Episode 39: What is L1-norm and L2-norm?
In this episode I explain the differences between L1 and L2 regularization that you can find in function minimization in basically any machine learning model.
Jul 19, 2018•22 min•Ep. 34
Data Science at Home
Cutting through AI bullsh*t.
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Episodes
Episode 38: Collective intelligence (Part 2)
In the second part of this episode I am interviewing Johannes Castner from CollectiWise, a platform for collective intelligence. I am moving the conversation towards the more practical aspects of the project, asking about the centralised AGI and blockchain components that are essential part of the platform. References Opencog.org Thaler, Richard H., Sunstein, Cass R. and Balz, John P. (April 2, 2010). "Choice Architecture". doi : 10.2139/ssrn.1583509 . SSRN 1583509 Teschner, F., Rothschild, D. &...
Jul 17, 2018•47 min•Ep. 33
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!
Jul 12, 2018•31 min•Ep. 32
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 ...
Jul 09, 2018•26 min•Ep. 31
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 organizati...
Jul 03, 2018•22 min•Ep. 30
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 i...
Jun 29, 2018•29 min•Ep. 29
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 idea...
Jun 22, 2018•59 min•Ep. 27
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 the...
Jun 11, 2018•18 min•Ep. 26
Episode 32: I am back. I have been building fitchain
I know, I have been away too long without publishing much in the last 3 months. But, there's a reason for that. I have been building a platform that combines machine learning with blockchain technology. Let me introduce you to fitchain and tell you more in this episode. If you want to collaborate on the project or just think it's interesting, drop me a line on the contact page at fitchain.io
Jun 04, 2018•23 min•Ep. 25
Founder Interview – Francesco Gadaleta of Fitchain
Cross-posting from Cryptoradio.io Overview Francesco Gadaleta introduces Fitchain, a decentralized machine learning platform that combines blockchain technology and AI to solve the data manipulation problem in restrictive environments such as healthcare or financial institutions. Francesco Gadaleta is the founder of Fitchain.io and senior advisor to Abe AI . Fitchain is a platform that officially started in October 2017, which allows data scientists to write machine learning models on data they ...
May 24, 2018•31 min•Ep. 24
Episode 31: The End of Privacy
Data is a complex topic, not only related to machine learning algorithms, but also and especially to privacy and security of individuals, the same individuals who create such data just by using the many mobile apps and services that characterize their digital life. In this episode I am together with B.J.n Mendelson, author of “Social Media is Bullshit” from St. Martin’s Press and world-renowned speaker on issues involving the myths and realities involving today’s Internet platforms. B.J. has a n...
Apr 02, 2018•39 min•Ep. 23
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.
Nov 21, 2017•22 min•Ep. 22
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.
Nov 11, 2017•14 min•Ep. 21
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 definitel...
Nov 04, 2017•21 min•Ep. 20
Episode 27: Techstars accelerator and the culture of fireflies
In the aftermath of the Barclays Accelerator, powered by Techstars experience, one of the most innovative and influential startup accelerators in the world, I’d like to give back to the community lessons learned, including the need for confidence, soft-skills, and efficiency, to be applied to startups that deal with artificial intelligence and data science. In this episode I also share some thoughts about the culture of fireflies in modern and dynamic organisations....
Oct 30, 2017•18 min•Ep. 19
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.
Oct 23, 2017•54 min•Ep. 18
Episode 25: How to become data scientist [RB]
In this episode, I speak about the requirements and the skills to become data scientist and join an amazing community that is changing the world with data analyticsa
Oct 16, 2017•16 min•Ep. 17
Episode 24: How to handle imbalanced datasets
In machine learning and data science in general it is very common to deal at some point with imbalanced datasets and class distributions. This is the typical case where the number of observations that belong to one class is significantly lower than those belonging to the other classes. Actually this happens all the time, in several domains, from finance, to healthcare to social media, just to name a few I have personally worked with. Think about a bank detecting fraudulent transactions among mil...
Oct 09, 2017•21 min•Ep. 16
Episode 23: Why do ensemble methods work?
Ensemble methods have been designed to improve the performance of the single model, when the single model is not very accurate. According to the general definition of ensembling, it consists in building a number of single classifiers and then combining or aggregating their predictions into one classifier that is usually stronger than the single one. The key idea behind ensembling is that some models will do well when they model certain aspects of the data while others will do well in modelling o...
Oct 03, 2017•19 min•Ep. 15
Episode 22: Parallelising and distributing Deep Learning
Continuing the discussion of the last two episodes, there is one more aspect of deep learning that I would love to consider and therefore left as a full episode, that is parallelising and distributing deep learning on relatively large clusters. As a matter of fact, computing architectures are changing in a way that is encouraging parallelism more than ever before. And deep learning is no exception and despite the greatest improvements with commodity GPUs - graphical processing units, when it com...
Sep 25, 2017•20 min•Ep. 14
Episode 21: Additional optimisation strategies for deep learning
In the last episode How to master optimisation in deep learning I explained some of the most challenging tasks of deep learning and some methodologies and algorithms to improve the speed of convergence of a minimisation method for deep learning. I explored the family of gradient descent methods - even though not exhaustively - giving a list of approaches that deep learning researchers are considering for different scenarios. Every method has its own benefits and drawbacks, pretty much depending ...
Sep 18, 2017•15 min•Ep. 13
Episode 20: How to master optimisation in deep learning
The secret behind deep learning is not really a secret. It is function optimisation. What a neural network essentially does, is optimising a function. In this episode I illustrate a number of optimisation methods and explain which one is the best and why.
Aug 28, 2017•19 min•Ep. 12
Episode 19: How to completely change your data analytics strategy with deep learning
Over the past few years, neural networks have re-emerged as powerful machine-learning models, reaching state-of-the-art results in several fields like image recognition and speech processing. More recently, neural network models started to be applied also to textual data in order to deal with natural language, and there too with promising results. In this episode I explain why is deep learning performing the way it does, and what are some of the most tedious causes of failure.
Aug 09, 2017•16 min•Ep. 11
Episode 18: Machines that learn like humans
Artificial Intelligence allow machines to learn patterns from data. The way humans learn however is different and more efficient. With Lifelong Machine Learning, machines can learn the way human beings do, faster, and more efficiently
Mar 28, 2017•42 min•Ep. 1
Episode 17: Protecting privacy and confidentiality in data and communications
Talking about security of communication and privacy is never enough, especially when political instabilities are driving leaders towards decisions that will affect people on a global scale
Feb 15, 2017•18 min•Ep. 1
Episode 16: 2017 Predictions in Data Science
We strongly believe 2017 will be a very interesting year for data science and artificial intelligence. Let me tell you what I expect and why.
Dec 23, 2016•21 min•Ep. 1
Episode 15: Statistical analysis of phenomena that smell like chaos
Is the market really predictable? How do stock prices increase? What is their dynamics? Here is what I think about the magics and the reality of predictions applied to markets and the stock exchange.
Dec 05, 2016•10 min•Ep. 1
Episode 14: The minimum required by a data scientist
Why the job of the data scientist can disappear soon. What is required by a data scientist to survive inflation.
Sep 27, 2016•17 min•Ep. 1
Episode 13: Data Science and Fraud Detection at iZettle
Data science is making the difference also in fraud detection. In this episode I have a conversation with an expert in the field, Engineer Eyad Sibai, who works at iZettle, a fraud detection company
Sep 06, 2016•17 min•Ep. 1
Episode 12: EU Regulations and the rise of Data Hijackers
Extracting knowledge from large datasets with large number of variables is always tricky. Dimensionality reduction helps in analyzing high dimensional data, still maintaining most of the information hidden behind complexity. Here are some methods that you must try before further analysis (Part 1).
Jul 26, 2016•16 min•Ep. 1
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