FOX News Media CEO Suzanne Scott participated in a fireside chat on Thursday with University of South Carolina students, discussing media operations and the business of journalism.

In this episode, Eleanor and Kerry chat to Yasmine Boudiaf, a researcher, artist and creative technologist who uses technology in beautiful and interesting ways to challenge and redefine what we think of as'good'.

What follows is the second part of our coverage of the "Radiology: Artificial Intelligence Fireside Chat" conducted at RSNA 2021.

What follows is the second part of our coverage of the "Radiology: Artificial Intelligence Fireside Chat" conducted at RSNA 2021. The in-depth discussion, for which excerpts are presented here, was well-facilitated by Dania Daye, MD, PhD, Massachusetts General Hospital/Harvard Medical School; and Paul Yi, MD., University of Maryland School of Medicine; with RSNA Journal Radiology: AI Editor Charles E. Kahn, Jr., MD, MS, Perelman School of Medicine, University of Pennsylvania. Featured panelists included: John Mongan, MD, PhD, University of California, San Francisco; Jayashree Kalpathy-Cramer, MS, PhD, Athinoula A. Martinos Center for Biomedical Imaging; and Linda Moy, MD, NYU Grossman School of Medicine. Q: The successes we have seen in AI are clear. There is cutting-edge research emerging, but with every success, we are identifying multiple obstacles.

ORLANDO, Fla. – The healthcare business is an unusual one: Although trillions of dollars are poured into the industry, billions of people worldwide don't have reasonable access to care. Part of the solution to that gap, explained Amazon Web Services Chief Medical Officer and Director of Machine Learning Dr. Taha Kass-Hout, may be found in artificial intelligence, and in technology more broadly. "Innovations like precision medicine, conversational bots, AI scribes and APIs for data interoperability are great examples of how we can help improve care, close gaps in care, provide more efficiencies and also provide more equitable care," said Kass-Hout in a fireside chat at the HIMSS22 Machine Learning and AI for Healthcare Forum on Monday. Additionally, given the move toward the digitization of health data, particularly via the cloud, the question becomes how to use that information for the benefit of patients. One hurdle, as other HIMSS22 panelists pointed out earlier in the day, is the sheer amount of unstructured data being created.

On Tuesday, the tech and gaming industries woke up to shocking news that Microsoft would buy embattled gaming company Activision Blizzard, in one of the largest acquisitions in history. The acquisition is expected to be completed by June 2023, pending regulatory approval. Over the past six months, Activision Blizzard has been besieged by lawsuits from a California state agency, shareholders and employees who allege a "frat boy" corporate culture leading to sexual harassment and gender-based discrimination.

What can AI do for you? A lot, as the recent Intel AI Summit 2021 has shown. Artificial intelligence opens up a broad range of possibilities, from tiny devices to the massive cloud. Suppose you don't know where to start or how to develop and scale up your ideas and innovation further. In that case, the two-day summit's contents are now available on-demand to inspire you with the latest from the Intel AI technology stack, as well as successful customer use cases from the Asia Pacific and Japan Territory (APJ-T).

It's been a hot, hot year in the world of data, machine learning and AI. Just when you thought it couldn't grow any more explosively, the data/AI landscape just did: rapid pace of company creation, exciting new product and project launches, a deluge of VC financings, unicorn creation, IPOs, etc. It has also been a year of multiple threads and stories intertwining. One story has been the maturation of the ecosystem, with market leaders reaching large scale and ramping up their ambitions for global market domination, in particular through increasingly broad product offerings. Some of those companies, such as Snowflake, have been thriving in public markets (see our MAD Public Company Index), and a number of others (Databricks, Dataiku, Datarobot, etc.) have raised very large (or in the case of Databricks, gigantic) rounds at multi-billion valuations and are knocking on the IPO door (see our Emerging MAD company Index – both indexes will be updated soon). But at the other end of the spectrum, this year has also seen the rapid emergence of a whole new generation of data and ML startups. Whether they were founded a few years or a few months ago, many experienced a growth spurt in the last year or so. As we will discuss, part of it is due to a rabid VC funding environment and part of it, more fundamentally, is due to inflection points in the market. In the last year, there's been less headline-grabbing discussion of futuristic applications of AI (self-driving vehicle, etc.), and a bit less AI hype as a result. Regardless, data and ML/AI-driven application companies have continued to thrive, particularly those focused on enterprise use cases. Meanwhile, a lot of the action has been happening behind the scenes on the data and ML infrastructure side, with entire new categories (data observability, reverse ETL, metrics stores, etc.) appearing and/or drastically accelerating. To keep track of this evolution, this is our eighth annual landscape and "state of the union" of the data and AI ecosystem – co-authored this year with my FirstMark colleague John Wu. (For anyone interested, here are the prior versions: 2012, 2014, 2016, 2017, 2018, 2019 (Part I and Part II) and 2020.) For those who have remarked over the years how insanely busy the chart is, you'll love our new acronym – Machine learning, Artificial intelligence and Data (MAD) – this is now officially the MAD landscape! We've learned over the years that those posts are read by a broad group of people, so we have tried to provide a little bit for everyone – a macro view that will hopefully be interesting and approachable to most; and then a slightly more granular overview of trends in data infrastructure and ML/AI for people with deeper familiarity with the industry. This (long!) post is organized as follows: Let's start with the high level view of the market. As the number of companies in the space keeps increasing every year, the inevitable questions are: why is this happening?

Just when you thought it couldn't grow any more explosively, the data/AI landscape just did: the rapid pace of company creation, exciting new product and project launches, a deluge of VC financings, unicorn creation, IPOs, etc. It has also been a year of multiple threads and stories intertwining. One story has been the maturation of the ecosystem, with market leaders reaching large scale and ramping up their ambitions for global market domination, in particular through increasingly broad product offerings. Some of those companies, such as Snowflake, have been thriving in public markets (see our MAD Public Company Index), and a number of others (Databricks, Dataiku, DataRobot, etc.) have raised very large (or in the case of Databricks, gigantic) rounds at multi-billion valuations and are knocking on the IPO door (see our Emerging MAD company Index). But at the other end of the spectrum, this year has also seen the rapid emergence of a whole new generation of data and ML startups. Whether they were founded a few years or a few months ago, many experienced a growth spurt in the past year or so. Part of it is due to a rabid VC funding environment and part of it, more fundamentally, is due to inflection points in the market. In the past year, there's been less headline-grabbing discussion of futuristic applications of AI (self-driving vehicles, etc.), and a bit less AI hype as a result. Regardless, data and ML/AI-driven application companies have continued to thrive, particularly those focused on enterprise use trend cases. Meanwhile, a lot of the action has been happening behind the scenes on the data and ML infrastructure side, with entirely new categories (data observability, reverse ETL, metrics stores, etc.) appearing or drastically accelerating. To keep track of this evolution, this is our eighth annual landscape and "state of the union" of the data and AI ecosystem -- coauthored this year with my FirstMark colleague John Wu. (For anyone interested, here are the prior versions: 2012, 2014, 2016, 2017, 2018, 2019: Part I and Part II, and 2020.) For those who have remarked over the years how insanely busy the chart is, you'll love our new acronym: Machine learning, Artificial intelligence, and Data (MAD) -- this is now officially the MAD landscape! We've learned over the years that those posts are read by a broad group of people, so we have tried to provide a little bit for everyone -- a macro view that will hopefully be interesting and approachable to most, and then a slightly more granular overview of trends in data infrastructure and ML/AI for people with a deeper familiarity with the industry. Let's start with a high-level view of the market. As the number of companies in the space keeps increasing every year, the inevitable questions are: Why is this happening? How long can it keep going?

We're about a week away from the AWS Machine Learning Summit and if you haven't registered yet, you better get on it! On June 2, 2021 (Americas) and June 3, 2021 (Asia-Pacific, Japan, Europe, Middle East, and Africa), don't miss the opportunity to hear from some of the brightest minds in machine learning (ML) at the free virtual AWS Machine Learning Summit. This Summit, which is open to all, brings together industry luminaries, AWS customers, and leading ML experts to share the latest in ML. You'll learn about science breakthroughs in ML, how ML is impacting business, best practices in building ML, and how to get started now without prior ML expertise. This post is your guide to navigating the Summit.