His idea unfolded amid a massive shift in the world of business as companies as diverse as JPMorgan Chase and General Motors became more reliant on computers as part of their infrastructure. It happened in incremental steps, from big mainframes to personal computers to the era of smartphones and cloud computing. Understanding all these shifts was critical for IT executives, and Mr. Gartner positioned his firm as a vital guide to understanding and embracing the idea that those changing systems were a critical part of the corporate equation.

It's been two weeks since Google fired Timnit Gebru, a decision that still seems incomprehensible. Gebru is one of the most highly regarded AI ethics researchers in the world, a pioneer whose work has highlighted the ways tech fails marginalized communities when it comes to facial recognition and more recently large language models. Of course, this incident didn't happen in a vacuum. Case in point: Gebru was fired the same day the National Labor Review Board (NLRB) filed a complaint against Google for illegally spying on employees and the retaliatory firing of employees interested in unionizing. Gebru's dismissal also calls into question issues of corporate influence in research, demonstrates the shortcomings of self-regulation, and highlights the poor treatment of Black people and women in tech in a year when Black Lives Matter sparked the largest protest movement in U.S. history. In an interview with VentureBeat last week, Gebru called the way she was fired disrespectful and described a companywide memo sent by CEO Sundar Pichai as "dehumanizing." To delve further into possible outcomes following Google's AI ethics meltdown, VentureBeat spoke with five experts in the field about Gebru's dismissal and the issues it raises.

The following week, she took part in several workshops at NeurIPS, the largest annual AI research conference, which over 20,000 people attended this year. It was "therapeutic," she says, to see how the community she'd helped build showed up and supported one another. Now, another week later, she's just winding down and catching her breath--and trying to make sense of it all. On Monday, December 14, I caught up with Gebru via Zoom. She recounted what happened during her time at Google, reflected on what it meant for the field and AI ethics research, and gave parting words of advice to those who want to keep holding tech companies accountable.

While impressive, the technology wasn't yet capable of replacing the existing expensive and time-consuming experimental methods for determining what these proteins look like. However, its latest software comes close. In November, AlphaFold again outperformed all the other competing groups at CASP. The technology solved protein structures other labs had been working on for years. Scientists think the technology could have immense implications for the way proteins are studied.

Earlier this fall, A.I. ethicist Timnit Gebru submitted a paper for consideration at an academic conference about predictive language models: on their environmental cost, and how they could learn racist and sexist language and also spread misinformation. Since she was working for Google, the company first wanted to review the paper--which Gebru wrote with several of her colleagues--and sign off on it. She was then told by senior managers that the paper didn't meet Google's publication bar, and that she should retract it or remove the names of Google employees. Gebru wanted more clarity on why they wanted it retracted and said that if Google couldn't provide that information, she would resign. This kicked off a few days of wrangling and several intense emails--until a manager emailed Gebru's boss, saying they had accepted her resignation.

It was only a year ago that we convened in southern California for AIMed19 with over 600 of us in attendance, and not one of us would have predicted that it was to be the last such gathering for most of us for a long while. This year, in the spirit of celebrating artificial intelligence during the COVID-19 pandemic, we launched the AIMed AI Champions Awards in several categories during the week-long virtual sessions focused on the hot topics of this year of artificial intelligence in healthcare. The first topic of the week was Education of AI in Healthcare. This session had an inspiring gathering of clinicians at all levels of education of artificial intelligence. The winner of the AI Champion Rising Star award, Dr. Addison Gearhart, eloquently stated that she did not allow her lack of formal artificial intelligence education to be a deterrent in creatively using it as a resource for completing projects and building programs.

To gain an insight into these and other essential 2021 trends for businesses, Digital Journal caught up with Robert Prigge, CEO of Jumio. Addressing bias in AI algorithms will be a top priority causing guidelines to be rolled out for machine learning support of ethnicity for facial recognition. Prigge explains: "Enterprises are becoming increasingly concerned about demographic bias in AI algorithms (race, age, gender) and its effect on their brand and potential to raise legal issues. Evaluating how vendors address demographic bias will become a top priority when selecting identity proofing solutions in 2021." Prigge adds: "According to Gartner, more than 95 percent of RFPs for document-centric identity proofing (comparing a government-issued ID to a selfie) will contain clear requirements regarding minimizing demographic bias by 2022, an increase from fewer than 15 percent today. Organizations will increasingly need to have clear answers to organizations who want to know how a vendor's AI "black box" was built, where the data originated from and how representative the training data is to the broader population being served."

The past week was a momentous occasion for protein structure prediction, structural biology at large, and in due time, may prove to be so for the whole of life sciences. CASP14, the conference for the biennial competition for the prediction of protein structure from sequence, took place virtually over multiple remote working platforms. DeepMind, Google's premier AI research group, entered the competition as they did the previous time, when they upended expectations of what an industrial research lab can do. The outcome this time was very, very different however. At CASP13 DeepMind made an impressive showing with AlphaFold but was ultimately within the bounds of the usual expectations of academic progress, albeit at an accelerated rate. At CASP14 DeepMind produced an advance so thorough it compelled CASP organizers to declare the protein structure prediction problem for single protein chains to be solved. In my read of most CASP14 attendees (virtual as it was), I sense that this was ...

Cheryl Cran is a future of work expert and founder of NextMapping. She is a globally known Hall of Fame Keynote Speaker and has a long term successful track record with clients that include small, medium and Fortune 500's businesses. She has also been named as #1 Future of Work Influencer. She is also the author of over 5 books, on business and leadership. Bringing with her vast experience, we are happy to have someone of her stature on our interview series today. We have the pleasure of welcoming Cheryl Cran today to our interview series. Before we begin, just a quick intro of PeopleHum. We run the peopleHum blog and video channel which receives upwards of 200,000 visitors a year and publish around 2 interviews with well-known names globally, every month. Welcome, Cheryl, we're thrilled to have you.

Back in February, when AI conferences were still held in-person, Turing Award winners Geoffrey Hinton, Yann LeCun and Yoshua Bengio shared a stage in New York at an AAAI event, which Syncedcovered in detail. LeCun told the audience that, after decades of skepticism, he had finally joined Hinton in support of the idea that self-supervised learning may usher in AI's next revolution. Unlike supervised learning, which requires manual data-labelling, self-supervised learning (SSL) is an approach that can automatically generate labels. Recent improvements in self-supervised training methods have established SSL as a serious alternative to traditional supervised training. Google's language representation model ALBERT for example utilizes a self-supervised training framework to leverage large amounts of text. It's no surprise then that NeurIPS 2020 (the Conference on Neural Information Processing Systems) would find itself at the forefront of this trend.