The Humans of Ai
Danny Ma on Data Science Mentorship.

Danny Ma on Data Science Mentorship.

September 16, 2020

Danny Ma is an influencer in the Data Science and Machine Learning community. In this episode of the Humans of Ai, we deep dive into Danny’s passion and journey into the field of Data Science and technology.

Danny throws light on the current state of Data Science and Machine Learning and provides tips on how to improve yourself as a Data Scientist, the importance of working with and around data, along with the pros and cons of the Data Science field. 

We then talk about Danny’s passion for mentorship and the challenges of finding the right mentor fit. Towards the end of the episode, Danny shares his views on the future of technology and the NEXT BIG THING in the field of Data Science.

 

Find out more about Danny & his #DatawithDanny movement here:

https://www.linkedin.com/in/datawithdanny/

 

Intro & Outro music by Simon Calcinai

Emre Kiciman & Amit Sharma - Causal Inference & Microsoft’s DoWhy Library

Emre Kiciman & Amit Sharma - Causal Inference & Microsoft’s DoWhy Library

August 13, 2020

Emre Kiciman is the Senior Principal Researcher at Microsoft Research Ai in the Information and Data Sciences group, and Amit Sharma is a Senior Researcher at Microsoft Research India. 

In this episode of the Humans of Ai, we discuss how Emre and Amit started in the field of Science and Technology and then dive into how they got started in Causal Science. We further explore the concepts around Causal Inference, such as Causal Graphs and Confounding Variables. We then discuss Amit & Emre's new software library, “DoWhy – A Library for Causal Inference,” the motivation behind its creation and its significance.

Towards the end of the episode, we talk about the advantages/disadvantages of Causal Inference and the ethical usage of bringing such sophisticated tools into Machine Learning.

 

Learning Resources Mentioned in the Podcast:

 

Causal Inference Course:

https://causalinference.gitlab.io/

 

Upcoming Book on Causal Inference by Emre & Amit:

https://causalinference.gitlab.io/causal-reasoning-book-chapter1/

 

Intro & Outro music by Simon Calcinai

Damian Brady - The emerging field of MLops

Damian Brady - The emerging field of MLops

May 7, 2020

MLOps is the application of the best practises from DevOps - that is the tools & techniques that are required to take software & put it into production - and applying these same principles to the field of Data Science to formalise the process of training a machine learning model all the way through to putting the model into production.

In this edition of the Humans of Ai, we interview Damian Brady - Senior Cloud DevOps Advocate at Microsoft & discuss how he got started in the field of Computer Science, then deep dive into Damian's field of expertise - MLops. Finally, we finish up the episode with what it is like to talk at a super conference - Microsoft Ignite.

Seth Juarez - What it takes: Ai Advocacy at Microsoft

Seth Juarez - What it takes: Ai Advocacy at Microsoft

March 12, 2020

Seth Juarez is a Principal Cloud Developer Advocate at Microsoft focussing on Artificial Intelligence, Machine Learning & Quantum Computing. In this episode we focus on Seth’s history and how he got involved in Ai, what it takes to be a Developer Advocate & how Seth is helping developers be successful with Ai in the cloud at Microsoft.

Uri Barash on Cortana & Azure Data Explorer

Uri Barash on Cortana & Azure Data Explorer

February 8, 2020

In celebration of Microsoft the Tour Sydney, the Humans of Ai is proud to release the latest podcast edition featuring Uri Barash. Uri Barash is the Principal Group Program Manager for Cortana, AI and Research. He is also the Principal Group Product Manager of Azure Data Explorer (Kusto).

In this podcast we deep dive into Uri’s Machine Learning experience, talking about how he was doing Natural Language Processing in the 90’s, what it was like to ship a machine learning production level product in the early 2000’s - including how he tackled distributed analysis on petabytes of data, the difference between corporate & startup cultures in America & Israel & finally discuss his latest project, Kusto now known as Azure Data Explorer.

Sunil Mallya, on AWS Deepracer & Sagemaker RL

Sunil Mallya, on AWS Deepracer & Sagemaker RL

December 6, 2019

Sunil Mallya is the Principal Deep Learning Scientist at Amazon Web Services Machine Learning Lab & is one of the key people behind AWS Deepracer & Sagemaker RL.

In this episode we explore the rich interaction that Sunil has had with machine learning over the course of his career including talking about what drew him to the field, his experience as an early phase machine learning entrepreneur, his knowledge in building distributed machine learning systems and finally the two biggest projects Sunil has taken on recently - Sagemaker RL & AWS Deepracer!

We also discuss many other topics including what the machine learning scene is like in San Francisco, some real world applications of Reinforcement Learning & what it takes to be a part of the Amazon's ML Lab.

We hope you enjoy this extremely interesting episode of The Humans of Ai. See you in the podcast ;)

Dr Stewart Worrall, on Engineering Autonomous Vehicle Systems

Dr Stewart Worrall, on Engineering Autonomous Vehicle Systems

October 3, 2019

Dr Stewart Worrall is a research fellow at the Australian Centre for Field Robotics. In this episode of the Humans of Ai, we deep dive with Dr Worrall on a Autonomous Vehicle Driving systems covering the history of autonomous driving, autonomous submarines!, the difference between different sensor suites, how successfull the "pure vision" approach might be & finally discuss how to engineer an autonomous vehicle system.

Dr Worrall's University of Sydney Campus self driving data set is available here: 

https://ieee-dataport.org/open-access/usyd-campus-dataset

With additional information relating to the dataset here:

http://its.acfr.usyd.edu.au/datasets/usyd-campus-dataset/

 

Dr Eugene Dubossarsky, Ai Antihype - the Perils & Remedies of a Career in Modern Data Science

Dr Eugene Dubossarsky, Ai Antihype - the Perils & Remedies of a Career in Modern Data Science

September 12, 2019

Dr Eugene Dubossarsky is a veteran of Data Science & legend of the Sydney community, being involved in the industry in one way shape or form since the late 80s.

In this episode of the Humans of Ai we briefly discuss Dr Dubossarsky's background in artificial intelligence before deep diving into some of the problems of the data science industry and potential solutions. We discuss the difference between what makes a bad Data Scientist vs. good and finally wrap things up with Eugene's best advice on how to get started in the field.

Steven Brown, on Deepmind’s PySC2 Starcraft Interface, Blizzcon 2017 & Alphastar.

Steven Brown, on Deepmind’s PySC2 Starcraft Interface, Blizzcon 2017 & Alphastar.

July 5, 2019

Steven Brown is one of the primary contributors to the PySC2 interface- the API that Deepmind used to defeat humans in Starcraft 2 earlier in January this year (2019). 

In this episode we discuss Steven’s history with how he got involved in the Starcraft 2 Ai community, what it was like to attend the original announcement of the Blizzard-Deepmind collaboration in 2017, discuss his contribution to the interface - and find out a little bit of info on what might on the horizon for the next phase of Starcraft Ai development.

Prof Maurice Pagnucco, A brief history of Machine Learning - Expert Systems, Bayesian Methods, Causal Inference and the Millennium Prize.

Prof Maurice Pagnucco, A brief history of Machine Learning - Expert Systems, Bayesian Methods, Causal Inference and the Millennium Prize.

June 13, 2019

Professor Maurice Pagnucco is the Deputy Dean of Education, and the Head of the School of Computer Science and Engineering at the University of New South Wales (UNSW) in Sydney Australia.

A talk which turned into a Brief History of Machine Learning & using Prof Pagnucco's wide & deep knowledge of the field - I asked him a couple of ML questions covering topics including Expert Systems, Bayesian Methods, Causal Inference, the Millennium Prize and the future of ML.

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