Data Science at Home

Neural networks are becoming massive monsters that are hard to train (without the "regular" 12 last-generation GPUs). Is there a way to skip that? Let me introduce you to Zero-Cost proxies     References https://www.technologyreview.com/2022/08/05/1056814/automation-ai-machine-learning-automl/ https://iclr-blog-track.github.io/2022/03/25/zero-cost-proxies/

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.  

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

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. 

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

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  

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  

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

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/  

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 NordVPN protects your privacy while you are online. Get secure and private access to the internet by surfing nordvpn.com/DATASCIENCE or use coupon code DATASCIENCE and get a massive discount. 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/