What is the difference between a data competition and the day to day work as a data scientist? what lessons and tricks can we learn from Kaggle and what pitfalls should we look out for? Nir Malbin, a Kaggle Master and one of the first kaggle competitors in Israel would share with us his thoughts and insights from his long time experience at Kaggle.

How to learn from noisy data? Can you use free text to generate labels in an unsupervised manner? Jonathan Laserson, Lead AI researcher is Zebra Medical, tells us how they built the world's largest data set of chest X-ray images (1M) and trained a network that detects over 40 findings. Jonathan did his PhD in computer science at Stanford, where he specialized in Bayesian methods and probabilistic graphical models.

The field of privacy in machine learning is becoming increasingly important. With legislation like GDPR, it is becoming necessary for us, data scientists, to be mindful about privacy concerns related to the applications we develop. In this episode we interview Ran Gilad Bachrach, a researcher at Microsoft Research, that tells us about privacy in machine learning. We'll talk about differential privacy, about homomorphic encryption and how it enables training models on encrypted data, and about secure multi party computation - a field who's goal is to help different parties train models together, even when they can't share their data with one-another.

Daniel Soudry is an assistant professor and a Taub Fellow at the Department of Electrical Engineering at the Technion. His first work focsed on Neuroscience, attempting to understand how neurons work in the brain. He then continued to a post-doc at Columbia University, where he discovered his interest in both the practical concerns and theory of deep neural networks. This episode focuses on Daniel's research work on questions such as how to make neural network work with low numerical precision, and when are SVM and Logistic Regression the same thing?

We also talk with him about his path in academia and the journey to discover his research interests.

AI and ML algorithms are becoming increasingly popular, being implemented in finance, health and law enforcement systems. Mistakes these algorithms can make can have tremendous impact on people’s lives, leading to many ethical and legal questions; how do we define fairness in this context? On what personal rights do these algorithms affect? How can people appeal decisions made by algorithms? These questions, in turn, pose computational challenges, like improving the explainability of algorithms and enforcing algorithmic fairness toward minority groups. In this episode we talk to Gal Yona, from Weitzmann institute, and Yafit Lev-Aretz, from City University of New York. Together they provide us with an introduction to the hot topic of fairness in AI, from computational and legal perspective.

Dafna Shahaf has so many cool research projects. In this episode we talked about a few of them - using metro maps to visualize information about events and storylines; an algorithm that judges jokes; a search engine that finds creative solutions using analogies; finding surprising facts in wikipedia.

She tells us how she comes up with these ideas, how she choses which ones to focus on and about her way of "failing fast".

David Golan was on the track to becoming a university professor. Then something happened that made him changed route. He co-founded viz.ai - a company that helps doctors detect strokes quickly. We talked about How to use Deep Learning when you don't have a lot of data; about the challenges in building an AI-oriented company in the field of medicine; and about how the time he spent as a researcher helps him as an entrepreneur.

Yair Mazor is the head of Data Science at windward. In this episode we talked about how to efficiently getting into new domain as a data scientist, about data science for maritime analytics and about different aspects in managing data science teams: the pros and cons of working as teams of data scientists vs. being a part of product and development teams; balancing between short and long term research goals; working with product managers and finally - can data scientists work in agile?

Ofra Amir did her PhD in computer science in Harvard, where she studied the interactions between humans and intelligent machines. In this episode we talk with Ofra about designing algorithms whose goal is to improve human performance in a given task, how to design metrics when some of your goals are not easily measurable, and how to explain the decisions of intelligent agents to humans.

Yo data scientists, Nimrod Priell has a very important message for you - Data Science is much more than just Machine Learning. It may sound trivial when you think about it, but yet it's very easy to forget these days. Nimrod, a Data Science team lead at facebook, told us about 7 different products of a data scientist (only one of which is a machine learning model!), about the two metrics he uses to evaluate a product, and how it helps him to manage data science projects and build a versatile and diverse team. We found this talk very interesting, and hope you do too.