Niki Trigoni discusses how Navenio has helped hospitals during the pandemic and shares her thoughts on digital transformation. Niki Trigoni is a professor of computer science at the University of Oxford where she leads the Cyber Physical Systems group. She has 15 years of experience in intelligent sensor systems and has won several awards for her group's work on indoor and underground positioning. She is also a founder of the Centre for Doctoral Training in autonomous and intelligent machines and systems, which aims to deliver highly trained individuals versed in the underpinning sciences of robotics, computer vision, wireless embedded systems, machine learning, control and verification. Currently Trigoni is the chief technology officer of Navenio, an AI-led indoor location-based platform that aims to improve workforce efficiency in hospitals.

In the June issue of Communications, Editor-in-Chief Andrew A. Chien suggested in his Editor's Letter (p. 5) that ACM consider bestowing two A.M. Turing Awards per year. Immediately upon reading your June Editor's letter, my reaction was "No!" because I thought two annual awards would reduce the stature of each and minimize the honor to recipients and even to Alan Turing. But I was hasty in forming my opinion. I reread your argument and changed my opinion--I now believe we need to think even bigger. The number "two" suggests a division between hardware and software.

The new Anthony Bourdain documentary, "Roadrunner," is one of many projects dedicated to the larger-than-life chef, writer and television personality. But the film has drawn outsize attention, in part because of its subtle reliance on artificial intelligence technology. Using several hours of Bourdain's voice recordings, a software company created 45 seconds of new audio for the documentary. The AI voice sounds just like Bourdain speaking from the great beyond; at one point in the movie, it reads an email he sent before his death by suicide in 2018. "If you watch the film, other than that line you mentioned, you probably don't know what the other lines are that were spoken by the AI, and you're not going to know," Morgan Neville, the director, said in an interview with The New Yorker.

Every company may want to put artificial intelligence to work, but most companies aren't blessed with the ability to hire battalions of data scientists–nor is that necessarily the right approach. As Gartner analyst Svetlana Sicular once argued, often the best possible data scientist is the person you already employ who knows your data and simply needs help figuring out how to unlock it. For many business line owners, it's this kind of approach that may make the most sense, as they seek to be smarter with the data they already have. One company working to enable this vision is Cambridge, Massachusetts-based machine learning startup Akkio, which pairs AI with low code in an attempt to democratize AI. I caught up with company co-founder and COO Jon Reilly to learn more.

All the sessions from Transform 2021 are available on-demand now. Women in the AI field are making research breakthroughs, spearheading vital ethical discussions, and inspiring the next generation of AI professionals. We created the VentureBeat Women in AI Awards to emphasize the importance of their voices, work, and experience, and to shine a light on some of these leaders. In this series, publishing Fridays, we're diving deeper into conversations with this year's winners, whom we honored recently at Transform 2021. Briana Brownell, winner of VentureBeat's Women in AI entrepreneur award, didn't enter this field to earn accolades.

Yesterday Olga Tokarczuk (2018 Nobel Prize in Literature) said in an interview that when she thinks about literature, she no longer thinks about books!!! So, how should we effectively tell the most important story in predictive modelling i.e. We (MI2DataLab) are currently working on an exciting and interdisciplinary experiment combining a classic textbook with a comic book, combining a description of methods and software with a description of process, combining a description of a specific use-case about COVID-19 data analysis with universal best practices. These 52 page long teaching materials describe how to build a predictive model, compare the developed models, and use XAI to analyze them, plus a bonus -- how to deploy model with explanations in a similar form to https://crs19.pl/. The material is prepared as a starter for predictive modelling. The included code examples can be executed and experimented with on your own (the first version has examples in R, but there will be albo translation for Python).

Early reviews of the new documentary film ROADRUNNER about the late food mogul Anthony Bourdain were overwhelmingly positive. Upon its official release last week, though, it started to get some backlash particularly after filmmaker Morgan Neville said he used artificial intelligence technology to create some quotes in Anthony's voice. In an interview with The New Yorker, Neville explained how his team "created an A.I. model of his [Bourdain's] voice" because there were three quotes wanted for the film that had no previous recordings before. By sending a software company hours of recordings and footage, they were able to splice together these quotes in Anthony's voice. "If you watch the film, other than that line you mentioned, you probably don't know what the other lines are that were spoken by the A.I., and you're not going to know," Neville told The New Yorker: "We can have a documentary-ethics panel about it later."

One night last fall, unable to sleep, Joshua Barbeau logged onto a mysterious chat website called Project December. It was Sept. 24, around 3 a.m., and Joshua was on the couch, next to a bookcase crammed with board games and Dungeons & Dragons strategy guides. He lived in Bradford, Canada, a suburban town an hour north of Toronto, renting a basement apartment and speaking little to other people. A 33-year-old freelance writer, Joshua had existed in quasi-isolation for years before the pandemic, confined by bouts of anxiety and depression. Once a theater geek with dreams of being an actor, he supported himself by writing articles about D&D and selling them to gaming sites. Many days he left the apartment only to walk his dog, Chauncey, a black-and-white Border collie. Usually they went in the middle of the night, because Chauncey tended to get anxious around other dogs and people. They would pass dozens of dark, silent, middle-class homes. Then, back in the basement, Joshua would lay ...

TechRepublic's Karen Roby spoke with Jahanzaib Ansari, co-founder and CEO of Knockri, a behavioral skills assessment platform, about unconscious bias in artificial intelligence. The following is an edited transcript of their conversation. Karen Roby: I think what makes this really interesting and why I wanted to talk to you, Jahanzaib, is because your desire to create this company was rooted in your own personal story. Jahanzaib Ansari: I was actually applying to jobs and I wouldn't hear back from employers. I have a long, ethnic name, which is Jahanzaib, and so my co-founder, Maaz, is like, "Why don't you just anglicize it?"

Abstract--Language instruction plays an essential role in the natural language grounded navigation tasks. However, navigators trained with limited human-annotated instructions may have difficulties in accurately capturing key information from the complicated instruction at different timesteps, leading to poor navigation performance. In this paper, we exploit to train a more robust navigator which is capable of dynamically extracting crucial factors from the long instruction, by using an adversarial attacking paradigm. Specifically, we propose a Dynamic Reinforced Instruction Attacker (DR-Attacker), which learns to mislead the navigator to move to the wrong target by destroying the most instructive information in instructions at different timesteps. By formulating the perturbation generation as a Markov Decision Process, DR-Attacker is optimized by the reinforcement learning algorithm to generate perturbed instructions sequentially during the navigation, according to a learnable attack score. Then, the perturbed instructions, which serve as hard samples, are used for improving the robustness of the navigator with an effective adversarial training strategy and an auxiliary self-supervised reasoning task. Experimental results on both Vision-and-Language Navigation (VLN) and Navigation from Dialog History (NDH) tasks show the superiority of our proposed method over state-of-the-art methods. Moreover, the visualization analysis shows the effectiveness of the proposed DR-Attacker, which can successfully attack crucial information in the instructions at different timesteps.