Ensuring Successful Enterprise AI Deployments, with Sol Rashidi

Prodigious Sol Rashidi has deployed nearly 40 large-scale data and A.I. projects at Fortune 100 companies. Her rich insights on doing this successfully fill her new book and are distilled into today's fun episode.

Sol ☀:

• Has been a C-suite data/analytics/A.I. leader at Estée Lauder, Merck pharmaceuticals, Sony Music and Royal Caribbean Cruise Lines.

• Was Senior Partner leading the Digital and Innovation Practice at EY and was the Partner who led the Watson go-to-market at IBM.

• Has been involved in over three dozen large-scale data/A.I. deployments.

• Is recognized with a string of international awards for her leadership.

• Holds eight patents with many more pending.

Today’s episode will be invaluable to anyone who’d like to succeed at deploying A.I. models commercially. In it, Sol details:

• Her straightforward system for selecting the enterprise A.I. projects that will be successfully deployed.

• What kinds of A.I. projects should always be avoided.

• Why larger enterprises drag their feet on impactful A.I. projects and how to overcome such corporate logjams.

• When you should patent an innovation.

• Why Chief Data Officers and related C-suite roles have such high turnover.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

How A.I. is Transforming Science

A.I. is not just a tool, but a driving force in reshaping the landscape of science. In today's episode, I dive into the profound implications A.I. holds for scientific discovery, citing applications across nuclear fusion, medicine, self-driving labs and more.

Here are some of the ways A.I. is transforming science that are covered in today's episode:

• Antibiotics: MIT researchers uncovered two new antibiotics in a single year (antibiotic discovery is very rare so this is crazy!) by using an ML model trained on the efficacy of known antibiotics to sift through millions of potential antibiotic compounds.

• Batteries: Similar sifting was carried out by A.I. at the University of Liverpool to narrow down the search for battery materials from 200,000 candidates to just five highly promising ones.

• Weather: Huawei's Pangu-Weather and NVIDIA's FourCastNet use ML to offer faster and more accurate forecasts than traditional super-compute-intensive weather simulations — crucial for predicting and managing natural disasters.

• Nuclear Fusion: AI is simplifying the once-daunting task of controlling plasma in tokamak reactors, thereby contributing to advancements in clean energy production.

• Self-Driving Labs: Automate research by planning, executing, and analyzing experiments autonomously, thereby speeding up scientific experimentation and unveiling new possibilities for discovery.

• Generative A.I.: Large Language Models (LLMs) tools are pioneering new frontiers in scientific research. From improving image resolution to designing novel molecules, these tools are yielding tangible results, with several A.I.-designed drugs currently in clinical trials. Tools like Elicit are streamlining the process of scientific literature review over vast corpora, allowing connections within or between fields to be uncovered automatically and suggesting new research directions.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Deep Learning's Neurobiology Origins

Interested in understanding what Deep Learning is? Here's a ten-minute talk I gave last month that visually explains the approach, how it originated in neuroscience research, and how it's now leading the A.I. revolution.

Thanks to Kate Strachnyi and Ravit Jain for organizing the fun "Holiday Book Party" that I gave this talk at. And thanks to Aglae Bassens for the stunning illustrations I used throughout.

Transformers for Natural Language Processing

This week's guest is award-winning author Denis Rothman. He details how Transformer models (like GPT-3) have revolutionized Natural Language Processing (NLP) in recent years. He also explains Explainable AI (XAI).

Denis:
• Is the author of three technical books on artificial intelligence
• His most recent book, "Transformers for NLP", led him to win this year's Data Community Content Creator Award for technical book author
• Spent 25 years as co-founder of French A.I. company Planilog
• Has been patenting A.I. algos such as those for chatbots since 1982

In this episode, Denis fills us in on:
• What Natural Language Processing is
• What Transformer architectures are (e.g., BERT, GPT-3)
• Tools we can use to explain *why* A.I. algorithms provide a particular output

We covered audience questions from Serg, Chiara, and Jean-charles during filming. For those we didn't get to ask, Denis is kindly answering via a LinkedIn post today!

The episode's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

AI Recruitment Technology & Deep Learning - Guest Appearance on the Engineered-Mind Podcast

Thanks to Jousef Murad for having me on the popular Engineered-Mind podcast.! Jousef had deeply insightful questions and I enjoyed the experience immensely :)

I spoke with Jousef back in April 2021, where we discussed:

- untapt and how AI powered recruiting works
- My background in neuroscience
- Where to get started when learning ML
- Tips for becoming a deep learning specialist
- What is I’m most excited about in terms of AI
- How I come up with the idea of writing a book

You can listen to the podcast anywhere podcasts are available including Apple Podcasts, Spotify, and Anchor.fm. You can also check out the video directly on YouTube here.