Technology in the so-called Smart Machine Age, which includes AI, virtual reality and robotics, will bring huge changes not just in headcount, but also in how people innovate and collaborate. That will require new approaches to how people think, listen and relate, says Edward D. Hess, a professor of business administration at the University of Virginia. In the "Smart Age" now evolving, ego has no place. Instead, the focus will need to be on the quality of ideas, accuracy, emotional intelligence and mindfulness. Hess writes about these issues in the just-released book he co-authored with Katherine Ludwig, titled Humility is the New Smart: Rethinking Human Excellence in the Smart Machine Age. Hess discussed his ideas on the Knowledge@Wharton show on Wharton Business Radio, SiriusXM channel 111.

At Curalate, we're fascinated by the future of computer vision and machine learning. Those technologies have made serious strides over the past few years and the future looks incredibly bright. Curalate is doing its part to define how artificial intelligence meets commerce with Intelligent Product Tagging -- technology that can analyze an image and use machine learning to identify the products depicted within that image. For example: If you have a photo of a woman wearing a floral dress, our technology can identify that dress, then visually match it with the corresponding product in a brand's catalog -- making the image shoppable. We expect to start introducing this tool to clients in 2017, but it's actually a much longer-term research effort for Curalate's talented product development team.

We propose a technique for making Convolutional Neural Network (CNN)-based models more transparent by visualizing input regions that are 'important' for predictions -- or visual explanations. Our approach, called Gradient-weighted Class Activation Mapping (Grad-CAM), uses class-specific gradient information to localize important regions. These localizations are combined with existing pixel-space visualizations to create a novel high-resolution and class-discriminative visualization called Guided Grad-CAM. These methods help better understand CNN-based models, including image captioning and visual question answering (VQA) models. We evaluate our visual explanations by measuring their ability to discriminate between classes, to inspire trust in humans, and their correlation with occlusion maps. Grad-CAM provides a new way to understand CNN-based models. We have released code, an online demo hosted on CloudCV, and a full version of this extended abstract.

Many claims, including the key one that "Blockchain technology has the potential to revolutionize applications and redefine the digital economy," were neither discussed nor backed up with evidence. From a scientific point of view, this is insufficient. Worse, like many blockchain proponents, Underwood failed, in my opinion, to raise the right questions. Instead of focusing on "what block-chain could do," one should address "what blockchain can do better than other technologies." In this context, blockchain is often compared to existing solutions rather than to existing technologies, as in the proverbial comparison of apples and oranges. There may be any number of reasons, including operational, economic, or social, why an existing solution (as inadequate as it may be) has not been replaced in the marketplace.

The following script is from "Artificial Intelligence," which aired on Oct. 9, 2016. Charlie Rose is the correspondent. The search to improve and eventually perfect artificial intelligence is driving the research labs of some of the most advanced and best-known American corporations. They are investing billions of dollars and many of their best scientific minds in pursuit of that goal. All that money and manpower has begun to pay off. In the past few years, artificial intelligence -- or A.I. -- has taken a big leap -- making important strides in areas like medicine and military technology. What was once in the realm of science fiction has become day-to-day reality. You'll find A.I. routinely in your smart phone, in your car, in your household appliances and it is on the verge of changing everything. On 60 Minutes Overtime, Charlie Rose explores the labs at Carnegie Mellon on the cutting edge of A.I. See robots learning to go where humans can'... It was, for decades, primitive technology.

Using a test that can analyze the patterns of small proteins in blood serum samples in just 30 minutes, researchers were able to differentiate between samples taken from patients diagnosed with cancer and those from patients diagnosed with benign prostate disease. The technique proved effective not only in men with normal and high PSA levels, but also in those whose PSA levels were marginally elevated (4 to 10 nanograms of antigen per milliliter of fluid), in whom it is difficult to rule out cancer without a biopsy. Although the technique is still under evaluation, researchers believe the analysis of protein patterns will be a useful tool in the future for deciding whether men with marginally elevated PSA levels should undergo biopsy. PSA levels are commonly used as a preliminary screen for prostate cancer, but 70 percent to 75 percent of men who undergo biopsy because of an abnormal PSA level do not have cancer. The new proteomic approach has a higher specificity - that is, of the samples the test identifies as cancer, a large percentage are in fact cancer, rather than some other benign disease.

For as long as humans have had consciousness -- two or two hundred millennia depending on who you ask -- human scholars have made great efforts to understand and define what that means. The most facile and purely conceptual description of consciousness might be that it is an awareness of the self within the context of the world. But without an understanding of the underlying mechanism, consciousness keeps chasing its tail. This is, in part, why neuroscientists have successfully interjected themselves in the ongoing conversation about consciousness by pointing to physical phenomena within the brain. But linking the metaphysical to the physical still results in the sort of quasi-scientific, quasi-philosophical overreach that gets academics laughed out of faculty lounges and labeled eccentric.

This article originally ran in Term Sheet, Fortune's newsletter about deals and dealmakers. Last year, the U.S. tech titans slowed their pace of acquisitions, but three companies bucked that trend: Google, Intel, and Salesforce.com. Of the three, Salesforce had the highest acceleration of deals. Last year the $53 billion software company did 120% more acquisitions than it did in 2015, according to CB Insights. I spoke with John Somorjai, the EVP of corporate development and Salesforce Ventures, about AI startups, startup valuations, how the company deals with coop-etition, and what's overhyped. This interview has been lightly edited for length and clarity.

Last year I wrote an overview post which defines a number of key concepts related to data science -- including data science itself -- and attempts to explain how these pieces fit together into a so-called "data science puzzle." As a new year begins, and a previous year worth of advances, insights, and accomplishments get rolled into our collective professional outlook, I thought it would be prudent to revisit this puzzle, noting and incorporating any changes and updates which may contribute to rearranging the puzzle for the foreseeable future, and to provide some addition commentary where warranted. Big Data is still important to data science. Take your pick of metaphors, but any way you look at it, Big Data is the raw material that has continues to fuel the data science revolution. As relates to Big Data, I believe that justification of data-acquisition and -retention from a business point of view, expectations that Big Data projects start providing actual financial returns, and the challenges related to data privacy and security will become the big Big Data stories not only of 2017 but moving forward in general.

Daniel Kwan and Daniel Scheinert, young directors who go by the joint film credit Daniels, are known for reality-warped miniatures--short films, music videos, commercials--that are eerie yet playful in mood. In their work, people jump into other people's bodies, Teddy bears dance to hard-core dubstep, rednecks shoot clothes from rifles onto fleeing nudists. Last year, their first feature-length project, "Swiss Army Man"--starring Daniel Radcliffe, who plays a flatulent talking corpse that befriends a castaway--premièred at Sundance, and left some viewers wondering if it was the strangest thing ever to be screened at the festival. The Times, deciding that the film was impossible to categorize, called it "weird and wonderful, disgusting and demented." Perhaps it is no surprise, then, that when the Daniels were notified by their production company, several years ago, that an Israeli indie pop star living in New York wanted to hire them to experiment with technology that could alter fundamental assumptions of moviemaking, they took the call. The musician was Yoni Bloch, arguably the first Internet sensation on Israel's music scene--a wispy, bespectacled songwriter from the Negev whose wry, angst-laden music went viral in the early aughts, leading to sold-out venues and a record deal. After breaking up with his girlfriend, in 2007, Bloch had hoped to win her back by thinking big. He made a melancholy concept album about their relationship, along with a companion film in the mode of "The Wall"--only to fall in love with the actress who played his ex. He had also thought up a more ambitious idea: an interactive song that listeners could shape as it played. But by the time he got around to writing it his hurt feelings had given way to more indeterminate sentiments, and the idea grew to become an interactive music video. The result, "I Can't Be Sad Anymore," which he and his band released online in 2010, opens with Bloch at a party in a Tel Aviv apartment. Standing on a balcony, he puts on headphones, then wanders among his friends, singing about his readiness to escape melancholy. He passes the headphones to others; whoever wears them sings, too. Viewers decide, by clicking on onscreen prompts, how the headphones are passed--altering, in real time, the song's vocals, orchestration, and emotional tone, while also following different micro-dramas. If you choose the drunk, the camera follows her as she races into the bathroom, to Bloch's words "I want to drink less / but be more drunk." Choose her friend instead, and the video leads to sports fans downing shots, with the lyrics "I want to work less / but for a greater cause."