Sifat Muhammad Abdullah

MLLM Security Research | ML Intern @HPE Labs | PhD @VT_CS

[Sifat Muhammad Abdullah @SifatMdAbdullah]

🎉Excited to start as ML research associate intern at @HPE_labs! I'll be exploring ways to enhance the reasoning capabilities of Hierarchical Multi-Agent LLM systems, developing novel methods to push their boundaries. If you are in San Jose, please feel free to reach out! 😃

[Sam Altman @sama]

gpt-oss is a big deal; it is a state-of-the-art open-weights reasoning model, with strong real-world performance comparable to o4-mini, that you can run locally on your own computer (or phone with the smaller size). We believe this is the best and most usable open model in the world.

We're excited to make this model, the result of billions of dollars of research, available to the world to get AI into the hands of the most people possible. We believe far more good than bad will come from it; for example, gpt-oss-120b performs about as well as o3 on challenging health issues. We have worked hard to mitigate the most serious safety issues, especially around biosecurity. gpt-oss models perform comparably to our frontier models on internal safety benchmarks.

We believe in individual empowerment. Although we believe most people will want to use a convenient service like ChatGPT, people should be able to directly control and modify their own AI when they need to, and the privacy benefits are obvious.

As part of this, we are quite hopeful that this release will enable new kinds of research and the creation of new kinds of products. We expect a meaningful uptick in the rate of innovation in our field, and for many more people to do important work than were able to before.

OpenAI’s mission is to ensure AGI that benefits all of humanity. To that end, we are excited for the world to be building on an open AI stack created in the United States, based on democratic values, available for free to all and for wide benefit.

[Ludwig Schmidt @lschmidt3]

Very excited to finally release our paper for OpenThoughts!

After DataComp and DCLM, this is the third large open dataset my group has been building in collaboration with the DataComp community. This time, the focus is on post-training, specifically reasoning data.

[Sifat Muhammad Abdullah @SifatMdAbdullah]

@ImtiazKarim23 @UTDCompSci @LifeAtPurdue @UT_Dallas Congratulations and all the very best! 😄

[OpenAI @OpenAI]

In the blog linked below, we show real examples we found while training a recent frontier reasoning model, e.g. a model in the same class as OpenAI o1 or OpenAI o3‑mini.

We found the model thinking things like, “Let’s hack,” “They don’t inspect the details,” and “We need to cheat to get the test passing,” while subverting tests and rewarding hacking in coding tasks.

Find out more: https://t.co/rA1ugR6FK9

[Sifat Muhammad Abdullah @SifatMdAbdullah]

@thinkymachines This looks really promising! All the best!

[Amazon Science @AmazonScience]

Announcing Amazon Nova, a new generation of foundation models that have state-of-the-art intelligence across a wide range of tasks, & industry-leading price performance.

Learn more about the new Amazon Nova models available in Amazon Bedrock: https://t.co/W87nCxmoMq #AWSreInvent

[Black Forest Labs @bfl_ai]

Today, we are excited to release FLUX.1 Tools, a suite of models designed to add control and steerability to our base text-to-image model FLUX.1, enabling the modification and re-creation of real and generated images. Learn more in our blogpost: https://t.co/J5Bc8fVGEc

[Sifat Muhammad Abdullah @SifatMdAbdullah]

@lilianweng I follow your blog regularly and hopefully it keeps continuing! All the best!

[Sifat Muhammad Abdullah @SifatMdAbdullah]

@OpenAI Working towards 'agent like real world actions' from 'human level reasoning'

[Andrew Brown @Andrew__Brown__]

So how did we get to these amazing videos for Meta Movie Gen? One of the things I’m proudest of is that we released a very detailed technical report (https://t.co/FU2PzloDhr…)

Lets dive into a technical summary of what we did & learnt

🧵 1/n

https://t.co/BJPvf7wC9v

[AI at Meta @AIatMeta]

As part of our continued belief in open science and progressing the state-of-the-art in media generation, we’ve published more details on Movie Gen in a new research paper for the academic community ➡️ https://t.co/2SY8yYUEYh

[Sifat Muhammad Abdullah @SifatMdAbdullah]

@ylongqi @Microsoft @COLM_conf @peizNLP @mengtingwan @sharma_ashish_2 @soshsihao @MSheshera Thank you for the post. I have emailed you with my resume.

[Sifat Muhammad Abdullah @SifatMdAbdullah]

This looks very promising!

[Karina @karinanguyen]

For the first time we are fundamentally changing how humans can collaborate with ChatGPT since it launched two years ago.

We’re introducing canvas, a new interface for working with ChatGPT on writing and coding projects that go beyond simple chat.

Product and model features:

1/ Ask for in-line feedback. With canvas, ChatGPT can better understand the context of what you’re trying to accomplish. You can highlight specific sections to indicate exactly what you want ChatGPT to focus on. Like a copy editor or code reviewer, it can give in-line feedback and suggestions with the entire project in mind.

2/ Directly edit the model's output and select a specific area for targeted editing. You control your creative work on canvas. You can directly edit text or code.

3/ Menu of shortcuts. There’s a menu of shortcuts for you to ask ChatGPT to adjust writing length, debug your code, and quickly perform other useful actions. You can also restore previous versions of your work by using the back button in canvas.

4/ Use search with canvas for research writing! As we are moving towards the new paradigm of reasoning we are fundamentally evolving the chat interface into a more collaborative human-AI interaction. Today you can say “browse / use browsing to find XYZ on the internet and write a report in canvas”

[Sifat Muhammad Abdullah @SifatMdAbdullah]

@karpathy But won't that take away the fundamental skill of thinking for ourselves, and instead be sth like entering a rabbit hole on youtube or doomscrolling on Instagram?

[Imtiaz Karim @ImtiazKarim23]

I’m on the job market and looking for tenure-track faculty or industry researcher positions. My research interests lie in systems and network security, specifically analyzing the security and privacy of wireless communication protocols (4G/5G, Bluetooth, vehicular, WiFi, and IoT)

[Sifat Muhammad Abdullah @SifatMdAbdullah]

Thank you Professor @bviswana for the shoutout. I have recently published at IEEE S&P'24 about analyzing robustness of deepfake image detectors using Stable Diffusion and StyleGAN-based text-to-image generators. I have also studied toxicity injection and mitigation on LLMchatbots

[Jason Wei @_jasonwei]

Super excited to finally share what I have been working on at OpenAI!

o1 is a model that thinks before giving the final answer. In my own words, here are the biggest updates to the field of AI (see the blog post for more details):

1. Don’t do chain of thought purely via prompting, train models to do better chain of thought using RL.

2. In the history of deep learning we have always tried to scale training compute, but chain of thought is a form of adaptive compute that can also be scaled at inference time.

3. Results on AIME and GPQA are really strong, but that doesn’t necessarily translate to something that a user can feel. Even as someone working in science, it’s not easy to find the slice of prompts where GPT-4o fails, o1 does well, and I can grade the answer. But when you do find such prompts, o1 feels totally magical. We all need to find harder prompts.

4. AI models chain of thought using human language is great in so many ways. The model does a lot of human-like things, like breaking down tricky steps into simpler ones, recognizing and correcting mistakes, and trying different approaches. Would highly encourage everyone to look at the chain of thought examples in the blog post.

The game has been totally redefined.

[Sifat Muhammad Abdullah @SifatMdAbdullah]

@armankhon will research internship positions be opened for summer 2025? I couldn't find them in this link

[Andrej Karpathy @karpathy]

Huge congrats to @AIatMeta on the Llama 3.1 release!
Few notes:

Today, with the 405B model release, is the first time that a frontier-capability LLM is available to everyone to work with and build on. The model appears to be GPT-4 / Claude 3.5 Sonnet grade and the weights are open and permissively licensed, including commercial use, synthetic data generation, distillation and finetuning. This is an actual, open, frontier-capability LLM release from Meta. The release includes a lot more, e.g. including a 92-page PDF with a lot of detail about the model:
https://t.co/48e3YJ8Sg9

The philosophy underlying this release is in this longread from Zuck, well worth reading as it nicely covers all the major points and arguments in favor of the open AI ecosystem worldview:
"Open Source AI is the Path Forward"
https://t.co/AdmpadCRM0
I like to say that it is still very early days, that we are back in the ~1980s of computing all over again, that LLMs are a next major computing paradigm, and Meta is clearly positioning itself to be the open ecosystem leader of it.

- People will prompt and RAG the models.
- People will finetune the models.
- People will distill them into smaller expert models for narrow tasks and applications.
- People will study, benchmark, optimize.

Open ecosystems also self-organize in modular ways into products apps and services, where each party can contribute their own unique expertise. One example from this morning is @GroqInc , who built a new chip that inferences LLMs *really fast*. They've already integrated Llama 3.1 models and appear to be able to inference the 8B model ~instantly:
https://t.co/b2kdSsz0fH
And (I can't seem to try it due to server pressure) the 405B running on Groq is probably the highest capability, fastest LLM today (?).

Early model evaluations look good:
https://t.co/RLR5YBpmks https://t.co/ipT4x4wCvy
Pending still is the "vibe check", look out for that on X / r/LocalLlama over the next few days (hours?).

I expect the closed model players (which imo have a role in the ecosystem too) to give chase soon, and I'm looking forward to that.

There's a lot to like on the technical side too, w.r.t. multilingual, context lengths, function calling, multimodal, etc. I'll post about some of the technical notes a bit later, once I make it through all the 92 pages of the paper :)