AI and Bots

O'Reilly's Mary Treseler talks with information architect Jorge Arango about the state of IA and the importance of designers' understanding of context and perspective.

Information Architecture's Role in UX Design

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

This week on the Radar Podcast, we're featuring the first episode of the newly launched O'Reilly Bots Podcast, which you can find on Stitcher, iTunes, SoundCloud and RSS. O'Reilly's Jon Bruner is joined by Pete Skomoroch, the co-founder and CEO of Skipflag, to talk about bots—about what's driving the sudden interest, what we can expect from the technology, and some interesting emerging applications.

Pete Skomoroch on the Current State and Future Potential of Bots

In episode five of the O’Reilly Bots podcast, Pete Skomoroch and I speak with Joshua Browder, the 19-year old founder and CEO of DoNotPay, a series of bots that help people with legal issues, including challenging parking tickets, challenging bank charges, and claiming government assistance for homelessness. Dubbed “the world’s first robot lawyer,” his bots have attracted 260,000 users and provided 175,000 successful parking-ticket appeals.

Joshua Browder on Bots That Fight Bureaucracy

The workplace is a rich venue for bots that help with productivity and collaboration. In this episode of the O’Reilly Bots Podcast, Pete Skomoroch and I focus on workplace bots. We begin by talking with Jassim Latif, head of partnerships at Slack, about bots written by outside developers for scheduling, organizing meetings, and managing human resources. “We’re building toward a future where the value of these apps and services that people are building on top of Slack far outweigh the value of Slack on its own,” Latif says.

Jassim Latif on Workplace Bots

This episode of the O’Reilly Podcast features my discussion with Jason Hand, DevOps Evangelist at VictorOps and author of the recent O'Reilly book, "ChatOps: Managing Infrastructure In Group Chat." We talk about ChatOps, managing incidents, and what it means to be a learning organization.

ChatOps: Supercharging DevOps With Group Chat

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

In this episode of the Data Show, I spoke with Christine Hung, head of data solutions at Spotify. Prior to joining Spotify, she led data teams at the NY Times and at Apple (iTunes). Having led teams at three different companies, I wanted to hear her thoughts on digital transformation, and I wanted to know how she approaches the challenge of building, managing, and nurturing data teams.

I also wanted to learn more about what goes into building a recommender system for a popular consumer service like Spotify. Engagement should clearly be the most important metric, but there are other considerations, such as introducing users to new or “long tail” content.

Machine Learning at Spotify: You Are What You Stream

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 Leo Meyerovich, co-founder and CEO of Graphistry. Graphs have always been part of the big data revolution (think of the large graphs generated by the early social media startups). In recent months, I’ve come across companies releasing and using new tools for creating, storing, and (most importantly) analyzing large graphs. There are many problems and use cases that lend themselves naturally to graphs, and recent advances in hardware and software building blocks have made large-scale analytics possible.

Starting with his work as a graduate student at UC Berkeley, Meyerovich has pioneered the combination of hardware and software acceleration to create truly interactive environments for visualizing large amounts of data. Graphistry has built a suite of tools that enables analysts to wade through large data sets and investigate business and security incidents. The company is currently focused on the security domain—where it turns out that graph representations of data are things security analysts are quite familiar with.

Graphs as the Front End for Machine Learning

In this episode of the Data Show, I spoke with Ameet Talwalkar, assistant professor of machine learning at CMU and co-founder of Determined AI. He was an early and key contributor to Spark MLlib and a member of AMPLab. Most recently, he helped conceive and organize the first edition of SysML, a new academic conference at the intersection of systems and machine learning (ML).

We discussed using and deploying deep learning at scale. This is an empirical era for machine learning, and, as I noted in an earlier article, as successful as deep learning has been, our level of understanding of why it works so well is still lacking. In practice, machine learning engineers need to explore and experiment using different architectures and hyperparameters before they settle on a model that works for their specific use case. Training a single model usually involves big (labeled) data and big models; as such, exploring the space of possible model architectures and parameters can take days, weeks, or even months. Talwalkar has spent the last few years grappling with this problem as an academic researcher and as an entrepreneur. In this episode, he describes some of his related work on hyperparameter tuning, systems, and more.

How to Train and Deploy Deep Learning at Scale

In this episode of the Data Show, I spoke Jesse Anderson, managing director of the Big Data Institute, and my colleague Paco Nathan, who recently became co-chair of Jupytercon. This conversation grew out of a recent email thread the three of us had on machine learning engineers, a new job role that LinkedIn recently pegged as the fastest growing job in the U.S. In our email discussion, there was some disagreement on whether such a specialized job role/title was needed in the first place. As Eric Colson pointed out in his beautiful keynote at Strata Data San Jose, when done too soon, creating specialized roles can slow down your data team.

We recorded this conversation at Strata San Jose, while Anderson was in the middle of teaching his very popular two-day training course on real-time systems. We closed the conversation with Anderson’s take on Apache Pulsar, a very impressive new messaging system that is starting to gain fans among data engineers.

What Machine Learning Engineers Need to Know

In this episode of the O’Reilly podcast, I spoke with Stephen Gates of Oracle Dyn. Gates joined the Oracle Dyn Global Business Unit from Zenedge, the web application security company recently acquired by Oracle. Gates and I discussed how growing malicious bot activity impacts organizations.

Stephen Gates on the Growing Risks Posed by Malicious Bots

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.

AI and Bots

Scott Shepherd