This article started out as an addendum to a chapter in our book, Data Visualization: A History of Visual Thinking and Graphic Communication (Friendly & Wainer, 2020). In this we claimed that much of the history of data visualization could be seen as combination of three forces: (1) important scientific problems of the day, (2) a developing abundance of data, and (3) the cognitive ability of some heroes in this history to conceive solutions to problems by visual imagination. In the book and what follows we make frequent reference to cognitive aspects of the visual understanding of phenomena and their expression in graphic displays: "inner vision", "graphic communication", "visual insight" are some of the terms we use. An early metaphor for this and an early title for our book was "A gleam in the mind's eye." We give some additional explanations and examples here. We also want to place this topic in a wider framework.

What will work look like when everything is digitally transformed, artificial intelligence is optimally deployed and robots are embedded in every aspect of our economy? That's hard to know, but one company that's focused on making work work better, and helping businesses digitally transform, is Box. Box, which went public in 2015, still acts very much like a Silicon Valley startup. Except, of course, grown up, with partnerships with Google and IBM and just about any other important company you can think of. Elements of this image furnished by NASA.

Sir Ronald Aylmer Fisher FRS[3] (17 February 1890 – 29 July 1962) was a British statistician and geneticist. For his work in statistics, he has been described as "a genius who almost single-handedly created the foundations for modern statistical science"[4] and "the single most important figure in 20th century statistics".[5] In genetics, his work used mathematics to combine Mendelian genetics and natural selection; this contributed to the revival of Darwinism in the early 20th-century revision of the theory of evolution known as the modern synthesis. For his contributions to biology, Fisher has been called "the greatest of Darwin's successors".[6] From 1919 onward, he worked at the Rothamsted Experimental Station for 14 years;[7] there, he analysed its immense data from crop experiments since the 1840s, and developed the analysis of variance (ANOVA).

Today's podcast features recent interviews with [two] experts in top AI management positions discussing recent developments within their companies and the overall sector: Dr. Danny Rittman, CTO of GBT Technologies Inc. (OTC PINK:GTCH), and Mr. Babak Hodjat, VP of Evolutionary AI, Cognizant Technology Solutions Corporation (NasdaqGS:CTSH). Listen to the podcast interview with Dr. Danny Rittman, CTO of GBT Technologies Inc. (OTC PINK:GTCH) discussing the company's recently announced implementation and development of recurrent relational reasoning (RRN) in its AI, and its applications in the medical field. In a recently published press release, GBT Technologies CTO, Dr. Danny Rittman explained the company's rationale for incorporating recurrent relational reasoning (RRN) into its Avant! "Our goal is to implement a fundamental part of human intelligence called relational reasoning, which is planned to enable Avant! to acquire expertise on its own by understanding object's relations. Avant! will include an advanced artificial neural network (ANN) capable of pattern recognition and reasoning about those patterns which is very similar to the human brain."

StradVision has raised $16.6M in total. We talked with Junhwan Kim, its CEO. How would you describe StradVision in a single tweet? StradVision is a pioneer in deep learning-based vision processing technology, providing the software that will allow Advanced Driver-Assistance Aystems (ADAS) in autonomous vehicles to reach the next level of safety, and usher in the era of the fully autonomous vehicle. How did it all start and why?

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Longtime Beth Israel Deaconess Medical Chief Information Officer Dr. John Halamka left that role six months ago after more than two decades – during which time he also became one of the most vocal health information technology champions and visible thought leaders during a pivotal time of IT uptake. Halamka has been traveling the world recently – more than 400,000 miles this year, he says – from Europe to Israel to Africa to China, back to his Sherborn, Massachusetts-based Unity Farm Sanctuary for a quick visit with Dudley, his shaggy Scottish Highland Bull, and then out again to explore the newest global trends in leading-edge digital health. Halamka will keynote the ConVerge2Xcelerate event in Boston on October 15, hosted by Blockchain in Healthcare Today – where he is editor-in-chief – and co-presented as part of the preconference activities of the Connected Health Conference. We caught up with him recently at another Boston event and asked him about what he's seeing on his travels. You were CIO of Beth Israel Deaconess for so long – twenty two years. How has your newish gig, as International Healthcare Innovation Professor, been going? A. That's my academic title, my Harvard Medical School title, and that was three years ago. But the official transition from CIO, a hospital based-title, was March 1, 2019. That's when the merger of Beth Israel and Lahey came together, and the CEO and I talked about: How do you create innovation? So what is next with healthcare innovation?

Mansour asked Vorhies about Google's business engagements in China. "Google has gotten in trouble in the past for doing business with the Communist government of China," Mansour said. "And I wanted to ask you were those efforts ongoing when you were with the company? Can you give us any insight into that? Because it was quite troubling."

The promise of AI is easily understood by anyone with an imagination, and for 40 years, venture capitalists have been enthusiastically investing in that promise. However, it's been significantly harder for founders to turn that investment into sustainable business models. Today we are going to look at why that is, and go over what might be a blueprint for startups to create business models around artificial intelligence. Tatsuo Nakamura founded Valuenex in 2006 with the goal of using artificial intelligence to supplement the work being done by patent attorneys, and their software was instrumental in the resolution of one of Japan's most famous, and most valuable, lawsuits. We also talk about how to sell to large companies as a small startup, the challenges in trying to make product strategy based on technology, why staying private longer is not always a good thing for startups, and how Valuenex technology accidentally discovered a secret collaboration between Honda and Google. It's a great discussion with the founder of one of Japan's most successful AI companies, and I think you will really enjoy it. Welcome to Disrupting Japan, straight talk from Japan's most successful entrepreneurs. Today, we're going to be talking about something that's frankly difficult to talk about on an audio podcast. Tatsuo Nakamura founded Valuenex in 2006 to use Artificial Intelligence and modern visualization techniques to help clients make sense of their patent portfolios and to keep an eye on what the competition is doing. In fact, this technology uncovered some of the core evidence that decided the famous blue LED case. It's highly effective but highly visual, so let me try to explain it. Valuenex creates a kind of topographical map that shows companies where in the market, their IP is strong and where it's weak.

From its inception, AI has had a rather ambivalent relationship to humans---swinging between their augmentation and replacement. Now, as AI technologies enter our everyday lives at an ever increasing pace, there is a greater need for AI systems to work synergistically with humans. To do this effectively, AI systems must pay more attention to aspects of intelligence that helped humans work with each other---including social intelligence. I will discuss the research challenges in designing such human-aware AI systems, including modeling the mental states of humans in the loop, recognizing their desires and intentions, providing proactive support, exhibiting explicable behavior, giving cogent explanations on demand, and engendering trust. I will survey the progress made so far on these challenges, and highlight some promising directions. I will also touch on the additional ethical quandaries that such systems pose. I will end by arguing that the quest for human-aware AI systems broadens the scope of AI enterprise, necessitates and facilitates true inter-disciplinary collaborations, and can go a long way towards increasing public acceptance of AI technologies.