Calling Clinicians: Help Us Build the Future of AI Therapy

I recently began supervising a PhD student in the Auckland robotic-engineering department and we are looking to partner with psychotherapists to develop a companion robot. Do you know anyone relevant/interested?

(I promise that our eventual robotic solution will not be a two-headed monstrosity featuring my face on a kiwi bird's body... but maybe it helped get your attention 😂)

Through several years of upcoming R&D at The University of Auckland (I will mostly be supervising remotely from New York!), our project aims to develop a therapeutic A.I. model (e.g., a multi-modal Large Language Model) to power the conversational, perceptual and (potentially) real-time video-generation capabilities of a companion robot that gives its user (which could be in a clinical or at-home setting) personalized therapy and support when a human therapist is unavailable.

A particularly prominent challenge for us in developing and testing this LLM (and, eventually, robotic embodiment) is access to data from real therapeutic conversations, although there are other immediate and long-term R&D challenges that we would love practicing therapists to help us with as well.

This is an exciting, impactful project that could markedly improve millions of lives around the world in the coming decades. I applaud PhD candidate Maryam Khakpour for tackling it head on! If you're a clinician who's keen to be involved with the A.I. revolution, now's your chance :)

A.I. Robotics at Home

This week's guest is mad genius Dave Niewinski, who creates A.I.-powered robots for use at home (e.g., cold beer retriever, flame-throwing weed killer) and to teach people A.I. robotics via his popular YouTube channel.

In the episode, Dave covers:
• The specific robotics hardware and open-source software incorporated into his wildest and most well-known robots
• Where machine vision algorithms, particularly deep learning models, are critical for enabling robot functionality
• His tips for folks who’d like to get started in A.I. robotics themselves
• How his interest in robotics led him to founding his Dave's Armoury Ltd. consulting business, which allows him to automate and improve real-world industrial processes with robots
• What excites him most about the societal impact A.I. robotics will have in our lifetimes

Specific robots of Dave's that we detail on the show include ones that:
• Play the sandbag-tossing game "cornhole"
• Defend his machine shop from kids by spraying them with a hose
• Exterminate weeds by throwing flames
• Carve pumpkins for Halloween
• Solve Rubik's cubes
• Use Lego pieces to create 2D artwork
• Race kids at assembling 3D Lego unicorns
• Bring cold beer from the fridge to wherever you are in the house

Thanks to Graham McCormick for introducing me to Dave! I learned so much from him and had such a good time hanging with him "on air".

The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com. You can check out the Dave's Armoury YouTube channel here!

Deep Reinforcement Learning for Robotics with Pieter Abbeel

Very special guest this week! Pieter Abbeel is a serial A.I. entrepreneur, host of star-studded The Robot Brains Podcast, and the world's most preeminent researcher of Deep Reinforcement Learning applications.

As a professor of Electrical Engineering and Computer Science at the University of California, Berkeley, Pieter directs the Berkeley Robot Learning Lab and co-directs the Berkeley A.I. Research Lab.

As an entrepreneur, he's been exceptionally successful at applying machine learning for commercial value. Gradescope, a machine learning company in the education technology space that he co-founded, was acquired in 2018. And the A.I. robotics firm Covariant, which he co-founded more recently, has raised $147 million so far, including raising $80 million in a Series C funding round in July.

In this episode, Pieter eloquently discusses:
• His exciting current research in the field of Deep Reinforcement Learning
• Top learning resources and skills for becoming an expert in A.I. robotics
• How academic robotics research is vastly different from R&D for industry
• Productivity tips
• Traits he looks for in data scientists he hires
• Skills to succeed as a data scientist in the coming decades

He also had time to answer thoughtful questions from distinguished SuperDataScience listeners Serg Masís and Hsieh-Yu Li.

Listen or watch here.

Machine Learning at NVIDIA

This week's guest is absolute rockstar Dr. Anima Anandkumar, who's both professor at prestigious Caltech and director of ML research at NVIDIA. The episode is exceptionally content-rich but also lots of fun; Anima was a joy to film with.

In the episode, Anima fills us in on:

• The cutting-edge interdisciplinary research she carries out (applying highly optimized mathematical operations to allow state-of-the-art ML models to be executed on NVIDIA's state-of-the-art GPUs)

• How this blending of leading software and leading hardware enables world-changing innovations across disparate fields, from healthcare to robotics

• What it's like in the workweek of a researcher bridging the academic and industrial realms

• The open-source data science tools and techniques that she most highly recommends

Listen or watch here.