Chinese lithium-ion electric batteries now cost per kWh about a seventh of what they cost a decade ago. DeepSeek is doing in AI exactly what China has done elsewhere. While the impact of this was most visible in electric vehicles (EVs), where China is now the world's biggest exporter, having cornered the supply chains and the science for battery technology, it stretches well beyond. Even in auto the Chinese manufacturers are now pushing the concept of "electric intelligent vehicles", in which conventional carmakers cannot compete, especially on software development. China's consumer electronics companies are shifting into car manufacturing, with "dark factories" operated 24/7 by armies of AI-powered robots, now also increasingly made in China.

DeepSeek exploded into the world's consciousness this past weekend. Given the US government's concerns over TikTok and possible Chinese government involvement in that code, a new AI emerging from China is bound to generate attention. ZDNET's Radhika Rajkumar did a deep dive into those issues in her article Why China's DeepSeek could burst our AI bubble. Instead, I'm putting DeepSeek R1 through the same set of AI coding tests I've thrown at 10 other large language models. The short answer is this: impressive, but not perfect.

TL;DR: The Ninja Dragon Phantom dual-camera smart drone is only 90 while supplies last (55% off) -- less than 50 are left in stock. Forget the stress of work, tax season, and other adult things for a minute. What does your inner child want today? We have a guess since you're here: a drone that can take pictures. Now that you have adult money, your parents can't stop you.

DeepSeek, a Chinese open-source AI system similar to ChatGPT, has risen to popularity at a peculiar time: in the midst of an ongoing legal battle over whether another Chinese tech platform, TikTok, should be allowed to run in the U.S. Some users are curious if the U.S. government would attempt to ban DeepSeek on the same grounds it has used to attempt to ban TikTok. In short, sure, the U.S. could ban DeepSeek if it wanted to. It has the capacity to ban things it doesn't like from countries it doesn't trust in order to protect its citizens' data. In the case of TikTok, lawmakers who voted in support of banning the app cited concerns about data privacy, national security, surveillance, and propaganda, primarily due to the app's Chinese ownership. These lawmakers argue that TikTok is controlled by a "foreign adversary" -- in this case, its Chinese parent company, ByteDance -- and it isn't in the U.S.'s interest to allow foreign adversaries access U.S. citizens' data.

The United States' recent regulatory action against the Chinese-owned social video platform TikTok prompted mass migration to another Chinese app, the social platform "Rednote." Now, a generative artificial intelligence platform from the Chinese developer DeepSeek is exploding in popularity, posing a potential threat to US AI dominance and offering the latest evidence that moratoriums like the TikTok ban will not stop Americans from using Chinese-owned digital services. DeepSeek, an AI research lab created by a prominent Chinese hedge fund, recently gained popularity after releasing its latest open source generative AI model that easily competes with top US platforms like those developed by OpenAI. However, to help avoid US sanctions on hardware and software, DeepSeek created some clever workarounds when building its models. On Monday, DeepSeek's creators limited new sign-ups after claiming the app had been overrun with a "large-scale malicious attack."

Meta is giving its AI assistant a better "memory" in an effort to make the chatbot more useful. The company's latest AI update allows the assistant to "remember certain details that you share with it in 1:1 chat" and uses your past activity on Facebook and Instagram to make more personalized recommendations. With the change, which will initially be available to the US and Canada, Meta AI will be able to track your preferences based on information you share in chat with it. In a blog post, the company uses the example of food allergies and other dietary restrictions so meta AI will "remember" to recommend recipes that fit your requirements. But the assistant will also be able to track other details about you, including information about your personal life and relationships.

The news media shape public opinion, and often, the visual bias they contain is evident for human observers. This bias can be inferred from how different media sources portray different subjects or topics.

We thank all the reviewers for their time and constructive comments. Reviewer 1. a) It is true that knowing the past optimal solution is not realistic, especially given that the considered In practice, the most cost-effective K might be determined through cross-validation. Reviewer 2. We do not report the running time in the current paper because the entire process is reasonably fast (less Reviewer 3. a) This paper does not carry out the analysis of the final approximation guarantee on the PM problem, and We will improve the description to make it clear. Reviewer 4.a) The existing methods (e.g., Tong & Du [1]) require that the diffusion model is known to us, while our Therefore, the method in Tong & Du [1] is not applicable to our setting, and it is not used as a competitor.

When we use the wisdom of the crowds, we usually rank the answers according to their popularity, especially when we cannot verify the answers. However, this can be very dangerous when the majority make systematic mistakes. A fundamental question arises: can we build a hierarchy among the answers without any prior where the higher-ranking answers, which may not be supported by the majority, are from more sophisticated people? To address the question, we propose 1) a novel model to describe people's thinking hierarchy; 2) two algorithms to learn the thinking hierarchy without any prior; 3) a novel open-response based crowdsourcing approach based on the above theoretic framework. In addition to theoretic justifications, we conduct four empirical crowdsourcing studies and show that a) the accuracy of the top-ranking answers learned by our approach is much higher than that of plurality voting (In one question, the plurality answer is supported by 74 respondents but the correct answer is only supported by 3 respondents. Our approach ranks the correct answer the highest without any prior); b) our model has a high goodness-of-fit, especially for the questions where our top-ranking answer is correct. To the best of our knowledge, we are the first to propose a thinking hierarchy model with empirical validations in the general problem-solving scenarios; and the first to propose a practical open-response based crowdsourcing approach that beats plurality voting without any prior.

The authors are working on incorporating the script as part of the HuggingFace datasets library to further streamline the downloading and usage of Multi-LexSum. We include a similar instruction on the project website, https://multilexsum. github.io,