Nvidia’s opportunities in AI are ‘large and still early,’ says Bernstein As models get bigger, so does the opportunity. https://
thetechnologyletter.com/the-posts/nvid
ias-opportunities-in-ai-are-large-and-still-early-says-bernstein
… $NVDA $INTC $AMD @Srasgon #investing #stocks #Nvidia #artificialintelligence #machinelearning #GPU
MACHINE LEARNING
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Nvidia’s AI Opportunities Remain Large and Early Stage
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LLM Compression Techniques and Practical Implementation
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Oh nice! I took a couple stabs at “LLM compression” as I’d call this, but to no avail. Need to try this.
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Autonomous AI Agents Tackle AI Alignment Problem
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I asked my autonomous AI to solve AI alignment.
— Yohei (@yoheinakajima) 3 avril 2023
Fascinating to watch.
Agents:
– Task execution: text-davinci-003
– Task creation: text-davinci-003
– Task reprioritization: text-davinci-003 pic.twitter.com/rmOnKIgaAzI asked my autonomous AI to solve AI alignment. Fascinating to watch. Agents:
– Task execution: text-davinci-003
– Task creation: text-davinci-003
– Task reprioritization: text-davinci-003 -
State of the Art Image Generation Results from 5 Years Ago
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Around 5 years ago we were very proud of these state of the art results in image generation, trained on 32×32 "images" of CIFAR-10. You can kind of make out little wheel shapes, car/plane parts, and organic structures and textures. Pretty cool right
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Von Neumann’s Hypothetical Transformer Architecture Hyperparameters
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I wonder if von Neumann had a large d_model, n_layer, head_size or block_size, or kv cache. All of these hyperparams might manifest slightly different.
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Training Models: Foundation for Effective Testing Processes
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Because if you don’t train it, then you have nothing to test?
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Carnegie Mellon Deep Learning Course 2022-23 Lectures and Resources
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Introduction to Deep Learning, Carnegie Mellon 2022-23 Lecture(2023): https://
youtube.com/playlist?list=
PLp-0K3kfddPwgBSCbDtT6NaVOd-gIHVMW
… Lecture(2022): https://
youtube.com/playlist?list=
PLp-0K3kfddPy99Ia2XeM7awOEmzTSzsSq
… Website: http://
deeplearning.cs.cmu.edu/S23/index.html (Some lectures are way too advanced. Might not be a good starting point if this is your first contact with ML) -
Introduction to Deep Learning Carnegie Mellon Complete Lecture Series
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Introduction to Deep Learning, Carnegie Mellon 2022-23 A nice set of lectures that covers the foundation of deep learning. Goes all the way from the history of DL, neural network theories, techniques & algorithms that are used in present times. Lectures: https://
youtube.com/playlist?list=
PLp-0K3kfddPwgBSCbDtT6NaVOd-gIHVMW
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GPT Calls as Thoughts: Building Agents with Reflection and Memory
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1 GPT call is a bit like 1 thought. Stringing them together in loops creates agents that can perceive, think, and act, their goals defined in English in prompts. For feedback / learning, one path is to have a "reflect" phase that evaluates outcomes, saves rollouts to memory,
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Training LLMs to Use Multiple LLMs as Tools
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– Toolformer showed you can train LLMs to use tools. – HuggingGPT showed that you can use LLMs as tools. Who wants to train an LLM that uses other LLMs that can use tools? Can 100+ small LLMs outperform #GPT4?