Data Science at Home

AI’s Impact on Software Engineering: Killing Old Principles? (Ep. 220)

In this episode, we dive into the ways in which AI and machine learning are disrupting traditional software engineering principles. With the advent of automation and intelligent systems, developers are increasingly relying on algorithms to create efficient and effective code. However, this reliance on AI can come at a cost to the tried-and-true methods of software engineering. Join us as we explore the pros and cons of this paradigm shift and discuss what it means for the future of software development.

Edge AI applications for military and space [RB] (Ep. 219)

Prove It Without Revealing It: Exploring the Power of Zero-Knowledge Proofs in Data Science (Ep. 218)

In this episode, we dive into the fascinating world of zero-knowledge proofs and their impact on data science. Zero-knowledge proofs allow one party to prove to another that they know a secret without revealing the secret itself. This powerful concept has numerous applications in data science, from ensuring data privacy and security, to facilitating secure transactions and identity verification. We explore the mechanics of zero-knowledge proofs, its real-world applications, and how it is revolutionizing the way we handle sensitive information. Join us as we uncover the secrets of zero-knowledge proofs and its impact on the future of data science.
 
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Deep learning vs tabular models (Ep. 217)

Deep learning methods are not as effective with tabular data. Here is why, and what to do about it.
 
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References
https://paperswithcode.com/methods/category/deep-tabular-learning 
https://m-clark.github.io/posts/2022-04-01-more-dl-for-tabular/
 

[RB] Online learning is better than batch, right? Wrong! (Ep. 216)

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.
 

Chatting with ChatGPT: Pros and Cons of Advanced Language AI (Ep. 215)

In this episode, I'll be discussing the capabilities and limitations of ChatGPT, an advanced language AI model. I'll go over its power to understand and respond to natural language, and its applications in tasks such as language translation and text summarization. However, I'll also touch on the challenges that still need to be overcome such as bias and data privacy concerns. Tune in for a comprehensive look at the current state of advanced language AI.
 
References
https://datascienceathome.com/have-you-met-shannon-conversation-with-jimmy-soni-and-rob-goodman-about-one-of-the-greatest-minds-in-history/
 

Accelerating Perception Development with Synthetic Data (Ep. 214)

In this episode I am with Kevin McNamara, founder and CEO of Parallel Domain. We speak about a very effective method to generate synthetic data that is currently in production at Parallel Domain.
Enjoy the show!
 
 
References
Parallel Domain Synthetic Data Improves Cyclist Detection (blog post):
https://paralleldomain.com/parallel-domain-synthetic-data-improves-cyclist-detection/ 
 
Beating the State of the Art in Object Tracking with Synthetic Data:
https://paralleldomain.com/beating-the-state-of-the-art-in-object-tracking-with-synthetic-data/ 
 
Parallel Domain Open Synthetic Dataset:
https://paralleldomain.com/open-datasets/bicycle-detection 
 
How Toyota Research Institute Trains Better Computer Vision Models with PD Synthetic Data (interview):
https://www.youtube.com/watch?v=QIYttoVxf2w
 
Career Opportunities:
https://paralleldomain.com/careers

Edge AI applications for military and space [RB] (Ep. 213)

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From image to 3D model (Ep. 212)

Is it possible to reconstruct a 3D model from a simple image?
Under certain constraints, it is! In this episode I tell you how.
 
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Amethix works to create and maximize the impact of the world’s leading corporations and startups, so they can create a better future for everyone they serve. We provide solutions in AI/ML, Fintech, Healthcare/RWE, and Predictive maintenance.
 
References
https://github.com/isl-org/Open3D
https://huggingface.co/docs/transformers/model_doc/glpn
https://arxiv.org/abs/2201.07436
 

Machine learning is physics (Ep. 211)

What if we borrowed from physics some theories that would interpret deep learning and machine learning in general?Here is a list of plausible ways to interpret our beloved ML models and understand why they works, or they don't.Enjoy the show!
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Amethix works to create and maximize the impact of the world’s leading corporations and startups, so they can create a better future for everyone they serve. We provide solutions in AI/ML, Fintech, Healthcare/RWE, and Predictive maintenance.