Deep learning models have shown very promising results in computer vision and sound recognition. As more and more deep learning based systems get integrated in disparate domains, they will keep affecting the life of people. Autonomous vehicles, medical imaging and banking applications, surveillance cameras and drones, digital assistants, are only a few real applications where deep learning plays a fundamental role. A malfunction in any of these applications will affect the quality of such integrated systems and compromise the security of the individuals who directly or indirectly use them.
In this episode, we explain how machine learning models can be attacked and what we can do to protect intelligent systems from being compromised.
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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