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.
Improving your AI by finding issues within data pockets (Ep. 195)
Fake data that looks, feels, and behaves like production.(Ep.194)
Batteries and AI in Automotive (Ep. 193)
Collect data at the edge [RB] (Ep. 192)
Bayesian Machine Learning with Ravin Kumar (Ep. 191)
What is spatial data science? With Matt Forest from Carto (Ep. 190)
Connect. Collect. Normalize. Analyze. An interview with the people from Railz AI (Ep. 189)
History of data science [RB] (Ep. 188)
Artificial Intelligence and Cloud Automation with Leon Kuperman from Cast.ai (Ep. 187)
Embedded Machine Learning: Part 5 - Machine Learning Compiler Optimization (Ep. 186)
Embedded Machine Learning: Part 4 - Machine Learning Compilers (Ep. 185)
Embedded Machine Learning: Part 3 - Network Quantization (Ep. 184)
Embedded Machine Learning: Part 2 (Ep. 183)
Embedded Machine Learning: Part 1 (Ep.182)
History of Data Science (Ep. 181)
Capturing Data at the Edge (Ep. 180)
[RB] Composable Artificial Intelligence (Ep. 179)
What is a data mesh and why it is relevant (Ep. 178)
Environmentally friendly AI (Ep. 177)
Do you fear of AI? Why? (Ep. 176)
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