#deep-learning

#deep-learning

A deep learning model that uses a single chest X-ray to predict the 10-year risk of death from a heart attack or stroke, stemming from atherosclerotic cardiovascular disease has been developed by researchers.Results of the study were presented today (November 29) at the annual meeting of the Radiological Society of North America (RSNA).

As you know, ODSC East brings together some of the best and brightest minds in data science and AI.They are experts in machine learning, NLP, deep learning, data engineering, MLOps, and data visualization.However, our speakers are also experienced and acclaimed instructors, able to impart their knowledge in a manner that is engaging and effective.

In an effort to learn more about our community, we recently shared a survey about machine learning topics, including what platforms you're using, in what industries, and what problems you're facing.In a series of articles, we'd like to share the results so you too can learn more about what the data science community is doing in machine learning.

The importance of AI to the success of organizations and institutions only continues to grow.If you haven't started thinking about how to incorporate it into your business, you may find yourself left in the dust by your competitors.Fortunately, ODSC Europe 2023 this June 14th-15th - both in-person and virtually - will be offering several opportunities for you to learn more about the AI products and services available today, as well as best practices for how to implement machine learning solutions in your organization.

This week we welcome Benjamin Bennett Alexander (@RealBenjizo ) as our PyDev of the Week!Benjamin is the author of 50 Days of Python: A Challenge a Day and Master Python Fundamentals and other books.You can catch up with Benjamin over on Medium.Let's spend a few moments getting to know Benjamin better!

This week we welcome Thomas Fan (@thomasjpfan) as our PyDev of the Week!Thomas is a core developer of the scikit-learn, a machine learning package for Python.If you'd like to see what else Thomas is up to you can check out Thomas's website or his GitHub profile.Let's take a few moments to get to know Thomas better!
Can you tell us a little about yourself (hobbies, education, etc):
I am a Staff Software Engineer at Quansight Labs, which aims to sustain and grow community-driven PyData open-source projects.

This week we welcome Janos Gabler (@JanosGabler) as our PyDev of the Week!Janos is the creator of estimagic, a Python package for nonlinear optimization.You can catch up with Janos on his website or by checking out Janos' GitHub Profile.Let's spend some time getting to know Janos better!Can you tell us a little about yourself (hobbies, education, etc.)
I am Janos.

So by now you have known about Data Phoenix through my previous articles.You can check out my previous articles about them below.Also, the latest digests by Data Phoenix can be found here.Data phoenix also publishes a section where latest research papers in the data world are put up, these research papers are chosen with interests in computer vision, artificial intelligence, deep learning, machine learning, etc.

Adludio, the global AI-powered platform that delivers interactive mobile advertising, today (November 16th, 2022) announces major enhancements to its technological capabilities which goes far beyond what is standard in the industry.With an emphasis on Deep Learning running through the platform, this is enabling a level of measurement and actionable insights for Adludio's global clients that will help them win the battle for brand attention.

Constitutional
With AI chatbots' propensity for making things up and spewing bigoted garbage, one firm founded by ex-OpenAI researchers has a different approach - teaching AI to have a conscience.As Wired reports, the OpenAI competitor Anthropic's intriguing chatbot Claude is built with what its makers call a "constitution," or set of rules that draws from the Universal Declaration of Human Rights and elsewhere to ensure that the bot is not only powerful, but ethical as well.

Geoffrey Hinton, a pioneer in machine learning who is best-known for his research of neural networks, has resigned from Google to speak freely about the dangers of AI.Hinton, 75, a computer science professor at the University of Toronto and a former Google top researcher, began studying neural nets long before they were in vogue.

* By Oliver Pickup


This story was first published by Digiday sibling WorkLife Why don't scientists trust atoms?Because they make everything up.When Greg Brockman, president and co-founder of OpenAI, demonstrated the possibilities of GPT-4 - Generative Pre-trained Transformer 4, the fourth-generation autoregressive language model that uses deep learning to produce human-like text - upon launch on Mar.

We couldn't be more excited to announce our first group of partners for ODSC East 2023's AI Expo and Demo Hall.These organizations are shaping the future of the AI and data science industries with their innovative products and services.Check them out below.At HPCC Systems® from LexisNexis® Risk Solutions you'll find "a consistent data-centric programming language, two processing platforms, and a single, complete end-to-end architecture for efficient processing."

On Wednesday, Nvidia announced a collaboration with Microsoft to build a "massive" cloud computer focused on AI.It will reportedly use tens of thousands of high-end Nvidia GPUs for applications like deep learning and large language models.The companies aim to make it one of the most powerful AI supercomputers in the world.

Your machine learning projects don't only need to succeed and produce results, but it's crucial to keep operations on time, on budget, and most importantly, on point.That's why MLOps is how organizations keep their ML projects on the right path.But for many teams, you might not know where to start.Don't worry, ODSC West has you covered this November 1st-3rd.

We recently shared a survey about the current state of machine learning in order to learn more about how practicing data scientists use machine learning, what hurdles they're seeing in their organization, and what tools they're using.While we're still looking for more responses to ensure we get a representative number, we'd like to share some of the initial responses.