IntelVerse

In episode twelve we talk with Andrew Ng, Chief Scientist at Baidu, we’re introduced to random features for large-scale kernel machines, and we take a listener question about the size of computing power in machine learning. 

The Economic Impact of Machine Learning and Using The Kernel Trick To Dig In To Big Data

In episode nineteen we chat with Hugo Larochelle, Ryan introduces us to autoencoders, plus we tackle the question of what is standing in the way of strong AI.

Strong AI and Autoencoders

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

Developer Network

Azure Machine Learning

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 Mike Tung, founder and CEO of Diffbot - a company dedicated to building large-scale knowledge databases. Diffbot is at the heart of many web applications, and it’s starting to power a wide array of intelligent applications. We talked about the challenges of building a web-scale platform for doing highly accurate, semi-supervised, structured data extraction. We also took a tour through the AI landscape, and the early days of self-driving cars.

Using AI to Build a Comprehensive Database of Knowledge

Artificial intelligence (AI) and virtual reality (VR) are often seen as different trends, but there is a lot of overlap in these areas, where you might not expect. Lucid VR's CTO Adam Rowell speaks&nbsp;today about how AI plays a role in making VR work, augmenting the accuracy of images and making a more immersive and convincing experience for users. Rowell also touches on non-gaming VR apps that he and his company are excited about launching&nbsp;in the future.

Lucid VR’s CTO Talks Machine Learning for Virtual Reality

When Google’s DeepMind won against one of the best modern Go champions, is used multiple AI approaches and exposed gaps in some individual strategies. This even has shed more light on AI, but also on the utility in combining approaches to AI for individual problems. Data security is one of these problem areas&nbsp;where multiple AI approaches is being used to make our information safer. Dr. Sal Stolfo has been a professor at Columbia in Computer Science since 1972 and is now also the CEO of Allure Security, with a focus on engineering network intrusion detection solutions using AI&nbsp;applications. In this episode, Stolfo talks about the various styles of AI and statical methods that have been and are being used&nbsp;to detect malicious activity, as well as how he believes the future of security is going to have to adapt as increasing amounts of data become available.

Network Intrusion Detection Using Machine Learning

Imagine there's an important decision to be made about someone, like a bank deciding whether to extend a loan, or a school deciding to admit a student--unfortunately, we're all too aware that discrimination can sneak into these situations (even when everyone is acting with the best of intentions!).  Now, these decisions are often made with the assistance of machine learning and statistical models, but unfortunately these algorithms pick up on the discrimination in the world (it sneaks in through the data, which can capture inequities, which the algorithms then learn) and reproduce it.

This podcast covers some of the most common ways we can try to minimize discrimination, and why none of those ways is perfect at fixing the problem.  Then we'll get to a new idea called "equality of opportunity," which came out of Google recently and takes a pretty practical and well-aimed approach to machine learning bias.

Attacking Discrimination in Machine Learning

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 the podcast, James first interviews Joakim Strid, head of European surveillance at Nasdaq, about how the exchange is incorporating machine learning into its surveillance program. They then discuss how AI is set to change surveillance as a whole in the years to come. After that interview, Emilia David, US reporter for Waters, joins James and Anthony to discuss how T+2 came about in the United States. Then finish things up by discussing must-watch TV for the fall now that Game of Thrones is over.

Nasdaq & Machine Learning
2:30 Joakim first talks about how Nasdaq is using machine learning to surveil its Nordic marketplace. 
http://www.waterstechnology.com/exchanges-trading-venues/3420051/nasdaq-boosts-surveillance-with-machine-learning 

5:15 He then looks at how the machine-learning algorithms work.
7:30 Why are machine-learning techniques useful when compared to existing techniques?
8:45 Joakim explains the genesis of the project.
10:45 Finally, he looks at what the possible future of machine learning holds for the surveillance sector.

T+2
14:00 What were the roadblocks keeping T+2 from being a reality in the US?
http://www.waterstechnology.com/industry-issues-initiatives/3414776/settling-down-how-the-us-finally-embraced-t2 

15:30 Why was this transition so difficult?
http://www.waterstechnology.com/industry-issues-initiatives/3419486/t2-settlement-begins-in-us-markets

18:15 How long until T+1? T+0?!
http://www.waterstechnology.com/industry-issues-initiatives/3417141/blockchain-not-yet-ready-to-power-t0-settlement 

23:30 TV shows you should start watching today.

86: Nasdaq's Use of Machine Learning & T+2 Update

This week on the podcast, Active IQ Technical Director Shankar Pasupathy joins us and tells us how AutoSupport’s infrastructure and backend evolved into Active IQ’s multicloud data pipeline. Learn how NetApp is using big data analytics and machine learning on ONTAP to improve the overall customer experience.

Episode 134 – The Active IQ Story: Building a Data Pipeline for Machine Learning

In this episode of the SuperDataScience Podcast, I chat with the Founder and Chief Technology Officer of Kylie.AI, Sinan Ozdemir. You will discuss Feature Engineering and why is it vital in interpreting and analyzing data models; you will also talk about Automated Customer Care Services and its importance in the future of enterprises, and get insights on why humans should ensure that AI is ethically responsible when serving humanity.

If you enjoyed this episode, check out show notes, resources, and more at www.superdatascience.com/161

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

SDS 161: Using AI to Automate Communication

In this episode of the Data Show, I spoke with Chang Liu, applied research scientist at Georgian Partners. In a previous post, I highlighted early tools for privacy-preserving analytics, both for improving decision-making (business intelligence and analytics) and for enabling automation (machine learning). One of the tools I mentioned is an open source project for SQL-based analysis that adheres to state-of-the-art differential privacy (a formal guarantee that provides robust privacy assurances).  Since business intelligence typically relies on SQL databases, this open source project is something many companies can already benefit from today.

What about machine learning? While I didn’t have space to point this out in my previous post, differential privacy has been an area of interest to many machine learning researchers. Most practicing data scientists aren’t aware of the research results, and popular data science tools haven’t incorporated differential privacy in meaningful ways (if at all). But things will change over the next months. For example, Liu wants to make  ideas from differential privacy accessible to industrial data scientists, and she is part of a team building tools to make this happen.

How Privacy-Preserving Techniques Can Lead to More Robust Machine Learning Models

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

Founder of EPIC Knowledge Society and SPACE Creative Culture Labs. I am a Futurist and Data Scientist with Professional Mastery and Pragmatic Insights on Digital Transformation and Innovation Management with extensive credentials on Digital Enterprise Architectures, Crypto Economics, Customer Experience Design, Social Entrepreneurship Incubation, Business Model Innovation, Innovation Ecosystems, Innovation Analytics, Innovation Consulting, Social Business Networks, Academic Research Networks, Industrial Research Alliances, New Service Incubation, New Product Incubation, New Platform Innovation, High performing Teams, Cross Competency Innovation etc.

IntelVerse

Gokul B. Alex