A data-driven look at AI’s transformative impact on the future of science

By: East Care December 24, 2024

The growing environmental impact of AI data centres energy demands PBS News Weekend

“A 17-year-old can learn to drive a car in about 20 hours, even less, sometimes, largely without causing any accident,” LeCun tells Newsweek. “We have millions of hours of training data of people driving cars around, and we still don’t have self-driving cars,” he says. “So that means, in terms of understanding the world, we’re missing something really, really big.” Researchers also observed that workers spend additional time reviewing AI outputs or managing new responsibilities.

MedPerf and Clinical Validation Emphasize Real-World Utility Over Lab Benchmarks

To delve deeper into the intersection of AI technology and scientific research — and its latest advancements — Fudan University and Shanghai Academy of AI for Science, in collaboration with Nature Research Intelligence, have launched AI for Science 2025. This comprehensive report examines how AI is reshaping scientific research paradigms and accelerating breakthroughs in frontier fields. At some point, I think the reality of Palantir’s actual growth will catch up with the sober overzealous sentiment currently surrounding the company. Ultimately, I believe this will result in a sell-off by growth investors seeking more robust prospects. In my eyes, augmenting software with more AI-centric capabilities is part of the first phase of broader investment in the technology. Throughout 2025, technology stocks have been whipsawed by the latest news or rumors surrounding the economy, interest rates, and tariffs.

The share who say people must use AI to keep up is close to the four in 10 who said so about the internet in 1999. That matches the levels seen in our 1999 polling on the internet, and we see similar demographic breaks now as we did then. Americans today are much likelier to say their understanding is somewhat good than very good, suggesting room for it to grow in the coming years.

Automated metadata scanning and stitching can provide that context as a first step in any integration effort. This context is essential to discovering data that might be useful to AI initiatives, and also to making sure it is aligned properly with other data to create a comprehensive business understanding. Nvidia is expected to continue releasing more GPU architectures, while cloud hyperscalers show no signs of slowing down AI capital expenditures (capex). As investors can see from the figures above, Palantir’s customer count is surging thanks to AIP’s popularity.

AI and genAI “are already changing the set of skills employers are demanding from the workforce,” the Fed survey suggested, with the percentage of job postings requiring AI-related skills increasing steadily. “Demand for AI skills is rising not just in computer and mathematical occupations but in a broader set of occupations, which they attribute to the increasing technical capabilities of AI to perform more tasks.” In addition, looking more closely at tech roles, at least 7 in 10 technology leaders surveyed by one major analyst firm, indicate they’re planning to increase head counts — at least within technology areas — to build generative AI capabilities. Kabir Khanna, Ph.D., is Director, Election Analytics & Technical Systems at CBS News. He analyzes elections and public opinion through survey research and data science tools. On election nights, he manages the team of experts responsible for projecting winners at the network Data Desk.

New AI-related tasks have emerged for 8.4% of workers, further diluting the potential productivity boost. Overall, the data undercuts expectations of a rapid AI-driven transformation in labor markets. Sasha Luccioni, an AI and energy researcher and the climate lead at open-source machine-learning platform Hugging Face, cautioned about leaning too hard on some of the conclusions of the new paper, given the amount of unknowns at play. Luccioni, who was not involved in this research, says that when it comes to truly calculating AI’s energy use, disclosure from tech giants is crucial. Tech giants have acknowledged in recent sustainability reports that AI is largely responsible for driving up their energy use. Google’s greenhouse gas emissions, for instance, have increased 48 percent since 2019, complicating the company’s goals of reaching net zero by 2030.

China’s flagship global infrastructure initiative is changing in the face of potent headwinds

“We are pleased by this strategic partnership, which reflects our commitment to shaping a smarter and resilient future for the business sector in the UAE. The technological solutions provided by the technology arm of Dubai Quality Group support effective decision-making through efficient data analysis. Dubai Quality Group continues to empower organisations with innovative technologies that align with the UAE’s vision for artificial intelligence and digital transformation. Through this alliance, we look forward to realising our vision and mission of promoting excellence and innovation in the business sector, thereby enhancing the level of our services.

That data, when paired with artificial intelligence (AI) models, can give businesses new insights into the way they make decisions and where to find opportunities for growth. By grounding the conversation about AI and energy in context about what is needed to tackle climate change, we can deliver better outcomes for communities, ecosystems, and the economy. The growth of electricity demand for AI and data centres is a test case for how society will respond to the demands and challenges of broader electrification. If we get this wrong, the likelihood of meeting our climate targets will be extremely low.

AI’s true labor impact

Looking at AI, he says, has grown more urgent over the past few years because of the widespread adoption of ChatGPT and other large language models that use massive amounts of energy. According to his research, worldwide AI energy demand is now set to surpass demand from bitcoin mining by the end of this year. In many organizations, data exists in a number of locations including mainframe, cloud, and distributed environments. Often, data experts don’t have an understanding of what data lives in which system, and how it’s all related. The report provides a data-driven analysis of the global development of AI and its applications in scientific research. A systematic review of AI-related publications from 2015 to 2024 reveals a sharp increase in the total number of publications in AI for scientific studies, ushering in a boom in the whole field of AI research since 2020.

• What makes the AI security crisis particularly acute is that these tools are designed to ingest, process, and generate content based on vast amounts of information.
• That matches the levels seen in our 1999 polling on the internet, and we see similar demographic breaks now as we did then.
• In Virginia, which has emerged as a hub of data centre activity, that figure is 25%.
• When AI models and tools have access to real-time data, the impact on business performance is substantial.

Human and machine: Rediscovering our humanity in the age of AI

Secure Multi-Party Computation allows these healthcare institutions to collaboratively train predictive models on how patients respond to novel therapies, without ever transferring raw clinical data. The company plans to use the funding, announced Tuesday, to scale up its AI infrastructure platform, enhance research and development for safe multimodal AI and expand enterprise partnerships globally. Founded in 2020, Centific offers an AI data foundry, a platform that provides the tools, infrastructure and human expertise needed to collect, curate and refine high-quality data for training, fine-tuning and safely deploying AI models at large scale. There’s even some evidence that it may help increase, rather than reduce jobs, particularly for technology occupations.

Mubadala, e&, Shorooq back Dubai dining platform Qlub’s $30mln funding

Through this partnership, both organizations aim to deliver cutting-edge AI-powered solutions and establish advanced data warehousing frameworks as a single source of truth for government and private sector entities. The goal is to enhance decision-making processes, streamline operations, and accelerate national digital transformation efforts. Healthcare organizations often wish to contribute to medical research but are bound by data-sharing restrictions. Federated Learning allows local model execution with result sharing, eliminating the need to transfer identifiable patient data. This opens research pathways for community hospitals to refine internal AI tools, be it claims accuracy in revenue cycle management (RCM) or mobility risk scoring in geriatric medicine.

By ensuring data is synchronized across platforms and systems as it changes, organizations can create a consistent, accurate foundation that AI can trust. As AI research output grows rapidly in China, the country’s total AI publications — including those in AI for science — have increased from 60,100 in 2015 to 273,900 in 2024, accounting for 28.7% of the global total. Palantir has emerged as one of the biggest winners in the software space thanks to demand for its artificial intelligence (AI) platforms.