Artificial Intelligence in Industry

What does it mean to tune an algorithm, how does it matter in a business context, and what are the approaches being developed today when it comes to tuning algorithms?&nbsp;This week's guest helps us answer these questions and more. CEO and Co-Founder Scott Clark&nbsp;of SigOpt takes time to explain the dynamics of tuning, goes into some of the cutting-edge&nbsp;methods for getting tuning done, and shares advice&nbsp;on how businesses using machine learning algorithms&nbsp;can continue to refine and adjust their parameters&nbsp;in order to glean greater results.

Tuning Machine Learning Algorithms with Scott Clark

Sales forecasting is big business. If you can better predict how much of a certain product or service you will sell in a given day, you can better stock inventory, better staff your facilities, and ultimately keep more margin in your business's accounts. This week on AI in Industry we interview Dr. John-Paul B Clarke, professor at Georgia Tech and co-founder / Chief Scientist at Pace (previously called "Prix"). Dr. Clarke shares details about how sales predictions are done today, and what AI advancements may allow for in helping businesses sell everything from groceries to hotel rooms. Read the full interview article online at:&nbsp; techemergence.com/ai-sales-forecasting-works-matters

AI for Sales Forecasting - How it Works and Where it Matters

Episode summary: In this episode of AI in Industry, we speak with Dr. Sanmay Das from the Washington University in St. Louis about risk prediction and management in industries like banking,  insurance and finance. Sanmay explores how are banks and other financial institutions are improving risk and fraud prevention measures with machine learning. In addition, he explores the ramifications of improved fraud detection in the coming 5 years ahead. Interested readers can listen to the full interview with Sanmay here:&nbsp;https://www.techemergence.com/machine-learning-for-credit-risk/

Machine Learning for Credit Risk - What's Changing, and What Does it Mean?

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

As it turns out, often times terms like predictive analytics and data science are used incorrectly. By the end of this podcast, you'll have greater clarity on five potentially vague AI and data science terms that are sometimes overused in conversations about AI in the enterprise. This week, I introduce you to German Sanches, who focused his PhD on NLP and has done a lot of AI work in business. He also helps us with our research projects. This episode is all about addressing use-cases in reference to five terms that a lot of folks get wrong.

Five AI and Data Science Terms People Get Wrong

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

When we polled our audience about what they were interested in, the most selected response was "business intelligence." As a follow-up, we asked them what business intelligence meant to them, and their responses boiled down to anything about understanding the data businesses are already collecting. That kind of broad definition gets to the heart of the confusion surrounding the differences between business intelligence and artificial intelligence. The line is starting to get blurry.&nbsp; Our guest this week is Elif Tutuk, Senior Director at Qlik. Tutuk talks about how business intelligence is evolving and how we might define it now that a lot of BI is becoming AI. Tutuk discusses where AI is making its way into business intelligence and what that might enable for businesses. Read our comprehensive definition of machine learning for business leaders here:&nbsp;https://bit.ly/2Ya2NxK

What's the Difference Between Business Intelligence and Artificial Intelligence? - With Elif Tutuk, Senior Director at Qlik Research

It's the first episode of the new style of AI in Industry, in which we spend a month at a time on a specific theme. This month is AI adoption. This week we speak with Vlad Sejnoha at Glasswing Ventures, an AI-focused VC firm. Sejnoha spent many years as the CTO at Nuance Communications. He talks to us about the table stakes AI insights the C-suite have to know and the dangers of relying entirely on consulting firms and vendor companies for these insights. In addition, Sejnoha discusses the need for a "BS-o-meter" for when someone is making a claim about AI to determine if it's real or hype. Lastly, Sejnoha discusses how he would go about choosing a first AI project.

Table Stakes AI Insights for the Enterprise - with Vlad Sejnoha of Glasswing Ventures

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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Machine Learning for Credit Risk - What's Changing, and What Does it Mean?

The AI in Business Podcast

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