IntelVerse

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

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

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

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

This week we interview a leader at Facebook. Jason Sundram is the lead of World.ai at Facebook, which is one of their efforts to work with public data around roads and population and other projects of that kind. But Sundram is also highly involved in the Boston office here, where Facebook will soon have around 650 employees. Many of them focus on data science and artificial intelligence. Last time we talked about personalization in AI with Hussein Mehanna, who was Director of Engineering at Facebook at the time. This time, we'll talk about two topics that all established sectors need to be focusing on:  How does one build ML and data science teams? How does one pick an AI project?  For business leaders who are considering hiring data science talent or thinking about how to start with AI in terms of making a difference in their bottom line, this should be a useful episode.

Facebook's Jason Sundram - How to Build Data Science Teams for AI Projects

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

Banks have several challenges to AI adoption. One of them is their legacy IT and data infrastructures which aren’t built or organized for machine learning. Nishant Chandra, PhD, Senior Director of Data Products at Visa, outlines how banks can transition their legacy systems to systems ready for AI. Chandra discusses the importance of leadership teams in cultivating a company culture necessary for adopting AI with any success, and he gives examples of times when he’s seen old systems hold a company back from adopting AI and when he’s seen leadership do the same. Discover how banks can overcome some of the pitfalls to AI adoption in this episode of the AI in Banking Podcast.

How Banking Leaders Can Overcome the Challenges to AI Adoption - With Nishant Chandra

According to Sergey Gribov, Partner at Flint Capital, “banks are the ultimate commodity.” Fintech startups can offer banking services with leaner teams and better customer experiences using artificial intelligence. But banks need to differentiate themselves somehow from not just fintech companies, but other banks, especially as they decide which banking functions they want to focus on in the coming decade. Banks will almost certainly become leaner operations to stay in business, and they’ll need to make strategic decisions that direct their future.&nbsp; In this episode of the AI in Banking podcast, Gribov offers a frank picture of the banking landscape in the coming years and how banks will need to respond to it.

How to Survive the Dangers of Fintech and AI Disruption - With Sergey Gribov

While AI is certainly finding its footing in finance, we still find most of our subscribers are in a phase where they're trying to catch up in terms of data and data infrastructure and figure out where there's real traction with AI in finance: in banking, investing, or insurance. In this episode, we explore AI use-cases in a number of these areas of the financial industry. We interview Carlos Pazos and&nbsp;Anwar Ghauche at Spark Cognition about how to maximize a smaller data science team at a financial institution, how AI and alternative data is being used for quantamental investing, and how AI is automating some financing and underwriting processes.

How to Maximize ROI From AI in Finance: Banking, Investing, and Insurance

This week, we speak with Gunnar Carlsson, co-founder of Ayasdi, one of the better-known anti-money laundering AI vendors and one of the highest ranking in our AI in Banking Vendor Landscape report. Carlson pioneered his own branch of mathematics that eventually became the backbone of Ayasdi's AI software. We speak to Carlson about methods of calibrating and understanding risk at banks, new sources of data and algorithmic approaches (and what they mean for banks), and the difference between supervised and unsupervised learning. Both machine learning techniques can be used to solve business problems in unique ways. Lastly, we discuss with Carlson something of critical importance in risk-related functions: knowing your customer.

How Banks Will Win With Unsupervised Learning - With Gunnar Carlsson

This week, we speak with Adam Hunt, CTO of RiskIQ, about how cybersecurity is shifting in the world of banking and financial services and why AI will be necessary to handle those changes. Fraud and cybersecurity startups selling to banks have raised more than vendors for any other business function, and we talk about why AI is such a good fit for cybersecurity in banking. Hunt also discusses how banks can secure AI talent in banking. Working on cybersecurity at a bank may not be as flashy as working on self-driving cars or working for Amazon, but there are things banks can do to attract AI talent they'll need to build and maintain cybersecurity systems.

How to Prepare for the Evolution of AI in Cybersecurity - With the CTO of RiskIQ

Discover how AI may shift the workflows and approaches to trading as we interview Ash Fontana, managing director at Zetta Venture Partners, a Silicon Valley-based venture capital firm focused on AI-first companies with B2B business models. Ash shares his valuable perspective on the present and future of AI in trading. Access Emerj's AI in Financial Services Vendor Landscape Brief here: emerj.com/vl1

The Present and Future of AI in Trading - with Ash Fontana of Zetta Ventures

Discover how to leverage the learning capabilities of AI for strategic advantage, as we interview H.P. Bunaes, former Chief Data Officer at Suntrust, now Director of Banking for Data Robot, a Boston-based startup which has raised over $400 million to innovate enterprise AI solutions. H.P. shares his valuable perspective on how strategic leaders can outmatch competition by enhancing the speed at which the organization can learn, with AI-driven technology. Download our guide "3 Keys to Thriving in the Post-COVID Automation Era": www.emerj.com/k3

How to Leverage AI-Powered Learning & Prediction for Strategic Advantage - With H.P. Bunaes

Today's interview is with Aki Ranin, Cofounder of Bambu, a robo-advisor platform based in Singapore. Bambu has raised millions to expand in Asian and American banking markets. Aki is also an adjunct lecturer on AI and machine learning at Singapore Management University. In this episode, he breaks down where AI fits into the world of robo-advisors and is frank about where AI isn't needed. For quick insights, download Emerj's AI in Financial Services Cheat Sheet: emerj.com/fin1

Do Roboadvisors Need AI? - with Aki Ranin of Bambu

Today's guest is the great and brilliant Dr. Ben Goertzel, the man who coined the term AGI with his eponymous book, "Artificial General Intelligence." Dr. Goertzel is known by many from his role as Chief Scientist of Hanson Robotics, where he lead the creation of Sophia, the first robot to be granted national citizenship. Dr. Goertzel founded OpenCog, an open-source software framework for AGI. He also founded SingularityNET, a decentralized marketplace for AI algorithms. Episode topics include: Dr. Goertzel's optimistic vision for AGI's future, how it might be born, and why decentralized frameworks may offer the most fertile ground for AI evolution. Dan and Ben first met in 2012, and we're grateful to Ben for keeping up the conversation. No series on the Future of AI would be complete without him.

[AI Futures] Creating Decentralized Artificial General Intelligence - with Ben Goertzel of SingularityNET (S1E11)

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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.

Lucid VR’s CTO Talks Machine Learning for Virtual Reality

The AI in Business Podcast

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