Are Data Scientists Still Needed in the Age of Generative AI?: The Rise of ChatGPT. https://
kdnuggets.com/2023/06/data-s
cientists-still-needed-age-generative-ai.html?utm_source=dlvr.it&utm_medium=twitter&utm_campaign=are-data-scientists-still-needed-in-the-age-of-generative-ai
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LLMS
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Data Scientists Still Needed in Generative AI Era
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LLMs Lack Many Assumed Capabilities, Beware Hype
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Today, in 2023, it's good to remember that most of the capabilities that the tech industry assumes LLMs to already possess aren't yet within reach. Tread this space carefully, and beware of shiny demos. Last year, I was repeatedly told that the upcoming GPT-4 was already AGI.
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Human-level language understanding remains years away despite progress
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In 2016, when I tweeted that human-level language understanding was many years away (which is still the case now, though we're closer), mind the context: this was in response to many people, including prominent VCs, claiming that then-current AI was nearly there and was about to
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Multimodal LLM with Vision-Language Transformer and LangChain
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Using the `Vision-and-Language Transformer` model and @LangChainAI to create a Multimodal LLM in @Streamlit! 🔥
— Charly Wargnier (@DataChaz) 23 juillet 2023
– Demo app: https://t.co/miwwtiUxv0
– ViLT model: https://t.co/nz8npLUs9o
– App creator: @nicolas_tch pic.twitter.com/iTWuMwgc8WUsing the `Vision-and-Language Transformer` model and @langchain to create a Multimodal LLM in @Streamlit
! – Demo app: https://
vilt-gpt-ppn83ly4c9.streamlit.app
– ViLT model: https://
huggingface.co/dandelin/vilt-
b32-finetuned-vqa
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– App creator: @nicolas_tch -
AI capability predictions: 2016 expectations versus 2023 reality
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In 2016, people didn't anticipate next-token prediction models (which were LSTMs then) to be this capable, but they did expect AI to soon have most of the capabilities they show now. Remember the chatbot mini-bubble of 2017? It was predicated on 2023 capabilities coming circa
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LLM capabilities have advanced far beyond 2016 expectations
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I don’t think many people in 2016 thought that LLMs would be so much more capable today. As you note, the use cases even when they’re not perfect are enormous.
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AI Progress Exceeded Expectations Despite Autonomous Vehicles Delay
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Yep. LLM‘s came a lot faster than people expected and self driving cars slower. But overall, I’d have to rate AI progress as surprising us on the upside, even people who were relatively optimistic like me. Don’t you agree?
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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 -
From Links to Answers: AI-Driven Task Completion Evolution
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Accelerate the transition from links –> answers, sifting –> learning, browsing –> getting things done.
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Llama API Integration Guide and Documentation Access
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Read more about llama-api and get a key here: https://
llama-api.com Documentation for the integration: https://
python.langchain.com/docs/modules/m
odel_io/models/chat/integrations/llama_api
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