Latest news with #2025AIIndex
Al Etihad
01-05-2025
- Business
- Al Etihad
UAE company to build data centre in Ankara
1 May 2025 21:26 ABU DHABI (ALETIHAD)Khazna Data Centers, UAE-based digital infrastructure company, has announced its expansion into Türkiye with plans to build a new AI-capable data centre in Başkent, OIZ, Ankara. The facility will have a potential capacity of up to 100 megawatts (MW), reinforcing Türkiye's ambitions to become a regional hub for artificial move follows a surge in AI investment in Türkiye, underpinned by a 198% rise in AI talent concentration between 2016 and 2024, as recorded in Stanford University's 2025 AI Index. It also comes on the heels of over $50 billion worth of agreements signed between Türkiye and the UAE in 2023, highlighting deepening bilateral Ankara facility will support a range of workloads, from AI to cloud computing, and will be delivered in modular phases, allowing for future scalability. Phase one will focus on cloud infrastructure, with the flexibility to adapt to emerging technology demands.'We're proud to be supporting the efforts being made in Türkiye to create an advanced economy with AI at its heart,' said Khazna CEO Hassan Alnaqbi. 'We believe this data center will add to the country's impressive economic growth.'Saeed Thani Hareb Al Dhaheri, UAE Ambassador to Türkiye, added that the expansion 'is a testament to the deepening ties between our countries' and reflects the UAE's broader strategy to strengthen international partnerships. Khazna is expected to appoint a general contractor by Q2 2025 and continue investing in Türkiye thereafter.
Mint
26-04-2025
- Business
- Mint
Companies are struggling to drive a return on AI. It doesn't have to be that way.
AI adoption among companies is stunningly high, but most of them are struggling to put it to good use. They intuit that AI is essential to their future. Yet intuition alone won't unlock the promise of AI, and it isn't clear to them which key will do the trick. As of last year, 78% of companies said they used artificial intelligence in at least one function, up from 55% in 2023, according to global management consulting firm McKinsey's State of AI survey, released in March. From these efforts, companies claimed to typically find cost savings of less than 10% and revenue increases of less than 5%. While the measurable financial return is limited, business is nonetheless all-in on AI, according to the 2025 AI Index report released in April by the Stanford Institute for Human-Centered Artificial Intelligence. Last year, private generative AI investment alone hit $33.9 billion globally, up 18.7% from 2023. The numbers reflect a 'productivity paradox," in which massive improvements in AI capabilities haven't led to a corresponding surge in national-level productivity, according to Stanford University economist and professor Erik Brynjolfsson, who worked on the AI Index. While some specific projects have been enormously productive, 'many companies are disappointed with their AI projects." For companies to get the most out of their AI efforts, Brynjolfsson advocates for a task-based analysis, in which a company is broken down into fine-grained tasks or 'atomic units of work" that are evaluated for potential AI assistance. As AI is applied, the results are measured against key performance indicators, or KPIs. He co-founded a startup, Workhelix, that applies those principles. Companies should take care to target an outcome first, and then find the model that helps them achieve it, says Scott Hallworth, chief data and analytics officer and head of digital solutions at HP. A separate report from McKinsey issued in January helps explain why AI adoption is racing ahead of associated productivity gains, according to Lareina Yee, senior partner and director at the McKinsey Global Institute. Only 1% of U.S. companies that have invested in AI report that they have scaled their investment, while 43% report that they are still in the pilot stage. 'One cannot expect significant productivity gains at the pilot level or even at the company unit level. Significant productivity improvements require achieving scale," she said. The critical question then, is how companies can best scale their AI efforts. Ryan Teeples, chief technology officer of 1-800Accountant, agrees that 'breaking work into AI-enabled tasks and aligning them to KPIs not only drives measurable ROI, it also creates a better customer experience by surfacing critical information faster than a human ever could." The privately held company based in New York provides tax, booking and payroll services to 50,000 active clients, with a focus on small businesses. The company isn't a Workhelix customer. Additionally, he says, companies should look beyond individualized AI usage, in which employees use GenAI chatbots or AI-equipped productivity tools to enhance their work. 'True enterprise adoption…involves orchestration and scaling across the organization. Very few organizations have truly reached this level, and even those are only scratching the surface," he said. The use of AI at 1-800Accountant begins with an assessment of whether the technology improves the client experience. If the AI provides customers with answers that are as good, better or faster than a human, it's a good use case, according to Teeples. In the past, the company scheduled hourlong appointments with advisers who answered simple client questions, such as the status of their tax return. Now, the company uses an AI agent connected to curated data sources to address 65% of customer inquiries, with 30% arranging a call with a human. (The remaining 5% drop out of the inquiry process for various reasons.) The company uses Salesforce's Agentforce to handle customer inquiries and its Einstein platform for orchestration across 1-800Accountant's back end. Teeples said the company is saving money on the cost of human advisers. 'The ROI in this case was abundantly clear," he said. Orchestrating AI across the enterprise requires the right infrastructure, especially when it comes to data, according to Gabrielle Tao, senior vice president for data cloud at Salesforce. It is important, she said, to harmonize data, for example, by creating a consistent way to refer to business concepts such as 'orders" and 'transactions," regardless of the underlying data source. AI deployments should target tasks that are both frequent and generalizable, according to Walter Sun, global head of artificial intelligence at SAP. Infrequent, highly specific tasks such as a marketing campaign for a single event might benefit from AI, but applying AI to regularly occurring tasks will achieve a more consistent ROI, he said. Historically, it has taken years for the world to figure out what to do with revolutionary general-purpose technologies including the steam engine and electricity, according to Brynjolfsson. It isn't unusual for general-purpose models to follow a 'J-curve," in which there's a dip in initial productivity, as businesses figure things out, followed by a ramp-up in productivity. He says companies are beginning to turn the corner of the AI J-curve. The transformation may occur faster than in the past, because businesses—under no small amount of pressure from investors—are working to quickly justify the massive amount of capital pouring into AI. Write to Steven Rosenbush at
Mint
21-04-2025
- Business
- Mint
DeepSeek's AI surprised the world. China's universities are the talent source.
In the global contest for artificial intelligence supremacy, the U.S. and China are often portrayed as the primary contenders. While the U.S. maintains a lead in AI innovation, China is rapidly closing the gap, driven by a surge in research output, substantial investments, and strategic governmental policies. 'It is not surprising that China now competes closely with the U.S. for leadership in AI talent. It trains by far a larger number of engineers and computer scientists than the U.S., although the U.S. still has an edge in attracting the best global talent, including from China," said Victor Shih, director of the 21st Century China Center at the University of California, San Diego. 'For now, the capital market for start-ups in the U.S. still functions much better than in China, but if the current turmoil turns into a prolonged recession, that advantage will be eroded," he told Barron's. China leads globally in the number of AI-related publications and patents, according to Stanford University's 2025 AI Index. This great leap is bolstered by significant government support and a strategic vision aiming for tech self-sufficiency. A pivotal moment in China's AI trajectory was the emergence of DeepSeek's R1 model, which rivals top U.S. models despite limited access to advanced computing resources—particularly semiconductor chips—due to U.S. export restrictions. DeepSeek's success intensified domestic competition—tech giants in China and start-ups tied to China's elite universities are vying for dominance in the sector. The landscape is characterized by fierce competition among established tech conglomerates—often referred to as the 'BAT" trio: Baidu, Alibaba Group Holding, and Tencent Holding—and a new wave of dynamic start-ups. Companies like Zhipu AI, MiniMax, and Moonshot AI have rapidly gained prominence, earning the nickname 'AI Tigers" from investors. These start-ups aren't only innovating at breakneck speed but are also attracting substantial investments, signaling a robust and competitive ecosystem. Tencent, for instance, has upgraded its Hunyuan T1 model to compete with DeepSeek and Alibaba. Baidu has launched new models, ERNIE 4.5 and ERNIE X1, which it plans to integrate into China's most popular search engine. This internal competition is further amplified by the open-source approach adopted by many Chinese firms. While open-sourcing AI models fosters collaboration and accelerates innovation, it also raises questions about revenue generation and potential exploitation by international competitors. Chinese universities have become pivotal to the nation's AI advancement, significantly contributing to research output and talent cultivation. Recent data place Peking University, Tsinghua University, and Zhejiang University at the top of the charts in AI research publications. Notably, Peking University has topped global lists of institutions ranked by AI research output since 2022, according to AIRankings. Peking University's AI institute said it referred requests for comment to professors, though none replied. Tsinghua University and Zhejiang University didn't respond to requests for comment. The success of AI start-ups like DeepSeek can be attributed, in part, to the robust talent pool emerging from these universities. DeepSeek's founder, Liang Wenfeng, is a graduate of Zhejiang University, and the company's team comprises young scientists, many of whom are fresh graduates from institutions such as Tsinghua and Peking University. This collaboration between academia and industry facilitates a seamless transition from cutting-edge research to real-world AI applications. 'When I was at Beida [Peking University], AI students were leaving or finishing their coursework to start companies—you'd hear about something new like every week," said James Liu, who is now pursuing a doctorate at MIT after receiving his bachelor's degree in China. Despite these advancements, China's AI sector faces significant challenges. U.S. export restrictions have limited China's ability to procure high-end AI chips, compelling domestic companies to seek alternatives and innovate with available resources. This constraint has spurred efforts to develop indigenous chip-making capabilities, but achieving parity with global leaders remains formidable. Moreover, the intense domestic competition necessitates strategic collaborations and a focus on niche areas to differentiate offerings. Companies are increasingly forming partnerships, both domestically and internationally, to leverage complementary strengths and navigate the complex AI landscape. Zhu Songchun—arguably the most renowned figure in the Chinese AI world—returned to China from the University of California, Los Angeles, in 2020 to head Peking University's Institute for Artificial Intelligence and its School of Intelligence Science and Technology. At a recent conference in Beijing, his keynote address summarized the zeitgeist. 'Creating world-class technology through Chinese thinking is our goal and our responsibility," he said. 'China is fully capable of taking the initiative in the era of general AI." Write to editors@
Leaders
12-04-2025
- Business
- Leaders
Prince Sultan University Leads AI Innovation, Women Empowerment
Prince Sultan University (PSU) solidifies Saudi Arabia's AI leadership, aligning with Vision 2030 to drive innovation and global competitiveness. The university's initiatives now bolster the Kingdom's rise in Stanford's 2025 AI Index, showcasing academic and research excellence. Empowering Women in AI The university champions women in tech by hosting the International Women in Data Science (WiDS) Conference, a flagship regional event. It also supports UNESCO's International Day of Women and Girls in Science, amplifying female voices in AI and sustainability. Undergraduate and Ph.D. programs in AI equip students with cutting-edge skills, while bootcamps and workshops address Saudi Arabia's demand for skilled professionals. Practical training bridges academia and industry needs seamlessly. PSU draws world-class researchers from Oxford, Cambridge, and Stanford, enriching its academic ecosystem. Partnerships with MIT, Berkeley, and Imperial College London further cement its global research footprint. Pioneering AI Research The university operates advanced AI labs, recognized for high-impact research output. Strategic MoUs with healthcare, energy, and transportation sectors accelerate innovation, aligning with national development goals. By merging academia, industry, and global networks, Prince Sultan University positions Saudi Arabia as a hub for AI-driven progress. Its efforts underscore the Kingdom's commitment to sustainable growth and technological leadership. Short link : Post Views: 1
