Sun, 6 May 2018
Episode summary: In this week’s episode of AI in Industry we speak with DataRobot CEO Jeremy Achin about the future of AI applications for people without a data science background. We specifically discuss how future AI tools might bypass the complexity of machine learning programming and make intuitive interfaces that function more like today’s everyday software. Our business leader listeners will be interested in Jeremy’s predictions about how the UX for AI-related tools might become more simplified and code-less in the coming 5 years.
Interested readers can listen to the full interview with Jermy here: https://www.techemergence.com/will-artificial-intelligence-become-easier-use/
Sun, 29 April 2018
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: https://www.techemergence.com/how-to-apply-ai-…h-larry-lafferty/
Sat, 21 April 2018
Episode summary: In the heavy industry sector, the cost of unpredicted repairs or machine failures can be very expensive. For example: A cargo train with an engine failure in will incur costs from it’s own repairs, from the transit required to reach the broken down engine, and with holding up other trains and cargo in the process.
Predictive maintenance has the potential to help businesses assess the condition of vehicles, equipment and parts in order to predict when maintenance should be performed. Using data collected by sensors on machines (including vibration, temperature, and more) heavy industry companies can potentially predict which machines or parts need imminent maintenance and which machines are least likely to breakdown.
In this week’s episode, we speak with Will McGinnis, Chief Scientist of Predikto, a predictive maintenance software provider based in Atlanta. Will speaks with us about predictive maintenance applied for the improvement railways and trains equipment, and how companies in the railway sector can use predictive maintenance to coax out patterns in maintenance schedules and heavy equipment data.
Interested readers can listen to the full interview with Will here:https://www.techemergence.com/will-mcginnis-predikto-predictive-maintenance-trains-mobile-heavy-industry
Wed, 18 April 2018
Episode summary: In this week’s episode of AI in Industry we speak with Rodney Brooks, Founder and CTO of Rethink Robotics, a collaborative robot manufacturers founded in Boston in 2008. Rodney explores robotic safety an regulations and he also paints a picture of what robots might be capable of in the next five years.
Executives in the logistics and manufacturing sectors considering adopting robots will find Rodney’s insights most valuable. Rodney explores what applications will move into the realm of robotics and what application won't in the near future and delves into what business executives need to know about human robot collaboration before considering their adoption.
Interested readers can see the full interview with Rodney Brooks from Rethink Robotics here: https://www.techemergence.com/improving-robot-safety-capability-artificial-intelligence-rodney-brooks/
Sat, 14 April 2018
Episode summary: One of the key challenges that enterprises face in adopting artificial intelligence is finding skilled data science talent; ). Business leaders want to know when it's best to hire AI talent, to "upskill" existing workers, or simply to bring in AI consultants - and the answers aren't always obvious.
In this episode of AI in Industry we speak with Nikolaos Vasiloglou from MLTrain about how AI consulting and AI training events can be used to upgrade an existing team’s skills. Nikolaos also distinguishes the right and wrong circumstances to bring on AI consultants, and shares his tips on how training, upskilling, and consulting can level up an existing company’s AI capabilities.
Listeners can find out how to set realistic goals for re-training existing teams for new AI skill sets. Lastly, we also explore how AI consultants can support developer and engineering teams to produce fruitful real-world AI applications (without developing unhealthy reliance on outside experts).
Interested readers can also listen to our previous episode of AI in Industry (here) where we look at overcoming the data and talent challenges of AI in life sciences
Interested readers can listen to the full interview with Nikolaos here:https://www.techemergence.com/whats-the-value-of-ai-events-and-consulting/
Mon, 9 April 2018
Episode Summary: Over the last couple of years there has been a definite but small shift from mobile as the primary interface focus for businesses to voice. With home assistant devices like the Amazon Echo and the Google Home becoming more commonplace, we aim to focus on how voice based AI applications are being used by businesses today and what this adoption will look like in the future.
In this week’s episode of AI in Industry, we speak with Peter Cahill, the founder and CEO of Voysis, a voice AI platform that enables voice-based natural language instruction, search, and discovery. Peter explores areas where voice related AI applications will be used by businesses in B2B and B2C spaces today and what this might look like in five years.
Interested readers can see the full interview with Peter Cahill from Voysis here: https://www.techemergence.com/spoken-voice-ai-applications-smart-home-peter-cahill-voysis/
Sun, 1 April 2018
In this week’s episode we focus on AI application in the customer service business function, - specifically in the context of call centers. We speak with Ali Azarbayejani, CTO of Cogito based in the Boston area, which works on coaching and providing feedback for call center agents in real time.
We aim to focus on what our readers and business executives can do today with AI in the context of call center applications, and how they can go about seeing measurable impacts over a predetermined period of time.
We speak with Ali about what is possible with analyzing voice in real-time today and what kind of ROI can businesses expect for this application. Lastly we touch-base on what factors will make AI inevitable for some companies in the next two to three years.
Interested readers can see the full interview with Ail here:
Sun, 25 March 2018
Episode summary: There are many challenges to bringing AI into an enterprise for example the lack of skilled AI talent, or issues around data organization. In this week's episode, we focus on AI adoption in the enterprise from an investor’s perspective.
We expect that founders looking to sell B2B enterprise AI-products and people in enterprises who are looking for the right qualities in an AI firm which would ease integration, would find this episode relatable. We speak with Rudina Seseri from Glasswing Ventures about what are the pain points for AI integration in the enterprise and at the other end of the spectrum, some factors that are aiding AI adoption.
Interested readers can see the full interview with Rudina here:
Sun, 18 March 2018
Episode summary: In this week's interview on the AI in Industry podcast, we speak with Amir Konigsberg, the CEO of Twiggle, about the future of product search - and how eCommerce and retail brands can use natural language processing (NLP) to improve their user experience.
Amir explains some of the factors that make eCommerce product search challenging, and the artificial intelligence approaches that can improve it today and within the next five years.
Interested readers can learn more about present and future use-cases for artificial intelligence applications in retail in our full article on that topic.
You can listen to the full interview with Amir Konigsberg from Twiggle here:
Sun, 11 March 2018
Episode Summary: Machine learning (ML) can be used to identify objects and pictures or help steer vehicles, but is not best suited for text-based AI applications says Robbie Allen, founder of Automated Insights.
In this episode of AI in Industry, we speak with Robbie about what is possible in generating text with AI and why rules based processes are a big part of natural language generation (NLG). We also explore which industries are likely to adopt such NLG techniques and in what ways can NLG help in business intelligence applications in the near future.
You can listen to the full interview with Robbie here: