The consumer Internet began life in 1969 with four nodes, three in California and one in Utah. We know how the Internet changed how we shop, watch movies and travel. Our moonshot mission, which Dr. Anthony Chang has called a pediatric Internet made history in 2021 as we ended the year with four edge zones, two in California, one in Pennsylvania and one in Italy. We are only beginning to understand how it will transform children's healthcare, globally. Thank you for believing in the vision and helping turn it into a reality. One of the powers of the edge cloud is using digital twin applications to standardize data coming from the machines.

In this contributed article, Ronen Korman, Founder and Co-CEO at Metrolink.ai, explores the problem of dark data through the prism of known and unknown unknowns, as used in intelligence analysis. Dark data is a known variable with an unknown value, as the exact benefits of its exploration are often unclear. True business value may in fact lie in the unknown unknowns, the data that the company has access to, but is not collecting at all due to a lack of a broader strategic outlook.

Graph Neural Networks (GNNs) have shown their great ability in modeling graph structured data. However, real-world graphs usually contain structure noises and have limited labeled nodes. The performance of GNNs would drop significantly when trained on such graphs, which hinders the adoption of GNNs on many applications. Thus, it is important to develop noise-resistant GNNs with limited labeled nodes. However, the work on this is rather limited. Therefore, we study a novel problem of developing robust GNNs on noisy graphs with limited labeled nodes. Our analysis shows that both the noisy edges and limited labeled nodes could harm the message-passing mechanism of GNNs. To mitigate these issues, we propose a novel framework which adopts the noisy edges as supervision to learn a denoised and dense graph, which can down-weight or eliminate noisy edges and facilitate message passing of GNNs to alleviate the issue of limited labeled nodes. The generated edges are further used to regularize the predictions of unlabeled nodes with label smoothness to better train GNNs. Experimental results on real-world datasets demonstrate the robustness of the proposed framework on noisy graphs with limited labeled nodes.

We consider a matrix completion problem that exploits social or item similarity graphs as side information. We develop a universal, parameter-free, and computationally efficient algorithm that starts with hierarchical graph clustering and then iteratively refines estimates both on graph clustering and matrix ratings. Under a hierarchical stochastic block model that well respects practically-relevant social graphs and a low-rank rating matrix model (to be detailed), we demonstrate that our algorithm achieves the information-theoretic limit on the number of observed matrix entries (i.e., optimal sample complexity) that is derived by maximum likelihood estimation together with a lower-bound impossibility result. One consequence of this result is that exploiting the hierarchical structure of social graphs yields a substantial gain in sample complexity relative to the one that simply identifies different groups without resorting to the relational structure across them. We conduct extensive experiments both on synthetic and real-world datasets to corroborate our theoretical results as well as to demonstrate significant performance improvements over other matrix completion algorithms that leverage graph side information.

Artificial intelligence for IT operations, or AIOps, has increased in demand over the last few years as enterprise IT teams battle complex data coming into their systems. Not only does AIOps automate mundane tasks, but it processes vast amounts of data. Networks, systems, and applications pump out tremendous amounts of data that has to be analyzed for patterns and correlation. AI and machine learning are perfect for rapidly looking for trends, patterns, and anomalies in massive amounts of data streams. Humans are overwhelmingly visual beings, and we are not designed to wade through massive amounts of textual data, hence the need for AIOps.

A new California law designed to prevent the warehouse industry from overworking employees doesn't name a specific company. But the legislation's target is clear: Amazon, which has given machines unparalleled control over workers and is accused of using the technology to impose unreasonable demands on them. Authored by Assemblywoman Lorena Gonzalez, the bill prohibits the use of monitoring systems that thwart basic worker rights such as rest periods, bathroom breaks and safety. The legislation will help determine whether governments can regulate human resources software that's expected to play an increasing role in deciding who gets hired and fired, how much workers are paid and how hard they work. "This is just the beginning of our work to regulate Amazon & its algorithms that put profits over workers' safety," Gonzalez, a San Diego Democrat, tweeted earlier this year.

Recent conversations around gender and AI have centred around the need to understand gender beyond the binary of male and female. For example, facial recognition technology used by Uber in the US has problems with correctly recognising transgender persons (see here and here). Yet Uber is no exception. The U.S. National Science Foundation, for example, has highlighted research that shows that "facial analysis services performed consistently worse on transgender individuals, and were universally unable to classify non-binary genders." According to CNN Business,¹ "The way a computer sees gender isn't always the same way people see it. A growing number of terms for describing one's gender are becoming common in everyday life."

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'Gutfeld!' panel discusses the year in review as 2021 comes to a close. This is a rush transcript from "Gutfeld!," December 30, 2021. This copy may not be in its final form and may be updated. EMILY COMPAGNO, FOX NEWS CHANNEL HOST: I know what you're thinking. Greg's never looked at this good in a dress. Like a tiny Ghost of Christmas Present, because I'm celebrating the holiday today. Because this year COVID robbed me of Christmas with my family. COVID robbed us of our studio audience. And it robbed me of my Christmas Eve Feast of the Seven Fishes. So to make up for it, we are having a feast tonight. COMPAGNO: In New Year's Eve news, Omicron fear mongers are warning people to stay away from New York's Times Square celebration. Even though previous crowds were exposed to something much worse. Thank God it'll be me hosting in Time Square this year. See you at 10:00 p.m. Eastern on Fox News. Germany's also banned large group gatherings. But you know who's never bans large gatherings of Germans? China's Wuhan Institute of virology recently hosted a conference on lab safety, to which the world responded a little (BLEEP) late, guys. In a recent segment on COVID Safety, CNN's Dr. Leana Wen admitted cloth masks don't stop transmission of the virus. Today in New York Mayor Bill de Blasio said he doesn't believe in shutdowns despite having shut down the city for months. He then added "I also oppose letting criminals roam free to murder people." Chris Tucker turned down a $10 million payday for a sequel to the awesome movie Friday, saying he's too mature to be seen behaving badly on screen anymore.

'The Five' panel react to an Amazon Alexa instructing a child to stick a penny in an electrical outlet. This is a rush transcript from "The Five," December 30, 2021. This copy may not be in its final form and may be updated. It's five o'clock in New York City, and this is The Five. The White House desperately trying to clean up President Biden's COVID debacle. As America hits a record number of new cases, the commander-in- chief failing to live up to his promise to shut down the virus as people wait hours in lines while states struggle with testing shortages. The president still has not signed the contract to send Americans millions of at-home tests, and he is bragging about a new test making facility that won't even be ready until -- listen to this -- 2024. And so much for following the science, the CDC is now cutting isolation period for people with COVID in half. But not to make us safer. ANTHONY FAUCI, DIRECTOR, NATIONAL INSTITUTE OF ALLERGY AND INFECTIOUS DISEASES: The reason is that now that we have such an overwhelming volume of cases coming in, many of which are without symptoms, there is the danger that this is going to have a really negative impact on our ability to really get society to function properly. So, the CDC made a decision to balance what is good for public health at the same time as keeping the society running. ROCHELLE WALENSKY, DIRECTOR, CENTERS FOR DISEASE CONTROL: It really had a lot to do with what we thought people would be able to tolerate. We really want to make sure that we have guidance in this moment where we were going to have a lot of disease that could be adhered to, that people were willing to adhere to and that spoke specifically to when people were maximally infectious. So, it really spoke to both behaviors as well as what people were able to do. MCENANY (on camera): President Biden is also being accused of sabotaging a key, life-saving treatment. Florida surgeon general claims the Biden administration has been, quote, "actively preventing monoclonal antibody treatments as states are running out of that therapeutic." Republicans are not happy about it. DAN CRENSHAW (R-TX): In Texas, we are one of those states that doesn't have these monoclonal antibodies anymore. We have been complaining to the administration about how these formulas are distributed.