Artificial Intelligence In Industry With Dan Faggella

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.

One of the promises of artificial intelligence is aiding humans in making smarter decisions. Whether it's in pharma, retail, or eCommerce companies, the idea of being able to pool together streams of data and coax out the insights that would help make the best call for the organization to reach its goals is the promise of artificial intelligence. As it turns out that same dynamic is sort of happening in the public sector where AI is now being used to inform policy. This week we interview Professor Joan Peckham at the University of Rhode Island. Previously, she was Program Director at the National Science Foundation. PhD in computer science and she runs the Data Science Initiatives at URI. The University of Rhode Island is home to DataSpark, an organization that helps policymakers inform the decisions that they're going to make about the economy, the environment, the opioid crisis, a variety of social issues, based on deeper assessments of the data. The ability to find objective insights might help policym

Sales is a big part of any sort of B2B firm. We speak this week with Micha Breakstone, co-founder of Chorus.ai. He holds a PhD in Cognitive Sciences from the Hebrew University in Jerusalem, and prior to starting his own company, he studied for a few years at MIT and was working on NLP at Intel. He speaks with us this week about where AI is being applied to sales, answering questions such as: How can managers better train salespeople? How can salespeople better find the patterns that lead to closing a deal? The next appointment? A bigger contract? This is a nascent domain. There are very few companies are actively leveraging artificial intelligence in their sales process, but in the two years ahead we'll likely see more and more firms who are. For more information on Ai for sales enablement, go to emerj.com

Close to a year ago, we had an interview here on the AI in Industry podcast with Jeremy Barnes of Element AI. We visited their headquarters in Montreal, and we'd interviewed Yoshua Bengio a couple years before that. Jeremy had brought up one point in that interview that I really like and that transfers its way into this conversation, which is that businesses should think not just about being more efficient with artificial intelligence, but places where they can actually make a real difference in the bottom line for the company beyond shaving off some savings. In this week's episode, we focus on compliance and analyzing contracts. At first, one might think about such an application in terms of cost savings. We speak with Shiv Vaithyanathan, an IBM fellow and Chief Architect of Watson Compare & Comply, about the following: What's possible with AI when it comes to analyzing contracts, and, most importantly Where is the business upside for AI as it relates to contract analysis. How can we analyze contracts n

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

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 autom

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

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

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

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

What makes a chatbot or a conversational interface actually work? What kind of work does one need to do to get a chatbot to do what one wants it to do? These are pivotal questions and questions that for most business leaders are still somewhat mysterious, but that’s exactly what we’re aiming to answer on this episode of the AI in Industry Podcast. This week we speak with Madhu Mathihalli, CTO and co-founder of Passage AI. We speak specifically about what kinds of tasks conversational interfaces are best at, what kinds of word tracks, what kind of questions and answer are they suited for and which are a bit beyond their grasp right now. In addition, we speak about what it takes to train these machines. In other words, how do we define the particular word tracks that we want to be able to automate and determine which of them might be lower hanging fruit for applying a chatbot or which of them might not? Read or listen to the full podcast here: https://www.techemergence.com/how-to-get-a-chatbot-to-do-what-one-wa

Episode Summary: In this episode of the AI in Industry podcast, we interview Rajat Mishra, VP of Customer Experience at Cisco, about the best practices for adopting AI in the enterprise and how business leaders should think about the man-machine balance at their companies. Mishra talks with us about how the executive team should be able to imagine the future of specific work roles that might integrate AI technology or envision how those roles will shift in the short-term. In other words, how will AI affect workflows?

In this episode of the AI in Industry podcast, we interview Nikhil Malhotra, Creator and Head of Maker's Lab at Tech Mahindra, about how artificial intelligence changed the nature of IT services and business services in general. Malhotra talks about what businesses should consider to make themselves relevant for the future. In addition, he discusses the philosophy shift that has to happen for people to be appreciative of the process of problem-solving, and to see profit and growth from AI. We hope business leaders in the IT services industry will take from this interview the low-hanging fruit applications in the IT services industry.

Episode Summary: Prominent technology companies like Google and Amazon lead the way in the B2C world, having access to streams of searches, clicks, and online purchases. They have access to large volumes of consumer data pointss numbering in the billions that can be used to train machine learning algorithms. B2B companies operate under a different model: "propensity to buy," as it's called. A typical B2B company might at most make a couple hundred sales per year, and many B2B companies make only dozens. In other words, every sale matters. In this episode of the AI in Industry podcast, we interview Kiran Rama, Director of Data Sciences Center of Excellence at VMWare, about purchasing external data and to leveraging internal data. Rama also talks about using data to determine how likely certain leads are to turn into high-value customers. In addition, he discusses with us the "propensity to buy." We hope that this interview can help business leaders determine if and how AI can help their organizations identify

For business leaders who are thinking about integrating AI into their company or who are just in the very beginning of that journey, this may be a useful episode of the podcast. Many times, people think that finding the right talent is the biggest challenge when it comes to integrating AI into the enterprise. Much of our own research and  conversations with machine learning vendors and the consultants trying to sell AI into the enterprise actually think there's another, bigger problem: combing the expertise of subject matter experts and that of data scientists to leverage information for future initiatives in business. This week, we interview Grant Wernick, CEO of Insight Engines in San Francisco. We speak with Grant about the initial challenges of organizing data and setting up a data infrastructure a business can use to leverage AI. We also talk about using data in leveraging normal workflows so that non-technical personnel can use it to drive better product innovation to help the company.

One of most fun parts about doing our geolocation pieces at TechEmergence is that we are able to interview so many people within a given country or city. Recently we did a huge piece on AI in India. We got to interview folks from the government and the bigger existing businesses, as well as a handful of people at the unicorns in Bangalore. One of those companies is Fractal Analytics. Fractal Analytics works in a number of spaces. One of them, consumer packaged goods, is an area on which we haven’t done much coverage. Many of our readers are in the retail space, but CPG has some pretty curious AI use cases. This week, we interview Prashant Joshi, Head of AI and Machine Learning at Fractal Analytics, about the different applications of machine learning in the CPG sector: doing chemical tests or finding new buyer segments within existing groups of consumers to determine who is buying from a company and who is buying from competitors. Hopefully, for those in retail, this interview will not only highlight some of