Manish Mehta

Over last few months, I am increasingly leveraging LLMs to write code. From 10% to nearly 78% currently (based on lines of code) of my code is from LLMs. I have my own pipeline to fine tuned LLMs to augment my needs. Observations - 1. I am producing more code --> MORE IMPACT (approx 2.3X) 2. I am reading more code --> MORE MAINTAINABLE CODE 3. I am learning more as now I can start from concept and expand out ---> MORE LEARNING 4. I am incorporating best practices --> BETTER CODE 5. Lesser typing --> LOWER REPETITIVE STRAIN INJURY #AIBUDDY

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Collaborative AI is gaining momentum at both MNC and Startups, they are investing into this EvolutionNext Robotics AI Transformative Experiential Learning Journey. I am extremely glad to put Singapore on the World map, by anchoring Robotics AI companies here, creating Jobs of the FutureNow as Roboticist. Do support my Leadership Initiative for Singapore as National Interest; help me enable our next-generation Singaporeans to take on Science, Technology, Engineering and Mathematics STEM Education and longer-term Career Practice into Industry 5.0 as Machine Intelligence of Human-Robot Collaborative, Human-centric Autonomy. * My STEM OneCareer Pinnacle and Finale as a 55-years old, always just another very ordinary Singapore-born Singaporean Scientist Engineer in Singapore, essentially a Nobody here 🌸

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"Excited to share that I've earned my NVIDIA Certificate in Generative AI! This certification has equipped me with the skills to harness the power of AI and unlock new possibilities in creative and technical fields. I'm looking forward to applying my knowledge and exploring the cutting-edge capabilities of generative AI. Special thanks to #Pradumn, #Pankaj, #Jayesh, #Santosh and #Rohith for their support, guidance, encouragement. Thanks to Tredence Analytics for the opportunity to upskill and reskill in this innovative area! #NVIDIACertificate #GenerativeAI #AItraining"

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Check out this highly recommended course for those interested in optimizing their models: - Introduction to Quantization - Symmetric and Asymmetric Quantization Modes - Hands-on Implementation from Scratch using PyTorch - covering Per Tensor, Per Channel (Axis), and Per Group techniques - Weights Packing: Exploring 2 or 4 Bits Quantization Methods https://lnkd.in/gMAuWXHm

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🎉 Excited to Share a New Achievement! 🎉 I’m thrilled to have earned the NVIDIA Generative AI LLMs (NCA-GENL) Certification! 🚀 This certification validates my foundational understanding of developing, integrating, and maintaining AI-driven applications using Generative AI and Large Language Models (LLMs) with NVIDIA solutions. Over the past few months, I’ve delved deep into a variety of resources, including courses and webinars on topics such as Hugging Face's NLP, Building RAG Agents for LLMs, Diffusion Models, DeepLearning.AI's Prompt Engineering, and LangChain for LLM Application Development, to name a few. The journey has been challenging, but incredibly rewarding. I’m particularly excited about the knowledge I’ve gained regarding NVIDIA’s AI tech stack, including DGX, AI Enterprise, NeMo, NIM, and TensorRT. This certification not only strengthens my understanding of the current AI landscape but also equips me with the skills to contribute to developing cutting-edge AI applications. A big thank you to all the amazing resources and communities that helped me along the way. I look forward to applying this knowledge to future projects and continuing to grow in this exciting field! #NVIDIA #GenerativeAI #LLMs #Certification #AI #DeepLearning #MachineLearning #LangChain #HuggingFace

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Last month, a paper from Sakana AI called “AI Scientist” introduced an intriguing idea. Now, don’t get me wrong, I love AI as an assistant. But after getting an LLM-generated review for my NeurIPS paper last month (which was total crap and useless), I’m a little skeptical about AI discovering true novelty. Here’s my take on the whole AI Scientist concept: The paper talks about a paradigm shift in research, where AI can ideate by identifying challenges and opportunities, build hypotheses, design and run experiments by generating code and simulations, analyze results, refine hypotheses, and even write research papers and reviews. The authors even generated three full research papers using prompt templates to prove the concept! Now, I agree that this could be a game-changer for interdisciplinary research, or for uncovering new problems at the intersection of fields—where human experts, limited by working memory and attention span, may struggle to grasp more than a handful of domains. LLMs, with their ever-expanding knowledge base, offer the potential for cross-pollination of ideas. But when it comes to deep, niche, and fundamental breakthroughs, I’m not buying it—hence my disappointment with that NeurIPS review :( Here’s the thing—mathematically, LLMs are trained to regenerate the data distribution they’ve already seen. So, it's inherently challenging, if not impossible, for them to produce something truly novel. I like to think of LLMs as vast lookup tables, which makes them amazing assistants, but let’s not call them “scientists” just yet!

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🚀 New Research Alert! 🌟 Excited to share a groundbreaking paper on **Recursive Introspection** from Yuxiao Qu, Tianjun Zhang, Naman Garg, and Aviral Kumar. This research introduces **RISE** (Recursive IntroSpEction), a cutting-edge method for fine-tuning language models, enabling them to self-improve by iteratively correcting their own mistakes. This innovation significantly boosts performance on complex math reasoning tasks in models like Llama2, Llama3, and Mistral. 📈 Before RISE, language models depended heavily on human annotations for refinement. Now, with RISE, models can autonomously evaluate and enhance their outputs, marking a major advancement in AI technology. Read more: [arXiv:2407.18219v1](https://lnkd.in/gJffMJ57) #AI #MachineLearning #NLP #LLMs #SelfImprovement

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I remember when this all started. Faris - invited me to catch up at our favorite spot around Blok M. I had no idea he’d just wrapped up a procedure at Pondok Indah Hospital! (He really knows how to add drama to our catch-ups.) We’d been on separate paths and projects since "the winding down project", but there he was, reminding me of our dream to ride this AI wave together again. It was a plan we’d hatched with Sahal Zain & Gurnoor Dhillon when the world of AI was still mostly theoretical and jargon-heavy. Somehow, the conversation continued with Gurnoor Dhillon while he was waiting for his meeting with someone who would eventually become one of CalvinBall’s key early adopters. It felt like everything was coming together, like a series of seemingly random dots connecting to form a bigger picture. Seeing how far we’ve come as a team at CalvinBall Technologies is truly exciting. What started as theoretical discussions has now evolved into practical Gen AI solutions that are solving everyday problems and hold endless possibilities to shape the industry. 👨‍🚀 👩‍🚀 Here’s to pushing boundaries and achieving new milestones together! Gurnoor Dhillon, Sandeep Ramesh, Faris -, Sahal Zain, Eva Zuliya, Ratu Nadiah Khairunnisa 🚀 #GenAI #AIInnovation #TechJourney #ArtificialIntelligence #TechTransformation #CalvinballTech #Calvinballers

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The next guest speaker in my LLM course is Ram Sriharsha, the CTO of Pinecone, who will talk about Retrieval Augmented Generation (RAG) on Monday October 28. ➡️ Register here: https://lu.ma/r7yokyvc Title: Knowledgeable Models Combine Retrieval with Generation: An Introduction to RAG Date: Monday October 28 from 1:45 PM to 3:15 PM ET Abstract: Large Language Models have emerged into mainstream over the last few years. While they have demonstrated some huge successes already, they have also had challenges with hallucinations, difficulty with dealing with new knowledge and in retrieving relevant, accurate knowledge for downstream tasks. In this talk, we will provide a brief overview of the problem, and discuss a promising approach to solving these problems through what is known as Retrieval Augmented Generation (RAG). Alongside, we will also introduce vector databases, what makes them different from traditional databases and how they fit into RAG. Bio: Dr. Ram Sriharsha is the CTO at Pinecone. At Pinecone, Ram is responsible for overseeing the company’s technical direction in both engineering and research. Prior to joining Pinecone, Ram held numerous senior positions at some of the most well-known companies in the tech industry. Most recently, he was Vice President of Engineering at Splunk. Before that, he was a product and engineering lead at Databricks for their unified analytics platform for genomics. Some years after receiving a PhD in Physics from the University of Maryland, Ram was a Principal Software Engineer and later a Principal Research Scientist at Yahoo. Supplementary Readings: ➡️ The original paper on RAG: "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks" (https://lnkd.in/e_e56Y54) ➡️ Long context considerations: "Lost in the Middle: How Language Models Use Long Contexts" (https://lnkd.in/ehED53Fe) ➡️ Article on contextual RAG from Anthropic AI (https://lnkd.in/ehK4GwMc)

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The BayLearn 2024 abstract submission deadline has been extended to Aug 5, 2024. If you are working on cutting-edge ML research, please consider submitting an abstract. BayLearn 2024 will be an in-person event, held on Oct 10, 2024: https://baylearn.org This is a great opportunity for student researchers to showcase their latest research work and get good feedback. Submissions are non-archival and the review process is quite informal. #machinelearning #research

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We in the Silicon Valley have taken a position that the only thing we should be doing is injecting AI in to everything we do. And the big 5 Indian outsourcing companies are also drilling the same in to the brains of everyone in the organization. AI or DIE Greg Head does his homework and brings us to the reality. We have seen this in the past. Rewriting everything in Java or committing to J2EE architecture will not revolutionize business. Moving all data to Hadoop won't guarantee better business intelligence. Moving everything to Cloud will not cut IT costs by 95%. Moving data warehouse to AWS RedShift is not a sliced bread kind of invention. What are the questions people are asking in the Silicon Valley? Can we use LLM to retire our complex ERP systems? (Oracle, SAP, MFG-PRO, Infor, Sage etc) Can we use LLM to retire our expensive and clunky HRMS ?(Oracle, Workday, SAP, ADP etc) Can we use LLM to retire CRM ?(SFDC, Oracle, SAP etc). Can we use LLM to rewrite our homegrown applications for high throughput? Why not? LLM advocates say if you give your requirements to (ERP, HRMS, CRM, Custom applications) LLM, you can have your own software that will work for your requirements and it will cost nothing. And no expensive SaaS fees, No multi-million upgrades and no money for expensive consultants. If this becomes true, it will be the end of multi trillion $ software industries. Dream on LLM fans. We are not there at. Similar promises were made by Indian offshore outsourcing companies. That they will take over the IT and run it from India for 1/5th the cost.

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That’s a great note which give me a clear list to review almost every general topic in ML. It’s very useful not only for the starters but also for people who want review some concepts quickly. Hope more people can see that!

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Talking about LMMs and Astrophysics soon! :)

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Fantastic news today on the general availability of Amazon Q. Amazon Q is the most capable generative AI-powered assistant for accelerating software development and leveraging companies’ internal data. It eliminates tedious work for developers and employees across organizations. #AmazonQ #GenerativeAI

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There is a lot of interest in combining Graph Models and Language Models recently. In 2022, I led an initiative at Twitter (now X) in collaboration with Vivek Kulkarni, Ahmed El-Kishky, Sneha Mehta, and executed by our wonderful summer intern Jinning Li called "NTULM: Enriching Social Media Text Representations with Non-Textual Units" on combining large scale graph content at Twitter with Tweet's text content for building better quality Tweet Embeddings. The internal version of the project required addressing quite a few challenges: 1. Finding the node embeddings for NTUs using Twitter Scale Heterogeneous Graph 2. Training BERT with the above scale node embeddings This work was a follow up to my previous collaboration led by Vivek Kulkarni in 2021 on combining social context like time and geography in language models called "LMSOC: An Approach for Socially Sensitive Pretraining". There are a lot of interesting follow ups in this area and combining Graphs with generative LLMs is something which is very effective on real world datasets. NTULM Talk: https://lnkd.in/g2zvmnhh NTULM Paper: https://lnkd.in/gQq6VCQP LMSOC: https://lnkd.in/ghamVwVk

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It’s been a while since I have shared my experiences of working on AI projects using LLM’s that I have worked on in last 2 years. Hope these experiences will help folks. 😊 so here is the first one   Language Classification with GPT – We were working on a problem scenario where 2 languages to be classified in separate classes were very much similar in terms of words available in them and up to a good extent semantically also, but the languages were actually different. As the languages were domain specific, it was expected that we have to empower GPT. We used different models starting with GPT 3.5, GPT4 Turbo 0125, and then GPT 4 Omni and at last GPT-4 Turbo 2024-04-09. Of all the models we got the best accuracy with the last one. The last 2 are the latest ones available and as per Microsoft recommendation the go-to GPT models. However with GPT-4 Turbo 2024-04-09 also we didn’t got the desired result by the same pipeline we were using (words filter + vector search + RAG). While searching for the method to resolve the issue, I came across a research paper around ‘Feedback loop’ and how it can improve the results. (Sorry didn’t remember the paper name now , will try to find and post that).  We applied the same with some experimentations and it worked. So the focus here is – if you have a use cases where GPT/LLM’s is getting confused in language classification after application of latest techniques like vector search and RAG, feedback loop can be introduced after the final output as a last step. It can enhance the accuracy in very confusing but important cases. Hope this will help community. 😊 #NLP #LLM #GPT #RAG #DataScience #AI #OpenAI #Chatgpt OpenAI

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The release of Llama 3.2, an open multimodal model, marks another significant milestone for the AI community. But as we explore the potential of models like Llama 3.2, we must also address critical concerns around adversarial attacks and robustness. Robustness is no longer an option, but a necessity, in ensuring the integrity and security of these advanced models. The journey to create robust, secure multimodal models like Llama 3.2 is one that the AI community must tackle head-on. Let’s continue pushing the boundaries of what’s possible while making sure our innovations are as resilient as they are cutting-edge! #AISafety #Llama32 #Multimodal #DeepLearning #AdversarialAttacks #Robustness #MetaAI #MachineLearning #AIResearch 

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