ChatGPT

In a fireside chat at SIGGRAPH 2024, NVIDIA founder and CEO Jensen Huang and Meta founder and CEO Mark Zuckerberg shared their insights on the potential of open source AI and virtual assistants. The conversation began with Zuckerberg announcing the launch of AI Studio, a new platform designed to democratise AI creation. This tool allows users to create, share, and discover AI characters, potentially opening up AI development to millions of creators and small businesses. Huang emphasised the ubiquity of AI in the future, stating, “Every single restaurant, every single website will probably, in the future, have these AIs …” Zuckerberg concurred, adding, “…just like every business has an email address and a website and a social media account, I think, in the future, every business is going to have an AI.” This vision aligns with NVIDIA’s recent developments showcased at SIGGRAPH. The company previewed “James,” an interactive digital human based on the NVIDIA ACE (Avatar Cloud Engine) reference design. James – a virtual assistant capable of providing contextually accurate responses – demonstrates the potential for businesses to create custom, hyperrealistic avatars for customer interactions. The discussion highlighted Meta’s significant contributions to AI development. Huang praised Meta’s work, saying, “You guys have done amazing AI work,” and cited advancements in computer vision, language models, and real-time translation. He also acknowledged the widespread use of PyTorch, an open-source machine learning framework developed by Meta. Both CEOs stressed the importance of open source in advancing AI. Meta has positioned itself as a leader in this field, implementing AI across its platforms and releasing open-source models like Llama 3.1. This latest model, with 405 billion parameters, required training on over 16,000 NVIDIA H100 GPUs, representing a substantial investment in resources. Zuckerberg shared his vision for more integrated AI models, saying, “I kind of dream of one day like you can almost imagine all of Facebook or Instagram being like a single AI model that has unified all these different content types and systems together.” He believes that collaboration is crucial for further advancements in AI. The conversation touched on the potential of AI to enhance human productivity. Huang described a future where AI could generate images in real-time as users type, allowing for fluid collaboration between humans and AI assistants. This concept is reflected in NVIDIA’s latest advancements to the NVIDIA Maxine AI platform, including Maxine 3D and Audio2Face-2D, which aim to create immersive telepresence experiences. Looking ahead, Zuckerberg expressed enthusiasm about combining AI with augmented reality eyewear, mentioning Meta’s collaboration with eyewear maker Luxottica. He envisions this technology transforming education, entertainment, and work. Huang discussed the evolution of AI interactions, moving beyond turn-based conversations to more complex, multi-option simulations. “Today’s AI is kind of turn-based. You say something, it says something back to you,” Huang explained. “In the future, AI could contemplate multiple options, or come up with a tree of options and simulate outcomes, making it much more powerful.” The importance of this evolution is evident in the adoption of NVIDIA’s technologies by companies across industries. HTC, Looking Glass, Reply, and UneeQ are among the latest firms using NVIDIA ACE and Maxine for applications ranging from customer service agents to telepresence experiences in entertainment, retail, and hospitality. As AI continues to evolve and integrate into various aspects of our lives, the insights shared by these industry leaders provide a glimpse into a future where AI assistants are as commonplace as websites and social media accounts. The developments showcased at SIGGRAPH 2024 by both NVIDIA and other companies demonstrate that this future is rapidly approaching, with digital humans becoming increasingly sophisticated and capable of natural, engaging interactions. See also: Amazon strives to outpace Nvidia with cheaper, faster AI chips 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 NVIDIA and Meta CEOs: Every business will ‘have an AI’ appeared first on AI News. View the full article

In a move that underscores the growing influence of AI in the financial industry, JPMorgan Chase has unveiled a cutting-edge generative AI product. This new tool, LLM Suite, is being hailed as a game-changer and is capable of performing tasks traditionally assigned to research analysts. According to an internal memo obtained by the Financial Times, JPMorgan has granted employees in its asset and wealth management division access to this large language model platform. The memo, jointly signed by key executives Mary Erdoes, head of JPMorgan’s asset and wealth management business, Teresa Heitsenrether, the bank’s chief data and analytics officer, and Mike Urciuoli, the asset and wealth management unit’s chief information officer, describes LLM Suite as a “ChatGPT-like product” designed for “general purpose productivity.” The purpose of the platform is to change how employees work with their daily tasks, and it has various functions, including writing, helping to generate ideas, and document summarisation. The memo states, “Think of LLM Suite as a research analyst that can offer information, solutions, and advice on a topic.” Additionally, it is not an AI tool that performs functions by itself but an addition to the firm’s existing applications for handling financial information carefully—Connect Coach and SpectrumGPT. JPMorgan began the process of providing access to the LLM Suite to several of its departments earlier this year, representing a gradual implementation of the new system. At this point, an estimated 50,000 employees, or about 15% of the organisation’s workforce, can access the new platform. The number of research analysts who work for JPMorgan is not revealed, but the fact that the innovation affects so many employees from various departments raises the question of its relevance to traditional roles. It is worth mentioning that this is one of the most extensive implementations of large language models on Wall Street. For example, Morgan Stanley has started developing AI products built by OpenAI to improve their wealth management businesses. However, JPMorgan distinguishes itself from other financial organisations by developing its own AI tool, LLM Suite. A powerful reason to create the LLM Suite internally is the high level of regulation in the financial services domain. According to the strict regulations typical for any financial organisation, JPMorgan workers are not allowed to use any AI chatbots developed by other companies for consumers. These include Anthropic’s Claude, OpenAI’s GPT, and Google’s Gemini. Another reason is to ensure that customer information ******** on the bank’s servers safe and sound. As a company actively involved in the use of AI for enhancing services to its clients, JPMorgan Bank CEO Jamie Dimon made a statement to investors in May: “AI is going to change every job. It may eliminate some jobs. Some of it may create additional jobs.” He went further to state, “But you can’t envision one app, one database, or one job where it’s not going to help, aid, or abet.” Such a statement reflects the bank’s forward-looking attitude towards AI. Already, AI technologies contribute a significant amount of money to the bank. According to Daniel Pinto, the president of the bank, the value of AI technology currently in use is $1 to $1.5 billion. This fact shows that the effect of AI adoption is significant for the bank, which means it is also beneficial for society. Despite the fact that the release of LLM Suite is a breakthrough in AI implementation in the financial sphere, it is still possible to note that the technology has some drawbacks. Specifically, like any other AI model, LLM Suite may not work with sufficient accuracy, may “hallucinate,” or provide false data as correct information. Nevertheless, the memo does not discuss these issues or whether they exist. (Photo by IKECHUKWU JULIUS UGWU) See also: Meta advances open source AI with ‘frontier-level’ Llama 3.1 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 JPMorgan introduces in-house AI chatbot for research analysis appeared first on AI News. View the full article

Galileo, a leading developer of generative AI for enterprise applications, has released its latest Hallucination Index. The evaluation framework – which focuses on Retrieval Augmented Generation (RAG) – assessed 22 prominent Gen AI LLMs from major players including OpenAI, Anthropic, Google, and Meta. This year’s index expanded significantly, adding 11 new models to reflect the rapid growth in both open- and closed-source LLMs over the past eight months. Vikram Chatterji, CEO and Co-founder of Galileo, said: “In today’s rapidly evolving AI landscape, developers and enterprises face a critical challenge: how to harness the power of generative AI while balancing cost, accuracy, and reliability. Current benchmarks are often based on academic use-cases, rather than real-world applications.” The index employed Galileo’s proprietary evaluation metric, context adherence, to check for output inaccuracies across various input lengths, ranging from 1,000 to 100,000 tokens. This approach aims to help enterprises make informed decisions about balancing price and performance in their AI implementations. Key findings from the index include: Anthropic’s Claude 3.5 Sonnet emerged as the best overall performing model, consistently scoring near-perfect across short, medium, and long context scenarios. Google’s Gemini 1.5 Flash ranked as the best performing model in terms of cost-effectiveness, delivering strong performance across all tasks. Alibaba’s Qwen2-72B-Instruct stood out as the top open-source model, particularly excelling in short and medium context scenarios. The index also highlighted several trends in the LLM landscape: Open-source models are rapidly closing the gap with their closed-source counterparts, offering improved hallucination performance at lower costs. Current RAG LLMs demonstrate significant improvements in handling extended context lengths without sacrificing quality or accuracy. Smaller models sometimes outperform larger ones, suggesting that efficient design can be more crucial than scale. The emergence of strong performers from outside the US, such as Mistral’s Mistral-large and Alibaba’s qwen2-72b-instruct, indicates a growing global competition in LLM development. While closed-source models like Claude 3.5 Sonnet and Gemini 1.5 Flash maintain their lead due to proprietary training data, the index reveals that the landscape is evolving rapidly. Google’s performance was particularly noteworthy, with its open-source Gemma-7b model performing poorly while its closed-source Gemini 1.5 Flash consistently ranked near the top. As the AI industry continues to grapple with hallucinations as a major hurdle to production-ready Gen AI products, Galileo’s Hallucination Index provides valuable insights for enterprises looking to adopt the right model for their specific needs and budget constraints. See also: Senators probe OpenAI on safety and employment practices 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 Anthropic to Google: Who’s winning against AI hallucinations? appeared first on AI News. View the full article

Amazon’s chip lab is churning out a constant stream of innovation in Austin, Texas. A new server design was put through its paces by a group of devoted engineers on July 26th. During a visit to the facility in Austin, Amazon executive Rami Sinno shed light on the server’s use of Amazon’s AI chips. This development is a bold step toward competing with Nvidia, the current leader in the field. The main reason Amazon is developing its own processor is this: it doesn’t want to rely on Nvidia and buy the company’s chips. The expensive Nvidia chips power a big part of the AI cloud business at Amazon Web Services. This business is the most significant growth engine of the company. Thus, the so-called “Nvidia tax” was pushing the company to look for a cheaper option. Amazon’s chip development program has a dual purpose. Firstly, the project is meant to provide customers with more affordable opportunities for complex calculations and large data volume processing. Secondly, the initiative was developed to preserve Amazon’s competitiveness in the volatile cloud computing and AI industry. This move was also supported by the directions of tech giants such as Microsoft and Alphabet, which are developing custom-made chips to maintain their leadership in the market. Rami Sinno, director of engineering for Amazon’s Annapurna Labs, a key element of the AWS ecosystem, emphasised that customer demand for more economical solutions to Nvidia’s products is growing. The acquisition of Annapurna Labs in 2015 was a savvy move by Amazon as it enabled the company to lay the groundwork to begin developing popular chips. Although Amazon’s chips for AI are in their early days, the company has been making and refining chips for other mainstream applications for nearly a decade, most notably its general-purpose chip, Graviton, which is now in its fourth generation. Amazon has announced that its Trainium and Inferentia chips, the company’s latest and strongest, are still in their early days and are specially designed processors. The impact is potentially huge because the impressive performance underscores the reports by David Brown, vice president of compute and networking at AWS. In this light, it should be acknowledged that Amazon’s in-house chips could deliver up to a 40-50% price-performance ratio improvement compared to Nvidia-based solutions. In turn, this potential improvement could mean considerable savings for AWS clientele deploying their AI workloads. AWS’ significance to Amazon’s overall business cannot be underestimated. In the first quarter of this year, AWS made up a little under a fifth of Amazon’s total revenue, as its sales soared by 17 per cent year over year to reach $25 billion. At the moment, AWS holds about a third of the global cloud computing market, and Microsoft’s Azure covers about a quarter, or 25%. Amazon’s commitment to its custom chip strategy was demonstrated during the recent Prime Day, a two-day sales event at Amazon.com. To handle the highly elevated level of shopping as well as streaming video, music, and other content, Amazon deployed an impressive 250,000 Graviton chips and 80,000 of its custom AI chips across its platforms. Adobe Analytics announced record Prime Day results of $14.2 billion in sales. It seems that as Amazon intensifies its work on the development of AI chips, the industry leader, Nvidia, is not going to remain at the same level. Nvidia’s CEO, Jensen Huang, has presented Nvidia’s latest Blackwell chips, which are scheduled for release later in the year. Their performance has increased significantly, and Huang promised that the new chips are twice as powerful for AI model training and five times faster for inference. Nvidia’s dominant position in the AI chip market is underscored by its impressive client list, which includes tech giants like Amazon, Google, Microsoft, OpenAI, and Meta. The company’s focus on AI has propelled its market value to a staggering $2 trillion, making it the third most valuable company globally, behind only Microsoft and Apple. As the AI chip race intensifies, Nvidia is also diversifying its offerings. The company has introduced new software tools to facilitate AI integration across various industries and is developing specialised chips for emerging applications such as in-car chatbots and humanoid robots. (Image by Gerd Altmann) See also: Nvidia: World’s most valuable company under French antitrust ***** 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 Amazon strives to outpace Nvidia with cheaper, faster AI chips appeared first on AI News. View the full article

Tech giants like Microsoft, Alphabet, and Meta are riding high on a wave of revenue from AI-driven cloud services, yet simultaneously drowning in the substantial costs of pushing AI’s boundaries. Recent financial reports paint a picture of a double-edged sword: on one side, impressive gains; on the other, staggering expenses. This dichotomy has led Bloomberg to aptly dub AI development a “huge money pit,” highlighting the complex economic reality behind today’s AI revolution. At the heart of this financial problem ***** a relentless push for *******, more sophisticated AI models. The quest for artificial general intelligence (AGI) has led companies to develop increasingly complex systems, exemplified by large language models like GPT-4. These models require vast computational power, driving up hardware costs to unprecedented levels. To top it off, the demand for specialised AI chips, mainly graphics processing units (GPUs), has skyrocketed. Nvidia, the leading manufacturer in this space, has seen its market value soar as tech companies scramble to secure these essential components. Its H100 graphics chip, the gold standard for training AI models, has sold for an estimated $30,000 — with some resellers offering them for multiple times that amount. The global chip shortage has only exacerbated this issue, with some firms waiting months to acquire the necessary hardware. Meta Chief Executive Officer Zuckerberg previously said that his company planned to acquire 350,000 H100 chips by the end of this year to support its AI research efforts. Even if he gets a bulk-buying discount, that quickly adds to billions of dollars. On the other hand, the push for more advanced AI has also sparked an arms race in chip design. Companies like Google and Amazon invest heavily in developing their AI-specific processors, aiming to gain a competitive edge and reduce reliance on third-party suppliers. This trend towards custom silicon adds another layer of complexity and cost to the AI development process. But the hardware challenge extends beyond just procuring chips. The scale of modern AI models necessitates massive data centres, which come with their technological hurdles. These facilities must be designed to handle extreme computational loads while managing heat dissipation and energy consumption efficiently. As models grow larger, so do the power requirements, significantly increasing operational costs and environmental impact. In a podcast interview in early April, Dario Amodei, the chief executive officer of OpenAI-rival Anthropic, said the current crop of AI models on the market cost around $100 million to train. “The models that are in training now and that will come out at various times later this year or early next year are closer in cost to $1 billion,” he said. “And then I think in 2025 and 2026, we’ll get more towards $5 or $10 billion.” Then, there is data, the lifeblood of AI systems, presenting its own technological challenges. The need for vast, high-quality datasets has led companies to invest heavily in data collection, cleaning, and annotation technologies. Some firms are developing sophisticated synthetic data generation tools to supplement real-world data, further driving up research and development costs. The rapid pace of AI innovation also means that infrastructure and tools quickly become obsolete. Companies must continuously upgrade their systems and retrain their models to stay competitive, creating a constant cycle of investment and obsolescence. “On April 25, Microsoft said it spent $14 billion on capital expenditures in the most recent quarter and expects those costs to “increase materially,” driven partly by AI infrastructure investments. That was a 79% increase from the year-earlier quarter. Alphabet said it spent $12 billion during the quarter, a 91% increase from a year earlier, and expects the rest of the year to be “at or above” that level as it focuses on AI opportunities,” the article by Bloomberg reads. Bloomberg also noted that Meta, meanwhile, raised its estimates for investments for the year and now believes capital expenditures will be $35 billion to $40 billion, which would be a 42% increase at the high end of the range. “It cited aggressive investment in AI research and product development,” Bloomberg wrote. Interestingly, Bloomberg’s article also points out that despite these enormous costs, tech giants are proving that AI can be a real revenue driver. Microsoft and Alphabet reported significant growth in their cloud businesses, mainly attributed to increased demand for AI services. This suggests that while the initial investment in AI technology is staggering, the potential returns are compelling enough to justify the expense. However, the high costs of AI development raise concerns about market concentration. As noted in the article, the expenses associated with cutting-edge AI research may limit innovation to a handful of well-funded companies, potentially stifling competition and diversity in the field. Looking ahead, the industry is focusing on developing more efficient AI technologies to address these cost challenges. Research into techniques like few-shot learning, transfer learning, and more energy-efficient model architectures aims to reduce the computational resources required for AI development and deployment. Moreover, the push towards edge AI – running AI models on local devices rather than in the cloud – could help distribute computational loads and reduce the strain on centralised data centres. This shift, however, requires its own set of technological innovations in chip design and software optimisation. Overall, it is clear that the future of AI will be shaped not just by breakthroughs in algorithms and model design but also by our ability to overcome the immense technological and financial hurdles that come with scaling AI systems. Companies that can navigate these challenges effectively will likely emerge as the leaders in the next phase of the AI revolution. (Image by Igor Omilaev) 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 The exponential expenses of AI development appeared first on AI News. View the full article

Mistral AI’s latest model, Mistral Large 2 (ML2), allegedly competes with large models from industry leaders like OpenAI, Meta, and Anthropic, despite being a fraction of their sizes. The timing of this release is noteworthy, arriving the same week as Meta’s launch of its behemoth 405-billion-parameter Llama 3.1 model. Both ML2 and Llama 3 boast impressive capabilities, including a 128,000 token context window for enhanced “memory” and support for multiple languages. Mistral AI has long differentiated itself through its focus on language diversity, and ML2 continues this tradition. The model supports “dozens” of languages and more than 80 coding languages, making it a versatile tool for developers and businesses worldwide. According to Mistral’s benchmarks, ML2 performs competitively against top-tier models like OpenAI’s GPT-4o, Anthropic’s Claude 3.5 Sonnet, and Meta’s Llama 3.1 405B across various language, coding, and mathematics tests. In the widely-recognised Massive Multitask Language Understanding (MMLU) benchmark, ML2 achieved a score of 84 percent. While slightly behind its competitors (GPT-4o at 88.7%, Claude 3.5 Sonnet at 88.3%, and Llama 3.1 405B at 88.6%), it’s worth noting that human domain experts are estimated to score around 89.8% on this test. Efficiency: A key advantage What sets ML2 apart is its ability to achieve high performance with significantly fewer resources than its rivals. At 123 billion parameters, ML2 is less than a third the size of Meta’s largest model and approximately one-fourteenth the size of GPT-4. This efficiency has major implications for deployment and commercial applications. At full 16-bit precision, ML2 requires about 246GB of memory. While this is still too large for a single GPU, it can be easily deployed on a server with four to eight GPUs without resorting to quantisation – a feat not necessarily achievable with larger models like GPT-4 or Llama 3.1 405B. Mistral emphasises that ML2’s smaller footprint translates to higher throughput, as LLM performance is largely dictated by memory bandwidth. In practical terms, this means ML2 can generate responses faster than larger models on the same hardware. Addressing key challenges Mistral has prioritised combating hallucinations – a common issue where AI models generate convincing but inaccurate information. The company claims ML2 has been fine-tuned to be more “cautious and discerning” in its responses and better at recognising when it lacks sufficient information to answer a query. Additionally, ML2 is designed to excel at following complex instructions, especially in longer conversations. This improvement in prompt-following capabilities could make the model more versatile and user-friendly across various applications. In a nod to practical business concerns, Mistral has optimised ML2 to generate concise responses where appropriate. While verbose outputs can lead to higher benchmark scores, they often result in increased compute time and operational costs – a consideration that could make ML2 more attractive for commercial use. Licensing and availability While ML2 is freely available on popular repositories like Hugging Face, its licensing terms are more restrictive than some of Mistral’s previous offerings. Unlike the open-source Apache 2 license used for the Mistral-NeMo-12B model, ML2 is released under the Mistral Research License. This allows for non-commercial and research use but requires a separate commercial license for business applications. As the AI race heats up, Mistral’s ML2 represents a significant step forward in balancing power, efficiency, and practicality. Whether it can truly challenge the dominance of tech giants ******** to be seen, but its release is certainly an exciting addition to the field of large language models. (Photo by Sean Robertson) See also: Senators probe OpenAI on safety and employment practices 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 Mistral Large 2: The David to Big Tech’s Goliath(s) appeared first on AI News. View the full article

Meta has unveiled Llama 3.1, marking a significant milestone in the company’s commitment to open source AI. This release, which Meta CEO Mark Zuckerberg calls “the first frontier-level open source AI model,” aims to challenge the dominance of closed AI systems and democratise access to advanced AI technology. The Llama 3.1 release includes three models: 405B, 70B, and 8B. Zuckerberg asserts that the 405B model competes with the most advanced closed models while offering better cost-efficiency. The CEO views this release as a turning point, predicting that most developers will shift towards primarily using open source AI models. He invites the tech community to join Meta in “this journey to bring the benefits of AI to everyone in the world.” The Llama 3.1 models are now accessible at llama.meta.com. (Photo by Dima Solomin) See also: Meta joins Apple in withholding AI models from EU users [Hidden Content] 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 Meta advances open source AI with ‘frontier-level’ Llama 3.1 appeared first on AI News. View the full article

If your business relies on web applications, you’re probably familiar with traditional network firewalls. And for good reason – they play an invaluable role filtering external threats looking to ******* your overall infrastructure. But as more and more of your essential operations shift online to intricate web apps and APIs, gaps have opened up that basic firewalls simply can’t see into. The new AI-powered threats of today demand a new approach to security. Without visibility into your custom application logic and data flows, major vulnerabilities can be exploited, allowing sensitive information theft, financial ******, and even operational disruption. While you still need perimeter firewall defenses, exclusively relying on them to safeguard increasingly powerful web properties leaves you playing a risky game of chance (with very real consequences). By adding specialised web application firewalls (WAFs) designed to analyse requests in the full context of your app environments – and enhanced by AI for even greater accuracy – you can lock things down and confidently build out advanced digital capabilities. With a layered defense-in-depth approach combining network and application-level protections, you can securely deliver the types of seamless, personalised digital experiences that form the foundation of lasting customer relationships and operational excellence in 2024. Gaps in traditional firewall defences The chances are you already have traditional firewall protection guarding your overall network (if you run any online services). These firewalls filter incoming traffic based on a set of predefined rules focused primarily around protocol, port number, IP address ranges, and basic connection state. For example, common firewall rules restrict outside access to private intranet resources, block unwanted traffic types like online gaming protocols, detect large-scale network scans, and mitigate distributed denial of service (DDoS) attacks. This perimeter protection works well for classic network-focused cyberthreats. But a traditional firewall lacks context about the application logic, user workflows, and data structures unique to custom web apps and APIs. It simply scans network packets as they arrive and attempts to allow or block them accordingly. This leaves it vulnerable to the evolving tactics of AI-powered attackers. Without insight into application internals, major vulnerabilities can sneak right past traditional firewall defences: SQL injection attacks: Inserting malicious code allowing remote access, data destruction, or information theft Broken authentication: Enabling unauthorised system access with stolen credentials Sensitive data exposure: Through improper encryption, backups, or logging Cross-site scripting (XSS): Injecting JavaScript or HTML to spread malware, ******* sessions, scrape data, or deface sites Hackers can also target configuration issues, flawed business logic flows, identity management gaps, and unsafe object level access once inside applications themselves. AI-powered attacks can exploit these vulnerabilities with alarming speed and precision—and your firewall wouldn’t see it coming. These exploitable application flaws allow attackers to steal sensitive business data and personal information, mine cryptocurrency illicitly on servers, hold systems ransom, take over client accounts, and both deny legitimate access and ******** backend resources. AI has only amplified these risks. Still, traditional firewalls remain extremely important as the first line of network perimeter defence. But for companies conducting operations online through modern web apps, additional safeguards tuned to application threats – and bolstered by AI’s threat detection capabilities – are essential. Why WAFs provide critical protection Web application firewalls address the application layer vulnerabilities and holes in logic that basic network firewalls miss. WAFs are designed specifically to protect web apps, APIs, microservices, and rich internet applications. AI further enhances their ability to identify and respond to these threats. A WAF will deeply inspect all traffic flowing to web properties using targeted rulesets and negative security models defining suspicious behaviour. From there, they analyse requests for indicators of common exploits and attacks seeking to ****** application behaviour and functionality. AI-powered analysis can detect subtle patterns that might otherwise go unnoticed. These might include: Extreme traffic spikes indicating possible DDoS events Suspicious geolocations of an IP addresses Repeated input submissions just below lockout thresholds Unusual HTTP headers, user agents, or protocols Known malicious payloads in POST requests Attempts to traverse directory structures in unpredictable ways Special characters and patterns indicating SQL injection or cross-site scripting Advanced WAFs combine this real-time threat detection with global threat intelligence to identify emerging exploits and bad actors as soon as new ******* patterns appear. AI and machine learning algorithms even allow some solutions to derive additional behavioral rules by examining your specific application traffic patterns over time. AI’s adaptability is crucial in this constantly shifting landscape. As traffic passes through, the WAF blocks dangerous requests while allowing legitimate users through with minimal latency impact. This protects the application itself, shielding both data and functionality from compromise. AI-powered WAFs can do this with remarkable speed and accuracy, keeping pace with the ever-changing threat landscape. Most WAF products also include capabilities like virtual patching, behavioral anomaly detection, automatic policy tuning, third-party integration, and positive security models for detecting verified use cases. Breaking down the key features of traditional firewalls vs WAFs FeatureTraditional FirewallWeb Application Firewall (WAF)Layer of operationNetwork (Layer 3/4)Application (Layer 7)Traffic analysisPackets, ports, IP addressesHTTP/HTTPS requests, content, parameters, headers******* protectionNetwork-level attacksWeb application-specific attacks (SQLi, XSS, CSRF, etc.)CustomisationLimitedExtensiveAdditional capabilitiesMay offer basic intrusion preventionOften include **** mitigation, DDoS protection, API securityAI integrationLimited or non-existentConsiderably more prevalent. Used to enhance threat detection and and incident response Creating an application security ladder Web applications underpin many essential business capabilities – internal operations management, customer experience, partner integration – the list goes on. As reliance on these application ecosystems grows, so does business risk exposure through underlying vulnerabilities. Strengthening application security closes major blindspots while allowing companies to pursue advanced digital transformation supporting key goals around: Improving self-service and convenience through customer portal expansion Accelerating development velocity using CI/CD pipelines and microservices Enabling real-time data exchanges through IoT integrations and open API ecosystems Increasing revenue with personalised interfaces and recommendation engines Combining network-layer perimeter defences from traditional firewalls with reinforced protections from specialised WAFs creates a security ladder effect. The traditional firewall filters allowed traffic at the network level based on IPs, protocols, and volume heuristics. This protects against basic attacks like worms, reconnaissance scans, and DDoS events. Then the WAF takes over at the application layer, scrutinising the full context of requests to identify attempts to exploit app logic and functionality itself using injection attacks, stolen credentials, unusual workflows, or other sneaky techniques security teams encounter daily. Together, this layered defence-in-depth approach secures both the overall network and the intricate web apps conducting an ever-larger percentage of essential business. Companies can then direct more development resources towards advancing capabilities rather than just patching vulnerabilities. Final word The costs of security incidents grow more severe year over year. And as companies rely increasingly on web apps to manage operations, serve customers, and drive revenue, application vulnerabilities present a serious (and immediate) business risk. Protecting systems with advanced application-aware defenses – powered by AI – means that your security supports rather than gets in the way of your key strategic initiatives With scalable and secure defenses guarding your web properties, you can confidently build capabilities supporting goals around better customer experience, smoother operations, increased sales growth, and expanded partner channels. In other words, you can focus on pushing your business forward with the peace of mind knowing that you’ve done your part in securing your perimeter and web apps in our ever AI-driven world. The post AI-powered WAFs vs traditional firewalls: Protecting your web applications appeared first on AI News. View the full article

Hey there, AI enthusiasts! If you’re anything like me, you’re always on the lookout for the best resources to discover the latest and greatest in artificial intelligence. Whether you’re a developer eager to showcase your cutting-edge tool or someone simply fascinated by the rapid advancements in AI, knowing where to find and promote these tools is crucial. That’s why I’ve put together this guide to the top five AI tool directories you absolutely need to check out. These platforms are not just directories; they’re vibrant communities and treasure troves of information that can help you navigate the ever-evolving world of AI. So, grab a cup of coffee, get comfy, and let’s ***** into these fantastic resources that will make your AI journey a whole lot easier and more exciting! How I chose these AI tool directories When it comes to finding the best directories, I took a multi-faceted approach. I scoured the web for directories that are not only popular, but also highly respected within the tech community. I looked for platforms that offer a mix of user reviews, community engagement, and ease of use. After a thorough search, I narrowed it down to these five stellar options. Each of these directories has its own unique strengths and features, making them invaluable resources for anyone involved in the AI space. So, without further ado, let’s explore these fantastic platforms. Top five AI tool directories 1. AI Parabellum AI Parabellum is a fantastic resource dedicated solely to AI tools. It’s like a treasure trove for anyone interested in artificial intelligence. The platform is user-friendly and allows you to explore, submit, and promote AI tools effortlessly. Key features: Focus on AI: Ensures that the tools listed are relevant and cutting-edge. User-friendly design: Easy to navigate and find exactly what you’re looking for. Expert recommendations: Handpicked lists of top AI tools by industry experts. Detailed filters: Narrow down your search by categories, features, pricing, and more. AI-powered search: Uses machine learning algorithms to provide the most relevant results. Whether you’re looking for AI-driven analytics, machine learning frameworks, or natural language processing tools, AI Parabellum has got you covered. This makes AI Parabellum not just a directory, but a vibrant community of AI enthusiasts and professionals. 2. SaaSHub SaaSHub is another excellent platform that serves as a directory for software alternatives, accelerators, and startups. While it covers a broad range of software categories, its section on AI tools is particularly robust. Key features: Wide range of software categories: Covers a broad spectrum, including AI tools. Community engagement: Strong discussions and reviews to help you gauge the effectiveness and popularity of different AI tools. User-friendly interface: Comprehensive search functionality to find exactly what you’re looking for. SaaSHub’s focus on alternatives means that it often highlights innovative and lesser-known tools, giving them a chance to shine. 3. G2 G2 is one of the most comprehensive software review platforms out there. It covers a wide array of software categories, including AI tools. Key features: Extensive user reviews: Detailed product comparisons and user feedback. Robust analytics: Helps you understand how your tool is performing in the market. Highly-engaged community: Provides detailed reviews and ratings to help make informed decisions. G2’s focus on transparency and user feedback makes it a trusted resource for anyone looking to discover or showcase AI tools. 4. AlternativeTo AlternativeTo is a unique platform that focuses on providing alternatives to popular software. It’s an excellent resource for discovering new AI tools that you might not find elsewhere. Key features: Focus on alternatives: Ensures innovative and lesser-known tools get their time in the spotlight. Community-driven platform: Users can submit tools and leave reviews. User-friendly interface: Comprehensive search functionality to find exactly what you’re looking for. If your AI tool offers a unique twist or serves as a better alternative to an existing tool, AlternativeTo is the place to be. 5. Product Hunt Product Hunt is a favorite among tech enthusiasts for discovering the latest and greatest in tech products, including AI tools. Key features: Community upvotes: The more upvotes your tool gets, the higher it appears on the list, increasing its visibility. Immediate feedback: Particularly useful for launching new AI tools and getting immediate feedback from a tech-savvy audience. Highly-engaged community: Provides detailed reviews and ratings to help make informed decisions. Product Hunt’s focus on innovation and community engagement makes it a trusted resource for anyone looking to discover or showcase AI tools. Conclusion Alright, folks, we’ve journeyed through some of the top AI tool directories out there, and I hope you’re as excited as I am about the possibilities they offer. These platforms are more than just lists; they’re gateways to innovation, collaboration, and growth in the AI space. Whether you’re looking to discover new tools, get expert recommendations, or connect with a community of like-minded individuals, these directories have got you covered. Remember, the world of AI is constantly evolving, and staying updated with the latest tools and technologies is key to staying ahead of the curve. So, take advantage of these resources, ***** into the community discussions, explore the curated lists, and don’t hesitate to try out new tools that could revolutionise your work or projects. The post Top 5 AI tool directories: Discover and showcase AI innovations appeared first on AI News. View the full article

Five prominent Senate Democrats have sent a letter to OpenAI CEO Sam Altman, seeking clarity on the company’s safety and employment practices. The letter – signed by Senators Brian Schatz, Ben Ray Luján, Peter Welch, Mark R. Warner, and Angus S. King, Jr. – comes in response to recent reports questioning OpenAI’s commitment to its stated goals of safe and responsible AI development. The senators emphasise the importance of AI safety for national economic competitiveness and geopolitical standing. They note OpenAI’s partnerships with the US government and national security agencies to develop cybersecurity tools, underscoring the critical nature of secure AI systems. “National and economic security are among the most important responsibilities of the ******* States Government, and unsecure or otherwise vulnerable AI systems are not acceptable,” the letter states. The lawmakers have requested detailed information on several key areas by 13 August 2024. These include: OpenAI’s commitment to dedicating 20% of its computing resources to AI safety research. The company’s stance on non-disparagement agreements for current and former employees. Procedures for employees to raise cybersecurity and safety concerns. Security protocols to prevent theft of AI models, research, or intellectual property. OpenAI’s adherence to its own Supplier Code of Conduct regarding non-retaliation policies and whistleblower channels. Plans for independent expert testing and assessment of OpenAI’s systems pre-release. Commitment to making future foundation models available to US Government agencies for pre-deployment testing. Post-release monitoring practices and learnings from deployed models. Plans for public release of retrospective impact assessments on deployed models. Documentation on meeting voluntary safety and security commitments to the Biden-Harris administration. The senators’ inquiry touches on recent controversies surrounding OpenAI, including reports of internal disputes over safety practices and alleged cybersecurity breaches. They specifically ask whether OpenAI will “commit to removing any other provisions from employment agreements that could be used to penalise employees who publicly raise concerns about company practices.” This congressional scrutiny comes at a time of increasing debate over AI regulation and safety measures. The letter references the voluntary commitments made by leading AI companies to the White House last year, framing them as “an important step towards building this trust” in AI safety and security. Kamala Harris may be the next US president following the election later this year. At the AI Safety Summit in the *** last year, Harris said: “Let us be clear, there are additional threats that also demand our action. Threats that are currently causing harm, and which to many people also feel existential… when people around the world cannot discern fact from fiction because of a flood of AI-enabled myths and disinformation.” Chelsea Alves, a consultant with UNMiss, commented: “Kamala Harris’ approach to AI and big tech regulation is both timely and critical as she steps into the presidential race. Her policies could set new standards for how we navigate the complexities of modern technology and individual privacy.” The response from OpenAI to these inquiries could have significant implications for the future of AI governance and the relationship between tech companies and government oversight bodies. (Photo by Darren Halstead) See also: OpenResearch reveals potential impacts of universal basic income [Hidden Content] 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 Senators probe OpenAI on safety and employment practices appeared first on AI News. View the full article

A study conducted by OpenResearch has shed light on the transformative potential of universal basic income (UBI). The research aimed to “learn from participants’ experiences and better understand both the potential and the limitations of unconditional cash transfers.” The study – which provided participants with an extra $1,000 per month – revealed significant impacts across various aspects of recipients’ lives, including health, spending habits, employment, personal agency, and housing mobility. In healthcare, the analysis showed increased utilisation of medical services, particularly in dental and specialist care. One participant noted, “I got myself braces…I feel like people underestimate the importance of having nice teeth because it affects more than just your own sense of self, it affects how people look at you.” While no immediate measurable effects on physical health were observed, researchers suggest that increased medical care utilisation could lead to long-term health benefits. The study also uncovered interesting spending patterns among UBI recipients. On average, participants increased their overall monthly spending by $310, with significant allocations towards basic needs such as food, transportation, and rent. Notably, there was a 26% increase in financial support provided to others, highlighting the ripple effect of UBI on communities. In terms of employment, the study revealed nuanced outcomes. While there was a slight decrease in overall employment rates and work hours among recipients, the study found that UBI provided individuals with greater flexibility in making employment decisions aligned with their circumstances and goals. One participant explained, “Because of that money and being able to build up my savings, I’m in a position for once to be picky…I don’t have to take a ******* job just because I need income right now.” The research also uncovered significant improvements in personal agency and future planning. UBI recipients were 14% more likely to pursue education or job training and 5% more likely to have a budget compared to the control group. ****** recipients in the third year of the program were 26% more likely to report starting or helping to start a business. Lastly, the study’s analysis revealed increased housing mobility among UBI recipients. Participants were 11% more likely to move neighbourhoods and 23% more likely to actively search for new housing compared to the control group. The study provides valuable insights into the potential impacts of UBI, offering policymakers and researchers a data-driven foundation for future decisions on social ******** programs. This major societal conversation may be necessary if worst case scenarios around AI-induced job displacement come to fruition. (Photo by Freddie Collins on Unsplash) See also: AI could unleash £119 billion in *** productivity [Hidden Content] 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 OpenResearch reveals potential impacts of universal basic income appeared first on AI News. View the full article

In the high-stakes world of sports ********, success hinges on the ability to predict outcomes with precision. Enter AI, which is revolutionising the industry by providing bettors with sophisticated tools to improve their odds. By leveraging data analytics, machine learning, and real-time processing, AI is turning the traditional approach to sports ******** on its head. This article delves into how AI algorithms are transforming sports ********, providing actual data, statistics, and insights that demonstrate their impact. The rise of AI in sports ******** Sports ******** has always been a game of numbers, but the advent of AI has taken it to another level. AI algorithms can analyse vast amounts of data, recognise patterns, and make predictions with remarkable accuracy. According to a sportbet.one blog, the AI in sports market is projected to reach $3.5 billion by 2026, growing at a compound annual growth rate (CAGR) of 28.32% from 2019 to 2026. This surge is driven by the increasing demand for data-driven decision-making in sports ********. Predictive analytics: The heart of AI ******** At the core of AI’s impact on sports ******** is predictive analytics. Machine learning models, such as regression analysis, neural networks, and decision trees, are employed to analyse historical data and predict future outcomes. For instance, a study published in the Journal of Sports Analytics found that machine learning models could predict NFL game outcomes with up to 75% accuracy, significantly higher than traditional methods. Data collection and processing AI algorithms thrive on data. They collect and process information from various sources, including past performance statistics, player conditions, weather forecasts, and even social media sentiments. According to a survey by the International Journal of Computer Applications, AI models that incorporate diverse data sources can improve prediction accuracy by up to 20%. Pattern recognition and anomaly detection One of AI’s greatest strengths is its ability to recognise patterns and detect anomalies. Machine learning algorithms can identify subtle trends that human analysts might miss. For example, AI can detect a player’s declining performance due to unreported injuries by analysing minute variations in their play style. This capability allows bettors to make more informed decisions and identify value bets that offer higher returns. Real-time analysis for live ******** Live ********, where odds change rapidly during a game, benefits immensely from AI’s real-time analysis capabilities. AI algorithms can process live data streams and adjust predictions on the fly. A report by the Journal of Gambling Studies found that AI-powered real-time ******** systems could increase bettors’ profits by 15-25% compared to traditional methods. This advantage is particularly pronounced in fast-paced sports like basketball and soccer. Sentiment analysis: Gauging public opinion Public sentiment can significantly influence sports outcomes. AI uses natural language processing (NLP) to analyse sentiments from social media, news articles, and other textual data. For instance, during the 2018 FIFA World Cup, an AI model analysed over 10 million tweets to gauge public sentiment and accurately predicted the outcomes of 70% of the matches. Sentiment analysis adds an extra layer of insight, helping bettors understand the psychological factors at play. Insights from the BMR sports ******** forum show that community discussions and sentiments can also significantly influence ******** decisions. Risk management and arbitrage opportunities AI excels in managing risk by analysing the probability of various outcomes and suggesting bets that maximise returns while minimising potential losses. This includes identifying arbitrage opportunities, where discrepancies in odds between different bookmakers can be exploited. According to a study by the ********* Journal of Operational Research, AI-driven arbitrage strategies can yield returns of up to 10% per annum, far surpassing traditional ******** methods. Automated ******** systems AI-powered automated ******** systems, or bots, are becoming increasingly popular. These systems can place bets based on predefined criteria and real-time data analysis, executing trades at high speed to ensure the best odds are taken advantage of. For example, an AI **** developed by the MIT Computer Science and Artificial Intelligence Laboratory achieved a 25% return on investment (ROI) over a six-month ******* by leveraging real-time data and machine learning. Player and team performance analysis Advanced AI models assess individual player performance and team dynamics, considering factors like injuries, player transfers, and team strategies. A study published in the IEEE Transactions on Knowledge and Data Engineering demonstrated that AI models could predict player performance with an accuracy of 85%, significantly aiding bettors in making informed decisions, particularly in tournaments like the UEFA Youth Championships. Market analysis: Finding value bets AI algorithms analyse ******** markets to identify where the best value bets are. By understanding how odds are moving and why, bettors can identify opportunities where the odds are in their favour. According to a report by Grand View Research, AI-driven market analysis can improve ******** efficiency by up to 30%, making it a valuable tool for serious bettors. Bottom line AI algorithms are transforming sports ******** by providing bettors with powerful tools to improve their odds. From predictive analytics and real-time analysis to sentiment analysis and risk management, AI is enhancing every aspect of the ******** process. As AI technology continues to evolve, its impact on sports ******** is likely to grow, offering even more sophisticated tools for bettors. However, it’s crucial to use these tools responsibly and ethically to ensure a fair and enjoyable ******** experience. The post How AI is transforming sports ******** for better odds appeared first on AI News. View the full article

Three hospital systems across England have begun a live clinical trial of AI technology designed to detect and grade prostate *******. The study – known as ARTICULATE PRO – is being led by the University of Oxford in collaboration with Paige, a pioneer in clinical AI applications for ******* diagnosis. The participating hospitals – North Bristol Trust Southmead Hospital, University Hospitals Coventry and Warwickshire, and Oxford University NHS Foundation Trust – are now incorporating Paige’s AI technology into their standard of care. This multisite trial aims to evaluate the potential of AI to improve patient outcomes against a backdrop of rising prostate ******* cases. Professor Clare Verrill, OUH Cellular Pathology Consultant, Associate Professor and Principal Investigator of ARTICULATE PRO, said “The central focus of ARTICULATE PRO is patients. We are striving towards our goal to safely and effectively ensure they benefit the most from powerful AI technology. “With the multisite live use of The Paige Prostate Suite, we can systematically study benefits to patients in clinical settings.” The Prostate Suite – the AI system being trialled – is designed to assist pathologists in detecting, grading, and measuring tumours in prostate biopsies and tissue samples. Pathologists at the three hospitals are assessing how this AI technology impacts their clinical decision-making, pathology service delivery, and resource utilisation in real-world settings. Dr Jon Oxley, Uropathologist and Bristol lead of ARTICULATE PRO, commented: “I have studied the ******** and progression of prostate ******* in clinical research for over 25 years, it is a significant advancement that Paige’s AI applications have achieved a level of validation and performance that allows safe and effective live clinical use. “Using Paige Prostate Suite alongside our standard of care has the promise to increase efficiency and improve reproducibility of results for patients.” The study is notable for its implementation across hospitals using different digital pathology scanners and information systems, serving distinct patient populations. This diversity allows for a comprehensive assessment of how Paige’s AI technology can best serve patients, histopathologists, and hospital systems in prostate ******* diagnosis. Dr Bidisa Sinha, Uropathologist at University Hospitals Coventry and Warwickshire, added: “We believe AI can help to improve the accuracy and consistency of grading ******* and assist in detection of small areas of ******* which are easy to miss. “This is world-leading research being carried out at UHCW. We are proud to be a global leader in the field of digital and computational pathology.” The ARTICULATE PRO study is funded by the Accelerated Access Collaborative (AAC) Artificial Intelligence in Health and Care Award, overseen by the Department of Health and Social Care. As prostate ******* rates continue to rise, the integration of AI in diagnosis could potentially lead to earlier detection, more accurate grading, and ultimately improved patient outcomes. The results of this trial could pave the way for wider adoption of AI in ******* diagnosis across the *** and beyond. (Image Credit: Paige) See also: AI could unleash £119 billion in *** productivity [Hidden Content] 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 *** hospitals begin live trial of prostate *******-detecting AI appeared first on AI News. View the full article

Mistral AI has announced NeMo, a 12B model created in partnership with NVIDIA. This new model boasts an impressive context window of up to 128,000 tokens and claims state-of-the-art performance in reasoning, world knowledge, and coding accuracy for its size category. The collaboration between Mistral AI and NVIDIA has resulted in a model that not only pushes the boundaries of performance but also prioritises ease of use. Mistral NeMo is designed to be a seamless replacement for systems currently using Mistral 7B, thanks to its reliance on standard architecture. In a move to encourage adoption and further research, Mistral AI has made both pre-trained base and instruction-tuned checkpoints available under the Apache 2.0 license. This open-source approach is likely to appeal to researchers and enterprises alike, potentially accelerating the model’s integration into various applications. One of the key features of Mistral NeMo is its quantisation awareness during training, which enables FP8 inference without compromising performance. This capability could prove crucial for organisations looking to deploy large language models efficiently. Mistral AI has provided performance comparisons between the Mistral NeMo base model and two recent open-source pre-trained models: Gemma 2 9B and Llama 3 8B. “The model is designed for global, multilingual applications. It is trained on function calling, has a large context window, and is particularly strong in English, French, *******, Spanish, Italian, Portuguese, ********, *********, Korean, Arabic, and Hindi,” explained Mistral AI. “This is a new step toward bringing frontier AI models to everyone’s hands in all languages that form human culture.” Mistral NeMo introduces Tekken, a new tokeniser based on Tiktoken. Trained on over 100 languages, Tekken offers improved compression efficiency for both natural language text and source code compared to the SentencePiece tokeniser used in previous Mistral models. The company reports that Tekken is approximately 30% more efficient at compressing source code and several major languages, with even more significant gains for Korean and Arabic. Mistral AI also claims that Tekken outperforms the Llama 3 tokeniser in text compression for about 85% of all languages, potentially giving Mistral NeMo an edge in multilingual applications. The model’s weights are now available on HuggingFace for both the base and instruct versions. Developers can start experimenting with Mistral NeMo using the mistral-inference tool and adapt it with mistral-finetune. For those using Mistral’s platform, the model is accessible under the name open-mistral-nemo. In a nod to the collaboration with NVIDIA, Mistral NeMo is also packaged as an NVIDIA NIM inference microservice, available through ai.nvidia.com. This integration could streamline deployment for organisations already invested in NVIDIA’s AI ecosystem. The release of Mistral NeMo represents a significant step forward in the democratisation of advanced AI models. By combining high performance, multilingual capabilities, and open-source availability, Mistral AI and NVIDIA are positioning this model as a versatile tool for a wide range of AI applications across various industries and research fields. (Photo by David Clode) See also: Meta joins Apple in withholding AI models from EU users [Hidden Content] 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 Mistral AI and NVIDIA unveil 12B NeMo model appeared first on AI News. View the full article

Taiwan Semiconductor Manufacturing Company (TSMC) has raised its revenue forecast for 2024, citing strong demand for chips in AI applications. The world’s largest contract chipmaker anticipates growth slightly above the mid-20% range in US dollar terms, up from its previous estimate. This adjustment comes as TSMC reports better-than-expected profits for the second quarter of 2024. During its earnings call, TSMC, according to Reuters, also addressed speculation about potential ****** ventures, particularly in the ******* States. The company reaffirmed its commitment to its current global expansion strategy, including significant investments in Arizona, Japan, and Europe. This stance on independent growth comes amid ongoing discussions about the worldwide distribution of semiconductor manufacturing capacity. TSMC’s chairman and CEO, C.C. Wei, painted a vivid picture of the current landscape: “AI is so hot; right now everybody, all my customers, want to put AI functionality into their devices.” TSMC’s success is intrinsically linked to the global AI *****, which has helped offset the tapering demand for pandemic-driven electronics. As a critical supplier to tech giants like Apple Inc and Nvidia, TSMC finds itself at the heart of the AI revolution, producing the advanced chips that power everything from smartphones to data centres. It is reflected in the company’s stellar performance based on TSMC’s recent financial results, with net profit for the April-June quarter soaring to T$247.8 billion ($7.60 billion), surpassing market expectations. Despite global economic uncertainties and geopolitical tensions, this robust growth comes, underscoring TSMC’s resilience and strategic positioning in the semiconductor industry. The company’s optimism extends into the near future, with CFO Wendell Huang projecting strong demand for TSMC’s leading-edge process technologies, particularly in smartphones and AI-related applications. This positive outlook is backed by concrete plans. TSMC adjusts its capital expenditure for the year to between $30 billion and $32 billion, signalling its commitment to expanding capacity and maintaining its technological edge. However, TSMC’s journey is not without challenges. The company faces intense pressure to meet the skyrocketing demand for advanced chips, with Wei describing the situation as “very, very tight.” TSMC is working to ramp up capacity to support customer needs through 2026 and beyond. In the face of these challenges, TSMC ******** committed to its global expansion strategy. The company is investing heavily in new facilities, including a $65 billion investment in three plants in Arizona and projects in Japan and potentially in Europe. This global footprint helps TSMC meet worldwide demand and positions the company strategically in an increasingly complex geopolitical landscape. Interestingly, TSMC has firmly rejected the idea of a ****** venture in the US despite recent comments from US political figures about Taiwan’s dominance in the chip industry. Wei reaffirmed the company’s commitment to its expansion plans, emphasizing TSMC’s independence and strategic vision. As TSMC continues to push the boundaries of semiconductor technology, its impact extends far beyond its balance sheet. The company’s innovations are driving advancements in AI, powering the next generation of smart devices, and shaping the future of global technology. In conclusion, TSMC’s latest financial results and optimistic forecasts paint a picture of a company at the forefront of the AI revolution. As the world increasingly relies on advanced computing power, TSMC’s role in providing the chips that make it all possible has never been more critical. With its ambitious expansion plans and unwavering focus on innovation, TSMC is not just riding the AI wave – it’s helping to create it. See also: Global semiconductor shortage: How the US plans to close the talent gap [Hidden Content] 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 TSMC forecasts record growth, rejects US ****** venture amid AI surge appeared first on AI News. View the full article

A Reuters survey released recently ***** bare a nuanced picture of ********* corporate acceptance and social attitudes toward technology. The survey, conducted by Nikkei Research, anonymously polled 506 companies from 3-12 July, with around half responding. It provides a broad view of how corporate Japan is striking a balance between incorporating AI and tightening cybersecurity amid changing social attitudes toward work. The survey revealed a striking divide in AI adoption across ********* businesses. While nearly a quarter of companies have already integrated AI into their operations, a significant portion – over 40% – have yet to make any immediate plans to leverage this cutting-edge technology. Specifically, 24% of respondents reported having introduced AI in their businesses, with an additional 35% planning to do so in the future. However, the remaining 41% indicated no intention to adopt AI, illustrating the varying degrees of technological embrace within corporate Japan. For companies venturing into AI territory, the motivations are clear and multifaceted. When asked about their objectives for AI adoption, 60% of respondents cited the need to address labour shortages—a pressing issue in Japan’s ageing society. Additionally, 53% aimed to reduce labour costs, while 36% saw AI as a means to accelerate research and development efforts. These figures highlight the potential of AI to address some of Japan’s most pressing economic challenges. However, the path to AI integration is not without its obstacles. Companies reported several hurdles in their AI adoption journey. A manager from a transportation company pointed to “anxiety among employees over possible headcount reduction” as a significant concern. Other challenges included a lack of technological expertise within organisations, the need for substantial capital expenditure to implement AI systems, and lingering concerns about the reliability of AI technologies. These factors collectively contribute to the hesitation some companies feel about embracing AI. The survey also shed light on the cybersecurity landscape facing ********* businesses. A concerning 15% of respondents reported experiencing cyberattacks over the past year, with an additional 9% indicating that their business partners had fallen victim to such attacks during the same *******. The impact of these cyber incidents was substantial, with 23% of affected companies or their partners reporting temporary business halts, and 4% suffering information leaks. In response to these digital threats, ********* companies are taking varied approaches to enhance their cybersecurity. Nearly half (47%) of the surveyed firms are outsourcing their defense measures, while 38% have opted to develop in-house expertise. The recent high-profile cyberattack on publisher Kadokawa has further spotlighted this issue, prompting the ********* government to work towards strengthening national cybersecurity measures. Shifting social norms: The surname debate Interestingly, the survey extended beyond technological concerns to gauge corporate attitudes towards social change, specifically regarding Japan’s marriage laws. Half of the surveyed firms expressed support for changing the law that currently requires married couples to share the same surname. This practice, which typically results in women adopting their husband’s name in more than 90% of marriages, has faced growing criticism for potentially infringing on individual identity and burdening women with extensive paperwork. The issue has gained renewed attention following the Keidanren business lobby’s recent appeal to the government to allow married individuals to retain their original surnames. In the survey, 50% of respondents supported such a legislative change, compared to 11% who opposed it. A manager at a machinery firm argued that “the current system is hurting individuals’ – and especially women’s – dignity and freedom,” while a steelmaker official described the proposed change as the “natural demand of the times.” However, not all views were aligned, with a manager at a non-ferrous metal manufacturer expressing concern that allowing separate surnames could “weaken family bonds.” When asked about the potential impact of this legal change on their businesses, 14% of respondents anticipated a boost in employee morale, and 10% expected it to aid in hiring activities. However, a majority (56%) foresaw no significant impact on their operations. This comprehensive survey provides valuable insights into the multifaceted challenges and opportunities facing ********* businesses today. From technological adoption and cybersecurity concerns to evolving social norms, the results paint a picture of a corporate landscape in transition, grappling with the demands of innovation while navigating complex social changes. See also: AI could unleash £119 billion in *** productivity [Hidden Content] 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 Over 40% of ********* firms lack AI adoption plans appeared first on AI News. View the full article

Meta has announced it will not be launching its upcoming multimodal AI model in the ********* Union due to regulatory concerns. This decision from Meta comes on the heels of Apple’s similar move to exclude the EU from its Apple Intelligence rollout, signalling a growing trend of tech giants hesitating to introduce advanced AI technologies in the region. Meta’s latest multimodal AI model – capable of handling video, audio, images, and text – was set to be released under an open license. However, Meta’s decision will prevent ********* companies from utilising this technology, potentially putting them at a disadvantage in the global AI race. “We will release a multimodal Llama model over the coming months, but not in the EU due to the unpredictable nature of the ********* regulatory environment,” a Meta spokesperson stated. A text-only version of Meta’s Llama 3 model is still expected to launch in the EU. Meta’s announcement comes just days after the EU finalised compliance deadlines for its new AI Act. Tech companies operating in the EU will have until August 2026 to comply with rules surrounding copyright, transparency, and specific AI applications like predictive policing. The withholding of these advanced AI models from the EU market creates a challenging situation for companies outside the region. Those hoping to provide products and services utilising these models will be unable to offer them in one of the world’s largest economic markets. Meta plans to integrate its multimodal AI models into products like the Meta Ray-Ban smart glasses. According to Axios, the company’s EU exclusion will extend to future multimodal AI model releases as well. As more tech giants potentially follow suit, the EU may face challenges in maintaining its position as a leader in technological innovation while balancing concerns about AI’s societal impacts. (Photo by engin akyurt) See also: AI could unleash £119 billion in *** productivity [Hidden Content] 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 Meta joins Apple in withholding AI models from EU users appeared first on AI News. View the full article

Workday has unveiled figures that suggest AI could unleash a £119 billion productivity boost for *** enterprises. This revelation comes at a crucial time, as the nation grapples with a productivity slump that has persisted for over a decade and a half. The report paints a picture of a country on the brink of a seismic shift in its economic landscape. With current productivity levels languishing 24% below pre-2008 projections, the promise of AI-driven efficiency gains offers a glimmer of hope for businesses and policymakers alike. According to the study, large businesses in the *** could save a staggering 7.9 billion employee hours annually through the strategic implementation of AI technologies. Breaking this down to an individual level, the numbers are equally impressive. Business leaders stand to save 1,117 hours per year – equivalent to 140 working days – while individual employees could reclaim 737 hours, or 92 working days. “Sizeable productivity growth has eluded *** workplaces for over 15 years – but responsible AI has the potential to shift the paradigm,” explained Daniel Pell, VP and country manager for ***&I at Workday. The report’s findings come at a time when political figures are also weighing in on the role of technology in governance. Former Labour Prime Minister Tony Blair recently commented that while Britain faces economic challenges, advances in technologies like AI mean there has “never been a better or more exciting time to be governing.” Despite the optimistic outlook, the path to AI adoption is not without obstacles. The report highlights that 93% of both employees and business leaders harbour concerns related to trust in AI. This underscores the need for responsible AI strategies, comprehensive education, and transparent communication initiatives. Other barriers to AI adoption include fears over safety, privacy, and bias (38%), the need for more time to educate teams (34%), and lack of investment (32%). Additionally, the report identified unengaged employees (41%), lack of incentives (41%), and inadequate technology (35%) as key factors hampering organisational productivity. The potential economic impact of AI is staggering. Based on the study’s findings, an additional 2.9 hours of work per day translates to £11,058 a year of added value for each average employee. With over 10 million employees in large businesses across the ***, the cumulative effect could reach £119 billion worth of productive work annually. However, the report also reveals a productivity paradox in the current workplace. In an 8-hour workday, employees and business leaders are genuinely productive for only 5.8 and 5.9 hours respectively—leaving over a quarter of the day unproductive. The promise of AI extends beyond mere time savings. By taking on mundane and repetitive tasks, AI has the potential to empower workers to focus on more meaningful and impactful work. This shift could address one of the biggest barriers to productivity identified in the report: unengaged employees. As *** businesses stand at the crossroads of this AI revolution, the report serves as both a wake-up call and a roadmap. It suggests a two-pronged approach to AI deployment: a concrete analysis of potential efficiencies coupled with a transparent strategy to tackle adoption barriers. Realising the full potential of AI in the *** economy will require a concerted effort from businesses, policymakers, and employees alike. The successful integration of AI technologies could well determine the ***’s economic trajectory for years to come. A full copy of Workday’s report can be found here (registration required) (Photo by Belinda Fewings) See also: Tech executives confident in AI skills, but adoption barriers persist [Hidden Content] 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 AI could unleash £119 billion in *** productivity appeared first on AI News. View the full article

Since the 1990s, the most ********* gamers have traditionally opted for PCs and laptops. Not only did this allow them to ***** deeper into their favourite games, even creating things like mods, but PC gaming also offered the ability to tinker with hardware. Many PC gamers enjoy improving their hardware by adding new parts. They also enjoy playing a diverse range of games. Some of the world’s most competitive eSports focus on PC hits, from FPSs like Counter-Strike: Global Offensive to MOBAs like Dota 2. Though console gamers are also present, most gamers associate these titles with PCs. Beyond the scope of hyper-visible eSports hits, PC gamers also have access to other titles. Casino games, for example, are often played via browser. Slots are a popular choice, offering dozens of formats and hundreds of themes for them to choose from. Straight from a laptop or PC, players can spin the reel. The same is true for DCCGs. These types of card games have taken off over the last decade, including new hits like Hearthstone and Marvel Snap. Though traditionally played in person, PCs are now the preferred format for many DCCG competitors. One of the most unique developments in the world of PC gaming is the rise of AI-driven laptops and PCs. These powerful devices are designed to handle even more complex processing challenges. Both their hardware and software are advanced, and designed to handle machine-learning tasks. If you’ve been considering an AI-driven laptop to improve your gaming experience, here are some of the products you should keep an eye on. The metrics The best AI-driven laptops and PCs have a performance-focused processor. Some of the best for AI-driven tasks are Intel Core i7 and i9—but you’ll have other options, too. Along with the processor, focus on the device’s graphics card—the higher, the better. But keep a look out for storage, as the same is true for RAM. Better storage relates to faster loading times and general responsiveness—which are hugely important for gamers who need to avoid even a millisecond of lag. The NVMe SSDs are considered the best in the industry in 2024. Alienware Aurora R14 In terms of actual products, the Alienware Aurora R14 is one of the best choices for AI-driven gaming. That’s because it has all of the most advanced hardware features, including a graphics card from NVIDIA GeForce, an Intel Core i9 processor, and 32GB of RAM. That’s fast, accurate, and advanced gaming at its best. [Hidden Content] MSI Trident X This PC is a little bit different in that it focuses on being more compact. Oftentimes, the more advanced the PC, the bulkier the hardware becomes. That’s not the case with the MSI Trident X. Despite its compact size, it has the Intel Core i9 processor, NVIDIA GeForce Graphics Card, and 32GB of storage—all the same as the Alienware Aurora, just in a slightly more manageable package. HP Omen Obelisk With the HP Omen Obelisk, the focus is on customisation. The AI-driven PC includes all the same features as the other two products—even down to the brands used. However, it also offers tool-free access to the interior, which makes both maintenance and upgrading a breeze. Unsurprisingly, it also has a glass side window that makes observing the hardware enjoyable. HP Spectre x360 14 If you need to get more out of your AI-driven PC than gaming sessions, then the HP Spectre is a great option. (Although it’s a laptop—not a PC.) Its robust features will take your gaming sessions to the next level while also handling other types of demanding tasks, from graphic design to video editing. The sheer range of options makes this one popular for anyone who also needs a professional device. ASUS ROG Zephyrus G14 This option is slightly more affordable than others on the list—though you’ll notice the more limited features. Specifically, it has less storage than the other AI-driven PCs, along with a slightly less powerful processor. But it still packs a huge punch into its space-saving laptop hardware, which makes it a solid option for most gamers. The post Best AI-driven laptops and PCs appeared first on AI News. View the full article

Deutsche Telekom, one of Europe’s leading telecoms companies, has unveiled a new AI-powered software-as-a-service solution called “Law Monitor” aimed at supporting corporate legal departments in quickly identifying changes to national and international laws. In an era of increasingly complex regulatory environments, companies across all industries are struggling to keep up with the constant stream of legal updates. The Law Monitor seeks to address this challenge by automating the time-consuming process of monitoring legislative changes. Dr Ferri Abolhassan, CEO of T-Systems and member of the Board of Management of Deutsche Telekom AG, said: “AI has come to stay. At T-Systems, we see ourselves as consultants for our customers and need to make AI tangible. The Law Monitor is a good example of this.” The AI-based model scans legal texts from the ******* Federal Law Gazette, using advanced algorithms to recognise and structure information from text, images, and tables. This processed data is then presented to legal department employees via an intuitive dashboard, allowing them to quickly identify relevant changes. Abolhassan highlighted a recent example from the automotive industry to illustrate the tool’s potential impact: “Since 7 July, for example, binding regulations have applied throughout the EU for the integration of specific assistance systems, such as emergency braking and lane departure warning systems in new cars. Law Monitor helps car manufacturers to make these regulations transparent in real time in order to adapt production at an early stage.” The need for such a tool is clear. Companies are facing an unprecedented density of regulations both in Germany and worldwide. For instance, the ********* packaging industry recently had to adapt to new requirements mandating that disposable plastic bottles be produced with attached lids to reduce plastic waste. While the Law Monitor currently focuses on legislative changes in Germany, Deutsche Telekom plans to expand its coverage to 19 additional countries – including specific states in the US – in the coming months. This expansion will allow the tool to display legal changes from different countries in a single interface, setting it apart from existing competing products. The solution is designed to be applicable across various industries and even the public sector. Deutsche Telekom reports that one customer from the automotive industry is already using the Law Monitor, with others from both the automotive and packaging industries expressing strong interest. By automating the process of monitoring legal changes, the Law Monitor not only saves time but also allows legal professionals to focus on more value-added tasks such as analysing the content and assessing the context of these changes. Deutsche Telekom’s initiative demonstrates how AI can be leveraged to address real-world challenges in the legal and compliance space, potentially revolutionising how companies stay informed about and adapt to legislative changes. (Photo by Conny Schneider) See also: Tech executives confident in AI skills, but adoption barriers persist 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 Deutsche Telekom turns to AI to support legal departments appeared first on AI News. View the full article

The US is ******** big on the future of semiconductor technology, launching a $1.6 billion competition to revolutionise chip packaging and challenge Asia’s longstanding dominance in the field. On July 9, 2024, the US Department of Commerce unveiled its ambitious plan to turbocharge domestic advanced packaging capabilities, a critical yet often overlooked aspect of semiconductor manufacturing. This move, part of the Biden-Harris Administration’s CHIPS for America program, comes as the US seeks to revitalise its semiconductor industry and reduce dependence on foreign suppliers. Advanced packaging, a crucial step in semiconductor production, has long been dominated by ****** countries like Taiwan and South Korea. By investing heavily in this area, the US aims to reshape the global semiconductor landscape and position itself at the forefront of next-generation chip technology, marking a significant shift in the industry’s balance of power. US Secretary of Commerce Gina Raimondo emphasised the importance of this move, stating, “President Biden was clear that we need to build a vibrant domestic semiconductor ecosystem here in the US, and advanced packaging is a huge part of that. Thanks to the Biden-Harris Administration’s commitment to investing in America, the US will have multiple advanced packaging options across the country and push the envelope in new packaging technologies.” The competition will focus on five key R&D areas: equipment and process integration, power delivery and thermal management, connector technology, chiplets ecosystem, and co-design/electronic design automation. The Department of Commerce anticipates making several awards of approximately $150 million each in federal funding per research area, leveraging additional investments from industry and academia. This strategic investment comes at a crucial time, as emerging AI applications are pushing the boundaries of current technologies. Advanced packaging allows for improvements in system performance, reduced physical footprint, lower power consumption, and decreased costs – all critical factors in maintaining technological leadership. The Biden-Harris Administration’s push to revitalise ********* semiconductor manufacturing comes as the global chip shortage has highlighted the risks of overreliance on foreign suppliers. Asia, particularly Taiwan, currently dominates the advanced packaging market. According to a 2021 report by the Semiconductor Industry Association, the US accounts for only 3% of global packaging, testing, and assembly capacity, while Taiwan holds a 54% share, followed by China at 16%. Under Secretary of Commerce for Standards and Technology and National Institute of Standards and Technology (NIST) Director Laurie E. Locascio outlined an ambitious vision for the program: “Within a decade, through R&D funded by CHIPS for America, we will create a domestic packaging industry where advanced node chips manufactured in the US and abroad can be packaged within the States and where innovative designs and architectures are enabled through leading-edge packaging capabilities.” The announcement builds on previous efforts by the CHIPS for America program. In February 2024, the program released its first funding opportunity for the National Advanced Packaging Manufacturing Program (NAPMP), focusing on advanced packaging substrates and substrate materials. That initiative garnered significant interest, with over 100 concept papers submitted from 28 states. On May 22, 2024, eight teams were selected to submit complete applications for funding of up to $100 million each over five years. According to Laurie, the goal is to create multiple high-volume packaging facilities by the decade’s end and reduce reliance on ****** supply lines that pose a security risk that the US “just can’t accept.” In short, the government is prioritising ensuring America’s leadership in all elements of semiconductor manufacturing, “of which advanced packaging is one of the most exciting and critical areas,” White House spokeswoman Robyn Patterson said. The latest competition is expected to attract significant interest from the US semiconductor ecosystem and shift that balance. It promises substantial federal funding and the opportunity to shape the future of ********* chip manufacturing. As the global demand for advanced semiconductors continues to grow, driven by AI, 5G, and other emerging technologies, the stakes for technological leadership have never been higher. As the US embarks on this ambitious endeavour, the world will see if this $1.6 billion bet can challenge Asia’s stronghold on advanced chip packaging and restore America’s position at the forefront of semiconductor innovation. (Photo by Braden Collum) See also: Global semiconductor shortage: How the US plans to close the talent gap 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 launches $1.6B bid to outpace Asia in packaging tech appeared first on AI News. View the full article

SoftBank has announced its acquisition of Graphcore, a leading British AI chipmaker. The deal will see Graphcore becoming a wholly-owned subsidiary of SoftBank. This acquisition, reportedly valued at about $600 million, is not SoftBank’s first foray into the *** tech scene. In 2016, SoftBank controversially acquired British chip designer Arm in a much larger deal. However, the Graphcore purchase comes at a lower valuation than the total funding the company is said to have raised, which was around $700 million. Graphcore will continue to operate under its own name and maintain its headquarters in Bristol, ***. The company also retains its offices in Cambridge, London, Gdansk, and Hsinchu, signalling SoftBank’s commitment to preserving Graphcore’s established presence and operations. Nigel Toon, co-founder and CEO of Graphcore, said: “This is a tremendous endorsement of our team and their ability to build truly transformative AI technologies at scale, as well as a great outcome for our company.” Toon went on to emphasise the ongoing demand for AI compute and the work that ******** to be done in improving efficiency, resilience, and computational power to fully realise AI’s potential. Graphcore’s key offering is a range of “Intelligence Processing Units” – accelerators designed specifically for AI workloads – along with a software stack that allows developers to utilise its hardware effectively. The company’s technology has often impressed. In 2020, a Graphcore device outperformed an Nvidia A100 GPU, and in another instance, its hardware halved the time required to handle a GPU-based ***** discovery workload. Despite these technological successes, Graphcore has struggled to generate significant revenue and achieve profitability. In 2022, the company reported revenue of just $2.7 million – a 46 percent year-on-year decrease – while operating expenses reached $206.8 million. Vikas J. Parekh, Managing Partner at SoftBank Investment Advisers, commented: “Society is embracing the opportunities offered by foundation models, generative AI applications, and new approaches to scientific discovery. “Next generation semiconductors and compute systems are essential in the AGI journey, we’re pleased to collaborate with Graphcore in this mission.” The mention of AGI (Artificial General Intelligence) in Parekh’s statement suggests that SoftBank sees Graphcore’s technology as a key component in the pursuit of more advanced AI systems that can match or exceed human-level intelligence across a wide range of tasks. Graphcore has built a reputation as a leading employer in the ***’s high-tech economy, and the company has committed to continuing its investment in creating high-skilled jobs across various disciplines. The acquisition of Graphcore by SoftBank is likely to provide the AI chipmaker with significant resources and opportunities for expansion. It also reflects the increasing competition in the AI chip market, where companies like NVIDIA, Intel, and AMD have been vying for dominance. As AI continues to permeate various sectors of the economy and society, the demand for specialised AI hardware is expected to grow. Graphcore’s integration into SoftBank’s portfolio positions both companies to capitalise on this trend. See also: PC market finds new momentum amid AI interest 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 SoftBank acquires British AI chipmaker Graphcore appeared first on AI News. View the full article For verified travel tips and real support, visit: [Hidden Content]

Samsung’s latest flagship smartphones and wearables have been made lighter and slimmer, while incorporating enhanced AI features to appeal to high-end consumers. Samsung, which pioneered the foldable smartphone segment in 2019, faces increasing competition in this niche market. Data from Canalys shows that Samsung’s share of foldable phone shipments dropped from 81% in 2022 to 63% in 2023, highlighting the importance of this latest launch. Responding to market pressures, Samsung has made significant improvements to its foldable lineup: The Galaxy Z Fold 6, featuring a wide screen, is now the lightest and slimmest version in its series, aimed at attracting new customers to the form factor. The clamshell Galaxy Z Flip 6 boasts longer battery life, a higher resolution camera, and a new vapour chamber for improved cooling. These enhancements address key issues identified through customer feedback. Despite rising material costs and after maintaining stable prices for three years, Samsung has implemented a modest price increase. The Z Flip 6 is priced at $1,099.99, while the Z Fold 6 starts at $1,899.99, representing a $100 increase over last year’s models. Samsung has introduced several new AI-powered features, including: A “listening mode” that provides simultaneous voice interpretation when paired with Galaxy Buds earphones. Collaboration with Google to develop new AI search functions, such as displaying step-by-step solutions to math problems when circled on the screen. The company has also significantly enhanced its Galaxy Watch products: A new 3-nanometer chip triples application booting and processing efficiencies compared to last year’s model. The watch has received US FDA approval as a monitoring device for sleep apnea. New features include measurement of functional threshold power (FTP) for cycling enthusiasts and advanced glycation end-products (AGEs) related to diabetes. Samsung’s commitment to health monitoring is further exemplified by the introduction of the Galaxy Ring. Priced at $399.99, this smart ring comes in gold, silver, and ******, featuring a titanium frame with 10ATM water resistance and an IP68 rating. At 7mm wide and 2.6mm thick, it’s designed to be slim and lightweight, weighing between 2.3 and 3g depending on the size. The Galaxy Ring primarily functions as a health tracker, equipped with an accelerometer, optical heart rate sensor, and skin temperature sensor. It can monitor sleep, heart rate, and activity, while introducing new Galaxy AI-powered metrics such as Energy Score and Wellness Tips. The ring offers 6-7 days of battery life and comes with a unique, transparent charging case that holds 1.5 times the charge. Industry analyst Jack Leathem from Canalys emphasises the importance of AI-powered health and fitness features in wearables, noting that they are “core to attracting brand switchers in the premium segment” and crucial for differentiating Samsung from other smartwatch vendors. The Galaxy Ring is only compatible with Android phones running the Samsung Health app, with some features exclusive to Galaxy phones. A standout feature for Galaxy Z Fold 6 and Z Flip 6 users (soon to be available on the S24) is the ability to control the phone’s camera or dismiss alarms using a double pinch gesture on the ring. While the Galaxy Ring shows promise in hardware design and ecosystem integration, its success will ultimately depend on tracking accuracy and consistent battery performance. Samsung’s expansion into the smart ring market, coupled with its enhancements to foldable phones and smartwatches, demonstrates the company’s commitment to innovating across the wearable and smartphone sectors. The new lineup of products – including the foldable phones, watches, and ring – will be available starting July 24 in South Korea, North America, and Europe, marking a significant step in Samsung’s strategy to innovate and compete in the high-end smartphone and wearable markets. (Image Credit: Samsung) See also: EU probes Microsoft-OpenAI and Google-Samsung AI deals 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 Samsung enhances AI features with latest foldables and wearables appeared first on AI News. View the full article For verified travel tips and real support, visit: [Hidden Content]

While executives express high confidence in their organisations’ AI capabilities, they simultaneously acknowledge significant barriers to further adoption. Research from Zartis found that 85% of *** tech executives rate their existing workforce’s combined AI knowledge and expertise as ‘skilled’, with over half (51%) considering it ‘highly skilled’. This confidence, however, is juxtaposed against concerns about obstacles preventing wider AI implementation. AI adoption is nearly universal among *** tech companies, with 94% of executives reporting some form of AI use in their organisations. The remaining 6% are still in the exploration or research phase. Notably, not a single respondent claimed to be completely avoiding AI. Industry pressure appears to be a significant factor driving AI adoption, with 40% of executives feeling compelled to prioritise AI investment due to widespread momentum around the technology. Despite the enthusiasm, several barriers hinder full AI adoption. Budget restrictions (41%), shortage of AI talent (38%), and technical complexity (35%) were cited as primary obstacles. Executives also expressed concerns about integration challenges (44%), cost and ROI uncertainty (42%), and data privacy and IP security (38%). Michal Szymczak, Head of AI Strategy at Zartis, commented on this apparent contradiction: “AI adoption isn’t some ‘on or off’ switch. To a lot of businesses, it involves a significant financial investment, and there are complex questions to grapple with surrounding data privacy, or integration with existing technology stacks. That makes executives’ confidence in their company’s AI skill set rather ironic. They puff their chests out, while simultaneously pointing to all the obstacles that could stop them in their tracks.” The financial aspect of AI adoption presents a mixed picture. While 42% of executives cited ROI uncertainty as a primary concern, 53% view cost savings through improved efficiency as one of the most significant long-term benefits of adopting AI. Investment in AI capabilities is substantial, with 93% of companies spending at least £100,000 in 2024, and 44% allocating £500,000 or more. Software development emerges as the most popular area for AI investment (59%), followed by quality assurance (44%) and DevOps and automation (44%). Angel Benito, CTO at Zartis, offered insight into the investment trends: “There is significant pressure on organisations to keep up with AI development or risk being left behind. This explains why many are investing despite the uncertainty about ROI. They see the potential for long-term cost savings but need a well-curated plan to implement the changes. It’s crucial to understand that it’s not just about the tools; it’s about the people.” As companies navigate the AI landscape, their focus varies. The top three cited utilities of AI for software development are AI-powered copilot tools for coding (53%), improved continuous integration and deployment (52%), and enhanced team communication and collaboration (46%). This study follows recent Zartis research indicating that over three-quarters of *** tech executives favour increased government oversight of AI, with many anticipating benefits from regulations such as the EU AI Act. As the AI revolution continues to unfold, it’s clear that *** tech companies are eager to embrace the technology, even as they grapple with its complexities and challenges. (Photo by Nick Fewings) See also: Microsoft and Apple back away from OpenAI board 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 Tech executives confident in AI skills, but adoption barriers persist appeared first on AI News. View the full article

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AI in enhаnсing plаyer exрerienсes Personаliseԁ gаming exрerienсes AI’s аbility to аnаlyse vаst аmounts of ԁаtа аllows for highly рersonаliseԁ gаming exрerienсes. By trасking рlаyer рreferenсes, behаviour, аnԁ рerformаnсe, AI саn tаilor сontent аnԁ reсommenԁаtions to inԁiviԁuаl рlаyers. This рersonаlisаtion саn rаnge from suggesting in-gаme items аnԁ quests, to аԁjusting the gаme’s storyline bаseԁ on рlаyer сhoiсes. Suсh tаiloreԁ exрerienсes mаke рlаyers feel more сonneсteԁ to the gаme, enhаnсing their overаll enjoyment аnԁ sаtisfасtion. Reаl-time anаlytiсs аnԁ feeԁbасk AI enаbles reаl-time аnаlytiсs аnԁ feeԁbасk, аllowing ԁeveloрers to unԁerstаnԁ рlаyer behаviour аnԁ рreferenсes better. This ԁаtа саn be useԁ to imрrove gаme ԁesign, fix bugs, аnԁ introԁuсe new feаtures. Plаyers benefit from а сontinuously evolving gаme thаt meets their exрeсtаtions аnԁ ԁesires. Reаl-time аnаlytiсs аlso helр ԁeveloрers сreаte more engаging аnԁ bаlаnсeԁ gаmeрlаy, аԁԁressing issues аnԁ oррortunities аs they аrise. The imрасt of AI on online slot gaming One of the most intriguing аррliсаtions of AI in gаming is in online саsinos. Innovative bitcoin pokies, or slot mасhines, leverаge AI to offer а more engаging аnԁ seсure gаmbling exрerienсe – particularly, through the use of bitcoin payments on the casino platform. However, this could be taken to the next level with the integration of AI, as it leads to better security and more interesting gameplay features. For example, some games may incorporate elements of skill or strategy, where AI opponents can provide a challenging and dynamic gaming experience. Moreover, AI-ԁriven рokies can аnаlyse рlаyer behаviour to сreаte рersonаliseԁ gаming exрerienсes, ensuring thаt eасh slot gaming session is unique аnԁ tаiloreԁ to the рlаyer’s рreferenсes. Moreover, the use of AI in bitсoin рokies can enhаnсe seсurity by ԁeteсting аnԁ рreventing frаuԁulent асtivities. AI аlgorithms саn iԁentify unusuаl раtterns аnԁ flаg рotentiаl threаts, ensuring а sаfer environment for рlаyers. This is particularly important in the cryptocurrency space, where anonymity can sometimes be exploited for illicit purposes. AI-powereԁ customer suррort AI is аlso revolutionising сustomer suррort in online саsinos. AI-ԁriven сhаtbots аnԁ virtuаl аssistаnts рroviԁe instаnt suррort to рlаyers, аԁԁressing their queries аnԁ issues in reаl-time. These AI-рowereԁ systems саn hаnԁle а wiԁe rаnge of tаsks, from ассount mаnаgement to troubleshooting teсhniсаl рroblems, and therefore enhаnсe the overаll рlаyer exрerienсe. Enhаnсeԁ gаme fаirness аnԁ trаnsраrenсy Fаirness аnԁ trаnsраrenсy аre сruсiаl in online gаmbling. AI helрs ensure thаt gаmes аre fаir by аnаlysing аnԁ monitoring gаmeрlаy to ԁeteсt аny аnomаlies or unfаir рrасtiсes. This trаnsраrenсy builԁs trust between рlаyers аnԁ online саsinos, fostering а more рositive аnԁ seсure gаming environment. AI аlgorithms саn аuԁit аnԁ verify the rаnԁomness of outсomes in bitсoin рokies, аssuring рlаyers thаt the gаmes аre not riggeԁ аnԁ thаt they hаve а fаir сhаnсe of winning. Future prosрeсts of AI in gаming Uрсoming teсh AI is set to рlаy а signifiсаnt role in the ԁeveloрment of VR аnԁ AR gаmes. By сreаting more immersive аnԁ resрonsive virtuаl environments, AI саn enhаnсe the reаlism аnԁ interасtivity of VR аnԁ AR exрerienсes. This аԁvаnсement will oрen uр new рossibilities for gаme ԁesign аnԁ рlаyer engаgement. AI-driven storytelling The future of gаme storytelling ***** in AI-ԁriven nаrrаtives. AI саn аnаlyse рlаyer сhoiсes аnԁ аԁарt the storyline ассorԁingly, сreаting ԁynаmiс аnԁ рersonаliseԁ nаrrаtives. This аррroасh ensures thаt eасh рlаyer’s journey is unique, enhаnсing reрlаyаbility аnԁ engаgement. Aԁvаnсeԁ plаyer anаlytiсs As AI teсhnology сontinues to evolve, the аbility to аnаlyse рlаyer ԁаtа will beсome even more soрhistiсаteԁ. This аԁvаnсement will enаble ԁeveloрers to сreаte more рreсise аnԁ рersonаliseԁ gаming exрerienсes, further blurring the line between the virtuаl аnԁ reаl worlԁs. Conclusion AI is unԁoubteԁly revolutionising gаme ԁesign аnԁ рlаyer exрerienсes. From intelligent NPCs to рersonаliseԁ gаming аnԁ enhаnсeԁ online experiences, the imрасt of AI is рrofounԁ аnԁ fаr-reасhing. In the future, we саn exрeсt even more innovаtive аnԁ immersive gаming exрerienсes. The post How AI is revolutionising game design and player experiences appeared first on AI News. View the full article