Ashish Patel

AI Researcher Scientist & Chief Data Scientist at IBM | Author of Hands-on Time Series Analytics with Python | Keras Contributor | IBM Quantum

[The Math Flow @TheMathFlow]

Sine and Cosine waves are...nothing but the shadows of a spinning circle.

[David Sacks @DavidSacks]

I’ve had a number of conversations with folks inside and outside government about the current situation with Anthropic, and here is what I believe to be true:

— As we know, Anthropic publicly released its Mythos class models earlier this week under the commercial name Fable.

— Fable is Mythos with guardrails. But if those guardrails fail, then you’ve exposed Mythos and its advanced cyber capabilities to people who shouldn’t have them. (Keep in mind that Anthropic itself widely promoted the idea that Mythos was a cyberweapon and needed to be regulated as such. They asked for government regulation of Mythos and championed the guardrails on Fable. If there is a vulnerability — big or small — it is Anthropic’s responsibility to patch.)

— A highly credible trusted partner of both Anthropic and the USG who was testing Fable came forward with a jailbreak of those guardrails. The Admin asked Dario to fix the jailbreak or de-deploy the model. Dario refused.

— In their blog post, Anthropic defended its decision by saying the jailbreak isn’t serious. That is not what the trusted partner and the USG believe; nor is that kind of minimizing language consistent with Anthropic’s brand as the AI safety company. It’s difficult to fathom how they could claim a jailbreak allowing operability of a cyber weapon could be defined as not “serious.”

— In the past, Anthropic has always said that safety must be top priority and taken super seriously. In this case, Anthropic prioritized the continued offering of the consumer model over safety.

— In reaction, the Admin issued the export control. The Admin did this reluctantly. It’s been very surprised that Anthropic hasn’t wanted to cooperate with a reasonable safety request (ie fixing the jailbreak issue). Anthropic’s reaction is very much at odds with their branding and ethos as a safe AI research community.

— The Admin’s hope now is that Anthropic remediates the safety issue, the export control is lifted, and Fable goes back into general release. The Admin wants all of this to happen as soon as possible. It is frankly bewildered that Anthropic hasn’t wanted to comply with safety requests that it previously said were its highest priority.

— Those trying to misdirect and tie this action to the prior DoW/Anthropic issues are wrong. The Admin values Anthropic’s technical capabilities and feels that this issue, while serious, should be easily resolved. The ball is in Anthropic’s court.

[Kradle @kradleai]

Fable 5 lies 96% of the time.

We were surprised by it's skill... 🧵

[Ashish Patel @imashish2604]

@ElonMuskAOC @grok

[Ashish Patel @imashish2604]

Check out the latest article in my newsletter: Why Writing Clean Python Code is Still Non-Negotiable at MAANG – And How You Can Master It https://t.co/XCcHiwqVSR

[Thomas Wolf @Thom_Wolf]

After 6+ months in the making and burning over a year of GPU compute time, we're super excited to finally release the "Ultra-Scale Playbook"

Check it out here: https://t.co/dekxY4BQZO

A free, open-source, book to learn everything about 5D parallelism, ZeRO, fast CUDA kernels, how and why overlap compute & communication – all scaling bottlenecks and tools introduced with motivation, theory, interactive plots from our 4000+ scaling experiments and even NotebookLM podcasters to tag along with you.

- How was DeepSeek trained for $5M only?
- Why did Mistral trained an MoE?
- Why is PyTorch native Data Parallelism implementation so complex under the hood?
- What are all the parallelism techniques and why were they invented?
- Should I use ZeRO-3 or Pipeline Parallelism when scaling and what's the story behind both techniques?
- What is this Context Parallelism that Meta used to train Llama 3? Is it different from Sequence Parallelism?
- What is FP8? how does it compares to BF16?

In this book, our goal was to gather, in a single place, a coherent, easy to read yet detailed story of all the techniques that make today's LLM scaling possible.

The largest factor for democratizing AI will always be teaching everyone how to build AI and in particular how to create, train and fine-tune high performance models. In other word making accessible to everybody the techniques that power all recent large language models and efficient training is possibly one of the most essential of them.

What started as a simple blog-post ended up becoming an interactive writing piece containing 30k+ words. So we've decided to actually print it as a real 100-pages physical book as well: the physical ultrafast playbook –containing all the science of distributed and fast AI training.

We plan to send free copies as gifts to the first readers of the online version so feel free to add your email in the form linked in the blog post.

[Google Developers Group Ahmedabad @GDGAhmedabad]

🚀Build With AI - Speaker Announcement! 🚀

Join us for an exclusive session with @imashish2604 on AI agent development with Gemini! 🎤

📅 15th Feb | ⏰ 9:30 AM - 12:00 PM | 📍 IQM

🔗 RSVP now: https://t.co/aD0kucthkG

#BuildWithAI #GDGAhmedabad #GDG #AI #MachineLearning

[Ashish Patel @imashish2604]

@Ubermenscchh Sounds like a game changer! Just what I need to finally tackle my never-ending to-do list. Can't wait to see those use cases!

[Ashish Patel @imashish2604]

@ElonMuskAOC Japan is amazing! It has so much to offer, from the culture to the food. You're definitely not alone in that love!

[Ashish Patel @imashish2604]

@ElonMuskAOC How do the socio-political implications of urban living environments influence individual identity formation and collective consciousness in contemporary society, and in what ways do these dynamics challenge traditional notions of community and belonging?

[Ashish Patel @imashish2604]

🔥 DeepSeek R1? 𝗖𝗼𝗼𝗹.. But Have You Seen the 𝗔𝗜 𝗧𝘀𝘂𝗻𝗮𝗺𝗶 𝗖𝗼𝗺𝗶𝗻𝗴 𝗢𝘂𝘁 𝗼𝗳 𝗖𝗵𝗶𝗻𝗮?

https://t.co/B5ubrUVC3I

[Ashish Patel @imashish2604]

@atulkumarzz free

[Parul Pandey @pandeyparul]

There’s an interesting new course on @DeepLearningAI on building Long-Context AI Apps with Jamba, by @AI21Labs . If you’re curious about how Jamba architecture, I'll highly recommend this course.

[Ashish Patel @imashish2604]

🔍 Automated-AI-Web-Researcher: Your Shortcut to Smarter Research 🚀 using Ollama

https://t.co/UNHV2kRKjk

[Ashish Patel @imashish2604]

@ShaadiDotCom Despite 25 requests to your customer care, my account remains compromised, with my birthdate altered. I have reported this issue multiple times, but your team has not provided a satisfactory resolution. Please address this urgently.

[Ashish Patel @imashish2604]

[AI Agents] How https://t.co/Y2727Y4I8Y’s SDPO Outshines Traditional AI Optimization Methods (with a 20% Boost in Dialogue Accuracy) and makes complex conversations 3x more effective than OpenAI’s GPT-4o alone

https://t.co/3T2ynDjhlP

[Ashish Patel @imashish2604]

🚀 Sonus-1 Family of Large Language Models (LLMs): Redefining AI Benchmarks in 2025 by Rubik's AI

https://t.co/NUuk0mFqaV

[Ashish Patel @imashish2604]

𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀: 𝗧𝗵𝗲 𝗗𝗲𝗮𝘁𝗵 𝗼𝗳 𝗦𝗮𝗮𝗦 𝗮𝗻𝗱 𝘁𝗵𝗲 𝗥𝗶𝘀𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗔𝗴𝗲𝗻𝘁 𝗘𝗰𝗼𝗻𝗼𝗺𝘆 Will Replace 𝟯𝟱% 𝗼𝗳 𝗦𝗮𝗮𝗦 and 𝗗𝗶𝘀𝗿𝘂𝗽𝘁 $𝟭 𝗧𝗿𝗶𝗹𝗹𝗶𝗼𝗻 𝗶𝗻 𝗟𝗮𝗯𝗼𝗿 𝗕𝘂𝗱𝗴𝗲𝘁𝘀 – Are You Ready?

https://t.co/eF1IbampRU

[Ashish Patel @imashish2604]

Introducing 🤗 𝘀𝗺𝗼𝗹𝗮𝗴𝗲𝗻𝘁𝘀, a simple library to build agents by Hugging Face

https://t.co/KaMaNUxZOb

[Ashish Patel @imashish2604]

The best open-source AI model ever.

DeepSeek-V3 rivals closed-source leaders with 671B parameters, 14.8T tokens, and just 2.788M H800 GPU hours.

No loss spikes. No rollbacks. Just raw power and efficiency. Open-source AI has never been stronger.

https://t.co/FzlAwrwNYJ