Data Science at Home

Online learning is better than batch, right? Wrong! (Ep. 200)

In this episode I speak about online learning systems and why blindly choosing such a paradigm can lead to very unpredictable and expensive outcomes.Also in this episode, I have to deal with an intruder :)
 
 
Links
Birman, K.; Joseph, T. (1987). "Exploiting virtual synchrony in distributed systems". Proceedings of the Eleventh ACM Symposium on Operating Systems Principles - SOSP '87. pp. 123–138. doi:10.1145/41457.37515. ISBN 089791242X. S2CID 7739589.
 

What are generalist agents and why they can change the AI game (Ep. 199)

That deep learning alone is not sufficient to solve artificial general intelligence, is more and more accepted statement.
Generalist agents have great properties that can overcome some of the limitations of single-task deep learning models.
Be aware, we are still far from AGI, though.
 
So what are generalist agents?
 
References
https://arxiv.org/pdf/2205.06175
 
 

Streaming data with ease. With Chip Kent from Deephaven Data Labs (Ep. 198)

In this episode, I am with Chip Kent, chief data scientist at Deephaven Data Labs.
We speak about streaming data, real-time, and other powerful tools part of the Deephaven platform.
 
Links
Deephaven - https://deephaven.io
Deephaven Community Core Documentation - ​​https://deephaven.io/core/docs/
Deephaven Community Slack - https://join.slack.com/t/deephavencommunity/shared_invite/zt-11x3hiufp-DmOMWDAvXv_pNDUlVkagLQ
GitHub:
Deephaven Community Core - https://github.com/deephaven/deephaven-core
Barrage - https://github.com/deephaven/barrage
Deephaven web components - https://github.com/deephaven/web-client-ui
YouTube Channel - https://www.youtube.com/channel/UCoaYOlkX555PSTTJz8ZaI_w
Blog posts 
Real-time classification with Deephaven and SciKit-Learn - https://deephaven.io/blog/2022/02/02/learn-scikit/
Display a quadrillion rows of data in the browser - https://deephaven.io/blog/2022/01/24/displaying-a-quadrillion-rows/
A performance comparison between Materialize and Deephaven - https://deephaven.io/blog/2022/03/05/deephaven-materialize-study/
Careers https://deephaven.io/company/careers/
Community Slack http://deephaven.io/slack. 

Learning from data to create personalized experiences with Matt Swalley from Omneky (Ep. 197)

In this episode I speak with Matt Swalley, Chief Business Officer of Omneky, an AI platform that generates, analyzes and optimizes personalized ad creatives at scale.
We speak about the way AI is used for generating customized recommendation and creating experiences with data aggregation and analytics. And yes! respecting the privacy of individuals.
 
Links
Grow your business with personalized ads https://www.omneky.com/
Data Science at Home Podcast (Live) https://www.twitch.tv/datascienceathome

State of Artificial Intelligence 2022 (Ep. 196)

Let's take a break and think about the state of AI in 2022. In this episode I summarize the long report from the Stanford Institute for Human-Centered Artificial Intelligence (HAI)
Enjoy!
References
https://spectrum.ieee.org/artificial-intelligence-index
 

Improving your AI by finding issues within data pockets (Ep. 195)

In this episode I have a conversation with, Itai Bar-Sinai, CPO & Cofounder of Mona.
We speak about several interesting points about data and monitoring.Why is AI monitoring so different from monitoring classic software?How to reduce the gap between data science and business?What is the role of MLOps in the data monitoring field?
With over 10 years of experience with AI and as the CPO and head of customer success at Mona, the leading AI monitoring intelligence company, Itai has a unique view of the AI industry. Working closely with data science and ML teams applying dozens of AI solutions in over 10 industries, Itai encounters the wide variety of business use-cases, organizational structures and cultures, and technologies and tools used in today’s AI world.
 
References
https://www.monalabs.io
 

Fake data that looks, feels, and behaves like production.(Ep.194)

I am with Ander Steele, data scientist and mathematician with a passion for privacy and Shannon Bayatpur, product manager with a background in technical writing and computer science, from Tonic.ai. We speak about data. Fake data.
But all we say is authentic.
 
Links
Tonic website 
Career page
Neural networks for synthetic data

Batteries and AI in Automotive (Ep. 193)

In this episode my friend and I speak about AI, batteries and automotive.Dennis Berner, founder of Digitlabs has been operating in the field of automotive and batteries for a long time. His point of views are absolutely a must to listen to. Below a list of the links he mentioned in the show.
https://amethix.com
https://digitlabs.com
https://www.moia.io
https://www.elli.eco
https://www.uber.com
https://www.didiglobal.com/
https://waymo.com/
https://group.mercedes-benz.com/
https://www.fakultaet73.de
https://www.bmw.de
https://www.volkswagen.de
https://cariad.technology/
 

Collect data at the edge [RB] (Ep. 192)

In this episode I speak with Manavalan Krishnan from Tsecond about capturing massive amounts of data at the edge with security and reliability in mind.
 
This episode is brought to you by NordVPN
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and by Amethix Technologies
Amethix use advanced Artificial Intelligence and Machine Learning to build data platforms and predictive engines in domain like finance, healthcare, pharmaceuticals, logistics, energy. Amethix provide solutions to collect and secure data with higher transparency and disintermediation, and build the statistical models that will support your business.
 
References
https://tsecond.us/company/manavalan-krishnan/
 

Bayesian Machine Learning with Ravin Kumar (Ep. 191)

This is one episode where passion for math, statistics and computers are merged. I have a very interesting conversation with Ravin,  data scientist at Google where he uses data to inform decisions.
He has previously worked at Sweetgreen, designing systems that would benefit team members and communities through sustainable and healthy food, and SpaceX, creating tools that would ultimately launch rocket ships.
All opinions in this episode are his own and none of the companies he has worked for are represented.
 
This episode is brought to you by RailzAI
The Railz API connects to major accounting platforms to provide you with quick access to normalized and analyzed financial data. Get free access to their API and more. Just tell them you came through Data Science at Home podcast.
 
and by Amethix Technologies
Amethix use advanced Artificial Intelligence and Machine Learning to build data platforms and predictive engines in domain like finance, healthcare, pharmaceuticals, logistics, energy. Amethix provide solutions to collect and secure data with higher transparency and disintermediation, and build the statistical models that will support your business.
 
 
References
Bayesian Modeling and Computation in Python (Chapman & Hall/CRC Texts in Statistical Science) amazon.com
Bayesian Modeling and Computation in Python
https://twitter.com/canyon289