Vaibhav Goyal’s Post

This is the way. I think that current RAG patterns are very limited but there aren't better solutions yet, even though this is a promising one in the direction that I find the most interesting, which is a differentiable "soft" memory. ❌ Currently, a retriever extracts contexts (texts), which are sent to the LLM along with the user query. For a very good pipeline you need to have an embedding model, lexical search, query decomposition, hierarchical information chunks and lots of other things... that may not be enough for answering all kinds of queries. ✅ Have a sort of differentiable memory, which writes information into the LLM activations, that this model could request using cross-attention or some approach like this. Collect a dataset, research this memory and just let the neural net learn to do the task.

Great work Thomas Dybdahl Ahle and Phoebe Klett! Being able to generate citations and attribution is essential for most real-world GenAI use cases. For the most part roughly the same RAG approaches have been leveraged across the board between low-stakes consumer applications and higher-stakes enterprise applications. What happens if we are willing to take full advantage of an LLM's attention mechanism, meaning we ultimately make more queries to the VectorDB? We see that are able to achieve state-of-the-art improvements in reliability (eliminating fact-based hallucinations) and retrieval quality. "Very smart and simple approach that simplifies a lot the RAG architecture. Just let the LLM work!" Thanks for the shout Diego Peláez Paquico. In general, we believe at Normal that Enterprise AI architectures need to be built with fundamentally different assumptions in mind compared to the lower-stakes environments where we've designed today's consensus GenAI stack. Daniel Kahneman might say that enterprise needs the System 2 of AI -- typically more intensive, but significantly more reliable. Our latest release casts further light on specific approaches we are using that fill this category. Read more about the work and Hugging Face release here https://lnkd.in/dkq2e3Z8.

So Normal Computing just released a first version of what I believe is the way. To be able to generate text based on facts (memories) they combine the attention operations with a vector database, loaded with these external memories KV cache vectors. Then, at generation time, instead of computing attention against all the memories, calculate the usual attention queries, query the vector DB with them and continue the attention operations with the values retrieved! Very smart and simple approach that simplifies a lot the RAG architecture. Just let the LLM work! I see that the current challenges are to adapt this technique to other positional encodings and I suppose the generations speed slows significantly (didn't test this), because querying a vector DB at each transformer layer must add significant latency, but this is very promising. Thanks Phoebe Klett and Thomas Dybdahl Ahle ! https://lnkd.in/dHQ9QxHX

➡️ What is Machine Learning? 🎰 Machine Learning is a subset of artificial intelligence that focuses on developing algorithms and models that enable computers to learn from and make predictions or decisions based on data, without being explicitly programmed. It involves the use of statistical techniques to enable systems to improve their performance on a specific task through experience. Follow for more #interviewquestions #machineleaning

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Optimization and machine learning are deeply intertwined, with optimization serving as a fundamental tool in various aspects of machine learning. In machine learning, optimization algorithms are used to minimize or maximize an objective function, such as a loss function in training models or an evaluation metric in hyperparameter tuning. Optimization techniques like gradient descent, stochastic gradient descent, and their variants are central to optimizing model parameters and improving model performance. Additionally, optimization methods are employed in feature selection, constraint handling, ensemble learning, and other areas to enhance the overall effectiveness and efficiency of machine learning algorithms and models. Thus, optimization forms a critical foundation for the success and advancement of machine learning techniques and applications.

Theano: Bridging the Gap Between Theory and Practice in Machine Learning Theano: Bridging the Gap Between Theory and Practice in Machine Learning Introduction: Machine learning has revolutionized various industries by enabling computers to learn from data and make predictions or decisions without being explicitly programmed. This field has seen significant advancements in recent years, thanks to the development of powerful frameworks and libraries. One such framework […] https://lnkd.in/dSpj4SM4

𝐓𝐨𝐝𝐚𝐲'𝐬 𝐭𝐨𝐩𝐢𝐜 𝐦𝐚𝐤𝐞𝐬 𝐲𝐨𝐮 𝐭𝐡𝐢𝐧𝐤! ... Why does training Large Language Models (LLMs) take a significant amount of time? Let me guess some factors to consider: 𝟏. 𝐃𝐚𝐭𝐚 𝐕𝐨𝐥𝐮𝐦𝐞: LLMs require massive datasets for training. 𝟐. 𝐂𝐨𝐦𝐩𝐥𝐞𝐱 𝐌𝐨𝐝𝐞𝐥: Models like GPT-3 and GPT-4 have billions to trillions of parameters. 𝟑. 𝐇𝐚𝐫𝐝𝐰𝐚𝐫𝐞 𝐋𝐢𝐦𝐢𝐭𝐚𝐭𝐢𝐨𝐧: Training on GPUs or TPUs can still be time-consuming. NVIDIA become so powerful because of these offerings. 𝟒. 𝐈𝐭𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐑𝐞𝐟𝐢𝐧𝐞𝐦𝐞𝐧𝐭: Training involves multiple iterations for fine-tuning. and many other factors. But I believe the primary reason is quite fundamental. It usually comes down to thinking from First Principles. Let's not exaggerate too much; instead, let's understand where this issue begins. Matrix multiplication is a significant challenge and a current bottleneck. The complexity of performing matrix multiplications is substantial, typically being O(n^3). Although efforts have managed to reduce it to approximately O(n^2.34), it remains a substantial obstacle. 𝐖𝐡𝐲 𝐌𝐚𝐭𝐫𝐢𝐱 𝐌𝐮𝐥𝐭𝐢𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐌𝐚𝐭𝐭𝐞𝐫𝐬? 𝐑𝐞𝐩𝐫𝐞𝐬𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧: In LLMs, words, sentences, and code are represented as numerical vectors or matrices. Understanding the relationships between words and predicting the next word often involves linear algebra operations, primarily matrix multiplications. 𝐒𝐜𝐚𝐥𝐞: The matrices involved in LLMs are massive due to large vocabularies and long input sequences. Multiplying these repeatedly becomes incredibly computationally expensive. 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐞𝐫𝐬: Modern LLM architectures, like Transformers, heavily rely on attention mechanisms. Attention calculations essentially involve numerous matrix multiplications for each word in a sequence. In conclusion, the significant challenge in training large language models often stems from the fundamental bottleneck of matrix multiplication complexity. must read DeepMind publication: https://lnkd.in/geykHYVm Happy Sunday, Keep Learning Follow my group: https://lnkd.in/gD4E-jAU #dataanalytics #data #datascientist #machinelearningengineer #generatieveai #deeplearning #llm #llmops

Theano: Bridging the Gap Between Theory and Practice in Machine Learning Theano: Bridging the Gap Between Theory and Practice in Machine Learning Introduction: Machine learning has revolutionized various industries by enabling computers to learn from data and make predictions or decisions without being explicitly programmed. This field has seen significant advancements in recent years, thanks to the development of powerful frameworks and libraries. One such framework […] https://lnkd.in/dYGRAprn

Theano: Bridging the Gap Between Theory and Practice in Machine Learning Theano: Bridging the Gap Between Theory and Practice in Machine Learning Introduction: Machine learning has revolutionized various industries by enabling computers to learn from data and make predictions or decisions without being explicitly programmed. This field has seen significant advancements in recent years, thanks to the development of powerful frameworks and libraries. One such framework […] https://lnkd.in/dYGRAprn