Season 1

.NET

AI & ML

Podcasts

IntelVerse

It can take dozens of back-and-forth emails just to set up a quick meeting. Now, an artificial intelligence personal assistant can do it for you. Dennis Mortensen, CEO of x.ai, explains why you shouldn’t be afraid.

Goodbye to Personal Assistants

On this episode of Eat Sleep Code, guests Alex Bezuska and Eric Lathrop talk about game development, the Ludem Dare, Game Jam and how the started Two Scoop Games. Later, Jen Looper joins the show to talk about gaming with the internet of things.

Developer Network

Game Development

On this episode of Eat Sleep Code, guest Chris Michel from Treehouse talks about how he learned to develop software coming from a designer background. Community, conferences, and meetups are discussed.

The Self-taught Developer

On this episode of Eat Sleep Code, guests Michael Crump and Jen Looper discuss mobile development and e-books. We learn what it's like to try to enter the app stores and what tools are needed to get started in mobile development.

Mobile Development

In science-fiction, you’ll encounter a lot of computers that can feel... HAL 9000, Deckard, WALL-E. But what about computers that can really sense your emotions? MIT Professor Rosalind Picard takes a look at this artificial-feeling future.

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Computers that Know You're Mad

On this episode of Eat Sleep Code, guest Jimmy Bogard technical architect with Headspring, shares his experience with running a successful open source project. Later, Sam Basu and Michael Crump join the show to talk about Apple, Microsoft and Telerik open source initiatives.

Running a Successful Open Source Project

On this episode of Eat Sleep Code, Ed Charbeneau and guests David Giard, Sam Basu, and Michael Crump share their mobile devlopment experiences in a panel discussion. This episode was recorded with a live audience at Codestock 2015.

Mobile Panel Discussion

On this episode of Eat Sleep Code, guest Dave Fancher talk about Functional Programming. We discuss how functional programming has made its way back into modern development, clean coding, and more. 

http://developer.telerik.com/content-types/podcast/better-code-with-functional-programming/

Ed Charbeneau** :** Hello and welcome to Eat Sleep Code, the official Telerik podcast. I'm your host, Ed Charbeneau, and with me today is Dave Fancher.

Dave Fancher** :** Hello!

EC: And today Dave and I will be talking about functional programming in .NET. Dave is author of, The book of F#, from No Starch Press and a Microsoft MVP for .NET and also the owner of Achiiv Solutions. Dave is a Pluralsight author, and he's done a course called, "Building F# Type Providers" and he's working on a new course about functional programming with C#. So Dave what is functional programming?

DF: So ask ten different programmers what functional programming is, and you'll likely get ten different answers. But in general, they tend to boil down to three main characteristics. The first one is that functional programming is about controlling side effects, side effects are anything that changes the external state of the system. It could be as benign as writing to a log file, or it could be more nefarious like changing some shared data across the system. Under functional programming, we wanna control that as much as possible, also under functional programming, we tend to focus on expressions rather than statements, so C# tends to be statement-based. There are some expressions in there, but for the most part it's statement based, your if statements, your using statements, and so on, versus a functional language tends to be focused on expressions, rather than executing something for its effect we wanna execute something for its result. And then finally, functional programming is really centered on the idea of mathematical functions. And so functional languages want to treat functions as data. So you can pass around functions just as you would an integer or a string and that gives way to higher order functions, which is where most of the power of functional programming comes from.

EC: So you're controlling the mutability of the data in the application.

Find the full transcript on Telerik Developer Network http://developer.telerik.com/content-types/podcast/better-code-with-functional-programming/

Better code with functional programming

On this episode of Eat Sleep Code, guest Jennifer Marsman talks about Azure Machine Learning and how Microsoft is bringing AI to the masses.

Azure Machine Learning

What is A.I. or artificial intelligence but the 'space of possible minds', argues Murray Shanahan, scientific advisor on the movie Ex Machina and Professor of Cognitive Robotics at Imperial College London.

In this special episode of the a16z Podcast brought to you on the ground from London, Shanahan -- along with journalist-turned-entrepreneur Azeem Azhar (who also curates The Exponential View newsletter on AI and more) and The Economist Deputy Editor Tom Standage (the author of several tech history books) -- we discuss the past, present, and future of A.I. ... as well as how it fits (or doesn't fit) with machine learning and deep learning.

But where are we now in the A.I. evolution? What players do we think will lead, if not win, the current race? And how should we think about issues such as ethics and automation of jobs without descending into obvious extremes? All this and more, including a surprise easter egg in Ex Machina shared by Shanahan, whose work influenced the movie.

a16z Podcast: Artificial Intelligence and the 'Space of Possible Minds'

On this episode of Eat Sleep Code, guest Matt Millican talks about Microsoft's open source initiative. Why did Microsoft go open source, what products, and what to expect in the future.


Why Microsoft Open Source

On this special episode of Eat Sleep Code John Bristowe from the Telerik DevRel team interviews developers from Microsoft Build.

Build Day 1 Interviews

In this episode of the O’Reilly Data Show, I spoke with Danny Bickson, co-founder and VP at Dato, and the principal organizer of the Data Science Summit (full disclosure: I’m a member of the conference organizing committee). Among machine learning students and practitioners, recommender systems have become somewhat of a canonical use case and application. One of the early and popular building blocks was GraphLab’s collaborative filtering toolkit, a library originally written and maintained by Bickson. He has continued to keep tabs on the latest developments in recommenders and continues to help organize workshops on related topics throughout the world.

Building and Deploying Large-Scale Machine Learning Applications

In this episode of the Data Show, I spoke with Parvez Ahammad, who leads the data science and machine learning efforts at Instart Logic. He has applied machine learning in a variety of domains, most recently to computational neuroscience and security. Along the way, he has assembled and managed teams of data scientists and has had to grapple with issues like explainability and interpretability, ethics, insufficient amount of labeled data, and adversaries who target machine learning models. As more companies deploy machine learning models into products, it’s important to remember there are many other factors that come into play aside from raw performance metrics.

Building Machine Learning Solutions That Can Withstand Adversarial Attacks

On this episode of Eat Sleep Code guest David Giard discusses Microsoft Cognitive Services, functionalities, and how they're used. We brain storm ideas for the future of cognitive APIs and what UX problems they will eliminate.

Microsoft Cognitive Services And Friends

On this Episode of Eat Sleep Code, guest Michael Crump talks about why Azure is important to .NET developers. We learn how Azure goes beyond virtualization, hosting databases and deploying websites. Michael talks about his experience with Azure coming from a C# client app background and the “ah-ha!” moment with the benefits of moving to the cloud.

Azure For Developers

In our very first english speaking episode we are sending right from Microsoft Ignite in Orlando. And we have to special guests in our show: Donovan Brown and Damian Brady. With them we are talking about Azure News at Microsoft Ignite and what this DevOps thing is about. 

Find our Shownotes here: https://geeksprech.de

GeekSprech(EN)

#22 - GeekSprech(EN) - Microsoft Ignite - DevOps and Azure News

In this episode of the Data Show, I spoke with Andrew Burt, chief privacy officer and legal engineer at Immuta, a company building data management tools tuned for data science. Burt and cybersecurity pioneer Daniel Geer recently released a must-read white paper (“Flat Light”) that provides a great framework for how to think about information security in the age of big data and AI. They list important changes to the information landscape and offer suggestions on how to alleviate some of the new risks introduced by the rise of machine learning and AI.

We discussed their new white paper, cybersecurity (Burt was previously a special advisor at the FBI), and an exciting new Strata Data tutorial that Burt will be co-teaching in March.

How Machine Learning Impacts Information Security

Three generals are voting on whether to attack or retreat from their siege of a castle. One of the generals is corrupt and two of them are not. What happens when the corrupted general sends different answers to the other two generals?

A Byzantine fault is "a condition of a computer system, particularly distributed computing systems, where components may fail and there is imperfect information on whether a component has failed. The term takes its name from an allegory, the "Byzantine Generals' Problem", developed to describe this condition, where actors must agree on a concerted strategy to avoid catastrophic system failure, but some of the actors are unreliable."

The Byzantine Generals' Problem and associated issues in maintaining reliable distributed computing networks is illuminating for both AI alignment and modern networks we interact with like Youtube, Facebook, or Google. By exploring this space, we are shown the limits of reliable distributed computing, the safety concerns and threats in this space, and the tradeoffs we will have to make for varying degrees of efficiency or safety.

The Byzantine Generals' Problem, Poisoning, and Distributed Machine Learning with El Mahdi El Mahmdi is the ninth podcast in the AI Alignment Podcast series, hosted by Lucas Perry. El Mahdi pioneered Byzantine resilient machine learning devising a series of provably safe algorithms he recently presented at NeurIPS and ICML. Interested in theoretical biology, his work also includes the analysis of error propagation and networks applied to both neural and biomolecular networks. This particular episode was recorded at the Beneficial AGI 2019 conference in Puerto Rico. We hope that you will join in the conversations by following us or subscribing to our podcasts on Youtube, SoundCloud, iTunes, Google Play, Stitcher, or your preferred podcast site/application. You can find all the AI Alignment Podcasts here.

If you're interested in exploring the interdisciplinary nature of AI alignment, we suggest you take a look here at a preliminary landscape which begins to map this space.

Topics discussed in this episode include:

The Byzantine Generals' Problem
What this has to do with artificial intelligence and machine learning
Everyday situations where this is important
How systems and models are to update in the context of asynchrony
Why it's hard to do Byzantine resilient distributed ML.
Why this is important for long-term AI alignment
An overview of Adversarial Machine Learning and where Byzantine-resilient Machine Learning stands on the map is available in this (9min) video . A specific focus on Byzantine Fault Tolerant Machine Learning is available here (~7min)

In particular, El Mahdi argues in the first interview (and in the podcast) that technical AI safety is not only relevant for long term concerns, but is crucial in current pressing issues such as social media poisoning of public debates and misinformation propagation, both of which fall into Poisoning-resilience. Another example he likes to use is social media addiction, that could be seen as a case of (non) Safely Interruptible learning. This value misalignment is already an issue with the primitive forms of AIs that optimize our world today as they maximize our watch-time all over the internet.

The latter (Safe Interruptibility) is another technical AI safety question El Mahdi works on, in the context of Reinforcement Learning. This line of research was initially dismissed as "science fiction", in this interview (5min), El Mahdi explains why it is a realistic question that arises naturally in reinforcement learning

El Mahdi's work on Byzantine-resilient Machine Learning and other relevant topics is available on
his Google scholar profile.

The Byzantine Generals' Problem, Poisoning, and Distributed Machine Learning with El Mahdi El Mhamdi

Eat Sleep Code is the Official Telerik Podcast (Telerik by Progress). On Eat Sleep Code you'll hear from industry pros and developers just like you. Topics include all things software development and upcoming tech.

Progress believes in giving back to the developer community and we do this by writing, speaking and staying in touch with the software development community.

Azure Machine Learning

Eat Sleep Code Podcast