Sat, 15 July 2017
If you work in healthcare, or in an established business that is looking to implement AI for the first time - then this won't be an interview you'll want to miss.
AYASDI is one of those rare AI startups that has raised over $100MM since it's inception in 2008. This week on the "AI in Industry" podcast, Sangeeta Chakraborty of AYASDI breaks down some of AI's important recent applications in the healthcare field. She also explores how hospitals are "modernizing" their processes and systems to include data science and AI applications - and we pick apart those "modernizing" strategies in a way that makes them applicable to nearly any "stodgy" business or industry that is just beginning to implement AI.
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Fri, 7 July 2017
Marshall Brain discusses how wetware (the human brain) is increasingly becoming a part of a bigger system which may in itself be managed by software systems. The roles and relationships of humans and machines are rapidly changing. With the increasing advances in technology, there are fewer and fewer skills or activities that an enterprise needs from human beings, and they only need those until they can be replaced by software or hardware.
For example, computer vision systems are often still not as effective as the human eye, so we still need human vision systems to recognize text or to recognize object placement, and take action accordingly (in a store, warehouse, or other setting). A human can fill that role as a piece of wetware until the software or the hardware catches up. How will man and machine collaborate in the future? We explore these dynamics in depth in this week's interview.
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Sun, 2 July 2017
Machine learning currently faces a number of obstacles which prevent it from advancing as quickly as it might. How might these obstacles be overcome and what impact would this have on the machine learning across different industries in the coming decade? In this episode we talk to Dr. Hanie Sedghi, Research Scientist at the Allen Institute for Artificial Intelligence, about the developments in core machine learning technology that need to be made, and that researchers and scientists are working, on to further the application of machine learning in autonomous vehicles. We also touch on some of the impact that might be made if machine learning is able to overcome its own boundaries in terms of computational research, in terms of certain algorithms, and what kind of impact that might have in the arena of autonomous driving and in the realm of natural language processing (NLP).
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