emerj

AI EMERJ

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

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

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 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

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

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

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 interview Jan Kautz, Vice President of Learning and Perception Research at NVIDIA. Kautz talks about what people underestimate when they start an AI initiative. In addition, he emphasizes the critical value of data storage. Kautz dives into&nbsp;the importance of getting started with an AI project when you already have a barometer of success. Essentially, he talks about why it's important to select a first AI project in an area where you already have a way of measuring success. Learn more about AI adoption in our full report,  Getting Started With AI: Proven Best Practices for AI Adoption.

Misconceptions About AI Adoption, and How to Overcome Them - with Jan Kautz of NVIDIA

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

In this episode, we interview Yaron Lavie, VP of Products at Earnix. Yaron breaks shares powerful insights about how AI can be leveraged for customizing financial service products, as well as for other use cases in insurance and banking. For quick and valuable insights, download Emerj's AI in Insurance Cheat Sheet: emerj.com/ins1

AI for Pricing and Personalization in Financial Services - with Yaron Lavie of Earnix

Emerj.com is where business leaders turn to understand how AI is impacting their business and industry - and what to do about it.

Our objective, jargon-free research and industry overviews are designed to give executives and decision-makers exactly what they need for competitive insight, informed AI technology procurement and strategic planning around AI.

AI disruption will open up new vistas for opportunity, creating new business paradigms and making others extinct. 

Emerj market research helps leaders survive and thrive in an era of AI disruption.

www.emerj.com

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

The AI in Business Podcast