3) Grammarly. Grammarly is an AI-based writing assistant that checks for grammar, spelling, and punctuation errors. Features:
– Real-time writing suggestions
– Plagiarism detection
– Writing style improvements
– Integration with Microsoft Office and Google Docs
MACHINE LEARNING
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Grammarly AI writing assistant with grammar, spelling, and plagiarism detection
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IBM Watson API suite for building AI-powered solutions
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2) IBM Watson. IBM Watson is a suite of APIs that can be used to build AI-powered solutions. Features: – Natural language processing
– Speech-to-text
– Text-to-speech
– Personality insights
– Visual recognition -
Salesforce Einstein: AI for predictive lead scoring and automation
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1) Salesforce Einstein. Salesforce Einstein uses AI to analyze customer data, make predictions, and suggest actions. Features: – Predictive lead scoring
– Automated data entry and analysis
– Next-best action recommendations
– Automated lead nurturing -
AlphaFold Adoption Accelerates Lifesaving Medical Research Workflows
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I’ve been so impressed by how quickly scientists have adapted #AlphaFold into their daily workflows and use it to advance their important work on lifesaving treatments. This is exactly why we made it freely available to the research community. https://t.co/sxaXe3fBh6 pic.twitter.com/XyDlPfScWy
— Demis Hassabis (@demishassabis) 7 août 2023I’ve been so impressed by how quickly scientists have adapted #AlphaFold into their daily workflows and use it to advance their important work on lifesaving treatments. This is exactly why we made it freely available to the research community. https://
dpmd.ai/3YiMgmK -
AI Models Predict Patient Demographics From Medical Images
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Studies show AI models can predict patient demographics including race from medical images. HAI faculty affiliate @james_y_zou discusses the real concerns — and possible benefit — in this: https://
stanford.io/3KuVSVZ -
Fine-tuning smaller models for specialized tasks with custom data
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Yes, it's more impressive. But if you are a small company that specializes on a specific task and has some custom data, I'd say that finetuning a smaller (e.g. 1.5 B) model is the way to go.
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Fine-tuning Custom LLMs vs Few-Shot Prompting Performance Comparison
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2/3 For fairness: The DeBERTa-1.5B model was likely finetuned on the training data whereas Llama 2 was used via few-shot prompting. In that case, it highlights once more that finetuning custom LLMs remains worthwhile.
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Reference Papers for Llama 2 and DeBERTa Models
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3/3
Here are the papers for reference: Llama 2: Open Foundation and Fine-Tuned Chat Models: https://
arxiv.org/abs/2307.09288 DeBERTa: Decoding-enhanced BERT with Disentangled Attention: https://
arxiv.org/abs/2006.03654 -
DeBERTa-1.5B Outperforms Llama 2 on BoolQ Classification Task
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Interestingly, DeBERTa-1.5B (and encoder-only model) beats Llama 2 on BoolQ, which is a nice example that encoders still outperform large decoders on classification task. For fairness: The DeBERTa-1.5B model was likely finetuned on the training data 1/3
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PaddlePaddle Framework: Object Detection, Pose Estimation, Segmentation
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🎥New Videohttps://t.co/qJ6aEVc0o1
— Satya Mallick (@LearnOpenCV) 7 août 2023
Check out our awesome video, where we dive into the world of PaddlePaddle framework!
Discover the magic of object detection, pose estimation, and segmentation in PaddlePaddle using a Google Colab notebook! #PaddlePaddle #computervision #AI… pic.twitter.com/iQb3kOEAKQNew https://
youtube.com/watch?v=H_8nDp
2GRQU
… Check out our awesome video, where we dive into the world of PaddlePaddle framework!
Discover the magic of object detection, pose estimation, and segmentation in PaddlePaddle using a Google Colab notebook! #PaddlePaddle #computervision #AI