Richard Socher — The Challenges of Making ML Work in the Real World

Richard Socher, ex-Chief Scientist at Salesforce, joins us to talk about The AI Economist, NLP protein generation and biggest challenge in making ML work in the real world.

Richard Socher was the Chief scientist (EVP) at Salesforce where he lead teams working on fundamental research(einstein.ai/), applied research, product incubation, CRM search, customer service automation and a cross-product AI platform for unstructured and structured data. Previously, he was an adjunct professor at Stanford’s computer science department and the founder and CEO/CTO of MetaMind(www.metamind.io/) which was acquired by Salesforce in 2016. In 2014, he got my PhD in the [CS Department](www.cs.stanford.edu/) at Stanford. He likes paramotoring and water adventures, traveling and photography. More info:

- Forbes article:

https://www.forbes.com/sites/gilpress/2017/05/01/emerging-artificial-intelligence-ai-leaders-richard-socher-salesforce/) with more info about Richard's bio.

- CS224n - NLP with Deep Learning(http://cs224n.stanford.edu/) the class Richard used to teach.

- TEDx talk(https://www.youtube.com/watch?v=8cmx7V4oIR8) about where AI is today and where it's going.

Research:

Google Scholar Link(https://scholar.google.com/citations?user=FaOcyfMAAAAJ&hl=en)

The AI Economist: Improving Equality and Productivity with AI-Driven Tax Policies

Arxiv link(https://arxiv.org/abs/2004.13332), blog(https://blog.einstein.ai/the-ai-economist/), short video(https://www.youtube.com/watch?v=4iQUcGyQhdA), Q&A(https://salesforce.com/company/news-press/stories/2020/4/salesforce-ai-economist/), Press: VentureBeat(https://venturebeat.com/2020/04/29/salesforces-ai-economist-taps-reinforcement-learning-to-generate-optimal-tax-policies/), TechCrunch(https://techcrunch.com/2020/04/29/salesforce-researchers-are-working-on-an-ai-economist-for-more-equitable-tax-policy/)

ProGen: Language Modeling for Protein Generation:

bioRxiv link(https://www.biorxiv.org/content/10.1101/2020.03.07.982272v2), [blog](https://blog.einstein.ai/progen/) ]

Dye-sensitized solar cells under ambient light powering machine learning: towards autonomous smart sensors for the internet of things

Issue11, (**Chemical Science 2020**). paper link(https://pubs.rsc.org/en/content/articlelanding/2020/sc/c9sc06145b#!divAbstract)

CTRL: A Conditional Transformer Language Model for Controllable Generation:

Arxiv link(https://arxiv.org/abs/1909.05858), code pre-trained and fine-tuning(https://github.com/salesforce/ctrl), blog(https://blog.einstein.ai/introducing-a-conditional-transformer-language-model-for-controllable-generation/)

Genie: a generator of natural language semantic parsers for virtual assistant commands:

PLDI 2019 pdf link(https://almond-static.stanford.edu/papers/genie-pldi19.pdf), https://almond.stanford.edu

Topics Covered:

0:00 intro

0:42 the AI economist

7:08 the objective function and Gini Coefficient

12:13 on growing up in Eastern Germany and cultural differences

15:02 Language models for protein generation (ProGen)

27:53 CTRL: conditional transformer language model for controllable generation

37:52 Businesses vs Academia

40:00 What ML applications are important to salesforce

44:57 an underrated aspect of machine learning

48:13 Biggest challenge in making ML work in the real world

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