Deep Learning

Hold on to your calculators and buckle up for a wild mathematical ride in this episode! Brace yourself as we dive into the fascinating realm of Liquid Time-Constant Networks (LTCs), where mathematical content reaches new heights of excitement.
In this mind-bending adventure, we demystify the intricacies of LTCs, from complex equations to mind-boggling mathematical concepts, we break them down into digestible explanations.
 
References
https://www.science.org/doi/10.1126/scirobotics.adc8892
https://spectrum.ieee.org/liquid-neural-networks#toggle-gdpr
 

Get ready for an eye-opening episode! 🎙️
In our latest podcast episode, we dive deep into the world of LoRa (Low-Rank Adaptation) for large language models (LLMs). This groundbreaking technique is revolutionizing the way we approach language model training by leveraging low-rank approximations.
Join us as we unravel the mysteries of LoRa and discover how it enables us to retrain LLMs with minimal expenditure of money and resources. We'll explore the ingenious strategies and practical methods that empower you to fine-tune your language models without breaking the bank.
Whether you're a researcher, developer, or language model enthusiast, this episode is packed with invaluable insights. Learn how to unlock the potential of LLMs without draining your resources.
Tune in and join the conversation as we unravel the secrets of LoRa low-rank adaptation and show you how to retrain LLMs on a budget.
Listen to the full episode now on your favorite podcast platform! 🎧✨
 
References
LoRA: Low-Rank Adaptation of Large Language Models https://arxiv.org/abs/2106.09685
Low-rank approximation https://en.wikipedia.org/wiki/Low-rank_approximation
Attention is all you need https://arxiv.org/pdf/1706.03762.pdf
 

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/
 

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

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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.
 

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.
 

If you think that the problem of self-driving cars has been solved, think twice.As a matter of fact, the problem of self-driving cars cannot be solved with the technical solutions that companies are currently considering. Don't get fooled by marketing and PR on social media. Whoever is telling you they solved the problem of driving a vehicle fully autonomously, they are lying.Here is why.
 
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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.

There are many solutions to private machine learning. I am pretty confident when I say that the one we are speaking in this episode is probably one of the most feasible and reliable.I am with Daniel Huynh, CEO of Mithril Security,  a graduate from Ecole Polytechnique with a specialisation in AI and data science. He worked at Microsoft on Privacy Enhancing Technologies under the office of the CTO of Microsoft France. He has written articles on Homomorphic Encryptions with the CKKS explained series (https://blog.openmined.org/ckks-explained-part-1-simple-encoding-and-decoding/). He is now focusing on Confidential Computing at Mithril Security and has written extensive articles on the topic: https://blog.mithrilsecurity.io/. 
In this show we speak about confidential computing, SGX and private machine learning
 
References
Mithril Security: https://www.mithrilsecurity.io/ 
BindAI GitHub: https://github.com/mithril-security/blindai 
Use cases for BlindAI:Deploy Transformers models with confidentiality: https://blog.mithrilsecurity.io/transformers-with-confidentiality/
Confidential medical image analysis with COVID-Net and BlindAI: https://blog.mithrilsecurity.io/confidential-covidnet-with-blindai/ 
Build a privacy-by-design voice assistant with BlindAI: https://blog.mithrilsecurity.io/privacy-voice-ai-with-blindai/ 
Confidential Computing Explained: https://blog.mithrilsecurity.io/confidential-computing-explained-part-1-introduction/ 
Confidential Computing Consortium: https://confidentialcomputing.io/ 
Confidential Computing White Papers: https://confidentialcomputing.io/white-papers-reports/ 
List of Intel processors with Intel SGX:https://www.intel.com/content/www/us/en/support/articles/000028173/processors.html 
https://github.com/ayeks/SGX-hardware 
Azure Confidential Computing VMs with SGX:Azure Docs: https://docs.microsoft.com/en-us/azure/confidential-computing/confidential-computing-enclaves 
How to deploy BlindAI on Azure: https://docs.mithrilsecurity.io/getting-started/cloud-deployment/azure-dcsv3 
Confidential Computing 101: https://www.youtube.com/watch?v=77U12Ss38Zc 
Rust: https://www.rust-lang.org/ 
ONNX: https://github.com/onnx/onnx
Tract, a Rust inference engine for ONNX models: https://github.com/sonos/tract
 

How does an autonomous vehicle see? How does it sense the road? They are equipped of many sensors, of course. Are they all powerful enough? Small enough to hide them and make your car look beautiful? In this episode I speak about LIDAR, high resolution cameras and some machine learning methods adapted to a minimal number of sensors.
 
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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://patents.google.com/patent/US20220043449A1/en?oq=20220043449
 
 

Companies and other business entities are actively involved in defining data products and applied research every year. Academia has always played a role in creating new methods and solutions/algorithms in the fields of machine learning and artificial intelligence.However, there is doubt about how powerful and effective such research efforts are.Is studying AI in academia a waste of time?
 
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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.