Tanmay Bakshi @TajyMany
ML Architect @IBM Watson Apps, @Google Dev Expert, Instructor @ Uni. of Winnipeg, Bestselling Author, TED & Keynote Speaker. YouTube: https://t.co/PdDlP5Vq2V ca.linkedin.com/in/tanmay-baks… Ontario, Canada Joined July 2015-
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Loved keynoting at @GdscTmu's Networking Night! Impressed by students eager to tackle real-world tech challenges. Your commitment to mastering skills and making an impact is inspiring. Thanks to the team for a memorable event!
“Organizations might end up eventually realizing that there are certain data regulations and other kinds of technical issues to do even potentially with the determinism of the output of these systems and the reliability and controllability of them.” hlb.global/insights/surve…
“Not only does AI make it possible for organizations to do better, it makes it possible for them to do more. With the application of this technology, we increase the scope of what computing systems can even do in general.” hlb.global/insights/surve…
“AI is a moving goalpost. It’s whatever people consider to be AI at the time. Especially new terms like generative AI—they’re fundamentally ML with use cases so broad in scope that most won’t even realise where a vast majority of them are being applied.” hlb.global/insights/surve…
"Without the right infrastructure in place, there’s no chance for us to have what we call modern AI. I’ve seen companies that are bottlenecked in what they can do & deliver simply because they physically don’t have enough compute infrastructure." hlb.global/insights/surve…
I was asked why we never published metrics relative to GPT-4 for the pplx-chat and pplx-online LLMs and only compared to 3.5-turbo. Just like everyone else today, we are far away from GPT-4 capabilities, even on the narrow task of answering questions accurately with search…
Epic work, the thing I love about this is how small and clean the code is - literally reimplementing everything down to the metal instead of depending on thick layers of magic.
Epic work, the thing I love about this is how small and clean the code is - literally reimplementing everything down to the metal instead of depending on thick layers of magic.
Demystifying RLHF (some of the secret sauce behind modern "Gen AI" advancements 🧠), and other fun, important cutting-edge tech conversations at George Brown College Marketing Symposium!
Exciting AI & Cybersecurity conversations with curious young minds at Vaughn College! Thank you, Dr. Wright, for exposing them to real-world practitioners sharing the applications & limitations of next-gen tech. 📖the book signing was my favourite part!
Loved giving a hands-on code-level intro to #MachineLearning and #ArtificialIntelligence for Scotiabank interns! I was enthused to see the eagerness of everyone in the room—technical AND non-technical—to dive deep!
“Distribution shift” 👻 ML terms can sound really scary, but at their core, a lot of them have pretty simple intuitions that build on top of each other. I love distilling these concepts down for those learning them at a young age, as someone who had to do that myself!
We're hardwired to perceive intelligence even where there is... Code! Apps like ChatGPT check *just* the right boxes in our brains to get them to shout "💡 that's Artificial Intelligence!" Let's talk about our brains' quirks, AI, and more 👉 youtube.com/watch?v=-bG64I…
Look at what my friend sent from a bookstore in Korea 👀 @BaheerKamal our book finding new corners of the world!
Teaching Pepper the Robot to listen feels like one of the most magical applications I've built so far. Here's how I did it—a great example of how you can creatively integrate Machine Learning models into applications to deliver value 👉 youtube.com/watch?v=0Gs6Ga…
Get ready for an exciting week (July 17-21)! I'm thrilled to host an AMA with the Emerging Technologies community at ISACA Engage. Ask me about the future of tech, my journey, tools for ML, the risks and opportunities, or ... anything! Register: engage.isaca.org/communities/co…
Going live in 30 minutes at 1 PM ET! Join us as we build a compiler to generate #minecraft Redstone from high-level code. Ask us questions, give us suggestions, and code with us, live! youtube.com/live/IoDkLhB6J…
Join us LIVE as we build an #LLVM Compiler that turns high-level code into ... #Minecraft Redstone circuits?! We'll be streaming at 1 PM ET today, building the foundations for "RedVM"—learn, interact, and code with us live! youtube.com/live/IoDkLhB6J…
Exciting news — 'Tanmay Teaches Julia' has gone Japanese and 'Tanmay Teaches Go' is now in Korean! Seems like it's time for me to learn some new languages, but for the first time, not to code in 😆 manning.com/books/hello-sw… packtpub.com/product/cognit… mheducation.ca/catalogsearch/…
Gaurav Aggarwal @fooobar
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2K Followers 1K Following #outsideinnarketing #marketeer #communicator #early adopter #love technology which empower us. working #mobilemarketing #Branch. All views, tweets are my own.one of the most important things I know about deep learning I learned from this paper: "Pretraining Without Attention" this what I found so surprising: these people developed an architecture very different from Transformers called BiGS, spent months and months optimizing it and…
Anyone who tried to read any scientific article at least once knows that English cannot be used to clearly convey ideas. Most people, including the brightest of scientists, have a hard time writing clearly. I'm sure Nvidia CEO knows that too. What he is doing here is he is…
Jensen Huang, CEO of Nvidia, argues that we should stop saying kids should learn to code. He argues the rise of AI means we can replace programming languages with human language prompts thus enabling everyone to be a programmer. AI will kill coding.
Folks, you don't need anything special to have python automatically drop you into the debugger if there's an exception -- this capability is already built in! Here's a complete demonstration of how to do it, including an alias you might find pretty handy:
It's my pleasure to announce that I am joining the Board of Advisors at #Recruitics! Recruitics is at the cutting edge of the #AI revolution, making it easy for the world’s leading companies to attract and hire exceptional talent. #Technology info.recruitics.com/blog/recruitic…
@TajyMany @CreativeFdnWPG Keep it up. Amazing!!!
@TajyMany @samlightstone @RHigh Interesting perspective! It's crucial to adapt to technological advancements and use them to our advantage. Thanks for sharing this insightful discussion!
Many of us don’t believe a kid become role model. But I do believe they didn’t met @TajyMany #IBMCodeDay #Prodigy #QuantumComputing
6 months into my IBM journey, super excited about diving into a new project! 🌐 Building from the scratch , and what makes it even more exciting? Our 18-year-old architect, Tanmay Bakshi who kickstarted his IBM career at age 10, is leading the charge. 🚀 #Innovation #TeamIBM
PSA mlx-lm now works with Mixtral-8x7B and Phi-2: pip install -U mlx-lm Pre-quantized models are in the 🤗 MLX Community huggingface.co/mlx-community Here's 4-bit quantized Mixtral running on an M2 Ultra, (it would probably run fine on a 64GB machine):
my definition of AGI is data label efficiency
TLDR use the biggest LLM you possibly can in 4bit. Anything else is leaving performance on the table
Tried a LoRA on mistral again, and it immediately picked up multiturn conversations correctly with < 2k samples. Really shows how much better bigger models are over smaller models
Why am I recommending bigger models? I mean look at this. Even Qwen-1.8B-chat fails at properly associating turns... smaller models will not cut it
This to me is a very important feature / side-effect of larger models. Being able to steer them with just a prompt
I think one significant drawback that *cannot* be addressed by "number of tokens" is the in context learning capabilities... it is clearly not there for a model this size
There is a disturbingly large set of researchers in artificial intelligence who believe that if you give them a collection of data big enough and mountain of GPUs on which to train, they can build a mind. They are, respectfully, so very wrong.
Introducing *SequenceMatch*, training LLMs with an imitation learning loss Avoids compounding error in generation by: 1. Training against *different divergences* like χ^2 with more support OOD 2. Adding a *backspace* action: model can correct errors! arxiv.org/abs/2306.05426 1/7
Wow I just reran humaneval (code bench) on the new mistral-8X-7B and it scored 50% holy shit lmao, this is higher than codellama-34B. I asked it to return the completion in markdown