After deep learning, a new entry is about ready to go on stage. The usual journalists are warming up their keyboards for blogs, news feeds, tweets, in one word, hype.
This time it's all about privacy and data confidentiality. The new words, homomorphic encryption.
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Sponsors
This episode is supported by Amethix Technologies.
Amethix works to create and maximize the impact of the world’s leading corporations, startups, and nonprofits, so they can create a better future for everyone they serve. They are a consulting firm focused on data science, machine learning, and artificial intelligence.
References
Towards a Homomorphic Machine Learning Big Data Pipeline for the Financial Services Sector
IBM Fully Homomorphic Encryption Toolkit for Linux
Composable models and artificial general intelligence (Ep. 175)
Ethics and explainability in AI with Erika Agostinelli from IBM (ep. 174)
Is neural hash by Apple violating our privacy? (Ep. 173)
Fighting Climate Change as a Technologist (Ep. 172)
AI in the Enterprise with IBM Global AI Strategist Mara Pometti (Ep. 171)
Speaking about data with Mikkel Settnes from Dreamdata.io (Ep. 170)
Send compute to data with POSH data-aware shell (Ep. 169)
How are organisations doing with data and AI? (Ep. 168)
Don't fight! Cooperate. Generative Teaching Networks (Ep. 167)
CSV sucks. Here is why. (Ep. 166)
Reinforcement Learning is all you need. Or is it? (Ep. 165)
What's happening with AI today? (Ep. 164)
2 effective ways to explain your predictions (Ep. 163)
The Netflix challenge. Fair or what? (Ep. 162)
Artificial Intelligence for Blockchains with Jonathan Ward CTO of Fetch AI (Ep. 161)
Apache Arrow, Ballista and Big Data in Rust with Andy Grove RB (Ep. 160)
GitHub Copilot: yay or nay? (Ep. 159)
Pandas vs Rust [RB] (Ep. 158)
A simple trick for very unbalanced data (Ep. 157)
Time to take your data back with Tapmydata (Ep. 156)
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