Over the past six months, the Chinese government has rolled out a series of policy documents and public pronouncements that are finally putting meat on the bone of the country's governance regime for artificial intelligence (AI). Given China's track record of leveraging AI for mass surveillance, it's tempting to view these initiatives as little more than a fig leaf to cover widespread abuses of human rights. Anyone who wants to compete against, cooperate with, or simply understand China's AI ecosystem must examine these moves closely.

Tesla's "full self-driving" (FSD) feature has had something of a rocky history, to put things generously. It's been implicated in multiple crashes, seemingly persistent software bugs and a cavalcade of scrutiny from a panoply of regulatory bodies. Also, it's about to cost more money. The bump represents an additional $2,000 being tacked onto the not insubstantial price tag: a new grand total of $12,000, or most of the way to a Honda Civil. Nor is it the first time FSD has gotten more expensive.

Artificial intelligence should be carefully regulated as of now since the concept is still broad. What should be heavily regulated is its applications including autonomous driving, cybersecurity and the military. It's way too early to regulate a fundamental technology such as artificial intelligence. If you ask any expert as of today what should be regulated in AI, the answer would have to be, inevitably, "we don't know". While the rapid progress of the technology should be seen with a positive lens, it is important to exercise some caution and introduce laws that will help the progress of AI technology.

Hilsa is the national fish of Bangladesh. Bangladesh is earning a lot of foreign currency by exporting this fish. Unfortunately, in recent days, some unscrupulous businessmen are selling fake Hilsa fishes to gain profit. The Sardines and Sardinella are the most sold in the market as Hilsa. The government agency of Bangladesh, namely Bangladesh Food Safety Authority said that these fake Hilsa fish contain high levels of cadmium and lead which are detrimental for humans. In this research, we have proposed a method that can readily identify original Hilsa fish and fake Hilsa fish. Based on the research available on online literature, we are the first to do research on identifying original Hilsa fish. We have collected more than 16,000 images of original and counterfeit Hilsa fish. To classify these images, we have used several deep learning-based models. Then, the performance has been compared between them. Among those models, DenseNet201 achieved the highest accuracy of 97.02%.

Policymakers face a broader challenge of how to view AI capabilities today and where does society stand in terms of those capabilities. This paper surveys AI capabilities and tackles this very issue, exploring it in context of political security in digitally networked societies. We extend the ideas of Information Management to better understand contemporary AI systems as part of a larger and more complex information system. Comprehensively reviewing AI capabilities and contemporary man-machine interactions, we undertake conceptual development to suggest that better information management could allow states to more optimally offset the risks of AI enabled influence and better utilise the emerging capabilities which these systems have to offer to policymakers and political institutions across the world. Hopefully this long essay will actuate further debates and discussions over these ideas, and prove to be a useful contribution towards governing the future of AI.

Dramatic success in machine learning has created an explosion of new AI capabilities. Continued advances promise to produce autonomous systems that perceive, learn, decide, and act on their own. These systems offer tremendous benefits, but their effectiveness will be limited by the machine's inability to explain its decisions and actions to human users. This issue is especially important for the United States Department of Defense (DoD), which faces challenges that require the development of more intelligent, autonomous, and reliable systems. XAI will be essential for users to understand, appropriately trust, and effectively manage this emerging generation of artificially intelligent partners.

"There are still many senators that don't even use email--if you don't even use email how are you going to understand some of these other tools? We need folks that are running for office that understand these things." That was the call from Will Hurd, former congressperson and author of the upcoming American Reboot: An Idealist's Guide to Getting Big Things Done, during WIRED's virtual CES HQ on Wednesday. Hurd advocated for cybersecurity, privacy, and responsible AI issues during his congressional tenure from 2015 to 2021. And in conversation with WIRED this week, he emphasized the need for the US government and state and local legislatures across the country to sharpen their understanding of the role of technology as misinformation, data abuse, and emerging technologies like AI become increasingly influential in domestic affairs and geopolitics.

Over the past six months, the Chinese government has rolled out a series of policy documents and public pronouncements that are finally putting meat on the bone of the country's governance regime for artificial intelligence (AI). Given China's track record of leveraging AI for mass surveillance, it's tempting to view these initiatives as little more than a fig leaf to cover widespread abuses of human rights. Anyone who wants to compete against, cooperate with, or simply understand China's AI ecosystem must examine these moves closely. These recent initiatives show the emergence of three different approaches to AI governance, each championed by a different branch of the Chinese bureaucracy, and each at a different level of maturity. Their backers also pack very different bureaucratic punches.

Unlike many valuable resources, real-time data is both abundant and growing rapidly. But it also needs to be handled with great care. That was one of the key takeaways from an online workshop produced by Science Business' Data Rules group, which explored what the rapid growth in real-time data means for artificial intelligence (AI). Real-time data is increasingly feeding machine learning systems that then adjust the algorithms they use to make decisions, such as which news item to display on your screen or which product to recommend. "With AI, especially, you want to make sure that the data that you have is consistent, replicable and also valid," noted Chris Atherton, senior research engagement officer at GÉANT, who described how his organisation transmits data captured by the European Space Agency's satellites to researchers across the world.

As CES wraps up, we're still pulling together our favorite picks of the show. That includes finger-nibbling robots, smart beds and all kinds of TVs, laptops and gadgets. Yes, we've been able to see some of the products while not attending the show, but it has meant a lot of spec-sheet perusing and a fair dose of skepticism without getting a lot of the announcements in the flesh. For things like TVs, that's usually months later, but for tablets, phones and wearables, you can expect Engadget to be reviewing and stress-testing many of them sooner rather than later. Have a great weekend and see you back here Monday.