This tutorial was recorded at KDD 2020 as a live, hands-on tutorial. The content is available at

Why do people collaborate? Because we achieve more together than we can alone. The same is true of man and machine. Leaders need to add machine learning to their data and analytics strategies to unlock actionable insights that help employees make better business decisions. This is augmented intelligence. In the sixth installment of his “Journeys in Transformation” webchat series, Genpact’s Chief Digital Officer, Sanjay Srivastava, shares a framework for augmented intelligence. Joining him are:

• JoAnn Stonier, Chief Data Officer at MasterCard
• Henna Karna, Chief Data Officer at AXA XL
• Vikram Mahidhar, Leader, AI Growth Strategy at Genpact

Peter Albert Railton is an American philosopher who is Gregory S. Kavka Distinguished University Professor and John Stephenson Perrin Professor of Philosophy at the University of Michigan, Ann Arbor, where he has taught since 1979.

It’s been 7 years since my first interview with Gary Marcus and I felt it’s time to catch up with him. Gary is the youngest Professor Emeritus at NYU and I wanted to get his contrarian views on the major things that have happened in AI as well as those that haven’t happened. Prof. Marcus is an interesting interviewee not only because he is an expert in the field but also because he is a skeptic on the current approaches and progress towards Artificial General Intelligence but an optimist that we will eventually figure it all out.

During this 90 min interview with Gary Marcus we cover a variety of interesting topics such as: Gary’s interest in the human mind, natural and artificial intelligence; Deep Mind’s victory in Go and what it does and doesn’t mean for AGI; the need for Rebooting AI; trusting AI and the AI chasms; Asimov’s Laws and Bostrom’s paper-clip-maximizing AI; the Turing Test and Ray Kurzweil’s singularity timeline; Mastering Go Without Human Knowledge; closed vs open systems; Chomsky, Minsky and Ferrucci on AGI; the limits of deep learning and the myth of the master algorithm; the problem of defining (artificial) intelligence; human and machine consciousness; the team behind and the mission of Robust AI.

As always you can listen to or download the audio file above or scroll down and watch the video interview in full. To show your support you can write a review on iTunes, make a direct donation or become a patron on Patreon.

Title: Break Into AI: A Q&A with Andrew Ng on Building a Career in Machine Learning
Speaker: Andrew Ng
Date: 12/4/2018

Andrew Ng will share tips and tricks on how to break into AI. He will discuss some of the most valuable skills for today’s machine learning engineers, how to gain the experience to successfully switch careers, and how to build a habit of lifelong learning. He will also take questions from aspiring engineers and business professionals who want to work on AI-powered products.

Andrew Ng, General Partner, AI Fund; CEO, Landing AI;
Adjunct Professor, Stanford University
Dr. Andrew Ng, a globally recognized leader in AI, is a General Partner at AI Fund and CEO of Landing AI. As the former Chief Scientist at Baidu and the founding lead of Google Brain, he led the AI transformation of two of the world’s leading technology companies. A longtime advocate of accessible education, Dr. Ng is the Co-founder of Coursera, an online learning platform, and founder of, an AI education platform. Dr. Ng is also an Adjunct Professor at Stanford University’s Computer Science Department.

Juan Miguel de Joya, UN ITU; ACM Practitioners Board
Juan Miguel de Joya is the in-house consultant for Artificial Intelligence and Emerging Technologies at the United Nations International Telecommunications Union. Prior to this role, he served as a contractor at Facebook/Oculus and Google, worked at Pixar Animation Studios and Walt Disney Animation Studios, and was an undergraduate researcher in graphics at the Visual Computing Lab at the University of California, Berkeley. In his spare time, he is part of the ACM Practitioners Board, the ACM Professional Development Committee, and the ACM SIGGRAPH Strategy Group. His current interests include artificial intelligence, computer vision, mixed reality, computational physics, the web, and the human impact of computing in society at large.

Geordie Rose, Founder and CTO of D-Wave Systems describes some of the challenges the team had to overcome in building the first commercial quantum computer.

Learn more about D-Wave and the first commercial quantum computers at

Recorded at the GAIA conference on April 10th 2018 in collaboration with Ericsson.

Artificial Intelligence and Machine Learning have made enormous progress during the last decade, and AI techniques are already widely used in industry and society. Expectations on these technologies are high, often so high that the technology we have today cannot come close to fulfil them. We will discuss what modern AI is, what is possible and what problems remain to be solved. We will present what RISE AI does within the area, how to create more interpretable machine learning models, approaches to bridge the gap between learning and reasoning, and what we believe is a way forward towards more general, large-scale AI-systems.

Our opinions of AI have been formed by mainstream media, but that stops now. We’re joined by New York Times best-selling author, Martin Ford to talk about what’s exciting, what’s scary, and what’s to come from machine learning. In his new book, Ford records the conversations he has had with the very people developing artificial intelligence.

Get Your Copy of “Architects of Intelligence” at is an NYC based artificial intelligence company that’s raised over $30 million. I sat down with the founder Dennis Mortensen to learn more about how they’re positioning and selling the product to the enterprise.

Talking to him got me super curious about the rise of artificial intelligence, and I hope this interview does similar for you.

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In this Keynote Session, some of Google’s leading minds on artificial intelligence and machine learning discuss their vision for a future where artificial intelligence can improve the lives of everyone.

Rate this session by signing-in on the I/O website here →

Watch more machine learning sessions from I/O ’18 here →
See all the sessions from Google I/O ’18 here →

Subscribe to the Google Developers channel →


Book review for

Architects of Intelligence: The truth about AI from the people building it
by: Martin Ford

Sam Harris and Joe Rogan discuss the future of Artificial Intelligence, the inevitability of General AI and how we could be giving birth to the very thing that will destroy us.


JRE Films is a cinematic exploration of the ideas and individuals featured on the Joe Rogan Experience. Our goal is to create short films that transform Joe’s conversations with guests into thought-provoking and dramatic visual stories.

JRE MMA Show #804 with Sam Harris:
Footage provided by @JoshuaHalvatzis

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Learn more about AWS Startups at – 
Yuaho Zheng, Director of Engineering at DataVisor, talks about building a fraud detection platform using AI and Big Data at the 2019 AWS Santa Clara Summit.

Why were early AI pioneers drawn to collaborating with architects?

This video is part of ‘Design Nonfiction’, Tellart’s documentary series of over 50 interviews with world-renowned designers and artists working with science and technology.

Visit to learn more.

© 2019 TELLART

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Yoshua Bengio (MILA) discusses the obstacles we are likely to face on the path to beneficial artificial general intelligence.

The Beneficial AGI 2019 Conference:

After our Puerto Rico AI conference in 2015 and our Asilomar Beneficial AI conference in 2017, we returned to Puerto Rico at the start of 2019 to talk about Beneficial AGI. We couldn’t be more excited to see all of the groups, organizations, conferences and workshops that have cropped up in the last few years to ensure that AI today and in the near future will be safe and beneficial. And so we now wanted to look further ahead to artificial general intelligence (AGI), the classic goal of AI research, which promises tremendous transformation in society. Beyond mitigating risks, we want to explore how we can design AGI to help us create the best future for humanity.

We again brought together an amazing group of AI researchers from academia and industry, as well as thought leaders in economics, law, policy, ethics, and philosophy for five days dedicated to beneficial AI. We hosted a two-day technical workshop to look more deeply at how we can create beneficial AGI, and we followed that with a 2.5-day conference, in which people from a broader AI background considered the opportunities and challenges related to the future of AGI and steps we can take today to move toward an even better future.

“There’s a proliferation of unstructured data. Companies collect massive amounts of news feed, emails, social media, and other text-based information to get to know their customers better or to comply with regulations. However, most of this data is unused and untouched. Natural language processing (NLP) holds the key to unlocking business value within these huge data sets, by turning free text into data that can be analyzed and acted upon. Join this tech talk and learn how you can get started mining text data effectively and extracting the rich insights it can bring. We will also demonstrate how you can build a text analytics solution with Amazon Comprehend and Amazon Relational Database Service.

Learning Objectives:
– Get an introduction to Natural Language Processing (NLP)
– Learn benefits of new approaches to analytics and technologies that help empower better decisions, e.g., NLP, data prep
– Build a text analytics solution with Amazon Comprehend and Amazon Relational Database Service in a step by step demo”

Presentation slides:

Building a Machine Learning Platform at Quora: Each month, over 100 million people use Quora to share and grow their knowledge. Machine learning has played a critical role in enabling us to grow to this scale, with applications ranging from understanding content quality to identifying users’ interests and expertise. By investing in a reusable, extensible machine learning platform, our small team of ML engineers has been able to productionize dozens of different models and algorithms that power many features across Quora.

In this talk, I’ll discuss the core ideas behind our ML platform, as well as some of the specific systems, tools, and abstractions that have enabled us to scale our approach to machine learning.

I’m building a machine learning/SaaS startup. In this video, I share the results my first 50 days of full-time work, explaining my business strategy, showing the core features I designed and programmed, and summarizing what I learned from my users. I also give a overview of all the programming frameworks and API’s I used.

I started the project out of Y Combinator Startup School. I have a software engineering background, but this is my first serious AI project, and I’m giving it my 100%. You can try it out here:


TensorFlow is a truly open source platform with over 1,900 contributors. On this episode of TensorFlow Meets, Laurence (@lmoroney) talks to Open Source Strategist Edd Wilder-James (@edd) about how things like TensorFlow’s Request for Comments process, Special Interest Groups, and the modularity of its codebase make it easier for the community to build TensorFlow together. They also discuss the upcoming O’Reilly TensorFlow World, which is accepting applications to participate now through April 23rd.

TensorFlow community →

Watch Edd’s talk at TF Dev Summit ‘19 →

O’Reilly TensorFlow World 2019 →

Subscribe to the TensorFlow channel →
Watch more episodes of TensorFlow Meets →

The Wow Room will break with the traditional onsite, blended and online education models to transform the learning experience through elements that include artificial intelligence, simulations in real time, big data analysis, interactive robots, emotion recognition systems, and the presence of experts using holograms.

Organizers: Jason (Jinquan) Dai Location: Room 151 A-C & G Time: 0900-1200 (Half Day — Morning) Description: Recent breakthroughs in artificial intelligence applications have brought deep learning to the forefront of new generations of data analytics. In this tutorial, we will pre-sent the practice and design tradeoffs for building large-scale deep learning applications (such as computer vision and NLP) for production data and workflow on Big Data platforms. In particular, we will provide an overview of emerging deep learn-ing frameworks for Big Data (e.g., BigDL, TensorFlow-on-Spark, Deep Learning Pipelines for Spark, etc.), present the underlying distributed systems and algorithms, and discuss innovative data analytics + AI application pipelines (with a focus on computer vision models and use cases) for Big Data platforms and workflows. Schedule: 0900 Motivation 0910 Overview 0930 Analytics Zoo for Spark and BigDL 1000 Morning Break 1030 Distributed Training and Inference 1100 Advanced Applications 1130 Real-World Applications 1150 Q&A

How can we harness the power of superintelligent AI while also preventing the catastrophe of robotic takeover? As we move closer toward creating all-knowing machines, AI pioneer Stuart Russell is working on something a bit different: robots with uncertainty. Hear his vision for human-compatible AI that can solve problems using common sense, altruism and other human values.

Recorded August, 2017

Visit the largest developers congress in Europe: WeAreDevelopers World Congress, 16 – 18 May 2018 in Vienna, Austria.

Conversational AI will dramatically change how users will interact with your software. The world is moving away from websites and apps to conversational interfaces, AI-powered bots and assistants. Advances in machine learning and state of the art research makes it possible for you to build your very own assistant. In this talk, Tom will show how you can get started building a conversational AI using open source software tools. You will get to know the architecture of conversational systems and the challenges faced when implementing them.

WeAreDevelopers Talents


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