The companies’ efforts follow the announcement of OpenAI’s latest LLMs, including the o1 generative pre-trained transformer model backed by Microsoft. The model is intended to tackle difficult tasks, paving the way for advances in science, coding, and mathematics.

During the conference, Kunal Zhilin, founder of Moonshot AI, underlined the importance of the o1 model, adding that it has the potential to reshape various industries and create new opportunities for AI startups.

Zhilin stated that reinforcement learning and scalability might be pivotal for AI development. He spoke of the scaling law, which states that larger models with more training data perform better.

“This approach pushes the ceiling of AI capabilities,” Zhilin said, adding that OpenAI o1 has the potential to disrupt sectors and generate new opportunities for startups.

OpenAI has also stressed the model’s ability to solve complex problems, which it says operate in a manner similar to human thinking. By refining its strategies and learning from mistakes, the model improves its problem-solving capabilities.

Zhilin said companies with enough computing power will be able to innovate not only in algorithms, but also in foundational AI models. He sees this as pivotal, as AI engineers rely increasingly on reinforcement learning to generate new data after exhausting available organic data sources.

StepFun CEO Jiang Daxin concurred with Zhilin but stated that computational power remains a big challenge for many start-ups, particularly due to US trade restrictions that hinder Chinese enterprises’ access to advanced semiconductors.

“The computational requirements are still substantial,” Daxin stated.

An insider at Baichuan AI has said that only a small group of Chinese AI start-ups — including Moonshot AI, Baichuan AI, Zhipu AI, and MiniMax — are in a position to make large-scale investments in reinforcement learning. These companies — collectively referred to as the “AI tigers” — are involved heavily in LLM development, pushing the next generation of AI.

More from the Apsara Conference

Also at the conference, Alibaba Cloud made several announcements, including the release of its Qwen 2.5 model family, which features advances in coding and mathematics. The models range from 0.5 billion to 72 billion parameters and support approximately 29 languages, including Chinese, English, French, and Spanish.

Specialised models such as Qwen2.5-Coder and Qwen2.5-Math have already gained some traction, with over 40 million downloads on platforms Hugging Face and ModelScope.

Alibaba Cloud added to its product portfolio, delivering a text-to-video model in its picture generator, Tongyi Wanxiang. The model can create videos in realistic and animated styles, with possible uses in advertising and filmmaking.

Alibaba Cloud unveiled Qwen 2-VL, the latest version of its vision language model. It handles videos longer than 20 minutes, supports video-based question-answering, and is optimised for mobile devices and robotics.

For more information on the conference, click here.

(Photo by: @Guy_AI_Wise via X)

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

The post OpenAI’s latest LLM opens doors for China’s AI startups appeared first on AI News.

The problem:

In 2023, Neon Internet CEO and co-founder Misch Strotz was struck by a clever idea: give Luxembourg residents the power to easily generate local images using AI. Within a month, Luxembourg-focused LetzAI V1 went live.

Encouraged by strong local demand, Strotz and his team began working on a global version of the platform. The vision? An opt-in AI platform empowering brands, creators, artists, and individuals to unlock endless creative possibilities by adding their own images, art styles, and products. “Other AI platforms scrape the internet, incorporating people and their content without permission. We wanted to put the choice and power in each person’s hands,” Strotz explains.

Before long, the team began working on V2. In addition to generating higher quality and more personalised AI-generated images, V2 would drive consistency across objects, characters, and styles. After uploading their own photos and creating their own models, users can blend them with other models created by the community to create an endless number of unique images.

However, LetzAI faced a significant hurdle in training and launching V2 – a global GPU shortage. With limited resources to train its models, LetzAI needed a reliable partner to help evolve its AI-driven platform and keep it operating smoothly.

The solution:

In the search for a fitting partner, Strotz spoke to major vendors including hyperscalers and various Europe-based providers. Meeting Gcore’s product leadership team made the decision clear. “It was amazing to meet executives who were so knowledgeable about technology and took us seriously,” recalls Strotz.

Gcore’s approach to data security and sovereignty further solidified the decision. “We needed a trusted partner who shared our commitment to European data protection principles, which we incorporated into the development of our platform” he continues.

The result:

LetzAI opted for Gcore’s state-of-the-art NVIDIA H100 GPUs in Luxembourg. “This was the perfect option, allowing us to keep our model training and development local. With Gcore, we can rent GPUs rather than entire servers, making it a far more cost-effective solution by avoiding unnecessary costs like excess storage and idle server capacity,” Strotz explains. This approach provided flexibility, efficiency, and high performance, tailored specifically for AI workloads.

LetzAI was able to adapt its app to run in containers, configure model training tasks to run on GPU Cloud, and use Everywhere Inference for image generation and upscaling. “Everywhere inference reduces the latency of our output and enhances the performance of AI-enabled apps, allowing us to optimise our workflows for more accurate, real-time results,” Strotz says.

In just two months, LetzAI V2 launched to serve users around the world. And Strotz and team were already developing its successor.

Empowering creativity with scalable, high-performance AI infrastructure

With Gcore’s continued support, LetzAI quickly deployed V3. “The Gcore team was incredibly responsive to our needs, guiding us to the best solution for our evolving

requirements. This has given us a powerful and efficient infrastructure that can flex according to demand,” says Strotz.

Running V3 on Gcore means LetzAI users experience fast, reliable performance. Artists, individuals, and brands are already putting V3 to use in interesting ways. For example, in response to what LetzAI calls its ‘AI Challenges’, a Luxembourg restaurant chain prompted residents to create thousands of images using its model of a pizza.

In another example, LetzAI teamed with digital agency LOOP to dress PUMA’s virtual influencer and avatar, Laila, in a Moroccan soccer jersey. According to Strotz, “PUMA had struggled to make clothing look realistic on Laila. When they saw our images, they said the result was 1,000 times better than anything they had tried.”

That wasn’t the only brand intrigued by V3’s possibilities. After LetzAI posted V3-generated images of models wearing Sloggi underwear, Sloggi’s creative agency STAN Studios asked LetzAI to generate more images for market testing.

Always looking for new ways to support creators, LetzAI also launched its Image Upscaler feature, which enhances images and doubles their resolution. “Our creators can now resolve common AI image issues around quality and resolution. Everywhere Inference is pivotal in delivering the power and speed needed for these dynamic image enhancements,” noted Strotz.

Platform evolution and AI innovation without limits

As its models exceed user expectations worldwide, LetzAI can rely on Gcore to handle a high volume of requests. Confident about generating a limitless number of high-quality images on the fly, LetzAI can continue to scale rapidly to become a sustainable, innovation-driven business.

“As we further evolve—such as by adding video features to our platform – our partnership with Gcore will be central to LetzAI’s continued success,” Strotz concluded.

Photo by Tim Arterbury on Unsplash

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

The post How LetzAI empowered creativity with scalable, high-performance AI infrastructure appeared first on AI News.

That was something Ahmed Bukhamseen wanted to change, and his company Quant set out to transform the property investment market and open up pricing information to buyers, speculators, and investors.

As you might expect, at the core of Quant is data – advertised and realised prices, mapping, details of proposed and ongoing construction, geo-spacial data, imagery, and much more. We spoke to Ahmed at the recent MWC (Mobile World Congress) in Barcelona, to talk about the company’s vision and its journey to date. But first we asked what his chosen data platform of choice was – the basis on which the business runs.

“Our main challenge was to first comply with data protection. So, Saudi implemented new regulation laws about data protection, similar to GDPR, run by the Saudi Data and AI Authority. And for that we needed to have a local host for data protection. So that was our main driver for moving from Azure – Microsoft here in Europe – back to Saudi.”

Saudi Arabia, the Middle East, Africa and the APAC are target markets for Chinese tech giant Huawei, but it wasn’t an immediate given for Quant. “We discovered that Huawei offer cloud services late […] about this time last year. We started comparing Huawei with others.”

As Quant’s business model developed, so too did its requirements. The company started out just supplying information it drew from the Saudi municipal data registries, but has since enriched that core information with local data, and high-res satellite imagery.

“We were using a data centre [here] and the data had to go from Saudi to Europe and back. We were running AI on the edge, on images without storing them. But now we have different use cases, so we needed to store data before and after processing, after enrichment or maximisation. There was a huge cost, and we didn’t expect it at the beginning.”

Part of the issue was the imagery the company was pulling down from satellites to get up-to-date pictures of existing buildings on the ground, and those in the process of construction. It was grabbing detailed images once every two weeks, and is about to go live with a daily satellite photography cadence.

“We started testing Huawei in terms of latency, especially, because we streamed high volume data, and would like the individuals using our mobile application to get a seamless experience, and start navigating in-app.”

The app released by Quant opens the property market to anyone: individuals buying their first place, portfolio managers scoping new possibilities, investors of all sizes, landlords, and those – like Ahmed was once himself – hoping just to find a place to rent without the massive variance in prices that was the norm before Quant began its service.

Quant combines different data sources such a property prices, maps, satellite imagery, and government-approved planning documents among them to give its app users everything they need to find property or land to buy and sell.

The future will see Quant revising its data, further enriching it, and adding value for its clients. “We need to develop specific feature detection or object detection. For example, municipality data could state a company has zoning for a warehouse or extension, and we don’t know which one the owner decided on. But from space, we can build models and see the exact the building plan.”

Although Quant likely has the technical ‘chops’ to build its own models, it’s turning to Huawei:

“With our new development, computing the model [ourselves] would take us six months to deploy. Now when we run it, it takes four hours. And last time I checked with the team, we run it every week,” Ahmed said.

Quant’s plans include a service for retailers that will advise on the best areas to site their stores, given the known demographics of a neighbourhood – all data it collects, collates, enriches, and presents. Plus, there is a project on the table that extends the data scope to take in the rental market.

Given the speed at which Saudi Arabia develops, Quant and delivery partner Huawei need to move at least as fast. “We want to see you in Saudi, and see the return on real estate! We don’t talk about 2% or 5% growth: We’re talking about multiplying investments similar to the Bitcoin market. Foreign investors will be able to buy property or land with just the touch of a button, and they need data for that. They need to know the growth areas, how much it costs in perspective, before making decisions.”

You can download the Quant app, read more about the company on its website, and check out Huawei’s service offerings for Saudi and beyond.

(Image source: European Space Agency, licensed under CC BY-SA 3.0 IGO.)

The post Dream properties at the touch of a button: Quant and Huawei in Saudi Arabia appeared first on AI News.

“Artificial intelligence is quickly becoming central to security and economic strength,” the White House fact sheet declares, framing the controls as a decisive action “to ensure that US technology undergirds global AI use and that adversaries cannot easily abuse advanced AI.”

The new AI chips export controls split the global technology landscape into three distinct tiers, fundamentally reshaping how nations can access and develop AI capabilities. Access to advanced AI processors remains unrestricted for 18 key allies, so-called tier-one nations, including Japan, Britain, and the Netherlands.

However, the administration has implemented strict AI chips export quotas for other nations, creating a new global AI development hierarchy. The 18 allies possess “robust technology protection regimes and technology ecosystems aligned with the national security and foreign policy interests of the US,” the policy document states.

For other countries, the restrictions impose precise limitations – chip orders maxing out at roughly 1,700 advanced GPUs can proceed without licences, primarily benefiting academic and research institutions.

Impact on global AI development

The reverberations through the AI industry were immediate. Nvidia, whose AI accelerators power many of the world’s most advanced artificial intelligence systems, saw its shares decline 2%. Vice President of Government Affairs Ned Finkle warned that the export curb “threatens to derail innovation and economic growth worldwide.”

The stakes are exceptionally high for Nvidia, which derives 56% of its revenue from international markets. Cloud computing giants face a complex recalibration of their AI infrastructure.

Under the new framework, US-headquartered providers must adopt a precise mathematical approach to their global operations: no more than 50% of their AI computing power can be deployed outside the country, with a maximum of 25% beyond tier-one countries, and just 7% in any single non-tier-one nation.

US-China AI technology battle intensifies

The timing and scope of these AI chip export controls reveal their primary target: China’s rapidly advancing AI capabilities. The White House document explicitly warns about “countries of concern” that “actively employ AI — including US-made AI” in ways that could “undermine US AI leadership.” 

With China accounting for 17% of Nvidia’s sales, the commercial impact aligns directly with the administration’s strategic goals. China’s Commerce Ministry’s swift response – promising to “take necessary measures to safeguard its legitimate rights and interests” – signals a new chapter in the technological cold war between the world’s leading AI powers.

The restrictions specifically target China’s ability to develop advanced AI systems, particularly those that could enable “the development of weapons of mass destruction, supporting powerful offensive cyber operations, and aiding human rights abuses.”

Global response and future implications

The US’s European allies have raised concerns about the broad reach of the controls. EU Executive Vice-President Henna Virkkunen and Commissioner Maroš Šefčovič emphasized the need for continued access to advanced AI technology, stating they are “looking forward to engaging constructively with the next US administration” to maintain “a secure transatlantic supply chain on AI technology and supercomputers.”

US National Security Adviser Jake Sullivan frames the controls within a broader technological revolution: “The US has to be prepared for rapid increases in AI’s capability in the coming years, which could have a transformative impact on the economy and our national security.”

Set to take effect in 120 days, the AI chip export controls represent more than just Biden’s final policy move – they establish a new paradigm for global AI development. As former Trump administration national security official Meghan Harris notes, “How effective the rule ends up being in the next 10 to 15 years is now up to the incoming team.”

The regulations mark a defining moment in both US-China relations and global AI development, creating boundaries and alliances that will shape the future of artificial intelligence well beyond the current administration. With these controls, Biden’s final act may be remembered as the moment that redefined the global AI technology landscape.

See also: South Korea wants to develop 50 types of AI chips by 2030

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

The post US-China tech war escalates with new AI chips export controls appeared first on AI News.

The facility stands out for its advanced infrastructure and focus on energy efficiency, reflecting broader efforts to meet the rising demands of AI computing sustainably.

The AI Factory is part of Firmus’s ongoing initiative to transform existing ST Telemedia Global Data Centres (STT GDC) into GPU-powered AI computing platforms. The redesigned centres are equipped with state-of-the-art hardware and efficient cooling systems, enabling them to meet both enterprise and research needs with improved energy performance metrics.

As artificial intelligence continues to need more power, energy efficiency has become a major issue. Firmus has addressed the issue for nearly a decade with its AI Factory platform, which combines advanced immersion cooling technology with dependable design, build, and operation services. The company states its platform has several significant advantages, including:

• Energy efficiency: 45% more FLOP per utility picoJoule than traditional data centres,
• Cost-effectiveness: Up to 30% cheaper total cost of ownership (TCO) than direct-to-chip cooling platforms,
• Scalability and sustainability: Supports high-density AI workloads while reducing environmental effects,
• Global expertise: A track record in building and operating immersion-cooled data centres in Singapore and Australia.

The deployment of the AI Factory in Singapore shows how innovative approaches to data centre infrastructure can address the energy demands of AI. The project highlights a potential pathway for sustainable AI development by achieving a pPUE of 1.02 and a reduction in energy consumption of 45%. The achievement aligns with Singapore’s National AI Strategy 2.0, which emphasises sustainable growth in AI and data centre innovation.

Tim Rosenfield, co-CEO of Firmus Technologies, explained the broader vision behind the project, noting that it’s about balancing AI growth with sustainability. “By rethinking data centre design, we have created a platform that supports the growth of AI while promoting environmental sustainability. If we can do it in Singapore, where space is constrained and the humid climate is against us, we can do it anywhere,” he said.

Firmus has recently changed its leadership team, adding Dr. Daniel Kearney as chief technology officer. Previously AWS’s Head of Technology for the ASEAN Enterprise business, Kearney leads the engineering team at Firmus. He pointed out how sustainable AI infrastructure is becoming essential as AI technologies expand. “This win against established data centre players recognises the importance of technology like ours in meeting the growth of AI and the energy challenges it brings,” he said.

The company has been advancing its work through the Sustainable Metal Cloud (SMC), an initiative aimed at improving the efficiency and sustainability of AI infrastructure. Recent updates from Firmus include:

• Power efficiency benchmarks: Firmus became the first to publish comprehensive power consumption data alongside performance results for the MLPerf Training benchmark,
• Policy contributions: Insights from Tim Rosenfield contributed to the Tony Blair Institute for Global Change’s policy agenda on managing the energy demands of the AI sector,
• Industry discussions: At ATxSG24, Firmus’s Chairman, Edward Pretty, joined a panel featuring organisations like NVIDIA, the World Bank, and Alibaba Cloud to explore the balance between sustainability and the computational needs of AI,
• Hypercube expansion: Firmus’s team of 700 is installing the first fleet of Sustainable AI Factories, known as HyperCubes in multiple regions.
• Engagement at NVIDIA GTC 2024: The company participated in two panels at NVIDIA’s GTC event, discussing sustainable AI infrastructure alongside partners like NVIDIA, Deloitte, and WEKA.

See also: The AI revolution: Reshaping data centres and the digital landscape 

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

The post Singapore-based Firmus wins recognition for AI data centre design appeared first on AI News.

Released in November 2024, the Commission’s annual report outlined 32 recommendations that could fundamentally alter how the two countries interact, with artificial intelligence taking centre stage in a new chapter of strategic rivalry.

US-China: the AGI moonshot and critical tech controls

At the heart of the report lies an ambitious proposal: establishing a government-backed programme to develop AGI – AI systems that could match and potentially exceed human cognitive abilities. 

However, the recommendation is just one piece of a larger technological puzzle, including export controls, investment screening, and new trade policies to preserve US technological advantages. 

The proposed AGI initiative would provide multi-year contracts to leading AI companies, cloud providers, and data centre operators. It would be backed by the Defense Department’s highest priority, “DX Rating” – a designation typically reserved for critical national security projects. 

This level of government involvement in AI development mirrors the urgency seen in previous technological races. It raises crucial questions about the role of state intervention in an industry primarily driven by private sector innovation.

The Commission’s tech-focused recommendations extend beyond AI. Notable proposals include restricting imports of Chinese-made autonomous humanoid robots with advanced dexterity, locomotion, and intelligence capabilities. 

The report also targets energy infrastructure products with remote monitoring capabilities, reflecting growing concerns about connected technologies in critical infrastructure. The report builds on existing export controls in the semiconductor space by recommending stronger oversight of technology transfers and investment flows. 

This comes as China continues to build domestic chip-making capabilities despite international restrictions. The Commission suggests creating an Outbound Investment Office that prevents US capital and expertise from advancing China’s technological capabilities in sensitive sectors.

Reshaping trade relations and investment flows

Perhaps most significantly, the report recommends eliminating China’s Permanent Normal Trade Relations (PNTR) status—a move that could reshape the technology supply chain and trade flows that have defined the global tech industry for decades. This recommendation acknowledges how deeply intertwined the US and Chinese tech ecosystems have become, while suggesting that this interdependence may now pose more risks than benefits.

Data transparency is another key theme, with recommendations for expanded reporting requirements on investments and technology transfers. The Commission calls for better tracking of investments flowing through offshore entities, addressing a significant blind-spot in current oversight mechanisms.

The report’s release comes at a critical juncture in technological development. China’s push for self-sufficiency in vital technologies and its “new quality productive forces” initiative demonstrates Beijing’s determination to lead in next-generation technologies. Meanwhile, AI capabilities and quantum computing breakthroughs have raised the stakes in technology competition.

However, the Commission’s recommendations face practical challenges. Achieving AGI remains a complex scientific challenge that may not yield quick results, regardless of funding levels. Additionally, restrictions on technology transfers and investment could have unintended consequences for global innovation networks that have historically benefited both nations.

If these recommendations are implemented, the tech industry may need to navigate an increasingly complex regulatory landscape. Companies would face new compliance requirements for international investments, technology transfers, and collaborative research projects.

Challenges and future implications

The effectiveness of the proposed measures will likely depend on coordination with allies and partners who share similar technological capabilities and concerns. The report acknowledges this by recommending multilateral approaches to export controls and investment screening.

US-China technological competition has entered a new phase where government policy may play a more direct role in shaping development. Whether this approach accelerates or hinders innovation remains to be seen, but the tech industry should prepare for increased scrutiny and regulation of international technological collaboration.

(Photo by Nathan Bingle)

See also: Chinese firms use cloud loophole to access US AI tech

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

The post Manhattan Project 2.0? US eyes AGI breakthrough in escalating China rivalry appeared first on AI News.

While maintaining Amazon’s minority stake in Anthropic, the investment represents a significant development in the company’s approach to AI technology and cloud infrastructure. The expanded collaboration goes beyond mere financial investment. Anthropic has now designated AWS as its “primary training partner” for AI model development, in addition to Amazon’s role as a primary cloud provider. 

Amazon’s investment will see Anthropic utilizing AWS Trainium and Inferentia chips for training and on which to deploy its future foundational models, including any updates to the flagship Claude AI system.

AWS’s competitive edge

The continuing partnership provides Amazon with several strategic advantages in the competitive cloud computing and AI services market:

1. Hardware innovation: The commitment to use AWS Trainium and Inferentia chips for Anthropic’s advanced AI models validates Amazon’s investment in custom AI chips and positions AWS as a serious competitor to NVIDIA in the AI infrastructure space.
2. Cloud service enhancement: AWS customers will receive early access to fine-tuning capabilities for data processed by Anthropic models. This benefit alone could attract more enterprises to Amazon’s cloud platform.
3. Model performance: Claude 3.5 Sonnet, Anthropic’s latest model available through Amazon Bedrock, has demonstrated exceptional performance in agentic coding tasks, according to Anthropic.

Amazon’s multi-faceted AI strategy

While the increased investment in Anthropic is impressive in monetary terms, it represents just one component of Amazon’s broader AI strategy. The company appears to be pursuing a multi-pronged approach:

1. External partnerships: The Anthropic investment provides immediate access to cutting-edge AI capabilities from third-parties.
2. Internal development: Amazon continues to develop its own AI models and capabilities.
3. Infrastructure development: Ongoing investment in AI-specific hardware like Trainium chips demonstrates a commitment to building AI-focussed infrastructure.

The expanded partnership signals Amazon’s long-term commitment to AI development yet retains flexibility thanks to its minority stakeholding. This approach allows Amazon to benefit from Anthropic’s innovations while preserving the ability to pursue other partnerships with external AI companies and continue internal development initiatives.

The investment reinforces the growing trend where major tech companies seek strategic AI partnerships rather than relying solely on internal development. It also highlights the important role of cloud infrastructure in the AI industry’s growth. AWS has positioned itself as a suitable platform for AI model training and deployment.

The post Amazon stakes $4bn more in Anthropic–the next tech arms race? appeared first on AI News.

Even laws designed to protect crime victims might end up protecting the names of abusive officers by labelling them as victims of crime in cases like resisting arrest or assaulting an officer. Such accusations are often used in cases of excessive force, keeping cops’ names out of the spotlight.

For example, a recent New Jersey law emerged from a tragic event in which a government employee faced violence, sparking a legislative response. Known as “Daniel’s Law,” it was created after the personal information of a federal judge’s family was used by a murderer to track them down. Instead of a broader privacy law that could protect all residents of New Jersey, it focused exclusively on safeguarding certain public employees.

Under the law, judges, prosecutors, and police officers can request that their personal information (addresses and phone numbers, for example) be scrubbed from public databases. Popular services that people use to look up information, such as Whitepages or Spokeo, must comply. While this sounds like a win for privacy, the protections stop there. The average citizen is still left exposed, with no legal recourse if their personal data is misused or sold.

At the centre of the debate is a lawyer who’s taken up the cause of protecting cops’ personal data. He’s suing numerous companies for making this type of information accessible. While noble at first glance, a deeper look raises questions.

It transpires that the lawyer’s company has previously collected and monetised personal data. And when a data service responded to his demands by freezing access to some of the firm’s databases, he and his clients cried foul — despite specifically requesting restrictions on how their information could be used.

It’s also worth noting how unevenly data protection measures are to be applied. Cops, for instance, frequently rely on the same tools and databases they’re now asking to be restricted. These services have long been used by law enforcement for investigations and running background checks. Yet, when law enforcement data appears in such systems, special treatment is required.

A recent anecdote involved a police union leader who was shown a simple property record pulled from an online database. The record displayed basic details like his home address and his property’s square footage — information anyone could find with a few clicks. His reaction was one of shock and anger – an obvious disconnect.

For everyday citizens, this level of data exposure is a given. But for law enforcement, it requires a level of granular exclusion that’s not practical.

Perhaps everyone, including law enforcement personnel deserves better safeguards against data harvesting and misuse? But what Daniel’s law and later events involving police officers point to is the need for the type of improvements to the way data is treated for all, not just one group of society.

Instead of expanding privacy rights to all New Jersey residents, the law carves out exceptions for the powerful — leaving the rest of the population as vulnerable as ever.

(Photo by Unsplash)

See also: EU AI legislation sparks controversy over data transparency

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

The post NJ cops demand protections against data brokers appeared first on AI News.

Announced on December 3, 2024, the public preview release lets organisations orchestrate AI workloads through a unified control plane, whether those workloads are running on cloud, on-premises, or in air-gapped infrastructure. The platform can work with any AI model and hardware accelerator including GPUs, CPUs, and TPUs.

“Clarifai has always been ahead of the curve, with over a decade of experience supporting large enterprise and mission-critical government needs with the full stack of AI tools to create custom AI workloads,” said Matt Zeiler, founder and CEO of Clarifai. “Now, we’re opening up capabilities we built internally to optimise our compute costs as we scale to serve millions of models simultaneously.”

The company claims its platform can reduce compute usage by 3.7x through model packing optimisations while supporting over 1.6 million inference requests per second with 99.9997% reliability. According to Clarifai, the optimisations can potentially cut costs by 60-90%, depending on configuration.

Capabilities of the compute orchestration platform include:

• Cost optimisation through automated resource management, including model packing, dependency simplification, and customisable auto-scaling options that can scale to zero for model replicas and compute nodes,
• Deployment flexibility on any hardware vendor including cloud, on-premise, air-gapped, and Clarifai SaaS infrastructure,
• Integration with Clarifai’s AI platform for data labeling, training, evaluation, workflows, and feedback,
• Security features that allow deployment into customer VPCs or on-premise Kubernetes clusters without requiring open inbound ports, VPC peering, or custom IAM roles.

The platform emerged from Clarifai customers’ issues with AI performance and cost. “If we had a way to think about it holistically and look at our on-prem costs compared to our cloud costs, and then be able to orchestrate across environments with a cost basis, that would be incredibly valuable,” noted a customer, as cited in Clarifai’s announcement.

The compute orchestration capabilities build on Clarifai’s existing AI platform that, the company says, has processed over 2 billion operations in computer vision, language, and audio AI. The company reports maintaining 99.99%+ uptime and 24/7 availability for critical applications.

The compute orchestration capability is currently available in public preview. Organisations interested in testing the platform should contact Clarifai for access.

The post New Clarifai tool orchestrates AI across any infrastructure appeared first on AI News.

With data centres burning vast amounts of energy, the growing demand for GenAI has sparked a debate about its sustainability.

The cost of intelligence: Generative AI’s carbon footprint

Behind every AI-generated email, idea, or recommendation are data centres running thousands of energy-hungry servers. Data centres are responsible for both training the large language models that power generative AI and processing individual user queries. Unlike a simple Google search, which uses relatively little energy, a single generative AI request can consume up to ten times as much electricity.

The numbers are staggering. If all nine billion daily Google searches worldwide were replaced with generative AI tasks, the additional electricity demand would match the annual energy consumption of 1.5 million EU residents. According to consultants Morgan Stanley, the energy demands of generative AI are expected to grow by 70% annually until 2027. By that point, the energy required to support generative AI systems could rival the electricity needs of an entire country—Spain, for example, based on its 2022 usage.

UK consumers want greener AI practices

The survey also highlights growing awareness among UK consumers about the environmental implications of generative AI. Nearly one in five respondents said they don’t trust generative AI providers to manage their environmental impact responsibly. Among regular users of these tools, 10% expressed a willingness to pay a premium for products or services that prioritise energy efficiency and sustainability.

Interestingly, over a third (35%) of respondents think generative AI tools should “actively remind” users of their environmental impact. While this appears like a small step, it has the potential to encourage more mindful usage and place pressure on companies to adopt greener technologies.

Efforts to tackle the environmental challenge

Fortunately, some companies and policymakers are beginning to address these concerns. In the United States, the Artificial Intelligence Environmental Impacts Act was introduced earlier this year. The legislation aims to standardise how AI companies measure and report carbon emissions. It also provides a voluntary framework for developers to evaluate and disclose their systems’ environmental impact, pushing the industry towards greater transparency.

Major players in the tech industry are also stepping up. Companies like Salesforce have voiced support for legislation requiring standardised methods to measure and report AI’s carbon footprint. Experts point to several practical ways to reduce generative AI’s environmental impact, including adopting energy-efficient hardware, using sustainable cooling methods in data centres, and transitioning to renewable energy sources.

Despite these efforts, the urgency to address generative AI’s environmental impact remains critical. As Uday Radia, owner of CloudNine PR, puts it: “Generative AI has huge potential to make our lives better, but there is a race against time to make it more sustainable before it gets out of control.”

(Photo by Unsplash)

See also: The AI revolution: Reshaping data centres and the digital landscape 

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

The post Generative AI use soars among Brits, but is it sustainable? appeared first on AI News.

Their combined spending on AI is projected to exceed a jaw-dropping $240 billion. Why? Because AI isn’t just the future—it’s the present, and the demand for AI-powered tools and infrastructure has never been higher. The companies aren’t just keeping up; they’re setting the pace for the industry.

The scale of their investment is hard to ignore. In the first half of 2023, tech giants poured $74 billion into capital expenditure. By Q3, that number had jumped to $109 billion. In mid-2024, spending reached $104 billion, a remarkable 47% rise over the same period a year earlier. By Q3, the total hit $171 billion.

If this pattern continues, Q4 might add another $70 billion, bringing the total to a truly staggering $240 billion for the year.

Why so much spending?

AI’s potential is immense, and companies are making sure they’re positioned to reap the rewards.

• A growing market: AI is projected to create $20 trillion in global economic impact by 2030. In countries like India, AI could contribute $500 billion to GDP by 2025. With stakes this high, big tech isn’t hesitating to invest heavily.
• Infrastructure demands: Training and running AI models require massive investment in infrastructure, from data centres to high-performance GPUs. Alphabet increased its capital expenditures by 62% last quarter compared to the previous year, even as it cut its workforce by 9,000 employees to manage costs.
• Revenue potential: AI is already proving its value. Microsoft’s AI products are expected to generate $10 billion annually—the fastest-growing segment in the company’s history. Alphabet, meanwhile, uses AI to write over 25% of its new code, streamlining operations.

Amazon is also ramping up, with plans to spend $75 billion on capital expenditure in 2024. Meta’s forecast is not far behind, with estimates between $38 and $40 billion. Across the board, organisations recognise that maintaining their edge in AI requires sustained and significant investment.

Supporting revenue streams

What keeps the massive investments keep on coming is the strength of big tech’s core businesses. Last quarter, Alphabet’s digital advertising machine, which is powered by Google’s search engine, generated $49.39 billion in ad revenue, a 12% year-over-year increase. This as a solid foundation that allows Alphabet to pour resources into building out its AI arsenal without destabilising the bottom line.

Microsoft’s diversified revenue streams are another example. While the company spent $20 billion on AI and cloud infrastructure last quarter, its productivity segment, which includes Office, grew by 12% to $28.3 billion, and its personal computing business, boosted by Xbox and the Activision Blizzard acquisition, grew 17% to $13.2 billion. These successes demonstrate how AI investments can support broader growth strategies.

The financial payoff

Big tech is already seeing the benefits of its heavy spending. Microsoft’s Azure platform has seen substantial growth, with its AI income approaching $6 billion. Amazon’s AI business is growing at triple-digit rates, and Alphabet reported a 34% jump in profits last quarter, with cloud revenue playing a major role.

Meta, while primarily focused on advertising, is leveraging AI to make its platforms more engaging. AI-driven tools, such as improved feeds and search features keep users on its platforms longer, resulting in new revenue growth.

AI spending shows no signs of slowing down. Tech leaders at Microsoft and Alphabet view AI as a long-term investment critical to their future success. And the results speak for themselves: Alphabet’s cloud revenue is up 35%, while Microsoft’s cloud business grew 20% last quarter.

For the time being, the focus is on scaling up infrastructure and meeting demand. However, the real transformation will come when big tech unlocks AI’s full potential, transforming industries and redefining how we work and live.

By investing in high-quality, centralised data strategies, businesses can ensure trustworthy and accurate AI implementations, and unlock AI’s full potential to drive innovation, improve decision-making, and gain competitive edge. AI’s revolutionary promise is within reach—but only for companies prepared to lay the groundwork for sustainable growth and long-term results.

(Photo by Unsplash)

See also: Microsoft tries to convert Google Chrome users

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

The post Big tech’s AI spending hits new heights appeared first on AI News.

The Playbook looks at stories from various industries to see what problems AI is solving, who’s driving these initiatives, and how it’s reshaping strategies.

Salesforce, well known for its CRM software used by over 150,000 companies like Amazon and Walmart, is no stranger to innovation. It also owns Slack, the popular workplace communication app. Salesforce is now stepping up its AI game with Agentforce, a platform that lets businesses to build and deploy digital agents to automate tasks such as creating sales reports and summarising Slack conversations.

What problem is it solving?

Salesforce has been working with AI for years. In 2016, it launched Einstein, an AI feature baked into its CRM platform. Einstein handled basic scriptable tasks, but the rise of generative AI brought a chance to do more. Smarter tools could now make better decisions and understand natural language.

This sparked a transformation. First came Einstein GPT, then Einstein Copilot, and now Agentforce—a platform designed for flexibility with prebuilt and customisable agents to handle diverse business needs.

“Our customers wanted more. Some wanted to tweak the agents we offer, while others wanted to create their own,” said Tyler Carlson, Salesforce’s VP of Business Development.

The tech behind it

Agentforce is powered by Salesforce’s Atlas Reasoning Engine, developed in-house. The platform connects with AI models from major players like OpenAI, Anthropic, Amazon, and Google, giving businesses access to a variety of tools.

Slack has become a testing ground for these AI agents. Currently in beta, Agentforce’s Slack integration puts automations where employees already spend their time. “Slack makes these tools easy to use and accessible,” Carlson added.

Smarter, more flexible AI

Agentforce uses ReAct prompting, a technique that helps agents break down problems into smaller steps and adjust their approach as they go. This leads to more accurate responses and hands-off task management, from answering questions to scheduling meetings.

Agentforce works with Salesforce’s proprietary LLMs and third-party models, giving clients plenty of options. To ensure security, Salesforce enforces strict data privacy policies, including limits on data retention.

Making it work for businesses

With tools like Agentbuilder, companies can design AI agents tailored to their needs. For example, an agent could sort emails or answer specific HR questions using internal data. One example is Salesforce’s collaboration with Workday to create an AI service agent for employee queries.

Salesforce is already seeing results, with Agentforce resolving 90% of customer inquiries in early trials. The goal? Broader adoption, more capabilities, and higher workloads handled by these agents.

“We’re building a bigger ecosystem of partners and skills,” Carlson said. “By next year, we want Agentforce to be a must-have for businesses.”

(Photo by Unsplash)

See also: Paul O’Sullivan, Salesforce: Transforming work in the GenAI era

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

The post Salesforce launches AI platform for automated task management appeared first on AI News.