AI and Bots

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The O'Reilly Data Show explores the opportunities and techniques driving big data and data science. Through interviews and analysis, we highlight the people putting data to work.

The O'Reilly Data Show Podcast

Embedding a Culture of Governance, Risk and Compliance Across the Organization
Shellye Archambeau, Chief Executive Officer, MetricStream
Horst Simon, Director- Risk Management, Horwath MAK 
M.Ali Baig, Head of Internal Audit and Controls, Dana Gas 
Moderator: Dheeraj Prasad, VP-Customer Advocacy & Production Engineering, MetricStream
Youtube: http://www.youtube.com/watch?v=Tb3IDZy4fto&list   

Embedding A Culture Of Governance, Risk And Compliance Across The Organization

Guest : Stephen Elkins
Title: Chief Information Officer, City of Austin, Texas

Striving for leaner business operations, quicker actions and lower costs is an ongoing goal. The drive to always stay ahead of disruptions and degradation and get the most of our assets will never stop being desirable. Getting insight into your operations real time might seem like the answer to all your problems, but how can you truly optimize business operations real time? What is that ideal state, and how can you reasonably reach it? In looking at new solutions and real time data analytics, we explore the drivers behind making this change in your operations and if this update can really give you the quantum leap you need to stay competitive.

Optimizing Business Operations in Real Time

Greylock Partners investor Sarah Guo joins us for episode two of our new pop-up podcast on bots and conversational interfaces. She’s written insightfully on bots and has worked on several investments in bot startups.

Sarah Guo on the Case for Bots

In episode four of the O’Reilly Bots podcast, Pete Skomoroch and I speak with Cathy Pearl, director of user experience at Sensely, and author of the forthcoming O’Reilly book “Designing Voice User Interfaces.” She’s also a speaker at O’Reilly’s upcoming Bot Day on October 19, 2016, in San Francisco.

Cathy Pearl on Voice User Interfaces for Bots

In this episode of the O’Reilly Bots Podcast, I speak with Dennis Mortensen, founder and CEO of X.ai, a personal assistant bot that handles meeting scheduling through email.

Dennis Mortensen on Email Bots

In this episode of the Bots Podcast, we peer into the giant companies that are beginning to adopt messaging and bots. My guest is Tom Hadfield, founder of Message.io, a service that syndicates bots across many different messaging platforms.

Tom Hadfield on Bots in the Enterprise

Jordan Kong, Director of Business Operations and Board Observer at FoodTech startup Ando, talks about what working in Business Operations is like.

Some of the areas that Jordan touches upon in this episode include:
1. What's a venture studio (eg: Expa) and how it's great for people who want to work at a startup but dont have an idea
2. What does someone in Biz Ops do
3. Examples of projects someone in Biz Ops may work on 
4. How this can be a highly cross-functional role and requires working with teams across the organization
5. How this is a data heavy role
6. How this role can take on either a strategic finance bent or an operations bent
7. How Biz Ops can be similar to an internal consulting role at times
8. Interesting and challenging aspects of the job
9. How this is a relatively new function and so the career path is not very clear
10. How to assess the importance of the function on the outside/while recruiting
11. Qualities in someone who would enjoy this job
12. How to stand out in a cold mail
13. Recruiting and interviewing tips for interested candidates

Thank you for listening!!

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Ep 93: Business Operations in Tech, Jordan Kong, Director Biz Ops @Ando

Do you know somebody who is apprehensive in getting into data science because they think it's too complex?

If you enjoyed this episode, check out show notes, resources, and more at https://www.superdatascience.com/102

The Super Data Science Podcast: Big Data | Analytics Careers | Mentors | Success

SDS 102: Intuition vs Mathematics in Data Science

Roughly one third of the top 100 business schools have started Masters in Business or Data Analytics programs, in response to the growing need for workers who can leverage data assets for competitive advantage.

What do MBA applicants need to know about MBA careers in the growing industry of data and business analytics?

Darren invites Shane Rai, Co-Founder of TenPoint7, a machine learning-as-a-service company, to share his experience as a technology consultant turned entrepreneur in the data analytics and service industry.

This is Part 4 of our MBA Career Series. Looking for more career-related podcasts? Click here - http://touchmba.com/category/application-strategy/career-goals-careers/

Questions:
Shane’s background (3:39)
The difference between data science, data analytics and business analytics (7:00)
How to break into the industry (11:00)
What background & experience are data-centric companies looking for? (15:13)
Is AI going to take over the world? (20:30)
Selling data intelligence services (24:30)
Interviewing for a data analytics role – what you need to know (26:41)
Real world examples of data analytics in action (33:24)
Worst parts of the job (37:50)
Shane’s recommended resources (39:46)
Shane’s final thoughts on the data analytics industry (41:20)

About Our Guest:
After graduating from the University of Washington with a degree in Computing and Software Systems, Shane Rai worked 7 years for Deloitte Consulting in the technology service area, and then 2 years as Product Manager for SDL Social, which provides social media analytics and intelligence tools. Shane currently leads product development for TenPoint7, a machine-learning-as-a-service and data consulting provider based in Seattle.

Episode Sponsor:
The IESE MBA in Barcelona is consistently ranked Top 15 worldwide by the Financial Times. Do you want to challenge yourself and take your career to the next level? IESE’s full time MBA program will transform your capacity to think strategically, explore new perspectives and be a leader in today’s global world. The IESE MBA: Give us 19 months and we give you a lifetime of impact.

Get informal feedback on your profile at https://mba.iese.edu/ or visit us on campus at our Open Days.

Need school selection help? Get a free expert profile review at http://touchmba.com.

Touch MBA Admissions Podcast

Breaking into Data and Business Analytics (as an MBA)

In this episode of the Data Show, I spoke with Kristian Hammond, chief scientist of Narrative Science and professor of EECS at Northwestern University. He has been at the forefront of helping companies understand the power, limitations, and disruptive potential of AI technologies and tools. In a previous post on machine learning, I listed types of uses cases (a taxonomy) for machine learning that could just as well apply to enterprise applications of AI. But how do you identify good use cases to begin with?

Bringing AI Into the Enterprise

In this episode of the Data Show, I spoke with Jerry Overton, senior principal and distinguished technologist at DXC Technology. I wanted the perspective of someone who works across industries and with a variety of companies. I specifically wanted to explore the current state of data science and AI within companies and public sector agencies. As much as we talk about use cases, technologies, and algorithms, there are also important issues that practitioners like Overton need to address, including privacy, security, and ethics. Overton has long been involved in teaching and mentoring new data scientists, so we also discussed some tips and best practices he shares with new members of his team.

Teaching and Implementing Data Science and AI in the Enterprise

In this episode of the Data Show, I spoke with Roger Chen, co-founder and CEO of Computable Labs, a startup focused on building tools for the creation of data networks and data exchanges. Chen has also served as co-chair of O'Reilly's Artificial Intelligence Conference since its inception in 2016. This conversation took place the day after Chen and his collaborators released an interesting new white paper, "Fair value and decentralized governance of data." Current-generation AI and machine learning technologies rely on large amounts of data, and to the extent they can use their large user bases to create “data silos,” large companies in large countries (like the U.S. and China) enjoy a competitive advantage. With that said, we are awash in articles about the dangers posed by these data silos. Privacy and security, disinformation, bias, and a lack of transparency and control are just some of the issues that have plagued the perceived owners of “data monopolies.”

Acquiring and Sharing High-Quality Data

In this episode of the Data Show, I speak with Cassie Kozyrkov, technical director and chief decision scientist at Google Cloud. She describes "decision intelligence" as an interdisciplinary field concerned with all aspects of decision-making, and which combines data science with the behavioral sciences. Most recently she has been focused on developing best practices that can help practitioners make safe, effective use of AI and data. Kozyrkov uses her platform to help data scientists develop skills that will enable them to connect data and AI with their organizations' core businesses.

We had a great conversation spanning many topics, including:

How data science can be more useful

The importance of the human side of data

The leadership talent shortage in data science

Is data science a bubble?

Make Data Science More Useful

In this episode of the Data Show, I speak with Alex Ratner, project lead for Stanford’s Snorkel open source project; Ratner also recently garnered a faculty position at the University of Washington and is currently working on a company supporting and extending the Snorkel project. Snorkel is a framework for building and managing training data. Based on our survey from earlier this year, labeled data remains a key bottleneck for organizations building machine learning applications and services.

Ratner was a guest on the podcast a little over two years ago when Snorkel was a relatively new project. Since then, Snorkel has added more features, expanded into computer vision use cases, and now boasts many users, including Google, Intel, IBM, and other organizations. Along with his thesis advisor professor Chris Ré of Stanford, Ratner and his collaborators have long championed the importance of building tools aimed squarely at helping teams build and manage training data. With today’s release of Snorkel version 0.9, we are a step closer to having a framework that enables the programmatic creation of training data sets.

Labeling, Transforming, and Structuring Training Data Sets for Machine Learning

In this episode of the Data Show, I speak with Kesha Williams, technical instructor at A Cloud Guru, a training company focused on cloud computing. As a full stack web developer, Williams became intrigued by machine learning and started teaching herself the ML tools on Amazon Web Services. Fast forward to today, Williams has built some well-regarded Alexa skills, mastered ML services on AWS, and has now firmly added machine learning to her developer toolkit.

Becoming a Machine Learning Practitioner

In this episode of the Data Show, I speak with Michael Mahoney, a member of RISELab, the International Computer Science Institute, and the Department of Statistics at UC Berkeley. A physicist by training, Mahoney has been at the forefront of many important problems in large-scale data analysis. On the theoretical side, his works spans algorithmic and statistical methods for matrices, graphs, regression, optimization, and related problems. On the applications side, he has contributed to systems used for internet and social media analysis, social network analysis, as well as for a host of applications in the physical and life sciences. Most recently, he has been working on deep neural networks, specifically developing theoretical methods and practical diagnostic tools that should be helpful to practitioners who use deep learning.

Understanding Deep Neural Networks

O'Reilly Media spreads the knowledge of innovators. At O’Reilly, a big part of our business is paying attention to what’s new and interesting in the world of technology. We have a pretty good record at having anticipated some of the big technology developments in recent history. For instance, we launched the first commercial Web site, GNN, in 1993; we organized the meeting at which the term “open source” was first adopted; we were early investors in Blogger, which helped launch the blogging revolution; and more recently, our Web 2.0 conference launched a world-wide meme. We call this predictive sense the “O’Reilly Radar.” And while we’re certainly not always right, we are, at least, good at making interesting guesses.

Our methodology is simple: we draw from the wisdom of the alpha geeks in our midst, paying attention to what’s interesting to them, amplifying these weak signals, and seeing where they fit into the innovation ecology. Add to that the original research conducted by our Research team, and you start to get a good picture of what the technology world is thinking about.

Teaching and Implementing Data Science and AI in the Enterprise

O'Reilly Radar