MPs have called for a ban on two Chinese CCTV systems that are used by councils, schools, and police forces across the UK. A group of 67 MPs and Lords including Lib Dem leader Sir Ed Davey and four ex-Conservative ministers is urging the government to ban the sale and use of Hikvision and Dahua cameras. The calls come amid concerns the CCTV cameras can recognise faces, eavesdrop on conversations, and judge people's moods. The Uyghurs, a predominantly Muslim ethnic group, are the majority population in Xinjiang. More than a million Uyghurs and other minorities are estimated to have been detained at camps in Xinjiang, where allegations of torture, forced labour and sexual abuse have emerged.

Since humans invented art, sometime in the Paleolithic era, they've produced lots of pictures--"The Starry Night," some memes, that photo of Donald Trump staring at the eclipse. What does it all add up to? A few years ago, a company called OpenAI fed a good deal of those images, along with text descriptions, into the neural network of an artificial intelligence named DALL-E. DALL-E was being trained to create original art of its own, in any style, depicting in uncanny detail almost anything desired, based on written prompts. But a mastery of the entire universe of human imagery makes for difficult choices.

Any good thing on earth can be used and misused. It seems that AI is no exception in this regard. Things become more complicated if such acts of misuse have a futuristic orientation. China, which has already shown a clear strategy of calibrating the great AI power to its advantage has become a classic case here. The government of China is getting ready to impose an AI prosecution system for prosecuting people for crimes they are yet to commit.

Fifth Generation (5G) networks must support billions of heterogeneous devices while guaranteeing optimal Quality of Service (QoS). Such requirements are impossible to meet with human effort alone, and Machine Learning (ML) represents a core asset in 5G. ML, however, is known to be vulnerable to adversarial examples; moreover, as our paper will show, the 5G context is exposed to a yet another type of adversarial ML attacks that cannot be formalized with existing threat models. Proactive assessment of such risks is also challenging due to the lack of ML-powered 5G equipment available for adversarial ML research. To tackle these problems, we propose a novel adversarial ML threat model that is particularly suited to 5G scenarios, and is agnostic to the precise function solved by ML. In contrast to existing ML threat models, our attacks do not require any compromise of the target 5G system while still being viable due to the QoS guarantees and the open nature of 5G networks. Furthermore, we propose an original framework for realistic ML security assessments based on public data. We proactively evaluate our threat model on 6 applications of ML envisioned in 5G. Our attacks affect both the training and the inference stages, can degrade the performance of state-of-the-art ML systems, and have a lower entry barrier than previous attacks.

In this week's geeked-out edition of the Good News Roundup, Ukraine's jaw-dropping battlefield victories with HIMARS are documented using OSINT, South Africa implements AI technology to track dangerous locust swarms, biologists and naturalists overwhelmingly agree that gay sex is normal throughout the animal kingdom, and BirdNet proves reliable at crowdsourcing the task of identifying wild birds by their songs. In wholesome news for sci fi/space fantasy fans everywhere, Ukraine's president Zelensky continues attending technology trade shows through holograms in which he promises that Ukraine will defeat the Empire. Ukrainians are also using 3d imaging technology to preserve the cultural heritage of their country from looters and bombs, storing their data in a digital archive that will support restoration work when the invaders have been defeated. And in good news for new Ukrainian parents, the non-profit Embrace Global is making headlines for using innovative technology to provide incubators for babies in Ukraine at a tiny fraction of their usual cost. You can see their TED talk by entrepreneur Jane Chen here.

You are free to share this article under the Attribution 4.0 International license. Will artificial intelligence be a help or a hindrance in the response to climate change? In the journal Nature Climate Change, a team of experts in AI, climate change, and public policy present a framework for understanding the complex and multifaceted relationship of AI with greenhouse gas emissions and suggest ways to better align AI with climate change goals. "AI affects the climate in many ways, both positive and negative, and most of these effects are poorly quantified," says coauthor David Rolnick, assistant professor of computer science at McGill University and a core academic member of Mila – Quebec AI Institute. "For example, AI is being used to track and reduce deforestation, but AI-based advertising systems are likely making climate change worse by increasing the amount that people buy." "Climate change should be a key consideration when developing and assessing AI technologies," says Lynn Kaack, assistant professor of computer science and public policy at the Hertie School, and lead author of the report.

AI's little guys are getting into the Washington influence game. Tech giants and defense contractors have long dominated AI lobbying, seeking both money and favorable rules. And while the largest companies still dominate the debate, pending legislation in Congress aimed at getting ahead of China on innovation, along with proposed bills on data privacy, have caused a spike in lobbying by smaller AI players. A number of companies focused on robotics, drones and self-driving cars are all setting up their own Washington influence machines, positioning them to shape the future of AI policy to their liking. A lot of it is spurred by one major piece of legislation: The Bipartisan Innovation Act, commonly referred to as USICA -- an acronym for its previous title, and its goal to out-innovate China.

The tech media outlet Toolbox featured the views of 10 experts on "How will AI evolve in the next year?" Edge technology that experts should pay attention to next year was also intensively discussed. The first place was occupied by MIT's Neil Thompson research team featuring an article on the cost of energy to train deep learning systems. As a result of analyzing the improvements of the image classifier, the research team found that "to cut the error rate in half, it can be expected that 500 times more computational resources are required." "The rising cost requires researchers to devise more efficient ways to solve these problems, otherwise we will give up research on these problems, and progress will be difficult," he said.

Russian President Vladimir Putin has waged "economic terrorism" in Ukraine by stocking its farmlands and Black Sea ports full of explosive mines, an official working to de-mine Kyiv told Fox News. "There's 20 plus years of mining work already in Ukraine and for every day of war there's an additional 30 days of mining work that will be required," Cameron Chill, CEO of drone company Draganfly Inc. (DPRO), explained to Fox News upon his return from Kyiv. Chill has been working with Ukraine's emergency management services to deploy drones to efficiently detect buried mines left behind by Russian forces after they withdrew from areas surrounding Kyiv in late March. Draganfly officials help Ukrainians detect landmines after Russian forces withdrew from Kyiv. However, the drone expert said where Russian troops left the explosive devices says a lot about the war Putin is raging against its former Soviet neighbor.

Abstract: We present a model of sense-making that greatly facilitates the collaboration between an intelligent analyst and a knowledge-based agent. It is a general model grounded in the science of evidence and the scientific method of hypothesis generation and testing, where sense-making hypotheses that explain an observation are generated, relevant evidence is then discovered, and the hypotheses are tested based on the discovered evidence. We illustrate how the model enables an analyst to directly instruct the agent to understand situations involving the possible production of weapons (e.g., chemical warfare agents) and how the agent becomes increasingly more competent in understanding other situations from that domain (e.g., possible production of centrifuge-enriched uranium or of stealth fighter aircraft) Abstract: There is a growing desire to create computer systems that can communicate effectively to collaborate with humans on complex, open-ended activities. Assessing these systems presents significant challenges. We describe a framework for evaluating systems engaged in open-ended complex scenarios where evaluators do not have the luxury of comparing performance to a single right answer.