Imagine you work in a large organization with tens of thousands of employees across multiple countries, a business that’s been around for over a hundred years, and all of a sudden you have people in one department who are interested in applying chatbots, colleagues in another department who wish to implement sentiment analysis and still another department that wants to begin using AI for fraud and risk analysis. How do you manage to put all these pieces together? That is exactly the situation that Muriel Serrurier Schepper found herself in. &nbsp;Muriel is the Business Consultant Advanced Data Analytics &amp; Artificial Intelligence at Rabobank Digital Bank in Naarden, Netherlands. In this episode, Muriel and I discuss the Artificial Intelligence Center of Excellence at Rabobank, where she manages projects and has connected ad virtual and physical team across the company which is comprised of over 60,000 employees spread across the world. For more interviews on the applications of AI in industry, visit: www.TechEmergence.com

Bringing AI into an Old, Large, Existing Business - with Muriel Serrurier Schepper of Rabobank

This week, AI in Industry features Jeremy Barnes, Chief Architect at Element AI. Jeremy talks about the common mistakes some businesses might make while adopting AI to solve broad business problems. He also sheds light on the problem areas that could raise the market value of businesses through AI adoption, hiring the right talent with the right combination of subject matter expertise and business experience, and the business and technical aspects executives should consider before contemplating the adoption of AI. For more insights on the B2B applications of AI, go to techemergence.com

Avoiding Common Mistakes in Applying AI to Business Problems - with Jeremy Barnes of Element AI

Episode summary: In this week’s episode of AI in Industry, we speak with Larry Lafferty, the President and CEO of Veloxiti. Larry has been building large AI projects for DARPA and other large private companies for the last 30 years. In this interview, Larry explains three critical factors to applying artificial intelligence in the enterprise (with insights especially relevant for companies who aren’t very familiar with AI and data science). AI vendors and business leaders should find the “how to” insights in this interview useful – particularly Larry’s details on organizing data and defining an AI-applicable business problem. Interested readers can listen to the full interview with Larry here:&nbsp;https://www.techemergence.com/how-to-apply-ai-…h-larry-lafferty/

How to Apply AI to an Existing Business with Larry Lafferty

This week on AI in Industry, we are talking about the ethical consequences of AI in business. If a system were to train itself to act in unethical or legally reprehensible ways, it could take actions such as filtering or making decisions about people in regards to race or gender. When machine learning is integrated into technology products, could a misbehaving system put the company at financial and legal risk? Our guest this week, Otto Berkes, Chief Technology Officer of New York-based CA Technologies, speaks to us about realistic changes in the technology planning and testing process that leaders need to consider. We discussed how businesses could integrate machine learning into the products and services, while still protecting themselves from potential legal downsides. See the full interview article featuring Otto Berkes live at:&nbsp;https://www.techemergence.com/?p=13752&amp;preview=true

AI in Industry: How AI Ethics Impacts the Bottom Line - An Overview of Practical Concerns

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

If one wants to start a general search engine, they're going to have to compete with Google. If one wants to start a general eCommerce platform, they'll have to compete with Amazon. But the same dynamics play out on a smaller scale. There are going to be some established players, some big tech giant,&nbsp;be it IBM or someone else, who already has a product. When it comes to getting a new AI product out to market, how does&nbsp;one compete with the big guys? This week's guest is Mike Edelhart, who runs Social Starts and Joyance Partners, seed stage investment firms out in the Bay Area. Edelhart has invested in a number of companies, and in this episode, we get his perspective on not only the patterns among successful AI startups and where AI plays a role in their competitive strategy, but what a "land and expand" strategy looks like for a new product that already has larger and more established competitors.

What Makes a Successful AI Company? - A Venture Capitalist's Perspective

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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What Makes a Successful AI Company? - A Venture Capitalist's Perspective

The AI in Business Podcast

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