Tesla has introduced a long-awaited update to its Autopilot software in China, adding city navigation features that allow for automatic lane changes and traffic light detection. However, many Chinese Tesla owners were disappointed, expressing that the update did not meet the high expectations set by CEO Elon Musk. The new features, while similar to the company’s Full Self-Driving (FSD) system, are less advanced in China due to insufficient data on local roads and traffic rules.
Tesla faces stiff competition from Chinese automakers like Huawei, Xiaomi, and BYD, which offer advanced driver-assistance systems at lower prices or even for free. These rivals have already launched vehicles capable of navigating complex Chinese traffic, leaving Tesla behind in the race for smart-driving technology. Despite this, Tesla continues to charge its customers nearly $9,000 for the limited version of its FSD software, which many feel does not live up to the promises made by the company.
The delays in rolling out full FSD in China are partly due to regulatory hurdles and restrictions on data transfer between China and the US. Tesla is working on gaining approval from Beijing for its advanced systems, but China currently only requires registration for level-two autonomous features like Autopilot. Tesla is also looking into establishing a data centre in China to train its AI software, though the process has been complicated by strict Chinese data laws.
While Tesla’s Autopilot update is seen as a step forward, it faces growing criticism for not keeping pace with the rapidly evolving smart-driving features offered by local competitors. Tesla’s challenge in China highlights the complex balance the company must maintain between innovation, regulatory compliance, and local competition.
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Chinese AI startup DeepSeek has announced plans to release five new code repositories next week, reinforcing its commitment to open-source artificial intelligence.
The company, which gained global attention with its R1 reasoning model rivaling Western AI systems, described the release as ‘small but sincere progress’ towards full transparency. These repositories, tested in real-world applications, will provide essential infrastructure to support the AI models DeepSeek has already made public.
DeepSeek has set itself apart in China‘s AI sector by embracing open-source practices, a rare move in a market that typically favours closed-source models.
Founder Liang Wenfeng has emphasised the cultural significance of open-source over commercial gains, highlighting the satisfaction that comes from driving innovation and earning industry respect.
His approach has sparked global interest, particularly after the release of the R1 model, known for its strong performance and cost efficiency.
The company has also recently introduced a new algorithm, Native Sparse Attention (NSA), designed to improve efficiency in long-context training and inference.
DeepSeek’s popularity has surged, becoming China’s leading chatbot service with 22.2 million daily active users, surpassing long-established platforms like Douban. The growing user base and commitment to open-source are positioning DeepSeek as a major player in the global AI landscape.
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AI search startup Genspark has secured $100 million in a Series A funding round, raising its valuation to $530 million as it aims to disrupt Google’s dominance in the search engine market.
Backed by United States and Singapore-based investors, the Palo Alto-based firm now boasts over two million monthly active users. The funding follows a $60 million seed round last June, continuing Genspark’s rapid growth.
Led by CEO Eric Jing, formerly head of Baidu’s AI-powered Xiaodu unit, Genspark is positioning itself as a major contender in the AI-driven search space. Its platform uses multiple AI models working together to conduct detailed online research, aiming to provide users with streamlined, citation-backed answers—an approach that contrasts Google’s traditional list of links.
Google is also testing AI-enhanced search results, highlighting the growing pressure to innovate in the industry. Genspark’s latest funding positions it to expand its technology and user base as the battle for AI-powered search heats up.
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Google has quietly disclosed the pricing for its Veo 2 video-generating AI model, setting the cost at 50 cents per second of video. This translates to $30 per minute or $1,800 per hour, positioning Veo 2 as a premium tool for AI-generated video content.
While unlikely to produce big-budget epics, the model can create clips of two minutes or longer, as highlighted in Google’s initial announcement.
Jon Barron, a Google DeepMind researcher, compared Veo 2’s cost to Hollywood productions, noting that Avengers: Endgame had a production cost of around $32,000 per second.
Though Veo 2 operates at a fraction of that price, its output serves different purposes, targeting creators seeking efficient video generation without traditional production expenses.
The AI video generation space has grown increasingly competitive, with OpenAI recently releasing its Sora model to ChatGPT Pro subscribers for $200 a month. Google’s move to price Veo 2 publicly reflects the broader push to commercialise AI video tools as demand surges among content creators and businesses.
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The National Institute of Standards and Technology is set to cut up to 500 staff members, a move that could devastate the US AI Safety Institute and its related programme, Chips for America. Recent reports indicate that these cuts are primarily aimed at probationary employees, with some already receiving verbal notice of termination.
Established under a previous US presidential directive, the AI Safety Institute has faced an uncertain future ever since its inception. The current government’s plans to reduce its workforce are raising concerns among experts, who warn that such reductions will hinder the nation’s capacity to develop critical safety standards in AI development.
Critics from various AI safety and policy organisations have voiced their alarm, emphasising that these cuts occur at a time when specialised expertise is essential. The potential loss of institutional knowledge could leave the government ill-equipped to manage emerging risks in artificial intelligence.
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A new digital wellbeing companion, known as Sonny, is now being introduced in several schools across nine districts. Developed by Sonar Mental Health, this tool combines artificial intelligence with human oversight to provide initial mental health support to students.
Students can send their queries via text, with the AI suggesting responses that are ultimately reviewed by a dedicated team of professionals experienced in psychology, social work, and crisis intervention. This approach comes at a time when many schools are facing a severe shortage of qualified counsellours, with recent data revealing that 17 per cent of high schools lack a dedicated counsellour.
CEO Drew Bavir has emphasised that Sonny is not meant to replace professional therapy. Instead, it acts as a first point of contact, with Sonar staff ready to work alongside schools and parents to secure access to further specialist support when needed. This initiative represents a significant step forward in addressing the growing mental health challenges within schools.
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London-based model Alexsandrah Gondora is now utilising an AI replica of herself for photo shoots, allowing designers and retailers to book her digital double without the need for her physical presence. This innovative approach not only saves time but also cuts down the costs traditionally associated with high-budget campaigns.
While this technology opens up endless creative possibilities, it has also sparked concerns among industry professionals. Critics fear that the widespread use of AI-generated images could eventually displace traditional models, not just in the UK, but globally. Replacing make-up artists, photographers, and even promote a homogenised standard of beauty.
Gondora, however, remains optimistic about the change, emphasising that she retains control over her digital likeness and benefits from the new model. The rise of such digital innovations is prompting calls for new regulations to ensure that models are fairly compensated and their rights protected in this evolving field.
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A groundbreaking study from the University of Copenhagen has demonstrated that AI can decode emotions in animals. By training a machine-learning model to analyse the vocal patterns of seven ungulate species, including cows, pigs, and wild boars, the research achieved an impressive accuracy rate of 89.49%. This study, the first of its kind to cross species, marks a significant step forward in understanding animal emotions.
The AI model identified key acoustic indicators of emotional states, such as duration, frequency, and amplitude of vocalisations, revealing that emotional expressions are evolutionarily conserved across species. This discovery could revolutionise animal welfare, enabling real-time monitoring of animals’ emotional well-being, particularly in livestock management, veterinary care, and conservation efforts.
The implications for animal welfare are profound. Early detection of stress or discomfort could lead to timely interventions, improving animals’ lives. Additionally, promoting positive emotions could enhance overall welfare. The researchers have made their emotional call database publicly available to support further studies and encourage more AI-driven research in animal welfare and conservation.
This study not only sheds new light on animal emotions but also offers insights into the evolutionary roots of human language, opening up exciting possibilities for future scientific exploration and better understanding of animal behaviour.
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Researchers from the University of Tokyo and Waseda University have developed the world’s largest biohybrid robot hand, which utilises cultivated human muscle tissue to perform complex movements. The 18-centimetre hand, complete with five individually moving fingers, incorporates a novel “sushi roll” design that bundles multiple thin muscle tissue strings to ensure sufficient nutrient supply and strength. Gold electrodes are employed to stimulate the tissues, translating straight muscle contractions into the rotational motion of the joints.
This innovative Japanese device is capable of delicate tasks such as grasping small objects, although its muscle tissues exhibit a decrease in force after approximately ten minutes of continuous action, recovering fully after an hour of rest. Such a breakthrough overcomes the limitations of earlier biohybrid devices, which were restricted to single-joint movements and were much smaller due to nutrient diffusion challenges in thicker muscle constructs.
Professor Shoji Takeuchi, specialising in mechanical engineering, believes that this research paves the way for the development of humanlike robots and precision prosthetic hands. The team’s findings, published in Science Robotics, mark a significant advance in the fusion of biological elements with mechanical systems, promising a future where robotics more closely mimic the capabilities of the human body.
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Qatar has signed a five-year agreement with Scale AI to integrate AI into government services. The partnership aims to enhance efficiency through predictive analytics, automation, and advanced data analysis, according to the country’s communications and information technology ministry.
Scale AI will explore over 50 AI applications tailored for Qatar’s public sector during the contract period. Trevor Thompson, the company’s global head of growth, described the initiative as a model for other governments worldwide and an opportunity to accelerate impactful AI adoption. The financial details of the agreement remain undisclosed.
Qatar is intensifying efforts to position itself as a leader in AI, competing with regional players such as Saudi Arabia and the UAE.
The San Francisco-based Scale AI, founded in 2016, specialises in providing high-quality labelled data for AI training and serves major clients, including Microsoft, Morgan Stanley, and OpenAI.
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