By this point, most industries have experienced some sort of digital revolution. As evidenced by this year's Digital Transformation Index, no one wants to be a “digital laggard.” But, some industries have shown up a little later to the party; manufacturing included. One particulary company, Xometry, has taken on that challenge. By creating an online platform for businesses and individuals to request manufacture of a particular part or product and simultaneously curating a collection of suppliers, Xometry is expediting the traditional manufacturing process, exponentially.

This week, host Kelly Lynch chats with Bill Cronin, Chief Revenue Officer for Xometry, to learn more about the company and what makes them truly unique. You’ll walk away this week with a better understanding of the manufacturing industry, why digitization has created an even bigger pie for suppliers and customers alike, and why distributed access to these types of technologies results in better outcomes for all.

What you'll hear this episode:

• What is Xometry? 01:00
• The old world vs. the new world of manufacturing 02:25
• How an agnostic supplier attitude leads to better outcomes 03:50
• Types of projects, like BMW Z4 parts, uploaded to Xometry 04:26
• How digital manufacturing reduces number of suppliers while expanding capabilities 05:03
• What happens if a natural disaster strikes where a factory resides? 06:07
• The new digital way to make parts 07:50
• Applying AI and ML to an age-old process 08:51
• What on earth is injection molding? 11:54
• Sustainability as a forethought, not an afterthought 12:34
• Maximizing the capacity of the world to build ideas, big and small 14:43

Without video conferencing tools, higher internet speeds and personal computers capable of handling individual-specific workloads, productivity would have been nearly impossible in 2020. Technology has enabled us to continue our lives as undisrupted as possible while staying home. But how will 2021 prove to be another year of incredible advancement in the realm of technology?

This week, John Roese joins host Kelly Lynch to offer his perspective on a number of emerging technologies. Whether it's the further expansion of 5G or the movement towards a more viable quantum computer, John predicts that next year will bring about several improvements across a variety of different technologies.

In this episode:

• Mixed reality and what that means (01:30)
• An overview of expected technological changes in the new year (02:55)
• The hybrid cloud for the hybrid workforce (04:17)
• Is everything moving to the Edge? (07:16)
• The four use cases for utilizing Edge capabilities (07:41)
• How Edge relates to the multi-cloud world (09:55)
• Is 5G just 4G on steroids? (11:48)
• What we can really except to see from 5G in 2021 (13:40)
• Re-creating human-to-human interaction with digital tools (15:55)
• Moving towards heterogeneous compute, thanks to innovation in semiconductors (19:10)
• How quantum computing actually works and the realities behind it (22:25)
• Will the "new normal" continue to be increasingly virtual? (26:02)

Voice assistants. Movie recommendations. Car insurance. Facial recognition software. All of these things use artificical intelligence and machine learning in one way or another. For the most part, we don't feel the need to question why we get a particular movie recommendation on whatever streaming application we use. However, when you start moving into applications that have the potential for more serious outcomes, there becomes a need for understanding that "why."

This is where explainable AI comes in. Unlike traditional programming, AI and ML algorithms are trained using batches of data; not a set of specific instructions. So when we look to understand exactly why we're told some rom-com is a 97% match for us, it's not always clear why that's the case. With things like autonomous vehicles (which are developed primarily with AI/ML algorithms) increasingly becoming a reality, comprehending why that car stopped when it did becomes much more imperitive.

This week, Roberto Stelling and Adriana Prado, two researchers from the Office of the CTO Research Office, join host Kelly Lynch to talk through exactly what explainable AI is, why it is such an important factor in the future development of algorithms, and how, once we can fully trust it, AI has the potential to positively impact our lives.

In this episode:

• Bridging the gap between fear and trust in AI (01:19)
• Review of traditional programming vs. AI (01:45)
• Re-imaginging traditional programming vs. AI in the context of a paper shredder (02:30)
• Difficulty understanding outputs from AI and ML algorithms (04:20)
• Why it would be impossible to write a program for an autonomous vehicle (05:02)
• The best definition for explainable AI (06:36)
• A clear example of the need for explainability through the lens of autonomous cars (07:13)
• Explainable AI as Responsible AI (07:59)
• Is explainability only going to help the internals of AI algorithms? (09:07)
• How voice assistants are becoming more and more regionally aware (10:20)
• Reaching a new level of trust with AI (11:30)