Episode Summary: Recently, we were called upon by the World Bank to do a good deal of research on the potential of applying artificial intelligence to health data in the developing world. Diagnostics was a very big focus of the information that we presented. It appears as though diagnostics is an area of great promise with regards to AI, and that's what we're focusing on in this episode the podcast. This week, we speak with Yufeng Deng, Chief Scientist of Infervision, a company that focuses on computer vision for medical diagnostics. We speak with Deng about the expanding capability of machine vision, including what kind of data one needs to collect and what is now possible with the technology. In addition, Deng also speaks about how Infovision found a business problem to solve using AI, and in that he provides transferable lessons to business leaders in a variety of industries.

Artificial intelligence plays a role in the future of retail in terms of a deeper understanding of customers going beyond intuition. This week, we speak with Pedro Alves, CEO of a company called Ople, based in San Francisco. Alves was previously the Head of Data Science at a number of companies in addition to being Director of Data Science at Sentient Technologies, one of the best known AI firms in the Bay Area. Sentient has raised upwards of $200 million. We talk with Pedro about the future of retail, the future of understanding customers with artificial intelligence. Essentially asking under what circumstances would a retailer need to go beyond intuition in order to inform their understanding and their ability to influence the actions of their customers or their users. In addition to that, Alves talks with us about what has to happen to AI as a technology to become more accessible and within reach of existing enterprises. Knowing now all the points of friction for bringing AI into an existing business, he talks about the transition points that he thinks are going to have to happen over the course of the years ahead in order to make these technologies more accessible to companies.

A lot of machine learning applications in business can be boiled down to some form of decision support. There are big decisions like deciding whether or not to merge or acquire another company, and there might be smaller decisions like whether or not a tumor has enough traits that make it seem like it's worth a surgical procedure or if it's worth leaving alone. In this particular interview, we talk about the domain of decision support, specifically in tax and accounting. There are few firms that know more about tax and accounting than Ernst & Young, and there are few people at Ernst & Young who know more about artificial intelligence than Sharda Cherwoo. Cherwoo is a partner at EY, and she is also the Intelligent Automation Leader for the Americas division of its tax practice. Cherwoo talks about where decision support is being influenced by machine learning in accounting and tax today, the initial experimentation traction, and results. She also paints a picture of bigger decisions that might be automatable by machine learning software. The focus of this episode may be on tax and accounting, but here are transferable lessons for business leaders in all industries that revolve around how machine learning can help inform decisions made by human experts.

We spoke with Robert Golladay, General Manager, Europe at CognitiveScale, which offers AI software that helps both wealth advisors personalize insights and identify new opportunities for clients. According to Golladay, AI is being applied to wealth management services in two areas today: Personalization: Helping financial advisors identify the investment preferences of a client and provide personalized advice to a degree that was not possible before. This might involve taking into account factors such as declared, observed, and inferred information around client goals or attitude towards risk. Engagement: Helping wealth advisors communicate the most relevant insights for a client at a preferred time and channel.

This week, we're going to be talking about the defense sector. We interview Ryan Welch, CEO of Kyndi, a company working on explainable AI. We focus specifically on the unique data challenges of the defense industry, as well as the general use case of AI in defense writ large. Many of the challenges that the defense sector has to deal with transfer to other spaces and sectors. Business leaders that deal with extremely disjointed text information, what is sometimes called "dark data," and information in various languages or different dialects, will be able to resonate with some of the unique challenges talked about in this episode, and maybe even gain some insights for how to handle them.   Read the full interview article on Emerj.com

Whether we're talking about customer service, marketing, or building developer teams, what we try to do on our AI in Industry podcast is bring to bear lessons that are transferable. There are few more transferrable ideas than what makes a company ready to adopt AI. When it comes to the willingness and the ability to integrate AI into a company strategy and to fruitfully adopt the technology to really see an ROI, what do the companies that do so successfully have in common? What do the companies that are not ready or too fearful to do it have in common? There are probably few companies in the AI vendor space that are aiming to sell AI more ardently into the enterprise than Salesforce, and there are few people that know more about how that process is going than Allison Witherspoon, Senior Director of Product Marketing for Salesforce Einstein, which is their artificial intelligence layer on top of the Salesforce product. We speak to Witherspoon about the telltale signs of a company that understands the use cases of AI in their industry and that have a good chance of driving value with AI. We also talk about the common qualities of companies that might not ready for I adoption. Read our full interview article on Sunday at Emerj.com

Episode Summary: This week we talk to Alejandro Giacometti, the data science lead at a company called EDITED, based in London. The company claims to help retailers with inventory optimization, and we speak with Alejandro about how artificial intelligence can be used to search the web for the product clusters and individual products of major retailers to help inform other retailers on what products might be popular. There are two primary takeaways from this episode. The first is the broad capability of monitoring the competition with artificial intelligence, something that can be applied across industries, not just in retail. The second is that EDITED is generating information from what is freely available on the web, and so it would seem their software doesn't require businesses to integrate it into inventory management systems in order to train the algorithm behind it. I'm not necessarily lauding the company; I haven't used their product nor read all of their case studies. That said, it's worth noting simply because its approach is fundamentally different than most AI vendors. Read the full interview article on emerj.com

Some businesses are going to require a sea change in the way that their computation works and the kinds of computing power that they're leveraging to do what they need to do with artificial intelligence. Others might not need an upgrade in hardware in the near term to do what they want to do with AI. What's the difference? That's the question that we decided to ask today of Per Nyberg, Vice President of Market Development, Artificial Intelligence at Cray. Cray is known for the Cray-1 supercomputer, built back in 1975. Cray continues to work on hardware and has an entire division now dedicated to artificial intelligence hardware. This week on AI in Industry, we speak to Nyberg about which kinds of business problems require an upgrade in hardware and which don't.

We speak this week with Aneesh Reddy, cofounder and CEO of Capillary Technologies. Capillary is a rather large firm based in Singapore. Aneesh is in Bangalore himself. The firm focuses on machine vision applications in the retail environment. How do we instrument a physical retail space so that, with cameras, we can pick up on the same kind of metrics that eCommerce stores can? Retail stores, as Reddy talks about in this episode, have to focus on the data that they get from the checkout counter, such as what kind of purchases were made, and potentially some kind of data about how many times the front door was opened or closed. That doesn’t really lay out that much detail about who came in, what percent of them converted, and what the average cart value was for different people. A lot of that is completely greyed out when looking at the numbers that are accessible to brick and mortar retailers. But some of that is changing. Reddy talks about what’s possible now with machine vision in retail, and what it opens up in terms of possibility spaces for understanding customers better in a physical environment. More importantly, Aneesh paints a bit of a future vision of where he believes retail is going to be when not just computer vision is included, but when audio and other kinds of sensor information are included.

In this episode of AI In Industry, we interview Nick Possley, the CTO of a company called AllyO, based in the San Francisco Bay area. We speak with Nick about where artificial intelligence and machine learning are playing a role in recruiting today and how picking the right candidates from a pool is in some way being informed by artificial intelligence. Whether a business leader is hiring dozens and dozens of people or whether they ’re just interested in understanding how AI can engage with individuals on more of a one-to-one basis, this should be a fruitful episode. In addition, the fundamentals of what we discuss in this episode, in terms of taking in data from profiles and responding and engaging with applicants, could be applied to all sorts of cases, such as customer service and marketing. Read the full interview article here: https://www.techemergence.com/how-to-use-ai-to-hire-and-recruit-talent