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Ask the CEO: Can you help us separate the realities of AI from the hype?

Transcription:

So a lot of hype around AI is around what Hallywood shows us. We're not building the Terminator anymore. But are we close to self-driving cars? Probably we are, but not that close yet.

But when you take that same concept and apply it to specific domains like agriculture, for example. Agricultural automation is so much easier than self-driving cars. You don't run over people when you're out in the fields. You don't have that risk that is inherent with a self-driving option.

Some of the applications we keep hearing about are advancing so fast. And we're seeing some amazing technologies coming out of them. But a lot of the hype is around things that could be done that we read in the science fiction books.

20.11.2019

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How to extract knowledge from documents with Google PaLM 2 LLM

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How to extract knowledge from documents with Google PaLM 2 LLM

PaLM 2 is Google's next generation large language model that builds on Google’s legacy of breakthrough research in machine learning and responsible AI. It excels at advanced reasoning tasks, including code and math, classification and question answering, translation and multilingual proficiency, and natural language generation better than previous state-of-the-art LLMs. It can accomplish these tasks because of the way it was built – bringing together compute-optimal scaling, an improved dataset mixture, and model architecture improvements. This article offers a quick and straightforward method for leveraging the PaLM 2 API to extract knowledge and ask questions from text.

DevFest West Coast 2020

Watch videos of some of the world's top AI experts discuss everything from Tensorflow Extended to Kubernetes to AutoML to Coral.

Video: Machine Learning Engineering with Tensorflow Extended

In this talk, Hannes is providing insights into Machine Learning Engineering with TensorFlow Extended (TFX). He introduces how TFX for machine learning pipeline tasks and how to orchestrate entire ML pipelines with TFX. The audience learns how to run ML production pipelines with Kubeflow Pipelines, and therefore, free the data scientist's time from maintaining production machine learning models.

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