Stay ConnectedThe AI in Business Podcast is for non-technical business leaders who need to find AI opportunities, align AI capabilities with strategy, and deliver ROI.
Each week, Emerj research staff and journalists interview top AI executives from Fortune 2000 firms and unicorn startups – uncovering trends, use-cases, and best practices for practical AI adoption.
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Copyright: © Daniel Faggella, 2014
This week, we speak with arguably one of the best-known folks in the domain of neural networks: Jurgen Schmidhuber. He's working on a lot of different applications now in heavy industry, self-driving cars, and other spaces.
We talk to him about the future of manufacturing and more broadly, how machines and robots learn. Schmidhuber uses the analogy of a baby learning about the world around it. He has a lot of interesting perspectives on how the general progression of making machines more intelligent will affect other industries outside of where AI is arguably best known today: consumer tech and advertising.
If you're in the manufacturing space, this will be an interesting interview to tune into. If you're just interested in what the next phase in AI might be like, I think Schmidhuber actually frames it pretty succinctly.
The AI In Industry podcast is often conducted over Skype, and this week's guest happens to be one of its early developers. Jaan Tallinn is recognized as sort of one of the technical leads behind Skype as a platform. I met Jaan while we were both doing round table sessions at the World Government Summit, and in this episode, I talk to Tallinn about a topic that we often don't get to cover on the podcast: the consequences of artificial general intelligence. Where's this going to take humanity in the next hundred years?
In this episode of the AI in Industry podcast, we speak with Marshall Choy, VP of Product at SambaNova, an AI hardware firm based in the Bay Area. SambaNova was founded by a number of Oracle and Sun Micro Systems alumni. We speak with Choy on two fundamental questions:
SambaNova is one of many firms that's going to be advertising at the Kisaco Research AI Hardware Summit in Beijing June 4th and 5th.
Danny Lange heads up the AI efforts at Unity, one of the better-known firms in terms of simulations and computer graphics. They work in several different industries, but this week we speak mostly about automotive.
This is a man that has been in the AI game since before it was cool, and now he is working on some cutting-edge projects with Unity. In this interview, we speak with Danny about where simulated environments are becoming valuable.
We hear about simulations mostly in the context of video games, and of course, Unity does apply their technology in that domain, but what about a space like automotive, where navigating within an environment is important?
Certainly we need to have physical cars on the road to drink in data from physical roads and physical environments, but is it possible to splinter some digital cars into digital environments that model the physics, that model the roads, that model the same number of pedestrian risks, and see how well they succeed in all these different environments with no real physical risk of damaging an actual vehicle or an actual person on the road?
As it turns out, there's value there.
Have you ever been frustrated with how Alexa or Siri don't always understand your verbal requests? If so, then you already understand the problem that our guest this struggles with. He's Tom Livine, co-founder and CEO of Verbit.ai.
Verbit is a company that focuses on AI for transcription. They use a combination of machine learning and human experts to transcribe audio in different accents, in different noise environments, with different diction, to give people more accurate results and hopefully help the process scale.
In this episode, Levine explains five different factors that go into getting transcription right and getting AI to be able to aid in the process. In addition, Tom talks about some of the critical factors for where transcription will come into play in terms of bringing value into business.
Have you ever been frustrated with how Alexa or Siri don't always understand your verbal requests? If so, then you already understand the problem that our guest this struggles with. He's Tom Livine, co-founder and CEO of Verbit.ai.
Verbit is a company that focuses on AI for transcription. They use a combination of machine learning and human experts to transcribe audio in different accents, in different noise environments, with different diction, to give people more accurate results and hopefully help the process scale.
In this episode, Levine explains five different factors that go into getting transcription right and getting AI to be able to aid in the process. In addition, Tom talks about some of the critical factors for where transcription will come into play in terms of bringing value into business.
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
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