Artificial Intelligence in Industry

One of the most memorable moments from this interview is when our guest mentioned that Larry Page hired him to solve intelligence; very few people can say this, and this says something about today’s guest, Dr. Nando de Freitas - a senior researcher at Google and professor at Oxford - as well as the gravity of his present work. Today, I speak with Nando about a topic well known through his research at Google, deep learning. de Freitas gives his perspective on the basics of deep learning, the applications in conversational interfaces and recognizing images and videos, and what the future of this technology might look like in the nearer future.

DeepMind's Nando de Freitas - Why Deep Learning is Like Building with Legos

In many ways, AI and finance are made for each other. Machine learning and other techniques make it easier to identify patterns that might otherwise not be detected by the human eye, and finance is quantitative to begin with so that it’s hard not to find traction. Financial firms have also invested heavily in AI in the past, and more are starting to tap into the  financial applications of machine learning (ML) and deep learning. This week, we’re joined by CEO and Co-founder of Kavout&nbsp;Alex Lu, whose company offers AI trading applications for enterprises and individuals. Lu speaks today about the kinds of patterns that traders now have access to in finance, and he gives examples of ways Kavout and other institutions are using artificial intelligence in stock trading to build better and more personalized products and services. &nbsp;

Artificial Intelligence in Stock Trading - Future Trends and Applications

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

Although machine learning in finance is far from new, it is merely at the cusp of a much wider set of applications (in all segments of finance, from insurance to bookkeeping and beyond). Already machine learning has overhauled so many aspects of the financial landscape, from accounting to trading, and it is destined to have more and more impact as it develops further. Guest Alexander Fleiss and his team at Rebellion Research are developing and using AI which uses quantitative analysis to pick investments. Fleiss discusses the current status of machine learning in the world of finance as well as lesser-known niche applications that don’t make headlines - but do make a big impact on how businesses are run. He then goes on to explore the effects of future innovative applications of AI in the financial domain.

Full episode notes and related finance and AI interviews online at:
https://www.techemergence.com/rebellion-researchs-alexander-fleiss-how-ai-is-eating-finance

TechEmergence Podcast

Rebellion Research's Alexander Fleiss - How AI is Eating Finance

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

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

In order to shed more light on the growing applications of natural language processing, we speak with Vlad Sejnoha (CTO of Nuance Communications) about the current and near-term applications of NLP for voice and text across industries. In this podcast interview, Vlad breaks down real-world NLP use-cases in industries like banking, healthcare, automotive, and customer service. For the full article of this episode, visit:  TechEmergence.com/natural-language-processing-current-applications-and-future-possibilities

Natural Language Processing - Current Applications and Future Possibilities

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?

It's curious to see how much more there is of sensor tech and internet of things than there was 18 months ago. This week, we speak with Cormac Driver, PhD and Head of Product Engineering at Temboo, an IoT vendor. We talk about how to spot AI and IoT opportunity where sensors and equipment in the physical world can actually deliver ROI and drive value for an enterprise. In addition, Cormac discusses how to get the most out of an IoT project and what's involved in terms of data and infrastructure. Finally, I ask Cormac in what sector IoT will become ubiquitous first.

How to get an ROI from AI Internet of Things Solutions

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

One of the best conversations I ever had on the topic of AI business strategy on the podcast was with the guest I've brought back this week: Madhu Shekar, Head of Digital Innovation for Amazon Internet Services in Bangalore. I wanted to do a deeper session with Madhu, who has seen a lot of companies go from no AI to beginning with AI, about where to start with AI adoption. How do companies build the expertise and experience with AI that lets them scale it to their organization? He also talks about how to prepare realistically for AI, including data requirements, integration times, and more.&nbsp;

AI Business Strategy Basics - Critical Insights on AI Adoption

Building an AI strategy - there's hardly anything more vague and open-ended than that. Business leaders have probably gotten the idea that they should develop one, but where should they start? That's what we talk about this week with Charles Martin, PhD. Martin talks about how to go about starting an AI strategy, what to avoid, and the challenges and struggles of applying AI at existing businesses. Also, Martin discusses what business leaders should ignore and what business leaders should tune into and prioritize for an effective AI strategy that will propel them toward success in the coming years.

How to Get Started With an Effective AI Strategy

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

Some say that the competitive dynamics between the US and China in terms of AI are overblown, but there's a lot of truth to them. The US has access to more of the base research, but China can orchestrate various organizations (corporations, government bodies) and secure government funding. That said, very few people talk about K-12 education and what countries are doing to prepare their future workforce for AI. David Touretzky talks to us about just that. He is a research professor in the Computer Science Department and the Center for the Neural Basis of Cognition at Carnegie Mellon University. He's heading up an initiative for K-12 education, and he discusses what countries should be doing to secure their positions and technological leadership in the 21st century.

China's AI Education Initiatives and What It Means for the US

This week, we interview Jay Budzik, CTO at ZestFinance, about where AI applies to the world of auto-lending. We speak with Budzik about how underwriting and credit scoring is evolving as a result of advances in machine learning. In addition, we talk about how companies might solve the "black box" of machine learning in finance, particularly how ZestFinance is focusing on transparent models. The financial sector has to contend with complex regulations that prevent certain information from being leveraged in credit models. It can be near impossible to determine how machine learning comes to the conclusions it does, but ZestFinance claims their software in part solves this problem.

How Lenders Can Win More Business with Machine Learning

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

This episode of the AI in industry podcast is all about where the rubber meets the road for AI in Insurance. We interview Jerry Overton,&nbsp;Head of AI and a Fellow at DXC Technology. He speaks to us about his experience implementing AI in insurance, about where there's real traction with AI in insurance, and where there's only hype. In particular, Overton discusses how anomaly detection technology is a natural fit for AI in the insurance sector. This is the last episode of its kind on AI in Industry. Starting next Tuesday, we'll be kicking off a new format for the show. Each month, we'll focus on a specific theme, and in August, we're focusing on AI adoption in the enterprise. We hope you'll join us. &nbsp;

Where AI is Driving Value in Insurance (and Where It's Not) - With Jerry Overton, Head of AI and Fellow at DXC Technology

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

This week we speak with David Carmona, General Manager of AI at Microsoft. Carmona discusses&nbsp;how redefining a business process is a very different kind of AI adoption project than working on something that is horizontal. He discusses how to attack both of these scenarios, which to handle first, and why.&nbsp; In addition, Carmona talks about proprietary data and things that are close to your own IP. How do you take advantage of the real strategic data value within your own organization? How should you be thinking about that differently? Carmona poses three different questions to determine where those valuable opportunities are for you.

Critical Questions to Ask Before Adopting Artificial Intelligence - With David Carmona of Microsoft

This week, we speak with Jan Neumann, Senior Director of Applied AI Research at Comcast. Comcast is an enormous company; it has lots of data, lots of application areas for AI, and a lot of opportunity for confusion about AI. As such, Neumann speaks with us about scaling AI expertise in the enterprise. Neumann talks about a very strong distinction between software and AI and how to think through problems to determine whether or not it's a software problem or an AI problem. He also talks about scaling the problem-solving abilities of business experts in the organization. Lastly, Neumann talks about his ideas for how to determine a first AI initiative.

Artificial Intelligence in Industry

Lau Yee Heng