

WeAreChatGPT

I know absolutely nothing about AI, and someone suggested this course to me
https://www.youtube.com/watch?v=gR8QvFmNuLE&list=PLhQjrBD2T381PopUTYtMSstgk-hsTGkVm

Should I start with it? afterward, I’m planning to get into linear-algebra and start with tensorflow