You can now use @abacusai to build a custom LLM on your data and solve some of the following problems: • ChatLLM: Search your knowledge base
• DataLLM: Get insights from your data
• TaskLLM: Teach an LLM to solve your tasks
MACHINE LEARNING
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Build Custom LLMs on Your Data with Abacus AI
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How Policymakers Can Accelerate AI Innovation
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How Policymakers Can Accelerate AI Innovation
#AI #AIio #BigData #ML #NLU #Futureofwork @IIoT_World @ipfconline1 @jblefevre60 @JimMarous @insurtechtalk @KirkDBorne @kuriharan @LouisSerge http://
ow.ly/RK5a30sw8C1 -
Knowledge Graphs and LLMs for Multi-Hop Question Answering
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Knowledge Graphs & LLMs: Multi-Hop Question Answering https://
bit.ly/43N4Yow #AI #MachineLearning #DeepLearning #LLMs #DataScience -
Serial-Position Effect in Transformer Training Data
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Yup! > The serial-position effect plays a role in understanding how humans develop short- and long-term memory. My guess is the transformer picks this up from the training data, which reflects how humans write articles as well.
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Designing and Building Metric Trees for AI Systems
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Designing & Building Metric Trees https://
bit.ly/3ol793d #AI #MachineLearning #DeepLearning #LLMs #DataScience -
Start Learning Machine Learning with Neural Networks Video Series
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Are you looking to break into Machine Learning? Here is a great place to start: This is the best video series about Neural Networks for anyone who wants to understand how they work. https://
3blue1brown.com/topics/neural-
networks
… Make sure you follow @abacusai
! There's a lot more coming this month! -
kNN and Gzip paper coverage planned after vacation
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*PS: I didn't include the kNN + Gzip paper in this one since the paper was originally uploaded to arxiv in December 2022 https://
x.com/rasbt/status/1
679680423671001090?s=20
… But I have another "special coverage" for that one in mind in a few weeks once I am back from vacation 🙂 -
LLM Context Lengths and Efficient Transformers: June-July Research Highlights
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From long context lengths in LLMs to more efficient transformers for diffusion models. It's been another month with lots of interesting research. I put together an annotated list of 24 highlights from June to July:
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spaCy’s approach to transformer architectures and library integration
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We're still using it in @spacy_io . But we don't want to try to make efficient transformer architectures in it. We've always called out to other libraries for that. This lets us wrap PyTorch implementations directly, which makes things a bit easier for us.
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Computer Vision Embeddings for Machine Learning Applications
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Computer Vision Embeddings for Machine Learning https://
bit.ly/3CzwZE7 #AI #MachineLearning #DeepLearning #LLMs #DataScience