Artificial Intelligence

Episode 38: Collective intelligence (Part 1)

This is the first part of the amazing episode with Johannes Castner, CEO and founder of CollectiWise. Johannes is finishing his PhD in Sustainable Development from Columbia University in New York City, and he is building a platform for collective intelligence. Today we talk about artificial general intelligence and wisdom.
All references and shownotes will be published after the next episode.Enjoy and stay tuned!

Episode 37: Predicting the weather with deep learning

Predicting the weather is one of the most challenging tasks in machine learning due to the fact that physical phenomena are dynamic and riche of events. Moreover, most of traditional approaches to climate forecast are computationally prohibitive. It seems that a joint research between the Earth System Science at the University of California, Irvine and the faculty of Physics at LMU Munich has an interesting improvement on the scalability and accuracy of climate predictive modeling. The solution is... superparameterization and deep learning.
 
References                  
Could Machine Learning Break the Convection Parameterization Deadlock?                               
Gentine, M. Pritchard, S. Rasp, G. Reinaudi, and G. Yacalis Earth and Environmental Engineering, Columbia University, New York, NY, USA, Earth System Science, University of California, Irvine, CA, USA, Faculty of Physics, LMU Munich, Munich, Germany
           

Episode 36: The dangers of machine learning and medicine

Humans seem to have reached a cross-point, where they are asked to choose between functionality and privacy. But not both. Not both at all. No data, no service. That’s what companies building personal finance services say. The same applies to marketing companies, social media companies, search engine companies, and healthcare institutions.
In this episode I speak about the reasons to aggregate data for precision medicine, the consequences of such strategies and how can researchers and organizations provide services to individuals while respecting their privacy.
 

Episode 35: Attacking deep learning models

Attacking deep learning models
Compromising AI for fun and profit
 
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.

Episode 34: Get ready for AI winter

Today I am having a conversation with Filip Piękniewski, researcher working on computer vision and AI at Koh Young Research America. His adventure with AI started in the 90s and since then a long list of experiences at the intersection of computer science and physics, led him to the conclusion that deep learning might not be sufficient nor appropriate to solve the problem of intelligence, specifically artificial intelligence.  I read some of his publications and got familiar with some of his ideas. Honestly, I have been attracted by the fact that Filip does not buy the hype around AI and deep learning in particular. He doesn’t seem to share the vision of folks like Elon Musk who claimed that we are going to see an exponential improvement in self driving cars among other things (he actually said that before a Tesla drove over a pedestrian).

Episode 33: Decentralized Machine Learning and the proof-of-train

In the attempt of democratizing machine learning, data scientists should have the possibility to train their models on data they do not necessarily own, nor see. A model that is privately trained should be verified and uniquely identified across its entire life cycle, from its random initialization to setting the optimal values of its parameters.How does blockchain allow all this? Fitchain is the decentralized machine learning platform that provides models an identity and a certification of their training procedure, the proof-of-train

Episode 30: Neural networks and genetic evolution: an unfeasible approach

Despite what researchers claim about genetic evolution, in this episode we give a realistic view of the field.

Episode 29: Fail your AI company in 9 steps

In order to succeed with artificial intelligence, it is better to know how to fail first. It is easier than you think.Here are 9 easy steps to fail your AI startup.

Episode 28: Towards Artificial General Intelligence: preliminary talk

The enthusiasm for artificial intelligence is raising some concerns especially with respect to some ventured conclusions about what AI can really do and what its direct descendent, artificial general intelligence would be capable of doing in the immediate future. From stealing jobs, to exterminating the entire human race, the creativity (of some) seems to have no limits. In this episode I make sure that everyone comes back to reality - which might sound less exciting than Hollywood but definitely more... real. 

Episode 26: Deep Learning and Alzheimer

In this episode I speak about Deep Learning technology applied to Alzheimer disorder prediction. I had a great chat with Saman Sarraf, machine learning engineer at Konica Minolta, former lab manager at the Rotman Research Institute at Baycrest, University of Toronto and author of DeepAD: Alzheimer′ s Disease Classification via Deep Convolutional Neural Networks using MRI and fMRI.
I hope you enjoy the show.