Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Tencent has unveiled “Hunyuan3D 2.0,” an AI system that turns single images or text descriptions into detailed 3D models within seconds. The system makes a typically lengthy process — one that can take skilled artists days or weeks — into a rapid, automated task. Following its predecessor, this new version of the model is available as an open-source project on both Hugging Face and GitHub, making the technology immediately accessible to developers and researchers worldwide. “Creating high-quality 3D assets is a time-intensive process for artists, making automatic generation a long-term goal for researchers,” the company’s research team writes in a technical report. The upgraded system builds upon its predecessor’s foundation while introducing significant improvements in speed and quality. How Hunyuan3D 2.0 turns images into 3D models Hunyuan3D 2.0 uses two main components: Hunyuan3D-DiT creates the basic shape, while Hunyuan3D-Paint adds surface details. The system first makes multiple 2D views of an object, then builds these into a complete 3D model. A new guidance system ensures all views of the object match — solving a common problem in AI-generated 3D models. “We position cameras at specific heights to capture the maximum visible area of each object,” the researchers explain. This approach, combined with their method of mixing different viewpoints, helps the system capture details that other models often miss, especially on the tops and bottoms of objects. A diagram showing how Hunyuan3D 2.0 transforms a single panda image into a 3D model through multi-view diffusion and sparse-view reconstruction techniques. (credit: arxiv.org) Faster and more accurate: What sets Hunyuan3D 2.0 apart The technical results are impressive. Hunyuan3D 2.0 produces more accurate and visually appealing models than existing systems, according to standard industry measurements. The standard version creates a complete 3D model in about 25 seconds, while a smaller, faster version works in just 10 seconds. What sets Hunyuan3D 2.0 apart is its ability to handle both text and image inputs, making it more versatile than previous solutions. The system also introduces innovative features like “adaptive classifier-free guidance” and “hybrid inputs” that help ensure consistency and detail in generated 3D models. According to their published benchmarks, Hunyuan3D 2.0 achieves a CLIP score of 0.809, surpassing both open-source and proprietary alternatives. The technology introduces significant improvements in texture synthesis and geometric accuracy, outperforming existing solutions across all standard industry metrics. The system’s key technical advance is its ability to create high-resolution models without requiring massive computing power. The team developed a new way to increase detail while keeping processing demands manageable — a frequent limitation of other 3D AI systems. These advances matter for many industries. Game developers can quickly create test versions of characters and environments. Online stores could show products in 3D. Movie studios could preview special effects more efficiently. Tencent has shared nearly all parts of their system through Hugging Face. Developers can now use the code to create 3D models that work with standard design software, making it practical for immediate use in professional settings. While this technology marks a significant step forward in automated 3D creation, it raises questions about how artists will work in the future. Tencent sees Hunyuan3D 2.0 not as a replacement for human artists, but as a tool that handles technical tasks while creators focus on artistic decisions. As 3D content becomes increasingly central to gaming, shopping, and entertainment, tools like Hunyuan3D 2.0 suggest a future where creating virtual worlds is as simple as describing them. The challenge ahead may not be generating 3D models, but deciding what to do with them. source

By Jamie Lennox ( January 28, 2025, 6:05 PM GMT) — The antitrust watchdog should consider sanctioning Microsoft over the harmful effect of its software licensing practices on the cloud computing market, an independent inquiry group said Tuesday…. Law360 is on it, so you are, too. A Law360 subscription puts you at the center of fast-moving legal issues, trends and developments so you can act with speed and confidence. Over 200 articles are published daily across more than 60 topics, industries, practice areas and jurisdictions. A Law360 subscription includes features such as Daily newsletters Expert analysis Mobile app Advanced search Judge information Real-time alerts 450K+ searchable archived articles And more! Experience Law360 today with a free 7-day trial. source

ChatGPT Fast Facts What is ChatGPT? A generative AI that can answer questions in natural-sounding language.Who developed ChatGPT? OpenAI, Inc.Pricing: ChatGPT is free. ChatGPT Plus costs $20/month. ChatGPT Teams, which is the business subscription for at least two people, costs $25/month. ChatGPT Pro, with unlimited access to OpenAI’s most powerful “reasoning” model, costs $200/month. The business world has embraced ChatGPT over the last year and found uses for the writing and image generation AI throughout many industries. This cheat sheet includes answers to the most common questions about ChatGPT and its competitors. What is ChatGPT? ChatGPT is an AI chatbot product developed by OpenAI and built on the structure of GPT-4. GPT stands for generative pre-trained transformer; this indicates it is a large language model that checks for the probability of what words might come next in sequence. A large language model is a deep learning algorithm — a type of transformer model in which a neural network learns context about any language pattern. That might be a spoken language or a computer programming language. OpenAI continues to update ChatGPT with faster, more capable AI models. In September, the company showed its next-generation model o1, which is optimized for complex reasoning in math and science. While o1 actually takes more time to process information than GPT-4o does, that slow and steady approach can produce more complicated code or mathematical processes. OpenAI o1 is available to ChatGPT Plus and Team users now, with Enterprise and Edu users set to gain access on the week of Dec 9. The model doesn’t “know” what it’s saying, but it does know what symbols (words) are likely to come after one another based on the data set it was trained on. The current generation of artificial intelligence chatbots, such as ChatGPT, its Google rival Bard, and others, don’t really make intelligently informed decisions. Instead, they’re the internet’s parrots, repeating words that are likely to be found next to one another in the course of natural speech. The underlying math is all about probability. The companies that make and use them pitch them as productivity genies, creating text in a matter of seconds that would take a person hours or days to produce. In ChatGPT’s case, that data set is a large portion of the internet. From there, humans give feedback on the AI’s output to confirm whether the words it uses sound natural. The public version of ChatGPT can call on current events information as recent as January 2022. ChatGPT Plus can call on current events information as recent as April 2023. In August 2023, OpenAI launched GPTBot, a web crawler meant to expand ChatGPT’s knowledge. The company provided technical details about GPTBot and ways to keep it from crawling a website. SEE: OpenAI’s probability assessments were trained on Microsoft’s Azure AI supercomputer. (TechRepublic) Several organizations have built chatbots into some of their software features. Microsoft, which provides funding for OpenAI, rolled out ChatGPT in Bing search and in Microsoft 365. Salesforce has added ChatGPT to some of its CRM platforms in the form of the Einstein digital assistant. Who made ChatGPT? ChatGPT was built by OpenAI, a research laboratory with both nonprofit and for-profit branches. At the time of its founding in 2015, OpenAI received funding from Amazon Web Services, InfoSys, and YC Research, and investors including Elon Musk and Peter Thiel. Musk has since cut ties with the company, while Microsoft provided $10 billion in funding for OpenAI in 2023. How much does ChatGPT cost? The base version of ChatGPT can strike up a conversation with you for free. For $20 per month, ChatGPT Plus gives subscribers priority access in individual instances, faster response times, and the chance to use new features and improvements first. For example, right now ChatGPT Plus subscribers will be running GPT-4, while anyone on the free tier will talk to GPT-3.5. For developers and organizations who don’t already have a specific contract with OpenAI, there is a waitlist for access to the ChatGPT API. In August 2023, OpenAI launched ChatGPT Enterprise, a subscription plan for business with more security enhancements and admin controls compared to the basic version. Organizations interested in pricing for ChatGPT Enterprise can contact OpenAI’s sales team. As of January 2024, 260 enterprise customers had signed up for ChatGPT Enterprise, according to Bloomberg. In January 2024, OpenAI opened ChatGPT Team, a subscription that allows access to OpenAI’s larger models and a collaborative workspace. It costs $25/month per user when billed per year or $30/month per user billed monthly. December 2024 brought a $200/per month Pro subscription. ChatGPT Pro includes access to heavy-duty models for research and business applications. For the money, users can talk via ChatGPT with OpenAI o1, o1-mini, or GPT-4o, and use the relatively versatile Advanced Voice mode. The subscription also includes access to o1 pro mode, a slower-thinking version of the advanced model best for complicated math, science, and coding problems. How to use ChatGPT It’s easy to use ChatGPT. Just follow these steps: Visit https://chat.openai.com/. Sign up for an account with OpenAI, which involves fetching a confirmation code from your email, or use ChatGPT without logging in. Type in the prompt box, where it says “Message ChatGPT,” to get started. For the Plus version, you’ll see an Upgrade To Plus button on the left side of the home page if you log in. OpenAI may use conversations with ChatGPT held without an account for AI training. There is a way to opt out of your conversations being used as training data if you are logged in: go to Settings and uncheck “Improve the model for everyone.” For businesses, ChatGPT can write and debug code, as well as create reports, presentations, emails, and websites. In general, ChatGPT can draft the kind of prose you’d likely use for work (“Write an email accepting an invitation to speak at a cybersecurity conference.”). ChatGPT can answer questions (“What are similar books to [xyz]?”) as well. Microsoft showed off these features in its announcement that

Artificial Intelligence has become a defining force of the 21st century, sparking debates about its role in shaping the future. While sometimes portrayed as a harbinger of dystopian automation, AI, when leveraged appropriately, can be a catalyst for profound, positive change.   AI’s ability to deliver a positive impact is not just a concept shared at tech shows or espoused by non-governmental organizations. The technology is already actively reshaping industries and addressing some of the world’s most pressing challenges.  As the global water crisis threatens nearly two billion people with absolute scarcity by 2025, AI is proving to be a key player in smart water management. By deploying advanced data-driven solutions, AI is optimizing how we manage water resources, identifying innovative approaches to desalination, reducing environmental impacts by minimizing overflows, and ensuring that water utilities achieve maximum returns on infrastructure investments by optimizing maintenance and operations for improved longevity.  In the telecommunications industry, AI is boosting network efficiency and informing how operators can expand access to underserved populations. For instance, one developing country leveraged AI to bring mobile network coverage to 95% of its population while saving $200 million in CapEx compared to a non-AI network planning approach.   Related:How AI Can Help (Or Deceive) Gamblers This latter example shows how AI can be a vital contributor to bridging the digital divide. The scenario above, achieved on a national scale, expanded broadband to rural areas much like the United States is looking to improve broadband penetration through the BEAD program. This altruistic yet practical example demonstrates the power of AI to fuel economic development and enhance access to vital services like education and healthcare. And it’s not just theoretical; the results are already being felt.  This is the impact of AI at its best — transforming technological innovation into tangible societal progress.  Amid the rapid pace of AI innovation, many companies, governments, and researchers have focused on technical possibilities rather than the positive realities of deploying AI at scale.  AI holds immense potential to drive social equity and inclusion. Consider the water management scenario above. In regions facing severe water scarcity, AI has optimized resource management and reduced pollution, potentially saving millions of lives and improving the quality of life for vulnerable communities.  In the broadband example, AI has helped bring education, telehealth, and employment services to underserved populations, acting as a great equalizer for many communities.   Related:China’s DeepSeek Dethrones ChatGPT as US Tech Stocks Plunge Yet AI’s ability to benefit society is dependent upon the humans using it. AI, on its own, is neither unethical nor capitalistic. The key to tapping AI’s power to generate positive impact lies in practitioners focusing on society’s biggest challenges, identifying how AI can play a role in solving them, and implementing a robust governance framework to carefully monitor the project and ensure it stays on an ethical and “greater good” track.  Having worked in AI and data science for a decade, we often encounter projects that we choose not to pursue. The power inherent in AI solutions compels us to look beyond the question of “Can we do this?” to a discussion about whether we should. AI can be deployed in many areas, and with great effect, so we prioritize projects that have a clear opportunity to benefit society.  The path forward demands a concerted effort from companies, particularly those with the resources and influence, to lead by example. It also requires AI partners who share the vision of using AI for initiatives that deliver real, positive impact.  In the end, the true measure of AI for social good won’t be in what AI can do, but in how it helps build a future where technology enhances and equalizes the human experience. The choices we make today — whether in deploying AI for water conservation or expanding digital access — will define AI’s trajectory in shaping and achieving that future.  Related:How Must Staffing Change in Relation to AI? source

The U.K.’s Competition and Markets Authority has published the provisional findings of an investigation into all cloud service providers in the country, following concerns raised by telecoms regulator Ofcom. The current conclusion is that the cloud services market is “not working as well as it could,” as it is difficult for customers to switch cloud providers or use multiple clouds. A lack of competition is likely leading to higher costs, less choice, less innovation, and lower quality of services. “The ability of UK businesses to put healthy pressure on cloud providers to offer better deals is key to ensuring good outcomes and to unlocking the potential benefits of cloud services,” the CMA said in a press release. 4 main concerns about the U.K. cloud services market In its investigation, the CMA’s independent inquiry group found four primary concerns: Cloud customers face a limited choice of providers and are not aware that multiple providers can offer the same services. Technical and commercial barriers make it difficult to switch cloud providers or use multiple clouds, leading to vendor lock-in. It is difficult for new providers to enter the market and compete due to the very large capital investment necessary to supply cloud services. Microsoft is making it harder for rivals, such as Google and Amazon Web Services, to compete for customers who want to use Microsoft software on the cloud. Microsoft and AWS each have a share of up to 40% of U.K. customer spend on cloud services, dominating the market considerably; the third largest provider, Google, has a much smaller share. Even if the dominant players are overcharging by just 5%, this could be costing U.K. businesses £430 million a year, according to the CMA. The investigation came on the heels of a 2023 report released by Ofcom in which the regulator identified a range of issues plaguing the cloud services market that presents implications for businesses and consumers. The CMA looked into egress fees, technical barriers, and committed spend discounts upon Ofcom’s recommendation, but provisionally found that only the former two harm competition. A decision about whether to make these provisional findings and recommendations final will be made by Aug. 4, 2025. AWS and Microsoft could be subject to the new Digital Markets, Competition and Consumers Act The inquiry group behind the investigation has recommended that the CMA considers giving AWS and Microsoft “Strategic Market Status” under the new Digital Markets, Competition and Consumers Act, which came into force on Jan. 1, 2025. The act was specifically designed to regulate the behaviour of major digital firms with significant market power in the U.K. The CMA conducts investigations into companies it expects to have Strategic Market Status. If companies are given that designation, regulators will draft bespoke conduct requirements for them to follow, preventing anti-competitive practices. The inquiry group said that regulating AWS and Microsoft under the DMCCA will allow it to take a “targeted and flexible approach to remedies” and “better provisions for ongoing monitoring and oversight.” Conduct requirements for the dominant cloud services providers might encourage technical standardisation, reduce data transfer charges incurred in switching providers or using multiple clouds, and ensure the fair licensing of software. Investigations into whether Google and Apple should receive SMS designations are ongoing. Both companies are both being looked at with regards to their mobile ecosystems. Google is also being investigated in search and search advertising services. SEE: UK Regulator Probes Apple’s Mobile Browser Dominance Cloud: Must-read coverage Industry reaction to the CMA’s investigation Reacting to the CMA’s move to investigate the U.K. cloud services market, Daniel Tremayne-Pitter, chief executive officer of Dark Matter, a U.K. technology research company, emphasized the need to democratize the cloud computing market. He said: “Even without the identification of anti-competitive practices, I believe there is a significant need to democratize the cloud computing landscape. Namely because of resiliency. Academics, sustainability experts, cloud architects and now, even regulators across the globe, are acknowledging that the power and intelligence a small number of providers hold is concerning.” Microsoft and Amazon respond to Ofcom’s survey In response to Ofcom’s cloud market survey, Amazon and Microsoft published lengthy responses. Here are brief excerpts from those responses. Amazon offered a counter-perspective. “We do not agree with the concerns raised in the Interim Report that ‘committed spend discounts’ can dampen competition by incentivizing customers to use a single provider for most or all of their cloud needs, or that we require customers to increase the amount of their committed spend upon renegotiation of their agreements,” the company said. “AWS prices are listed publicly on our website, and any customer can use our services at these listed prices as much or as little as they need.” Microsoft’s response read: “Azure does not exploit ‘locked in’ customers on price while it competes for new ones, not least because this dichotomy is false. Nor is there a realistic possibility that Microsoft or any other cloud vendor can profitably slow their rapid pace of innovation as a result of IT lock-in effects.” But Tremayne-Pitter had a different opinion, arguing that “Nearly every technologist describes ‘lock-in’ as being a very real risk factor.” He commented: “The exit cost to move data out of the cloud is usually disproportionate to the ‘free’ nature of putting it there in the first place. At serious volume, it could make it cost-prohibitive to even consider moving it. Cloud providers’ proprietary tooling is readily consumed by ambitious and innovative organizations; however, if your application or business-critical workloads are delivered through this proprietary tooling – you’re not moving anywhere unless you can spare the time and expense to re-develop your application in another environment.” CMA’s investigation may have profound implications The U.K.’s cloud service market has experienced tremendous growth in the last few years and is projected to reach $82.87 billion by 2029. However, with the CMA poised to dig deep into the activities going on in the U.K. public cloud market, the outcome could come with significant implications for various stakeholders, including

The practice of medicine has undergone an incredible, albeit incomplete, transformation over the past 50 years, moving steadily from a field informed primarily by expert opinion and the anecdotal experience of individual clinicians toward a formal scientific discipline. The advent of evidence-based medicine meant clinicians identified the most effective treatment options for their patients based on quality evaluations of the latest research. Now, precision medicine is enabling providers to use a patient’s individual genetic, environmental and clinical information to further personalize their care. The potential benefits of precision medicine also come with new challenges. Importantly, the amount and complexity of data available for each patient is rapidly increasing. How will clinicians figure out which data is useful for a particular patient? What is the most effective way to interpret the data in order to select the best treatment? These are precisely the challenges that computer scientists like me are working to address. Collaborating with experts in genetics, medicine and environmental science, my colleagues and I develop computer-based systems, often using artificial intelligence, to help clinicians integrate a wide range of complex patient data to make the best care decisions. The rise of evidence-based medicine As recently as the 1970s, clinical decisions were primarily based on expert opinion, anecdotal experience and theories of disease mechanisms that were frequently unsupported by empirical research. Around that time, a few pioneering researchers argued that clinical decision-making should be grounded in the best available evidence. By the 1990s, the term evidence-based medicine was introduced to describe the discipline of integrating research with clinical expertise when making decisions about patient care. The bedrock of evidence-based medicine is a hierarchy of evidence quality that determines what kinds of information clinicians should rely on most heavily to make treatment decisions. Certain types of evidence are stronger than others. While filtered information has been evaluated for rigor and quality, unfiltered information has not. CFCF/Wikimedia Commons, CC BY-SA Randomized controlled trials randomly place participants in different groups that receive either an experimental treatment or a placebo. These studies, also called clinical trials, are considered the best individual sources of evidence because they allow researchers to compare treatment effectiveness with minimal bias by ensuring the groups are similar. Observational studies, such as cohort and case-control studies, focus on the health outcomes of a group of participants without any intervention from the researchers. While used in evidence-based medicine, these studies are considered weaker than clinical trials because they don’t control for potential confounding factors and biases. Overall, systematic reviews that synthesize the findings of multiple research studies offer the highest quality evidence. In contrast, single-case reports detailing one individual’s experience are weak evidence because they may not apply to a wider population. Similarly, personal testimonials and expert opinions alone are not supported by empirical data. In practice, clinicians can use the framework of evidence-based medicine to formulate a specific clinical question about their patient that can be clearly answered by reviewing the best available research. For example, a clinician might ask whether statins would be more effective than diet and exercise to lower LDL cholesterol for a 50 year-old male with no other risk factors. Integrating evidence, patient preferences and their own expertise, they can develop diagnoses and treatment plans. As may be expected, gathering and putting all the evidence together can be a laborious process. Consequently, clinicians and patients commonly rely on clinical guidelines developed by third parties such as the American Medical Association, the National Institutes of Health and the World Health Organization. These guidelines provide recommendations and standards of care based on systematic and thorough assessment of available research. Dawn of precision medicine Around the same time that evidence-based medicine was gaining traction, two other transformative developments in science and health care were underway. These advances would lead to the emergence of precision medicine, which uses patient-specific information to tailor health care decisions to each person. The first was the Human Genome Project, which officially began in 1990 and was completed in 2003. It sought to create a reference map of human DNA, or the genetic information cells use to function and survive. This map of the human genome enabled scientists to discover genes linked to thousands of rare diseases, understand why people respond differently to the same drug, and identify mutations in tumors that can be targeted with specific treatments. Increasingly, clinicians are analyzing a patient’s DNA to identify genetic variations that inform their care. Output from the DNA sequencer used by the Human Genome Project. National Human Genome Research Institute/Flickr The second was the development of electronic medical records to store patient medical history. Although researchers had been conducting pilot studies of digital records for several years, the development of industry standards for electronic medical records began only in the late 1980s. Adoption did not become widespread until after the 2009 American Recovery and Reinvestment Act. Electronic medical records enable scientists to conduct large-scale studies of the associations between genetic variants and observable traits that inform precision medicine. By storing data in an organized digital format, researchers can also use these patient records to train AI models for use in medical practice. More data, more AI, more precision Superficially, the idea of using patient health information to personalize care is not new. For example, the ongoing Framingham Heart Study, which began in 1948, yielded a mathematical model to estimate a patient’s coronary artery disease risk based on their individual health information, rather than the average population risk. One fundamental difference between efforts to personalize medicine now and prior to the Human Genome Project and electronic medical records, however, is that the mental capacity required to analyze the scale and complexity of individual patient data available today far exceeds that of the human brain. Each person has hundreds of genetic variants, hundreds to thousands of environmental exposures and a clinical history that may include numerous physiological measurements, lab values and imaging results. In my team’s ongoing work, the AI models we’re developing to detect sepsis in infants

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More AI is already reshaping industries and society on a global scale. IDC predicts that AI will contribute $19.9 trillion to the global economy by 2030, comprising 3.5% of GDP. This momentum is exemplified by the recent announcement of “Project Stargate,” a partnership to invest up to $100 billion in new AI-focused data center capacity. This is all indicative of the tremendous activity going on with AI development. On a single day, AI made headlines for discovering proteins to counteract cobra venom, creating a Star Trek-style universal translator and paving the way for true AI assistants.  These and other developments highlight individual achievements, as well as their interconnected progress. This flywheel of innovation is where breakthroughs in one domain amplify advancements in others, compounding AI’s transformative potential. Separating signal from noise Even for someone who follows AI developments closely, the rapid technological breakthroughs and diffusion across industries and applications is dizzying, making it highly challenging to not only know and understand what is going on, but understand the relative importance of developments. It is challenging to separate the signal from noise.  In the past, I might have turned to an AI industry analyst to help explain the dynamics and meaning of recent and projected developments. This time, I decided instead to see if AI itself might be able to help me. This led me to a conversation with OpenAI’s o1 model. The 4o model might have worked as effectively, but I expected that a reasoning model such as o1 might be more effective.  I asked o1 what it thought were the top AI trends and why. I started by asking for the top 10 to 15, but over the course of our collaborative dialog, this expanded to 25. Yes, there really are that many, which is a testament to AI’s value as a general-purpose technology.  In dialog about leading AI trends with OpenAI’s o1 model. After about 30 seconds of inference-time “thinking,” o1 responded with a list of trends in AI development and use, ranked according to their potential significance and impact on business and society. I asked several qualifying questions and made a few suggestions that led to slight changes in the evaluation method and rankings.  Methodology Rankings of the various AI trends are determined by a blended heuristic that balances multiple factors including both quantitative indicators (near-term commercial viability) and qualitative judgments (disruptive potential and near-term societal impact) further described as follows:  Current commercial viability: The trend’s market presence and adoption. Long term disruptive potential: How a trend could significantly reshape industries and create new markets. Societal impact: Weighing the immediate and near-term effects on society, including accessibility, ethics and daily life. In addition to the overall AI trend rankings, each trend receives a long-term social transformation score (STS), ranging from incremental improvements (6) to civilization-altering breakthroughs (10). The STS reflects the trend’s maximum potential impact if fully realized, offering an absolute measure of transformational significance. Levels of social transformation associated with top AI trends. The development of this ranking process reflects the potential of human-AI collaboration. o1 provided a foundation for identifying and ranking trends, while my human oversight helped ensure that the insights were contextualized and relevant. The result shows how humans and AI can work together to navigate complexity. Top AI trends in 2025 For tech leaders, developers and enthusiasts alike, these trends signal both immense opportunity and significant challenges in navigating the many changes brought by AI. Highly-ranked trends typically have broad current adoption, high commercial viability or significant near-term disruptive effects. Table of top 10 trends for 2025 ranked on current commercial viability, long-term disruptive potential and potential for social impact. Specific use cases — like self-driving cars or personal assistant robots — are not considered individual trends but are instead subsumed within the broader foundational trends. Honorable mention list: AI trends 11 – 25 One can quibble whether number 11 or any of the following should be in the top 10, but keep in mind that these are relative rankings and include a certain amount of subjectivity (whether from o1 or from me), based on our iterative conversation. I suppose this is not too different from the conversations that take place within any research organization when completing their reports ranking the comparative merits of trends. In general, this next set of trends has significant potential but are either: 1) not yet as widespread and/or 2) have a potential payoff that is still several or more years away. While these trends did not make the top 10, they showcase the expanding influence of AI across healthcare, sustainability and other critical domains.  Table of top 11 to 25 trends for 2025 ranked on current commercial viability, long-term disruptive potential and potential for societal impact. Digital humans show the innovation flywheel in action One use case that highlights the convergence of these trends is digital humans, which exemplify how foundational and emerging AI technologies come together to drive transformative innovation. These AI-powered avatars create lifelike, engaging interactions and span roles such as digital coworkers, tutors, personal assistants, entertainers and companions. Their development shows how interconnected AI trends create transformative innovations.  The flywheel of AI innovation: Interconnected advancements in AI technologies drive transformative progress, where breakthroughs in one domain amplify developments in others, creating a self-reinforcing cycle of innovation leading to new uses. For example, these lifelike avatars are developed using the capabilities of generative AI (trend 1) for natural conversation, explainable AI (2) to build trust through transparency and agentic AI (3) for autonomous decision-making. With synthetic data generation, digital humans are trained on diverse, privacy-preserving datasets, ensuring they adapt to cultural and contextual nuances. Meanwhile, edge AI (5) enables near real-time responsiveness and multi-modal AI (17) enhances interactions by integrating text, audio and visual elements.  By using the technologies described by these trends, digital humans exemplify how advancements in one domain can accelerate progress in

In a move to establish itself as a global leader in AI-driven government, the government of Abu Dhabi has unveiled its ambitious Abu Dhabi Government Digital Strategy 2025-2027. This transformative plan, developed by the Department of Government Enablement (DGE) in collaboration with various governmental bodies, will see an investment of AED 13 billion over the next three years. The strategy’s goal is to create a fully AI-powered governance model, one that integrates the latest technologies across every facet of government operations, from cloud computing to automation, enhancing public service delivery and driving sustainable growth. The strategy’s cornerstone is the creation of a robust digital infrastructure that will enable 100% adoption of sovereign cloud computing for government operations, ensuring that all processes are not only digitalized but also fully automated. By transitioning to a completely cloud-based system, the government aims to enhance efficiency, reduce administrative overheads, and streamline public services for residents, businesses, and government entities alike. A key component of the strategy is the unified digital enterprise resource planning (ERP) platform, which will integrate various government functions into a single digital framework, improving productivity and simplifying management processes. This digital backbone will be crucial for the effective implementation of more than 200 innovative AI solutions across government services, further cementing Abu Dhabi’s role as a global hub for AI-driven innovation and digital governance. source

❣中國年臨門﹐在眾多的祝福裏﹐錦鉉滿懷尊敬的心與您分享對老茶的感悟：人生恰似葉經煎﹐歲月沉澱韻味綿｡半百春秋成老茶﹐千番歷練化清泉｡新春啟處茶香繞﹐舊歲辭時福運連｡一盞甘醇心沁潤﹐來年順遂夢皆圓｡===============越陳越香的老茶智慧學茶人 洪錦鉉 可能是五十春秋的閱歷，亦可能正處百年未有之大變局，香港迎來由治及興的新征程，我日漸喜歡上「品茶悟人生，靜心觀世界」。在茶香裊裊的時光裏，我漸漸領悟到，一片茶葉的歷程，恰似人的一生。從初製時的青澀，到歷經歲月陳化後的醇厚，宛如人生從年少輕狂逐漸走向成熟穩重，完成了一場深刻的蛻變。 新茶的香氣外揚，像極了少年的張揚，活力四溢；而老茶的香氣內斂，恰似智者的沉穩，韵味悠長。茶的湯，新茶明亮清澈，老茶則深沉厚重，色澤的變化見證了時間的魔力。茶性也在歲月中發生轉變，從新茶的性寒到老茶的性溫，恰似人生從鋒芒畢露到圓融通達。清代陸廷燦在《續茶經》中提到：「茶之爲物，擅甌閩之秀氣，鐘山川之靈禀。」老茶的溫潤，正是吸納天地精華後的升華，體現著生命的智慧。 宋代蔡襄在《茶錄》中講：「茶之爲物，擇地而處，擇人而交。」明確道出了環境對茶葉品質的重要影響。茶葉的儲存環境，對其品質起著决定性作用。適宜的溫度、濕度，以及與空氣的微妙接觸，是茶葉良好轉化的關鍵。這就如同人的成長，需要適度的磨礪和合適的環境，才能成就堅韌的品格。 時間的醞釀，是老茶品質的保證。茶葉在歲月中慢慢轉化，急不得，快不來。每一碗茶都有它最佳的品飲時機，就像人生的每一個階段都有其獨特的意義。同一批茶，儲存條件稍有差異，數年之後便可能天差地別。這如同人生的際遇，充滿變數，却也因此而精彩。 品飲老茶，是一場與時間的對話。當那醇厚的茶湯入口，仿佛能感受到歲月的流轉，生命的律動。皎然和尚曾說：「一飲滌昏寐，情來朗爽滿天地。」品茶，不僅是味覺的享受，更是心靈的覺醒，讓我們在喧囂的世界中尋得內心的寧靜。唐代陸羽提倡「精行儉德」的茶道精神，明代田藝蘅在《煮泉小品》中也說：「茶之爲飲，最宜精行修德之人。」這是對茶與人品格的深刻理解，也是對生命態度的詮釋。 日益歡喜老茶的智慧：懂得等待，懂得沉澱，懂得在歲月中修煉自己。讓我們如同老茶一般，在時間的長河中慢慢轉化，最終成就獨特的韵味與品格。這，或許就是茶道帶給我們最寶貴的人生啓示。 LinkedIn Email Facebook Twitter WhatsApp source

Microsoft is basically the biggest name in the business world, whether we’re talking about Microsoft Office apps or Windows running on our computers. One thing they have nailed down is recognizing that their products are well-loved for their simple and familiar interface, but still offering regular updates. Windows 11 Pro was specifically designed for business professionals. You’ll find new tools for productivity and balancing hybrid or remote work with life. With this deal, you can upgrade three devices to Windows 11 Pro — rated 4.5/5 stars by verified purchasers — for just $17.97 at TechRepublic Academy through Feb. 2. New look, new features The first thing you’ll notice is a redesigned user interface. Rounded app corners, a centered bottom taskbar, snap layouts and widgets all give your computer a refreshed, yet familiar, appearance while offering the latest tools. Then, there’s layers of security features like Microsoft Information Protection that protects your personal data from leaks and BitLocker device encryption that encrypts your hard drive with a key. Both of these are excellent for shielding your personal and work information from harm. Designed for the workforce If you’re a remote or hybrid worker, or a business owner or manager with employees around the globe, you’ll appreciate things like: Windows Information Protection allows you to separate work and personal data on the same device. Remote desktop access is included from anywhere. Connect to your Windows 11 Pro computer from another computer, a tablet or a smartphone. Group Policy Management tools allow enforcement of policies and compliance. Administrators can create settings or access for different devices, users and groups. Upgrade your operating system to Windows 11 Pro on three devices for only $17.97 (reg. $199), now at TechRepublic Academy, so be sure to take advantage of it before this offer ends on Feb. 2. Prices and availability are subject to change. source