99 episodes

Technology, AI, machine learning and algorithms

Data Science at Home Francesco Gadaleta

    • Technology
    • 4.3 • 64 Ratings

Technology, AI, machine learning and algorithms

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

    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.

     

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

    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
     
     

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

    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. 

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

    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

    • 24 min
    State of Artificial Intelligence 2022 (Ep. 196)

    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

     

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

    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

     

    • 33 min

Customer Reviews

4.3 out of 5
64 Ratings

64 Ratings

zerolagtime ,

Still Need A Degree

I have a BS in Computer Science. I've taken my share of math, including classes on statistics, graph theory, and optimization. I can barely keep up because my day job doesn't put me near this material. If I was working on my Masters in CS, this is great stuff. If I was trying to adapt existing projects to Big Data, this really helps me avoid some traps and pitfalls. It also helps me decode lingo from software vendors to find the liars. What it won't do is teach you Data Science.
I'd get lost for hours if Francesco uploaded his script to his blog with links to projects, papers, and products. It is a very dense lecture though that pulls valuable experiences into one place. Between that and the great improvements to the production process, I'm going to keep listening and gleaning.
In late 2019, Francesco turned the mic over to a friend with a soap box that rightly attacked recommender algorithms, but it stepped way far away from a technical, improvisational interview. Francesco has finally started to realize that ML is easily abused and I look forward to his leadership in guiding the ML industry in better methods to avoid traps imposed by data owners on their analysts.

ASobering ,

Such a wealth of knowledge! 🧠

This podcast is so insightful and I’ve enjoyed every episode I’ve listened to so far! Francesco is a very skilled interviewer - he does such a great job of sharing his wisdom and I love how he leads meaningful conversations with industry leaders who bring so much experience and actionable insight to the table. Highly recommend checking this show out - you won’t be disappointed!

okschu ,

Awesome

Good greatest

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