Wherever that will lead is, at the time of the writing of this article, still not certain, but regardless of the direction, it's clear that advancing progress with artificial intelligence is a key strategic element for both major parties. Over the course of the past few years, governments around the world have taken strong positions on advancing their strategies around AI adoption. Certainly heading into the new year it seems that the pace of adoption won't be slowing any time soon. At the recent Data for AI conference, we had an opportunity to get insights into how the government plans to continue and accelerate its adoption of AI in an interview with Ellery Taylor, Acting Director of the Office of Acquisition Management and Innovation Division, at the US General Services Administration (GSA). In this article he shares his outlook for the future of AI and how it is being adopted in the government.

So it turns out that Kim Kardashian whisking her friends and family off to a private island in the middle of a pandemic was only the second craziest thing about her 40th birthday celebration. On Thursday, Kardashian revealed what her husband, Kanye West, got her the birthday gift at the top of every woman's wish list: her very own hologram. And not just any hologram: It was a so-called holographic resurrection of her late father, Robert Kardashian, who died in 2003. Kaleida, a "multimedia hologram company," published a page to its website taking credit for the creation (Kardashian and West have not yet confirmed the hologram's origins). Reached by Slate, Kaleida director and producer Daniel Reynolds declined to discuss any specifics of the Kardashian hologram, but agreed to speak about the company and its technology more generally. How exactly do you order a hologram of a late relative?

It is 2020 and it's all about technology these days. You knowingly or unknowingly use machine learning in your day-to-day life. Let me give you some examples, Google spam filter, Netflix recommendation system, Facebook face recognition, weather forecast, and much more. All of these are Machine Learning. It has become one of the hottest industries and has become like a trend for beginners.

In early 2018, officials at University College London were shocked to learn that meetings organized by "race scientists" and neo-Nazis, called the London Conference on Intelligence, had been held at the college the previous four years. The existence of the conference was surprising, but the choice of location was not. UCL was an epicenter of the early 20th-century eugenics movement--a precursor to Nazi "racial hygiene" programs--due to its ties to Francis Galton, the father of eugenics, and his intellectual descendants and fellow eugenicists Karl Pearson and Ronald Fisher. In response to protests over the conference, UCL announced this June that it had stripped Galton's and Pearson's names from its buildings and classrooms. After similar outcries about eugenics, the Committee of Presidents of Statistical Societies renamed its annual Fisher Lecture, and the Society for the Study of Evolution did the same for its Fisher Prize. In science, these are the equivalents of toppling a Confederate statue and hurling it into the sea. Unlike tearing down monuments to white supremacy in the American South, purging statistics of the ghosts of its eugenicist past is not a straightforward proposition. In this version, it's as if Stonewall Jackson developed quantum physics. What we now understand as statistics comes largely from the work of Galton, Pearson, and Fisher, whose names appear in bread-and-butter terms like "Pearson correlation coefficient" and "Fisher information." In particular, the beleaguered concept of "statistical significance," for decades the measure of whether empirical research is publication-worthy, can be traced directly to the trio. Ideally, statisticians would like to divorce these tools from the lives and times of the people who created them. It would be convenient if statistics existed outside of history, but that's not the case.

The Department of Justice recently sued Google for allegedly monopolizing the market for search engines. The Department's complaint alleges that Google took numerous actions well before 2010 that formed part of the claimed antitrust violations. I have no comment about the merits. What I do want to call attention to, however, are the dates: a lawsuit beginning in 2020 to try to correct the market consequences of actions that began more than 10 years ago. The revolution that some scholars call "regulating by robot" is already underway.

It was August last year and I was in the process of giving interviews. By that point in time, I was already interviewing for Google India and Amazon India for Machine Learning and Data Science roles respectively. And then my senior advised me to apply for a role in Facebook London. Contacted a recruiter on LinkedIn, who introduced me to another one and my process started after a few days for the role of Machine Learning Engineer. Now Facebook has a pretty different process when it comes to hiring Machine learning engineers. They do coding rounds, system design, and machine learning design interviews to select future employees.

OneZero is partnering with the Big Technology Podcast from Alex Kantrowitz to bring readers exclusive access to interview transcripts with notable figures in and around the tech industry. This week, Kantrowitz sits down with Meredith Whittaker, an A.I. researcher who helped lead Google's employee walkout in 2018. This interview, which took place at World Summit A.I, has been edited for length and clarity. To subscribe to the podcast and hear the interview for yourself, you can check it out on Apple Podcasts, Spotify, and Overcast. When I interviewed Tristan Harris about The Social Dilemma earlier this month, my mentions filled with people saying, "You should speak to the people who were critical of the social web long before the film." One name stood out: Meredith Whittaker. An A.I. researcher and former Big Tech employee, Whittaker helped lead Google's walkout in 2018 amid a season of activism inside the company. On this edition of the Big Technology Podcast, we spoke not only about her views on the film, but also of the future of workplace activism inside tech companies in a moment where some are questioning if it belongs at all. Alex Kantrowitz: It seems like your perspective on The Social Dilemma is a little bit different from Tristan's.

The continuous growth of scientific literature brings innovations and, at the same time, raises new challenges. One of them is related to the fact that its analysis has become difficult due to the high volume of published papers for which manual effort for annotations and management is required. Novel technological infrastructures are needed to help researchers, research policy makers, and companies to time-efficiently browse, analyse, and forecast scientific research. Knowledge graphs i.e., large networks of entities and relationships, have proved to be effective solution in this space. Scientific knowledge graphs focus on the scholarly domain and typically contain metadata describing research publications such as authors, venues, organizations, research topics, and citations. However, the current generation of knowledge graphs lacks of an explicit representation of the knowledge presented in the research papers. As such, in this paper, we present a new architecture that takes advantage of Natural Language Processing and Machine Learning methods for extracting entities and relationships from research publications and integrates them in a large-scale knowledge graph. Within this research work, we i) tackle the challenge of knowledge extraction by employing several state-of-the-art Natural Language Processing and Text Mining tools, ii) describe an approach for integrating entities and relationships generated by these tools, iii) show the advantage of such an hybrid system over alternative approaches, and vi) as a chosen use case, we generated a scientific knowledge graph including 109,105 triples, extracted from 26,827 abstracts of papers within the Semantic Web domain. As our approach is general and can be applied to any domain, we expect that it can facilitate the management, analysis, dissemination, and processing of scientific knowledge.

Intel's new Tiger Lake processors for ultra-thin laptops are packed with new silicon building blocks for AI, graphics and other technologies to serve a "spectrum of needs" in the mobile computing space, according to top Intel engineer Boyd Phelps. In an interview with CRN, Phelps said the Santa Clara, Calif.-based company has taken a holistic and balanced approach to the engineering and design of the new processors, which are the first CPUs in the 11th-generation Intel Core family. This means that the company has devised ways, for instance, to offload certain AI workloads, like blurring the background in a Zoom video call, to new accelerators within the chip to make the workloads run faster while also saving on power. "For us, we thought about it in the context of how the different workloads have evolved and emerged. They all have kind of a different sweet spot, so for us, we geared Tiger Lake to meet that spectrum of needs," said Phelps, who is vice president of the Client Engineering Group and general manager of Client and Core Development Group at Intel.

This year's Nobel prize in physics has been awarded to Sir Roger Penrose (1/2), Reinhard Genzel (1/4), and Andrea Ghez (1/4) for their research on Blackhole. Even last year it was in astronomy and cosmology. These are exciting times for astronomy since the last one before that was in 2006. There is a common trait in astronomy and AI. The work started sometime in the 20th century and was not proved then due to the limitation of the technology. And now when the technologies are developed, we are able to provide pieces of evidence.