I hope that by the end of this episode of the AI in Industry podcast, you'll not only be able to hire better data scientists who will be a fit for your business problems and build better data science teams, but also pick the AI applications and use cases that you should bring into your business versus those that you shouldn't. This episode, we interview Brooke Wenig, the machine learning practice lead at Databricks. Databricks was founded by the folks who created Apache Spark. Those of you who are technically savvy with AI will be familiar with Apache Spark as an open source language for artificial intelligence and distributed computing. Wenig works with a lot of companies with Databricks. Databricks is now close to 700 folks and helps implement AI applications into, oftentimes, large enterprise environments. Wenig speaks with us this week about what to look for in an actual data scientist and how to find data science folks with the right skills to be able to communicate to business people, not just to work with models. What should people be capable of; how should they be capable of thinking? Hopefully, some of you will have better interview questions by the end of this podcast. In addition, we ask Brooke about what the value of covering the cutting edge applications of AI is, looking at what's working in industry. How does that help us in our own business make better decisions? Read the full article on Emerj.com

If you want to understand the international competitive dynamics of artificial intelligence, particularly the US and China, starting with the United Nations is probably not a bad move. This week, I spoke with Irakli Beridze, the head of the Center for Artificial Intelligence and Robotics at the UN, particularly under the wing called UNICRI, the organization's crime and justice division. Irakli was kind enough to invite me to speak at a recent event in Shanghai held by the UN and by the Shanghai Institutes for International Studies on national security, and when we were there, we talked a good deal about China's unique AI-related strengths. I spoke with Irakli about the strengths of the ecosystem in China for artificial intelligence and how that stacks up against the US. In addition, I asked Irakli about what it's going to look like to encourage more and more multilateral action. In other words, how do we get countries to be on the same page so AI doesn't become an arms race?

Discover how so-called autoML, or automated machine learning, could bring AI to more businesses by allowing users to build AI models faster and cheaper. Read the full article, where we go into further detail, at Emerj.com. Search for "AutoML and How AI Could Become More Accessible to Businesses"

AI has numerous use cases in legal, from document search to compliance and contract abstraction. This week, we speak with Lars Mahler, Chief Science Officer for LegalSifter, about what's possible with AI for legal departments today and how AI applications for legal teams, such as natural language processing-based contract analysis, work. In addition, Mahler discusses how lawyers at companies and data scientists work together to train machine learning algorithms. He provides some insight into how a company has to make its way into the legal space and the challenges of training an NLP system and collecting data for it. Read more about AI in legal at Emerj.com

There's a lot of venture money pouring into artificial intelligence in healthcare. From pharma to hospitals and beyond, the potential applications in healthcare are promising.  Late last year, we spoke for The World Bank about our proprietary AI in healthcare research, and speaking with governments, it's clear that there are hurdles that healthcare companies have to overcome to access data for training AI systems.  Broadly, most of the folks that we speak with who are innovating in AI and healthcare are frustrated with how hard it is to streamline the data to make use of it for applications such as diagnosing illnesses. But why is that? That's a question that we asked our guest this week. Our guest this week is Zhigang Chen, and he speaks about why this problem exists and how it can be overcome. In addition, Chen talks about the AI ecosystem in China and how it differs from Silicon Valley.

Saying that your company does artificial intelligence might still have a slightly cool ring to it if you're talking to one of your peers at a conference, but it doesn't mean very much to venture capitalists today, who've been battered with machine learning and artificial intelligence in every pitch deck they've seen for the last three or four years. I wondered, from a venture capitalist perspective, what makes an AI company's value proposition actually strong? What is it that makes an AI startup actually seem like a company that maybe could use AI to really win in the market? Not just to be another company that says they're going to do it or says they are doing it, but where can it actually provide enough of that competitive edge to make a VC want to pull the trigger? Getting a grasp of the answer to that question seems pretty critical. This week, we speak with Tim Chang, partner at Mayfield Fund in Menlo Park, California. Chang and I both spoke at the Trans Tech Conference, held every year in Silicon Valley, focused on wellness and health-related technologies. Chang talks about what it is about an AI company's pitch, product, and market that actually makes AI an enhancement to the business in a way that's compelling to someone who wants to invest potentially millions and millions of dollars.

If one wants to start a general search engine, they're going to have to compete with Google. If one wants to start a general eCommerce platform, they'll have to compete with Amazon. But the same dynamics play out on a smaller scale. There are going to be some established players, some big tech giant, be it IBM or someone else, who already has a product. When it comes to getting a new AI product out to market, how does one compete with the big guys? This week's guest is Mike Edelhart, who runs Social Starts and Joyance Partners, seed stage investment firms out in the Bay Area. Edelhart has invested in a number of companies, and in this episode, we get his perspective on not only the patterns among successful AI startups and where AI plays a role in their competitive strategy, but what a "land and expand" strategy looks like for a new product that already has larger and more established competitors.

A lot of AI in the press is CMOs or marketing people talking about what a company can do in a way that really is aspirational. They're speaking about what they can do, but in reality, the things that they're talking about, the capabilities won't be unlocked for maybe a year or more. These are just things on the technology road map, but people speak about them like they exist now. This week, we speak with Abinash Tripathy, founder and Chief Strategy Officer at Help Shift. They've raised upwards of $40,000,000 in the last six years to apply artificial intelligence to the future of customer service, and we speak about the hard challenges of chatbots and conversational interfaces, as well as how long it's going to be until those are actually robust. This in opposition to how people at large companies might put out a press release touting their own chatbots that simply aren't capable of doing what they say they can to any meaningful degree. We also talk about where AI can augment and make a difference in existing customer service workflows.  Even if we can't have all-capable chatbots to handle banking or insurance or eCommerce questions from people, where can AI easily slide it's way in and actually make a difference today? In this episode, we draw a firm line on where the technology currently stands. Overall, though, this episode is about the challenges of actually innovating in AI. We talk about why it really is the big companies that do a lot of the actual cutting edge breakthroughs of AI and why others are going to have to license those their technologies from large firms like Google and Amazon. We also discuss why companies maybe need to have a realistic expectation about where they can apply AI, as well as why actually innovating and coming up with new AI capabilities on their own might just be wholly unreasonable given their data, their company culture, and their density of AI talent. Read the full interview article on emerj.com

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 policymakers make better decisions about where they allocate budget and what decisions are made. Right now, policymakers are beginning to tune into artificial intelligence as a source of informing their decisions. The same dynamic will likely play out in the C-suite, particularly when the data is actually there. For more on AI in government, visit Emerj.com