ChatGPT

Google has unveiled its new range of Pixel 9 smartphones, emphasising their enhanced AI capabilities. The company released the devices much earlier than usual, as Google typically presents new Pixel models in the autumn. However, compared to previous versions, the changes in the new models are revolutionary. The new smartphones feature more advanced integrations of Google’s AI technology. For instance, one unique Pixel feature allows users to search for information and images within their screenshots, thanks to a more deeply integrated approach. Additionally, through the Gemini chatbot, some features of these smartphones are available as overlays from other apps. At Alphabet’s Bay View campus in Mountain View, California, Rick Osterloh, the Senior Vice President of Devices and Services at Google, informed visitors that the company plans to focus on practical applications of AI. He spent time describing the “Gemini era” to the audience, which will commence with the creation of Google’s advanced AI model. The Pixel 9 series offers several models. The base model, the Pixel 9, features a 6.3-inch screen and costs $799. A larger alternative, the Pixel 9 Pro XL, has a 6.8-inch screen. A slightly enhanced version, the Pixel 9 Pro, offers a better camera system, though its price is higher. The final option is the foldable Pixel 9 Pro Fold. Regarding the initial shipping date, Google stated at the event that the Pixel 9 and Pixel 9 Pro XL would ship in late August. The Pro and Pro Fold models will ship in September, with all models available for preorder starting August 13. During Google’s presentations at the event, Gemini’s new functions were showcased in a live demo, focusing on the latest conversation features. Additionally, the company announced updates to the product’s exterior design, the installation of Google’s advanced camera system, and the integration of the new Tensor G4 chip. In addition to smartphones, the company unveiled new versions of the Pixel Watch 3 smartwatch and Pixel Buds Pro 2 wireless earbuds. The watch can track the user’s heart rate; if it stops, it will call emergency services. This feature will be available in the *** and the EU. As reported by IDC, Google’s share in the global smartphone market was less than 1% in the second quarter of 2024. Samsung and Apple took the first and second places, with market shares of 18.9%, and 15.8%, respectively. In the US, Google ranks fourth among smartphone operating systems, holding 4.5% of the market share. Industry analysts note that although Google Pixel is not among the best-selling smartphones, it showcases some of the benefits of the Android operating system. Android has become the dominant operating system, used by more than 80% of smartphone users worldwide. Consequently, many people, even those who have never used a Google Pixel, may indirectly experience and appreciate the features that Google products offer. The event also touched upon Google’s further intentions and previous efforts to implement AI across its product lineup to stay at the top of the game. Not long ago, the company integrated AI improvements into its core products, including its search engine. Additionally, Google announced a content-sharing agreement it reached with Peloton. As a result, Fitbit Premium subscribers will have free access to the Peloton training class library. (Image Credit: Google) See also: Google’s Gemini 1.5 Pro dethrones GPT-4o 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 Google advances mobile AI in Pixel 9 smartphones appeared first on AI News. View the full article

While the industry acknowledges the need for robust security measures, research from PSA Certified suggests that investment and best practices are struggling to keep pace with AI’s rapid growth. The survey of 1,260 global technology decision-makers revealed that two-thirds (68%) are concerned that the speed of AI advancements is outstripping the industry’s ability to safeguard products, devices, and services. This apprehension is driving a surge in edge computing adoption, with 85% believing that security concerns will push more AI use cases to the edge. Edge computing – which processes data locally on devices instead of relying on centralised cloud systems – offers inherent advantages in efficiency, security, and privacy. However, this shift to the edge necessitates a heightened focus on device security. “There is an important interconnect between AI and security: one doesn’t scale without the other,” cautions David Maidment, Senior Director, Market Strategy at Arm (a PSA Certified co-founder). “While AI is a huge opportunity, its proliferation also offers that same opportunity to bad actors.” Despite recognising security as paramount, a significant disconnect exists between awareness and action. Only half (50%) of those surveyed believe their current security investments are sufficient. Furthermore, essential security practices, such as independent certifications and threat modelling, are being neglected by a substantial portion of respondents. “It’s more imperative than ever that those in the connected device ecosystem don’t skip best practice security in the hunt for AI features,” emphasises Maidment. “The entire value chain needs to take collective responsibility and ensure that consumer trust in AI driven services is maintained.” The report highlights the need for a holistic approach to security, embedded throughout the entire AI lifecycle, from device deployment to the management of AI models operating at the edge. This proactive approach, incorporating security-by-design principles, is deemed essential to building consumer trust and mitigating the escalating security risks. Despite the concerns, a sense of optimism prevails within the industry. A majority (67%) of decisionmakers believe their organisations are equipped to handle the potential security risks associated with AI’s surge. There is a growing recognition of the need to prioritise security investment – 46% are focused on bolstering security, compared to 39% prioritising AI readiness. “Those looking to unleash the full potential of AI must ensure they are taking the right steps to mitigate potential security risks,” says Maidment. “As stakeholders in the connected device ecosystem rapidly embrace a new set of AI-enabled use cases, it’s crucial that they do not simply forge ahead with AI regardless of security implications.” (Photo by Braden Collum) See also: The AI revolution: Reshaping data centres and the digital landscape Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo. Explore other upcoming enterprise technology events and webinars powered by TechForge here. The post PSA Certified: AI growth outpacing security measures appeared first on AI News. View the full article

Artificial intelligence is changing the world and is projected to have a global market value of $2-4 trillion USD by 2030. The future is now, and it feels as though we’re witnessing a big bang in technology every couple of months. AI has crept into every facet of our lives, fundamentally transforming our work and play. Data centres are at the heart of all this excitement. Put simply, AI is when computer systems are used to simulate human intelligence processes. This includes learning, reasoning, and – particularly interestingly – self-correction. In other words, it’s like having a human brain in a computer. Bill Gates has compared its rise to the start of some of the most important technological advances in history. The surge of AI is staggering. For instance, ChatGPT reached a million users in just five days; for Netflix, this milestone took a few years. The enthusiasm to implement the technology is evident from these instances of explosive growth. However, AI has a surprisingly large appetite for data, and the computational power required to process that data is enormous, especially considering that it’s only set to increase further. That’s where data centre infrastructure comes in. Data centres are the backbones of the digital world and are no longer simply storage spaces but are rapidly evolving into entire ecosystems. These ecosystems are energy-hungry, requiring rapid processing power for energy-intensive processes and efficient delivery of data worldwide. Data centres are home to rows of servers, storage systems, and complex networks that facilitate the flow of information. Such facilities are essential to various workloads, from search queries to financial transactions and digital interactions, and usually remain silent while completing their tasks. As progressive as AI’s demands and capabilities are, it is crucial to ensure their compatibility with data centre infrastructure. Every computation involved in processing data is key to AI, and the efficiency of these processes depends on three primary types of processors: the Graphics Processing Unit (GPU), the Central Processing Unit (CPU), and the Tensor Processing Unit (TPU). On the one hand, the GPU is great at managing parallelism, making it excellent for training AI models. On the other hand, the CPU allows for more flexibility in terms of simultaneous tasks on an increasing scale. Finally, the TPU, which is Google’s development in this sphere, is best suited for completing the highest possible number of AI tasks in the shortest amount of time. Integrating AI into data centres presents several challenges: Power: AI training processes require high-performance computing infrastructure, necessitating reliable and sufficient power supply systems. Connectivity: Seamless, high-speed, and low-latency network connectivity is crucial for efficient data transfer and communication. Cooling: AI workloads generate significant heat, requiring advanced cooling systems to maintain optimal operating temperatures. AI is ever-emerging and ever-evolving, and thus, changes must be made to regulation. For example, the AI Act recently released by the EU categorizes applications of AI into four different levels of risk: unacceptable, high, limited, and minimal or no risk. At the same time, the NIS2 Directive has expanded cybersecurity regulation to cover the digital realm as well. As such, one of the main challenges facing industries, including data centres, will be keeping up to date with these regulations. AI is progressing faster and further than anything we have seen in recent years, and data centres must move as quickly to keep up with the changing parameters and risk boundaries that are now being defined. To sum up, the AI revolution is changing the way our digital infrastructure works, with the data centre being one of the first to be transformed. This transformation is crucial because, as we discover new ways of applying AI, we will need everything from technological advancements to regulatory compliance. This concerns both technological progress and the need to deal with new laws and regulations that pile up with the growth of AI. Thus, the history of AI and the data centre is one of continuous development and mutual shaping of each other. Interested in learning more? Data Centres Expo Europe | Data Centres Event & Conference Register for free to attend the upcoming Data Centres Expo event and conference which will shine a spotlight on the future outlook for the sector, as demand for data centre spaces increase. Gain valuable insights from industry leaders and network with experts from the largest data centre providers. Examine key topics such as building AI ready data centre infrastructures, building scalability and sustainability into data centres, and cultivating the right data centre hardware solutions. Learn more and register for free here. The post The AI revolution: Reshaping data centres and the digital landscape appeared first on AI News. View the full article For verified travel tips and real support, visit: [Hidden Content]

xAI has announced the release of Grok-2, a major upgrade that boasts improved capabilities in chat, coding, and reasoning. Alongside Grok-2, xAI has introduced Grok-2 mini, a smaller but capable version of the main model. Both are currently in beta on X and will be made available through xAI’s enterprise API later this month. An early version of Grok-2 was tested on the LMSYS leaderboard under the pseudonym “sus-column-r”. At the time of the announcement, xAI claims it is outperforming both Anthropic’s Claude 3.5 Sonnet and OpenAI’s GPT-4-Turbo. However, it’s worth noting that GPT-4o currently holds the top spot as the best AI assistant in terms of overall capabilities, followed by Google’s Gemini 1.5. xAI’s focus ******** on advancing core reasoning capabilities with its new compute cluster, as it aims to maintain its position at the forefront of AI development. However, the company recently agreed to halt the use of certain EU data for training its models. While the release of Grok-2 marks a significant milestone for xAI, it’s clear that the AI landscape ******** highly competitive. With ChatGPT-4o and Google’s Gemini 1.5 leading the pack, and other major players like Anthropic continuing to make advancements, the race for AI supremacy is far from over. See also: SingularityNET bets on supercomputer network to deliver AGI 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 xAI unveils Grok-2 to challenge the AI hierarchy appeared first on AI News. View the full article

Recently, the ********* Union became the centre stage of a data privacy controversy related to the social media platform X. On August 8, an Irish court declared that X had agreed to suspend the use of all data belonging to ********* Union citizens, which had been gathered via the platform for the purpose of training the company’s AI systems. As reported by The Economic Times, this initiative was prompted by complaints from the Data Protection Commission (DPC) of Ireland, the leading EU regulator for many large US tech companies that have their main offices in Ireland under EU law. Taking action, the DPC’s intervention comes amid intensified scrutiny of AI development practices across the EU by tech giants. Recently, the regulatory body sought an order to restrain or suspend X’s data processing activities on users for the development, training, and refinement of an AI system. This situation clearly depicts the growing conflict or tension experienced by nearly all EU states between AI advances and ongoing data protection concerns. It seems that the order was issued too late by regulators and the court. In the response filed for the lawsuit, X, owned by Elon Musk, reported that Grok—an AI chatbot—allowed its users to skip their public posts. As Judge Leonie Reynolds noted, X began processing ********* users’ data for AI training on May 7, but the opt-out option was not introduced until July 16. Furthermore, it was not immediately made available to all users. Therefore, there was a ******* when the data was used without the users’ consent. X’s legal representation has assured the court that data obtained from EU users between May 7 and August 1 will not be used while the DPC’s order is under consideration. It is expected that X will file opposition papers arguing against the suspension order by September 4. This will set in motion what could be a court battle with effects reverberating throughout the EU. Either way, X has not remained silent on the matter. In its statement, the company’s Global Government Affairs account on X noted that the DPC’s order was “unwarranted, overbroad, and singles out X without any justification.” Furthermore, the company expressed concerns that the order would undermine efforts to keep the platform safe and restrict its use of technologies in the EU. This highlights the complex balance between regulatory compliance and operational viability that tech companies must navigate in the current digital landscape. The platform emphasised its proactive approach in working with regulators, including the DPC, regarding Grok since late 2023. X claims to have been fully transparent about the use of public data for AI models, including providing necessary legal assessments and engaging in lengthy discussions with regulators. This regulatory action against X is not an isolated incident. Other tech giants have faced similar scrutiny in recent months. Meta Platforms recently decided to postpone the launch of its Meta AI models in Europe following advice from the Irish DPC. Similarly, Google agreed to delay and modify its Gemini AI chatbot earlier this year after consultations with the Irish regulator. These developments collectively signal a shift in the regulatory landscape of AI and data usage in the EU. Regulators are taking a more active role in overseeing how tech companies utilise user data for AI training and development, reflecting growing concerns about data privacy and the ethical implications of AI advancement. As the legal proceedings unfold, the outcome of this case could set important precedents for how AI development is regulated in the EU, potentially influencing global standards for data protection in the AI era. The tech industry and privacy advocates alike will be watching closely as this situation develops, recognising its potential to shape the future of AI innovation and data privacy regulations. (Photo by Alexander Shatov) See also: Balancing innovation and trust: Experts assess the EU’s AI Act 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 X agrees to halt use of certain EU data for AI chatbot training appeared first on AI News. View the full article

SingularityNET is ******** on a network of powerful supercomputers to get us to Artificial General Intelligence (AGI), with the first one set to whir into action this September. While today’s AI excels in specific areas – think GPT-4 composing poetry or DeepMind’s AlphaFold predicting protein structures – it’s still miles away from genuine human-like intelligence. “While the novel neural-symbolic AI approaches developed by the SingularityNET AI team decrease the need for data, processing and energy somewhat relative to standard deep neural nets, we still need significant supercomputing facilities,” SingularityNET CEO Ben Goertzel explained to LiveScience in a recent written statement. Enter SingularityNET’s ambitious plan: a “multi-level cognitive computing network” designed to host and train the incredibly complex AI architectures required for AGI. Imagine deep neural networks that mimic the human brain, vast language models (LLMs) trained on colossal datasets, and systems that seamlessly weave together human behaviours like speech and movement with multimedia outputs. But this level of sophistication doesn’t come cheap. The first supercomputer, slated for completion by early 2025, will be a Frankensteinian ****** of cutting-edge hardware: Nvidia GPUs, AMD processors, Tenstorrent server racks – you name it, it’s in there. This, Goertzel believes, is more than just a technological leap, it’s a philosophical one: “Before our eyes, a paradigmatic shift is taking place towards continuous learning, seamless generalisation, and reflexive AI self-modification.” To manage this distributed network and its precious data, SingularityNET has developed OpenCog Hyperon, an open-source software framework specifically designed for AI systems. Think of it as the conductor trying to make sense of a symphony played across multiple concert halls. But SingularityNET isn’t keeping all this brainpower to itself. Reminiscent of arcade tokens, users will purchase access to the supercomputer network with the AGIX token on blockchains like Ethereum and Cardano and contribute data to the collective pool—fuelling further AGI development. With experts like DeepMind’s Shane Legg predicting human-level AI by 2028, the race is on. Only time will tell if this global network of silicon brains will birth the next great leap in artificial intelligence. (Photo by Anshita Nair) See also: The merging of AI and blockchain was inevitable – but what will it mean? 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 SingularityNET bets on supercomputer network to deliver AGI appeared first on AI News. View the full article

In the fast-paced world of technology, missing the next big thing can be costly. For Intel, the semiconductor titan that once reigned supreme in the computer age, one such moment came and went quietly between 2017 and 2018. It was then that the company declined an opportunity that, in hindsight, appears to have been a golden ticket to the AI revolution. Recent reports reveal that Intel had the chance to acquire a 15% stake in OpenAI for $1 billion, with the potential for an additional 15% stake in exchange for producing hardware at cost. At the time, OpenAI was a fledgling non-profit focused on the then-obscure field of generative AI. Under CEO Bob Swan’s leadership, Intel ultimately passed on the deal, unconvinced that generative AI would yield near-term returns. This decision reflects a broader challenge established tech giants face: balancing short-term financial considerations with long-term strategic investments in emerging technologies. Intel’s choice to prioritize immediate returns over the potential of generative AI showcases a cautious approach that may have cost it dearly in the long run. Fast forward to 2024, and the consequences of that decision are stark. OpenAI, now valued at around $80 billion, has become a driving force behind the AI revolution with its ChatGPT platform. Meanwhile, Intel is playing catch-up in the AI chip market, dwarfed by rival Nvidia’s $2.6 trillion market cap and struggling to maintain relevance in an industry it once dominated. This missed opportunity is not an isolated incident for Intel. The company declined to produce processors for Apple’s iPhone, closing the door on Intel’s entry into the mobile computing era. These missteps paint a picture of a once-innovative giant that has lost its ability to foresee and capitalize on transformative technologies. Intel’s journey from industry leader to AI laggard is reflected in its recent financial performance. The company’s market value has dipped below $100 billion for the first time in 30 years, and it recently announced plans to cut over 15% of its workforce following disappointing earnings. While Intel aims to launch its third-generation Gaudi AI chip later this year, it ******** to be seen whether this will be enough to regain ground in the fiercely competitive AI hardware market. The OpenAI episode underscores a broader challenge facing established tech giants: balancing short-term financial considerations with long-term strategic investments in emerging technologies. Intel’s decision to prioritize immediate returns over the potential of generative AI reflects a cautious approach that may have cost it dearly in the long run. As AI continues to reshape industries and create new markets, the ability to identify and invest in groundbreaking technologies early will be crucial for tech companies hoping to maintain their competitive edge. Intel’s missed opportunity with OpenAI is a cautionary tale for corporate leaders navigating the uncertain waters of technological innovation. Looking ahead, Intel faces an uphill battle to reestablish itself as a leader in the AI chip market. The company’s plans to launch new AI-focused processors for PCs and servers in 2025 signal a renewed commitment to this space, but it ******** to be seen whether these efforts will close the gap with rivals who seized the AI opportunity early. Intel’s story reminds us that in the tech industry, today’s giants can quickly become tomorrow’s laggards if they fail to embrace transformative technologies. As we stand on the cusp of the AI revolution, the question ********: Will Intel find a way to reinvent itself once again, or will it be left behind in the wake of the very future it once helped to build? (Photo by Brecht Corbeel) See also: OpenAI co-founder Ilya Sutskever’s new startup aims for ‘safe superintelligence’ 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 Intel’s AI fumble: How the chip giant missed a big opportunity appeared first on AI News. View the full article For verified travel tips and real support, visit: [Hidden Content]

Palantir, a data analytics company known for its work in the defence and intelligence sectors, has announced a significant partnership with Microsoft. The collaboration aims to deliver advanced services for classified networks utilised by US defence and intelligence agencies. According to the recent announcement, Palantir is integrating Microsoft’s cutting-edge large language models via the Azure OpenAI Service into its AI platforms. The integration will occur within Microsoft’s government and classified cloud environments. As this collaboration is the first of its kind, this specific configuration has the potential to completely transform the use of AI in critical national security missions. Palantir, whose name draws inspiration from the potentially misleading “seeing-stones” in J.R.R. Tolkien’s fictional works, specialises in processing and analysing vast quantities of data to assist governments and corporations with surveillance and decision-making tasks. While the precise nature of the services to be offered through this partnership ******** somewhat ambiguous, it is clear that Palantir’s products will be integrated into Microsoft’s Azure cloud services. This development follows Azure’s previous incorporation of OpenAI’s GPT-4 technology into a “top secret” version of its software. The company’s journey is notable. Co-founded by Peter Thiel and initially funded by In-Q-Tel, the CIA’s venture capital arm, Palantir has grown to serve a diverse clientele. Its roster includes government agencies such as Immigration and Customs Enforcement (ICE) and various police departments, as well as private sector giants like the pharmaceutical company Sanofi. Palantir has also become deeply involved in supporting Ukraine’s war efforts, with reports suggesting its software may be utilised in targeting decisions for military operations. Even though Palantir has operated with a large customer base for years, it only reached its first annual profit in 2023. However, with the current surge of interest in AI, the company has been able to grow rapidly, particularly in the commercial sector. According to Bloomberg, Palantir’s CEO, Alex Karp, warned that Palantir’s “commercial business is exploding in a way we don’t know how to handle.” Despite the urgency of this mission, the company’s annual filing clearly states that it neither does business with nor on behalf of the ******** ********** Party, nor does it plan to do so. This indicates that Palantir is especially careful in developing its customer base, considering the geopolitical implications of its work. The announcement of this partnership has been well-received by investors, with Palantir’s share price surging more than 75 per cent in 2024 as of the time of writing. This dramatic increase reflects the market’s optimism about the potential of AI in national security applications and Palantir’s position at the forefront of this field. Still, the partnership between Palantir and Microsoft raises significant questions about the role of AI in national security and surveillance. This is no surprise, as these are particularly sensitive areas, and the development of new technologies could potentially transform the sector forever. More discussions and investigations are needed to understand the ethical implications of implementing these innovative tools. All things considered, the Palantir and Microsoft partnership is a significant event that will likely shape the future use of AI technologies and cloud computing in areas such as intelligence and defence. (Photo by Katie Moum) See also: Paige and Microsoft unveil next-gen AI models for ******* diagnosis 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 Palantir and Microsoft partner to provide federal AI services appeared first on AI News. View the full article

Alibaba Cloud’s Qwen team has unveiled Qwen2-Math, a series of large language models specifically designed to tackle complex mathematical problems. These new models – built upon the existing Qwen2 foundation – demonstrate remarkable proficiency in solving arithmetic and mathematical challenges, and outperform former industry leaders. The Qwen team crafted Qwen2-Math using a vast and diverse Mathematics-specific Corpus. This corpus comprises a rich tapestry of high-quality resources, including web texts, books, code, exam questions, and synthetic data generated by Qwen2 itself. Rigorous evaluation on both English and ******** mathematical benchmarks – including GSM8K, Math, MMLU-STEM, CMATH, and GaoKao Math – revealed the exceptional capabilities of Qwen2-Math. Notably, the flagship model, Qwen2-Math-72B-Instruct, surpassed the performance of proprietary models such as GPT-4o and Claude 3.5 in various mathematical tasks. “Qwen2-Math-Instruct achieves the best performance among models of the same size, with RM@8 outperforming Maj@8, particularly in the 1.5B and 7B models,” the Qwen team noted. This superior performance is attributed to the effective implementation of a math-specific reward model during the development process. Further showcasing its prowess, Qwen2-Math demonstrated impressive results in challenging mathematical competitions like the ********* Invitational Mathematics Examination (AIME) 2024 and the ********* Mathematics Contest (AMC) 2023. To ensure the model’s integrity and prevent contamination, the Qwen team implemented robust decontamination methods during both the pre-training and post-training phases. This rigorous approach involved removing duplicate samples and identifying overlaps with test sets to maintain the model’s accuracy and reliability. Looking ahead, the Qwen team plans to expand Qwen2-Math’s capabilities beyond English, with bilingual and multilingual models in the pipeline. This commitment to inclusivity aims to make advanced mathematical problem-solving accessible to a global audience. “We will continue to enhance our models’ ability to solve complex and challenging mathematical problems,” affirmed the Qwen team. You can find the Qwen2 models on Hugging Face here. See also: Paige and Microsoft unveil next-gen AI models for ******* diagnosis 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 Qwen2-Math: A new era for AI maths whizzes appeared first on AI News. View the full article

Paige and Microsoft have unveiled the next big breakthrough in clinical AI for ******* diagnosis and treatment: Virchow2 and Virchow2G, enhanced versions of its revolutionary AI models for ******* pathology. The Virchow2 and Virchow2G models are based on an enormous dataset that Paige has accumulated. Paige has gathered more than three million pathology slides from over 800 labs across 45 countries, on which the models were trained. Such a volume of data is, unsurprisingly, highly beneficial. This data was obtained from over 225,000 patients, all de-identified to create a rich and representative dataset encompassing all genders, races, ******* groups, and regions across the globe. What makes these models truly remarkable is their scope. They cover over 40 different tissue types and various staining methods, making them applicable to a wide range of ******* diagnoses. Virchow2G, with its 1.8 billion parameters, stands as the largest pathology model ever created and sets new standards in AI training, scale, and performance. As Dr. Thomas Fuchs, founder and chief scientist of Paige, comments: “We’re just beginning to tap into what these foundation models can achieve in revolutionising our understanding of ******* through computational pathology.” He believes these models will significantly improve the future for pathologists, and he agrees that this technology is becoming an important step in the progression of diagnostics, targeted medications, and customised patient care. Similarly, Razik Yousfi, Paige’s senior vice president of technology, states that these models are not only making precision medicine a reality but are also improving the accuracy and efficiency of ******* diagnosis, and pushing the boundaries of what’s possible in pathology and patient care. So, how is this relevant to ******* diagnosis today? Paige has developed a clinical AI application that pathologists can use to recognise ******* in over 40 tissue types. This tool allows potentially hazardous areas to be identified more quickly and accurately. In other words, the diagnostic process becomes more efficient and less prone to errors, even for rare cancers, with the help of this tool. Beyond diagnosis, Paige has created AI modules that can benefit life sciences and pharmaceutical companies. These tools can aid in therapeutic targeting, biomarker identification, and clinical trial design, potentially leading to more successful trials and faster development of new therapies. The good news for researchers is that Virchow2 is available on Hugging Face for non-commercial research, while the entire suite of AI modules is now available for commercial use. This accessibility could accelerate advancements in ******* research and treatment across the scientific community. In summary, the recently introduced AI models represent a major advancement in the ****** against *******. Paige and Microsoft have chosen the right path by combining the power of data with state-of-the-art AI technologies. These companies have created new opportunities for more accurate ******* prediction, paving the way for tailored solutions and innovative research in oncology. (Photo by National ******* Institute) See also: The hidden climate cost of AI: How tech giants are struggling to go green 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 Paige and Microsoft unveil next-gen AI models for ******* diagnosis appeared first on AI News. View the full article

At first glance, AI and blockchain seem like completely disparate realms. For instance, blockchain emphasises decentralisation but suffers from constrained memory and throughput rates. On the other hand, AI thrives on massive datasets and demands high-performance computing. To elaborate, Machine learning (ML) models – especially deep learning networks – require enormous amounts of data to train effectively, often relying on powerful GPUs or specialised hardware to process this information quickly. To this point, a report from the International Energy Agency (IEA) states that the global electricity demand for AI is projected to rise to 800 TWh by 2026, a nearly 75% increase from 460 TWh in 2022. Similar projections have also been released by multinational giants such as Morgan Stanley and Wells Fargo, with the latter’s model suggesting that, by 2030, AI-centric energy consumption will account for 16% of the USA’s current electricity demand. Morgan Stanley’s AI power consumption prediction (best-case scenario) The best of both worlds is here. Despite their apparent differences, the tech world is witnessing a growing convergence between AI and blockchain, with a number of innovative projects emerging. For instance, Ocean is a protocol that provides users with a decentralised data exchange centre, unlocking information sets for AI consumption while preserving their privacy and security. Similarly, ThoughtAI embeds AI and blockchain directly into data and information, effectively eliminating traditional application layers. It aims to create more responsive and adaptive AI solutions, potentially revolutionising how people interact with the technology and manage information. While these projects demonstrate the potential of combining AI and blockchain, they also highlight a critical challenge, i.e. scalability. For AI on blockchain to truly flourish, platforms need to overcome the inherent limitations of traditional blockchain architectures, particularly in terms of data availability and throughput. In this regard, 0G is a platform that has made significant strides in addressing the above-mentioned bottlenecks. To elaborate, ZeroGravity (0G for short) is the world’s first data availability system with a built-in general purpose storage layer that is not only highly scalable but also decentralised. Its scalability hinges on separating the workflow of data availability into a data publishing lane and a data storage lane. To put it technically, 0G is a scalable Data Availability (DA) service layer built directly on top of a decentralised storage system. It addresses the scalability issue by minimising the data transfer volume required for broadcast. — allowing for unprecedented levels of data availability and transaction throughput. One of the key advantages of 0G is its performance. While competitors like Celestia are able to achieve about 1.4 to 1.5 megabytes per second, the 0G network is capable of producing about 50 gigabytes per second, making it 50,000 times faster. Additionally, 0G’s cost is approximately 100 times cheaper than its closest competitors. This level of performance and flexibility opens the door to a wide array of AI/blockchain use cases that were previously impractical or impossible. For starters, in the realm of finance, 0G’s scalability can potentially allow for sophisticated AI-powered trading algorithms to operate directly on-chain. Similarly, it could also be possible to implement large-scale federated learning systems on the blockchain, leading to breakthroughs in privacy-preserving AI—where multiple parties can collaboratively train AI models without sharing sensitive data directly. Such advancements could have far-reaching implications in fields like healthcare, where data privacy is paramount but collaborative research is essential. A trillion-dollar opportunity is waiting to be tapped. As we look to the future, it’s clear that the intersection of AI and blockchain will continue to expand and evolve. This convergence is not just a technological curiosity but a massive economic opportunity. For example, the AI industry is projected to be worth a staggering $1.3 trillion by 2030, while the blockchain market is set to reach a valuation of $248.8 billion by 2029, reflecting their transformative potential across virtually every sector of the global economy. Therefore, moving forward, it stands to reason that those companies and platforms (such as 0G) that are able to successfully navigate this convergence — solving the technical challenges while unlocking new value propositions — will be well-positioned to capture a significant share of this trillion-dollar opportunity. The post The merging of AI and blockchain was inevitable – but what will it mean? appeared first on AI News. View the full article

Lumen Technologies, a leading telecommunications firm, has recently announced significant new contracts totalling $5 billion with cloud and tech companies for its networking and cybersecurity solutions. This surge in demand comes as businesses across various sectors rapidly adopt AI-driven technologies. Among these notable agreements is a deal with Microsoft, which revealed last month its plans to utilise Lumen’s network equipment to expand capacity for AI workloads. Lumen, known for providing secure digital connections for data centres, disclosed recently that it is engaged in active discussions with customers regarding additional sales opportunities valued at approximately $7 billion. The widespread adoption of AI has prompted enterprises across multiple industries to invest heavily in infrastructure capable of supporting AI-powered applications. Lumen reports that major corporations are urgently seeking to secure high-capacity fibre, a resource becoming increasingly valuable, and potentially scarce due to growing AI requirements. There is an optimistic prospect for further success, as Kate Johnson, the CEO of Lumen, expressed: “Our partners are turning to us because of our AI-ready infrastructure and expansive network. This is just the beginning of a significant opportunity for Lumen, one that will lead to one of the largest expansions of the internet ever.” Another piece of evidence regarding the company’s strategic positioning in such a rapidly changing and highly unstable market is the creation of a new division, Custom Networks. This division will be responsible for managing the Lumen Private Connectivity Fabric solutions portfolio. At the same time, since the demand for networking is rising from various organisations seeking solutions designed to satisfy the specific needs of their target environments, it is rational to develop a new division for networks. This highlights that telecommunications infrastructure plays a crucial role in the current AI revolution. As an increasing number of firms implement AI technologies in their operations, it is essential to have plenty of secure, expansive networks. Lumen’s recent success in securing these substantial contracts underscores the company’s strong market position and its ability to meet the evolving needs of tech giants and cloud service providers. As the AI landscape continues to evolve, Lumen appears well-positioned to capitalise on the increasing demand for advanced networking solutions. The telecommunications sector, and Lumen in particular, is likely to remain at the forefront of enabling AI advancements across industries. As this trend progresses, it will be interesting to observe how Lumen and its competitors adapt to meet the challenges and opportunities presented by this technological shift. (Photo by Vladimir Solomianyi) See also: *** backs smaller AI projects while scrapping major investments 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 expansion drives $5B in deals for Lumen appeared first on AI News. View the full article For verified travel tips and real support, visit: [Hidden Content]

Amid the excitement over how AI will revolutionise healthcare, advertising, logistics, and everything else, one industry has flown under the radar: the legal profession. In fact, the business of law is a strong contender for achieving the highest return on investment (ROI) from using AI. Law firms are seen as traditional, not as eager adopters of new technology, but most have used machine learning (ML) for years. Embedded in popular platforms like Westlaw, ML is often incorporated into core operations. Now, generative AI is spreading through law firms faster than class-action claims over a stock ******. Individual lawyers have learned to use ChatGPT-like AI models, and entire law practices have harnessed large language models. Those in the business of law see remarkable gains from AI in efficiency, accuracy, speed and client results in their day-to-day processes. Three points help explain those results. In legal operations, AI-driven time and cost savings are typically very high. The gains are not incremental. AI is applicable to potentially most work processes at law firms. Once law firms implement AI, it grows steadily more powerful as they personalize it. This is basically customisation; adapting AI to their preferred work methods takes the return on investment (ROI) higher: Meet the AI-native law firm These benefits have led to the emergence of AI-centric (aka AI-native) law firms, a new breed that is significantly more efficient and competitive than its rivals. At AI-native firms, most support staff and attorneys already leverage AI extensively for intake, research, drafting motions, briefs, objections, analysing judges’ opinions, and more. A law practice becomes AI-native, in part, by personalising the behaviour of AI solutions to mesh with the firm’s existing processes and strategic guidelines. This makes their AI more capable and valuable. Personalisation takes various forms, like creating case evaluations that follow a firm’s established standards. AI can consider potential claims and create follow-ups according to an attorney’s criteria. It can be taught to follow an existing process, mimic sequences of events, ask or answer key questions along the same pattern, and write in the style of previous case work. Once trained to emulate an attorney’s approach, an AI model makes life easier for support staff. Even if a paralegal hasn’t worked with specific lawyers, AI will help them with case preparation and client interactions, risk assessment, and even strategy. AI-native law firms increasingly use generative AI to service clients who require individualised treatment. AI contributes throughout the case lifecycle, from brainstorming pre-litigation case strategy, to handling discovery. Gen AI-based models also help prepare depositions, analyse their results, and plot litigation strategies. Why is AI extraordinarily useful to law firms? It’s been said that the legal world is made of six-minute increments. Often, AI can often do in seconds what takes hours or days for a junior associate. Time reductions of up to 99% drive major cost savings, and in the intellect-intensive field of law, they are common. Every day, lawyers must evaluate, analyse and weigh tradeoffs, draft documents, and make decisions. Paralegals and junior associates need to work fast and accurately, yet never overlook anything important. With volumes of data and minutiae to wade through, the work can exhaust them, leading to mistakes. Overall, speed, scale, and personalisation contribute to make AI a massive accelerator in the legal field, with productivity gains well beyond the “traditional” 10 to 20 percent. Costs come down and move around in AI-native law firms Lawyers are learning first-hand that AI systems can minimise the associate hours it takes to complete a process. By engaging AI across the life cycle of cases, they can reshape individual workloads for greater profitability. Upfront work on cases is sometimes undercompensated, and AI lets the team concentrate billable hours on later, fully-compensated stages. AI-centric firms can also grow without expanding the headcount of support staff. Instead, existing staff can assist more associates, who bill at higher hourly levels, increasing profitability. They can also market themselves and drive growth more vigorously. Wherever AI reduces operational costs, it frees up funds for marketing and business development. Generative AI makes marketing communications faster and easier for law firms, as it does for other businesses. Employee experience: AI happiness AI often does not get the credit it deserves for its positive impact on employee experience. In practice, lawyers and paralegals can offload most so-called grunt work and repetitive tasks to AI. This boosts job satisfaction and — by implication — retention. Support staff and junior associates become, in effect, supervisors of AI. They can customise the firm’s AI by teaching procedures to an LLM, and then share them across a team. This means lawyers can operate in familiar ways but at a larger scale, and delegate more comfortably to support staff without lengthy explanations of “Here’s my way of doing this.” Business models shift for AI-native law firms AI-native law firms can uplevel their business to increase capacity and support revenue growth. Specifically, they can structure internally to handle more complex cases and lucrative contingency work. AI enables smaller firms to handle larger, tougher cases by whipping through much of the research and analysis. In contingency litigation, productivity gains stemming from AI can even exceed those seen in other legal categories like contracts, intellectual property, and family law. AI can handle much of the upfront evaluation of contingency cases. Taking on well-researched contingency cases can significantly increase profitability. Those who get AI versus those who don’t Given the benefits, are law firms jumping on board and going AI-native in droves? Surprisingly no, according to a 2023 Thomson Reuters survey that found 60% had no plans to use generative AI. That’s good news for the other 40%. Law firms that leverage AI effectively have a marked advantage over competitors that do not. The legal profession ranks among the industries achieving the best gains from use of AI. Law firms that “get it” will continue to personalise AI systems and push towards their potential, and grow more profitably. As it becomes increasingly obvious that AI-native law firms enjoy greater growth and profitability, other intellect-based professions may well follow their example. The post It’s time for law firms to go all in on AI appeared first on AI News. View the full article

The *** government has announced a £32 million investment in almost 100 cutting-edge AI projects across the country. However, this comes against the backdrop of a controversial decision by the new Labour government to scrap £1.3 billion in funding originally promised by the Conservatives for tech and AI initiatives. Announced today, the £32 million will bolster 98 projects spanning a diverse range of sectors, utilising AI to boost everything from construction site safety to the efficiency of prescription deliveries. More than 200 businesses and research organisations, from Southampton to Birmingham and Northern Ireland, are set to benefit. Rick McConnell, CEO of Dynatrace, said: This latest announcement is overshadowed by the Labour government’s decision to scrap a significant chunk of funding previously earmarked for major tech projects. These include £800 million for the development of a state-of-the-art exascale supercomputer at Edinburgh University and a further £500 million for AI Research Resource, which provides crucial computing power for AI research. While the £32 million investment signals continued support for AI development, the shadow of the £1.3 billion funding cut looms large. The long-term impact of this decision on the ***’s ability to foster groundbreaking technological advancements ******** to be seen. “Investing in AI-driven innovation will be essential to organisations’ ability to compete on the global stage. There is no doubt that, if implemented successfully, AI has the ability to improve efficiencies, turbocharge innovation, and streamline operations across all sectors,” McConnell concludes. (Photo by Steve Johnson) See also: Meta’s AI strategy: Building for tomorrow, not immediate profits 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 *** backs smaller AI projects while scrapping major investments appeared first on AI News. View the full article

The AI industry has always been the “futuristic view” for humans, whether in movies, cartoons, or real life. Computers work, think and act on behalf of futuristic humans – well, except in the Dune movies. In the past half-decade, artificial intelligence has become the hottest topic in the world, second only to the Covid 19 pandemic, with most people fascinated by the industry’s massive growth and the extent they can use it. This growth is expected to continue at a rapid pace into the last years of the decade, with Statista predicting the $184 billion industry will grow to nearly $900 billion by 2030. However, as the industry becomes a crucial part of our lives, which seems inevitable, it will shape how we think, interact with the world, and do the most basic and complex things in the future. We will be intertwined with it, probably more than we are today with the internet. While still in its infancy stages, most powerful AI systems and models are controlled by mega-corporations such as OpenAI, IBM Watson, Google AI, and Amazon Machine Learning. These Big Tech firms own large data hubs, to train, build, and sell these models to users. This raises a very pertinent and justifiable ***** amongst the common folk. Should we let this massive and dominant technological innovation be controlled by the billionaire de jour? Satoshi was wary of the centralised financial institutions post-2008 global financial crisis and created Bitcoin to solve the centralisation conundrum. In a similar breath, AI needs similar solutions to remove the heavy hand of mega-corporations on what could be the “most important technological advancement in the past few decades”, as Microsoft’s co-founder Bill Gates called it in a blog post in 2023. The problem with the current AI industry structure As stated above, AI technology will be a way of life for ‘almost’ everybody on Earth, helping us complete very menial tasks to greater tasks. For instance, the growth of artificial general intelligence (AGI) can be used to create “AI secretaries”, or AI agents, that can help organise your calendar, pay your monthly bills, create a weekly diet schedule, or create your playlist. (“Hey AI agent X, can you create an R&B playlist including Beyonce, Ne-Yo, etc”) While the data in the examples above may seem simplistic and elementary, such data is very important and personal for most people. Would you want to share such data with the Big Tech firms, who have time and again shown their willingness to use personal data only for profit? Even more unsettling is that AI is being trained in more ‘human-related’ jobs that millions, and probably billions, of people need such as therapists and coaches. Millions of people will share their innermost thoughts, longings, fears, ******* desires, confessions, and embarrassments. Who would trust big tech with such information? It is already happening with ChatGPT, with more and more people using the AI tool to look for answers to their deepest personal questions. This is the bottleneck of current AI systems and models – the centralisation of AI technology, monopolisation of data used to train the AI models, and privacy concerns by users. As such, several developers around the world are working on solutions that build sustainable AI models, without big tech firms’ prying eye on our personal data. Blockchain, a decentralised and privacy-preserving technology, is being integrated with AI to ensure users enjoy the benefits of the technology without the toxicity of Big Tech. A paradigm shift: The rise of decentralised AI services Blockchain technology has been used extensively to correct the centralisation impact in the financial world and most industries, from supply chain to health care, etc. Finally, the technology is extending its roots into artificial intelligence, helping democratise and decentralise the industry. The technology has enhanced data security and transparency through its immutable ledgers, transforming the global sharing of value and setting new standards for operational efficiency and transparency. Integrating two of the most sought after technologies today, AI and blockchain, could be the key to having a free, open, and decentralised AI ecosystem. The primary goal of decentralised AI technologies is to democratise access to AI resources, including data, models, and compute power. This is crucial in minimising the oligopolised structures in AI, which limits the number of entities in the space due to the computational complexity and huge costs of data sets that are needed to train AI models. For instance, NeurochainAI proposes an innovative solution to the challenges of centralised AI systems: a Decentralised AI Infrastructure As a Service (DeAIAS). Simply, NeurochainAI aims to break down the barriers of centralisation and monopolisation “by encouraging cooperation and coordination among various AI stakeholders,” its website reads. Decentralised AI benefits developers and the general public in several ways: Decentralisation: Unlike the current AI models, a decentralised AI ecosystem allows a community of users to share resources such as computing power, data storage, algorithm processing, and model validation. These could be costly for any one company trying to build their models but by tapping into a global community of users the costs are reduced significantly. Ready-to-use infrastructure: NeurochainAI provides developers with a ready-to-use platform helping them develop their AI dApps faster and up to five times more cost-effectively compared to traditional methods. This promotes more innovation across the ecosystem, unlike depending on a few companies for all technological advancements. Incentivisation: One of the biggest benefits of a decentralised AI platform is rewarding the community for providing their resources. For instance, NeurochainAI rewards contributors with $NCN rewards, fostering a collaborative ecosystem where each participant plays a role in shaping the future of AI technology. Privacy and security of data: Decentralised AI also introduces an element of privacy of data. Given blockchain technology allows users to be the custodians of their data, only they choose what data to give to train the AI models. Active participation by the community: NeurochainAI is developed by the community and for the community. This involves community members actively participating in crucial AI training processes such as data curation and validation, algorithm processing, and model validation. This democratises AI development and enriches the models with diverse, real-world inputs. The future of decentralised AI services The rapid growth of artificial intelligence has ensured that many companies/individuals cannot create or train their AI models due to the phenomenal amounts of computing power needed. While centralised cloud computing was a ready solution for previous challenges of computing power, AI is different. Decentralisation solves this problem by creating a network of nodes (computers) that harness the huge untapped computing power of CPUs across the world. This modular approach of decentralised physical infrastructure (DePIN) enhances scalability, provides a cheaper source of computing power than buying new servers, and increases community participation in training the AI models, allowing dApps to learn and share information with each other. While decentralised AI is still at its infancy, the creation of platforms such as NeurochainAI will give Big Tech a run for its money – solving the monopolised nature of AI, computational complexity, and privacy of data for users. The post Blockchain could solve the monopolised AI ecosystem appeared first on AI News. View the full article

OpenAI is facing a leadership crisis as three key figures announce their departure. The news comes amid a tumultuous year for the AI powerhouse, marked by legal battles and high-profile exits. John Schulman, a co-founder of OpenAI, is leaving for rival Anthropic. Schulman confirmed his departure in a statement on X. “I’ve made the difficult decision to leave OpenAI,” Schulman wrote. “This choice stems from my ******* to deepen my focus on AI alignment, and to start a new chapter of my career where I can return to hands-on technical work. I’ve decided to pursue this goal at Anthropic.” OpenAI CEO Sam Altman responded to Schulman’s departure on X, writing, “Thank you for everything you’ve done for OpenAI. We will miss you tremendously and make you proud of this place.” According to The Information, Peter Deng – who isn’t an OpenAI founder, but is a high-profile AI figure that joined the company last year after leading products at Meta, Uber, and Airtable – has also left. These departures come at a critical juncture for OpenAI. The company is currently embroiled in a legal battle with Elon Musk, who recently reignited a lawsuit against the company and two of its founders, including Altman and Brockman. Musk alleges that he was misled about OpenAI’s commitment to its non-profit status and its focus on ethical AI development. With its ranks becoming depleted, increasing competition, and a high-profile legal battle looming, OpenAI is facing an uphill battle. (Photo by Kevin Wang) See also: Google’s Gemini 1.5 Pro dethrones GPT-4o Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo. Explore other upcoming enterprise technology events and webinars powered by TechForge here. The post OpenAI hit by leadership exodus as three key figures depart appeared first on AI News. View the full article

The emergence of AI systems that can create songs presents the music industry with a new challenge. This phenomenon has sparked numerous discussions on concepts such as creativity, copyright, and the development of the music industry. Some artists, recording studios, and legal experts have taken an interest in this issue and raised important questions that highlight the necessity of finding the optimal balance between technology and human work. A recent example: AI attempts to mimic a Grammy nominee To illustrate the current state of AI in music, let us take the example of Tift Merritt, a country musician. Her track “Traveling Alone” is her most well-known piece due to its presence on Spotify. The song is a ballad that contemplates the open road and one’s capacity to travel alone. After requesting an AI music site, Udio, to create “Tift Merritt’s Americana song,” Udio promptly returned to the author with “Holy Grounds.” Specifically, the song contained lyrics about “driving old backroads” and “watching the fields and skies shift and sway.” Naturally, Tift Merritt’s work was unlikely to be entirely unique. Merritt, a Grammy-nominated singer and songwriter, is not particularly satisfied with the result. She stated that the “imitation” that Udio came up with “doesn’t make the cut for any album of mine.” However, the singer has a much more serious charge against the generated content – she doesn’t consider it a manifestation of creativity; in her opinion, it’s more like theft. “This is a great demonstration of the extent to which this technology is not transformative at all,” Merritt asserted. “It’s stealing.” Artists unite in concern Merritt’s stance resonates with many in the music industry. In April, she joined a cohort of high-profile artists including Billie Eilish, Nicki Minaj, and Stevie Wonder in signing an open letter. This document warned that AI-generated music, trained on their recordings, could potentially “sabotage creativity” and marginalise human artists. The industry takes legal action The issue is not confined to individual singers but affects giant record labels as well. Recently, Sony Music, Universal Music Group, and Warner Music have sued Udio and another music AI outfit, Suno. They are the first in the music industry to join the alarming copyright ****** over AI-made songs, a battle only beginning to be waged in the courtrooms. The significance of the situation was highlighted by Mitch Glazier, CEO of the Recording Industry Association of America (RIAA). He referred to the lawsuits as a response to “shameless copying of troves of recordings in order to flood the market with cheap imitations and drain away listens and income from real human artists and songwriters.” However, he also mentioned the potential in AI: “AI has great promise – but only if it’s built on a sound, responsible, licensed footing.” AI companies respond In their initial court responses, Suno and Udio have defended their technology. Additionally, the companies have referred to the industry’s past fears and concerns about the development of synthesisers, drum machines, and other technological advances that were expected to ruin the field by replacing all musically skilled humans. Both companies have maintained their original position, pleading not guilty and explaining that the lawsuits are a means to ******* lesser market players, as the apps they provide cannot be used to exactly replicate the top artists. Legal complexities and novel questions These cases raise new questions for the courts, such as whether AI can use copyrighted material to produce something original and whether the law should make an exception in such cases. The situation is further complicated by the fact that in music, text, melody, harmony, and rhythm of the created material might be mixed, making it much more challenging to determine a case of plagiarism. As mentioned by musicologist Brian McBrearty, who specialises in copyright cases, “Music has more factors than just the stream of words. It has pitch, and it has rhythm, and it has harmonic context. It’s a richer mix of different elements that make it a little bit less straightforward.” The fair use debate One of the key elements of both these cases is likely to be the notion of “fair use” in copyright law. Fair use is a provision of the law that allows some unauthorised uses of copyrighted works based on a few different conditions, one of them usually being whether the new use transforms the original work from which it was created. The AI companies argue that their use of existing recordings of music is “quintessential ‘fair use.'” However, legal experts suggest that music-generating AIs may not find it as easy to prove fair use as text-generating AIs did. The road ahead If such cases come to a conclusion in the courts, they are likely to set relevant precedents for the future of AI in the creative industries. Depending on the outcomes, it is possible that the matters discussed above will have widespread effects on producers of art, technology companies, and consumers alike. From the perspective of Tift Merritt, who is both a musician and a long-time activist for musicians on various points of law, the concerns feel valid: “Ingesting massive amounts of creative labour to imitate it is not creative. That’s stealing in order to be competition and replace us.” The music industry is now at a critical point due to the ongoing debate and legal disputes. The main question is whether the current issues will be resolved and how. Additionally, a conclusion must be made about whether it is possible to allow the development of technology for AI music creation while maintaining the copyright of human artists. (Photo by Lechon Kirb) See also: Elon Musk revives OpenAI legal battle with fresh allegations 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 music sparks new copyright battle in US courts appeared first on AI News. View the full article For verified travel tips and real support, visit: [Hidden Content]

Elon Musk’s revived lawsuit against OpenAI includes fresh allegations against the company and two of its founders, Sam Altman and Greg Brockman. This new legal salvo comes after Musk withdrew a previous lawsuit in June. The earlier suit had primarily focused on claims that OpenAI had breached its founding agreement to keep the company’s technology open source. The fresh complaint, however, takes a more aggressive stance. Musk’s legal team alleges that Altman and Brockman “assiduously manipulated Musk into co-founding their spurious non-profit venture” by making promises about OpenAI’s safety and transparency that set it apart from profit-driven alternatives. The lawsuit goes so far as to claim that assurances about OpenAI’s nonprofit structure were “the ***** for Altman’s long ****.” Marc Toberoff, Musk’s lawyer, told The New York Times, “This is a much more forceful lawsuit.” Indeed, the new suit ups the ante by accusing OpenAI of breaching federal racketeering laws in what it describes as a *********** to defraud Musk. The complaint paints a picture of extensive deception, alleging that Altman and OpenAI lured Musk into co-founding the organisation under false pretences of AI safety and openness. Musk claims he invested significant resources and recruited top scientists based on these assurances, only to see the company pivot towards a profit-making model that compromised its original mission. The lawsuit details allegations of self-dealing and conflicts of interest by Altman, which Musk argues led to their falling out and ultimately compromised OpenAI’s founding principles. Reports of withheld technology and a compromised Board of Directors have raised serious ethical concerns about the company’s operations and future direction. Furthermore, the lawsuit takes aim at OpenAI’s partnership with Microsoft. It claims that the contract between the two tech giants includes a clause that would revoke Microsoft’s rights to OpenAI’s technology once artificial general intelligence (AGI) is achieved. This allegation, if proven true, could have far-reaching implications for the future of AI development and corporate partnerships in the tech industry. The legal action seeks not only damages and restitution but also punitive measures against the defendants for allegedly exploiting Musk’s contributions. The tech billionaire is pushing for Altman to be divested of what the lawsuit describes as “ill-gotten gains” resulting from the alleged deception. Musk’s complaint goes beyond mere contractual disputes, invoking serious legal charges including ******, breach of contract, wire ******, and violations of RICO (Racketeer Influenced and Corrupt Organizations) law against Altman, Brockman, and OpenAI. The revival of Musk’s legal battle against OpenAI comes at a time of increasing scrutiny of AI technologies and their potential impact on society. As one of the original co-founders of OpenAI, Musk’s allegations carry significant weight and could potentially reshape the narrative around the company’s evolution from a non-profit to a for-profit entity. However, it’s worth noting that OpenAI has consistently maintained that its transition to a “capped-profit” model was necessary to secure the funding required for its ambitious AI research and development goals. The company has also emphasised its commitment to developing AI in a responsible and beneficial manner. The tech community and legal experts will be watching closely as this case progresses, given its potential to set precedents for how AI companies are held accountable to their stated missions and founding principles. (Photo by Jonathan Kemper) See also: Meta’s AI strategy: Building for tomorrow, not immediate profits 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 Elon Musk revives OpenAI legal battle with fresh allegations appeared first on AI News. View the full article For verified travel tips and real support, visit: [Hidden Content]

Google’s experimental Gemini 1.5 Pro model has surpassed OpenAI’s GPT-4o in generative AI benchmarks. For the past year, OpenAI’s GPT-4o and Anthropic’s Claude-3 have dominated the landscape. However, the latest version of Gemini 1.5 Pro appears to have taken the lead. One of the most widely recognised benchmarks in the AI community is the LMSYS Chatbot Arena, which evaluates models on various tasks and assigns an overall competency score. On this leaderboard, GPT-4o achieved a score of 1,286, while Claude-3 secured a commendable 1,271. A previous iteration of Gemini 1.5 Pro had scored 1,261. The experimental version of Gemini 1.5 Pro (designated as Gemini 1.5 Pro 0801) surpassed its closest rivals with an impressive score of 1,300. This significant improvement suggests that Google’s latest model may possess greater overall capabilities than its competitors. It’s worth noting that while benchmarks provide valuable insights into an AI model’s performance, they may not always accurately represent the full spectrum of its abilities or limitations in real-world applications. Despite Gemini 1.5 Pro’s current availability, the fact that it’s labelled as an early release or in a testing phase suggests that Google may still make adjustments or even withdraw the model for safety or alignment reasons. This development marks a significant milestone in the ongoing race for AI supremacy among tech giants. Google’s ability to surpass OpenAI and Anthropic in benchmark scores demonstrates the rapid pace of innovation in the field and the intense competition driving these advancements. As the AI landscape continues to evolve, it will be interesting to see how OpenAI and Anthropic respond to this challenge from Google. Will they be able to reclaim their positions at the top of the leaderboard, or has Google established a new standard for generative AI performance? (Photo by Yuliya Strizhkina) See also: Meta’s AI strategy: Building for tomorrow, not immediate profits 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 Google’s Gemini 1.5 Pro dethrones GPT-4o appeared first on AI News. View the full article

As AI takes centre stage in Silicon Valley, an inconvenient truth is emerging behind the scenes: AI has a massive carbon footprint. Tech giants like Microsoft, Google and Amazon have made bold commitments to slash greenhouse gas emissions in the coming years, but the technology they’re ******** their futures on is making those climate goals increasingly challenging to achieve. Microsoft revealed that its carbon emissions had surged nearly 30% since 2020, mainly due to the construction and operation of energy-hungry data centres needed to power its AI ambitions. Google reported an even steeper 48% rise in emissions compared to 2019. These trends highlight the growing tension between rapid AI development and environmental sustainability in the tech sector. The root of the problem ***** in AI’s immense appetite for computing power and electricity. Training large language models like GPT-3 requires vast amounts of data to be processed by thousands of specialized chips running around the clock in sprawling data centres. Once deployed, AI models consume significant energy with each query or task. “One query to ChatGPT uses approximately as much electricity as could light one light bulb for about 20 minutes,” explained Jesse Dodge, a researcher at the Allen Institute for AI, in an interview with NPR. “So, you can imagine that millions of people using something like that every day adds up to a really large amount of electricity.” Indeed, according to Goldman Sachs analysts, a typical ChatGPT query requires nearly ten times as much electricity as a standard Google search. As AI capabilities expand and usage skyrockets, so too does its energy demand. Goldman Sachs estimates that data centres will consume 8% of global electricity by 2030, up from about 3% today—a massive jump primarily driven by AI. The tech industry’s intense electricity consumption impacts regional power grids and even influences decisions around fossil fuel use. Data centre operators in Northern Virginia are expected to require enough electricity to power 6 million homes by 2030. In some areas, plans to decommission coal plants have been delayed to meet surging power needs. This puts tech giants in a difficult position as they try to balance their AI ambitions with climate commitments. Microsoft has pledged to become carbon-negative by 2030, removing more carbon from the atmosphere than it emits. That goal now appears increasingly challenging. The latest sustainability report acknowledges that “as we further integrate AI into our products, reducing emissions may be challenging due to increasing energy demands.” Google had long touted its carbon-neutral status, achieved through carbon offsets. But in 2023, it admitted it was no longer “maintaining operational carbon neutrality” due to emissions growth. The company still aims for net-zero emissions by 2030 but called that timeline “fraught with challenges.” Other major players in AI development, like OpenAI, have yet to disclose any emissions data, leaving the full scope of the industry’s climate impact unclear. However, Microsoft and Google’s trends paint a concerning picture. “We have an existential crisis right now. It’s called climate change, and AI is palpably making it worse,” warned Alex Hanna, director of research at the Distributed AI Research Institute, in an interview with NPR. To their credit, tech companies are not ignoring the problem. They’re investing heavily in renewable energy, exploring more efficient chip designs, and researching ways to reduce AI’s energy needs. Microsoft says it has expanded the use of low-power server states to cut energy use by up to 25% on some machines. Google is designing data centres that it claims will use zero water for cooling. However, these efforts are being outpaced by the breakneck speed of AI development and deployment. Every major tech firm is racing to integrate AI across their product lines, from search engines to productivity software to social media. The potential economic and competitive advantages are simply too large to ignore. This leaves the tech industry at a crossroads. Companies must find ways to dramatically improve AI’s energy efficiency or risk undermining their climate goals and facing growing criticism over their environmental impact. Regulators and the public may also need to grapple with difficult questions about the societal value of AI applications versus their climate costs. The coming years will be crucial in determining whether artificial intelligence becomes a powerful tool for addressing climate change or accelerates the very problem it could help solve. For now, as Microsoft’s president Brad Smith told Bloomberg, the company believes “the answer is not to slow down the expansion of AI but to speed up the work needed to make it more environmentally friendly.” Time will tell if that optimism is warranted or if more drastic measures will be needed to reconcile AI’s promise with its environmental price tag. (Photo by Li-An Lim) See also: Google’s dilemma: AI expansion vs achieving climate goals 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 hidden climate cost of AI: How tech giants are struggling to go green appeared first on AI News. View the full article

Where many have struggled to turn their cloud services into a profitable endeavour, Microsoft has stood out by integrating OpenAI’s successful AI technology. For instance, take TikTok. According to internal financial documents, as of March 2022, ByteDance’s TikTok was spending nearly $20 million every month on OpenAI’s AI model services, which TikTok accessed through Microsoft. This hefty amount accounted for almost 25% of Microsoft’s total revenue in that sector. At the time, Microsoft’s annual revenue from this business was expected to hit $1 billion, or around $83 million per month. But behind this success ***** a risk: high customer concentration. While Microsoft is relying on AI in its work with TikTok, the latter company has also developed its own AI-related plans. In particular, ByteDance wants to create software that can generate dialogue and images. This means that TikTok’s AI might soon become more competent than the one utilised by Microsoft, which could negatively impact the growth of Microsoft’s cloud business revenue. To reduce this risk, Microsoft is trying to attract more corporate clients, such as Walmart and financial software provider Intuit. These companies pay millions of dollars monthly to access OpenAI’s models through Microsoft. For Intuit, this subscription is a hopeful sign since the company used to rent its servers from Amazon. Microsoft is also adopting a diversified strategy by utilising AI in a number of ways. Their cloud service does not just consist of Azure OpenAI; they also have a service named Copilot, through which they sell AI-powered writing, coding, and summarising features to existing Office 365, and other enterprise software customers. Three months ago, CEO Satya Nadella mentioned that the subscription volume for Copilot had doubled, with major buyers including financial services. The success of Microsoft’s AI can be attributed to major customers like TikTok, which has significantly contributed to the company’s profits. Microsoft has also achieved success in pulling customers away from competitors like Google, Amazon, and Oracle. For instance, TikTok initially used the cloud services of these companies, but now spends money on using Microsoft’s cloud technology. In addition, Intuit previously rented servers from Amazon. This company developed a range of AI functions aimed at providing financial advice to customers based on their data. As Intuit CEO Sasan Goodarzi mentioned, since September, more than 24 million people have used this function. In the next fiscal year, the company intends to “accelerate investments” in this area. Walmart, one of the longest-term customers of Microsoft’s Azure OpenAI services, uses this technology at scale to deliver shopping recommendations to its customers. At the same time, a Microsoft customer from Abu Dhabi, G42, spends millions of dollars monthly on Azure OpenAI services, and has announced its partnership with OpenAI to create AI for Middle Eastern customers. However, some uncertainty still exists. It is unclear whether Walmart or TikTok use this technology to improve their own AI models. If they do, a share of their spending on Microsoft’s products will decrease once their technology matures. Although OpenAI prohibits using its technology to create competitive AI models, many customers still do so, and OpenAI appears to tolerate this practice. According to reports from the previous year, ByteDance trained its internal AI models using OpenAI’s GPT-4 model by having its chatbot produce text fragments that ByteDance’s model then incorporated. ByteDance responded by saying they were only “very limitedly” utilising OpenAI’s approach. In order to reduce the risk associated with a high customer concentration, Microsoft is working to increase both its clientele and sources of income. Microsoft has benefited greatly from OpenAI’s AI technologies, but the company is still highly reliant on big clients like TikTok. Microsoft must attract and retain more large clients to meet market expectations. In the hopes that these investments will someday pay off, the corporation has put billions of dollars into OpenAI’s technologies and data centre servers. Microsoft’s financial report was made public on Tuesday. It revealed a 29% increase in cloud revenue for the second quarter, which was less than anticipated by the market. This was followed by a more than 7% decline in Microsoft’s stock price, which impacted other tech stocks, such as Amazon, Datadog, and Snowflake. Despite the decline, Microsoft ******** upbeat and anticipates a spike in Azure revenue the following year. Additionally, Microsoft is selling a percentage of revenue from AI models directly to enterprises, with this year’s numbers unexpectedly surpassing those of Azure’s OpenAI services. Microsoft also generates billions in revenue annually by renting servers to OpenAI, allowing the startup to run ChatGPT and develop related technology, despite not having high profit margins from this. 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 Microsoft gains major AI client as TikTok spends $20 million monthly appeared first on AI News. View the full article

Meta has signalled a long-term AI strategy that prioritises substantial investments over immediate revenue generation. During the company’s Q2 earnings call, CEO and founder Mark Zuckerberg outlined Meta’s vision for the future and emphasised the need for extensive computational resources to support their AI initiatives. Zuckerberg revealed that Meta is “planning for the compute clusters and data we’ll need for the next several years,” with a particular focus on their next AI model, Llama 4. The company anticipates that training Llama 4 will require “almost 10x more” computing power than its predecessor, Llama 3, which is believed to have used 16,000 GPUs. Zuckerberg expressed his goal for Llama 4 “to be the most advanced [model] in the industry next year.” Meta’s financial commitment to AI development is substantial, with the company projecting capital expenditures between $37 and $40 billion for the full year, an increase of $2 billion from previous estimates. Investors were cautioned to expect “significant” increases in capital expenditures next year as well. Despite these massive investments, Meta CFO Susan Li acknowledged that the company does not expect to generate revenue from generative AI this year. Li emphasised the company’s strategy of building AI infrastructure with flexibility in mind, allowing for capacity adjustments based on optimal use cases. She explained that the hardware used for AI model training can also be utilised for inferencing and, with modifications, for ranking and recommendations. Meta’s current AI efforts, dubbed “Core AI,” are already showing positive results in improving user engagement on Facebook and Instagram. Zuckerberg highlighted the success of a recently implemented unified video recommendation tool for Facebook, which has “already increased engagement on Facebook Reels more than our initial move from CPUs to GPUs did.” Looking ahead, Zuckerberg envisions AI playing a crucial role in revolutionising Meta’s advertising business. He predicted that in the coming years, AI would take over ad copy creation and personalisation, potentially allowing advertisers to simply provide a business objective and budget, with Meta’s AI handling the rest. While Meta’s AI investments are substantial, the company ******** in a strong financial position. Q2 results showed revenue of $39 billion and net income of $13.5 billion, representing year-over-year increases of $7 billion and $5.7 billion, respectively. Meta’s user base continues to grow, with over 3.2 billion people using a Meta app daily, and its X competitor Threads is now approaching 200 million active monthly users. As Meta charts its course in the AI landscape, the company’s strategy reflects a long-term vision that prioritises technological advancement and infrastructure development over immediate financial returns. (Photo by Joshua Earle) See also: NVIDIA and Meta CEOs: Every business will ‘have an AI’ 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’s AI strategy: Building for tomorrow, not immediate profits appeared first on AI News. View the full article

Speech analytics driven by AI is speech recognition software that works using natural language processing and machine learning technologies. With speech analytics in call centres, you can convert live speech into text. After that, the program evaluates this text to reveal details about the needs, preferences, and sentiment of the customer. In contact centres, speech analytics tools helps: Analyse voice recordings. Provide feedback for agents. Improve customer experience. Increase sales. How does speech analytics driven by AI differ from the traditional one? What benefits can contact centres and businesses receive from it? Find the answers in this article. How does AI-driven speech analytics differ from traditional? They differ in several key aspects: Key components of AI-driven speech analytics Here is a list of common technologies driven by artificial intelligence. They are being used to optimise and improve the performance of contact centres and the applications they run: Artificial intelligence is a branch of computer technology that develops computer programs to solve complex problems by simulating behavior associated with the behaviour of intelligent beings. AI is able to reason, learn, solve issues, and self-correct. Machine learning is a subsection of AI that teaches computers through experience rather than additional programming. It is a method of data analysis that, without the need for programming, finds patterns in data and forecasts future events using statistical algorithms. Natural language processing allows a computer to understand spoken or written language. It can analyse syntax and semantics. In determining meaning and developing suitable answers, this is helpful. For example, it processes verbal commands given to intelligent virtual operators, virtual assistants that staff work with, or voice menus. Sentiment analysis is another application for this technology. More advanced natural language processing can “learn” to take into account context and read sarcasm, humor, and a variety of different human emotions. A part of natural language processing called natural language understanding enables a computer to comprehend written or spoken language. Grammatical structure, syntax, and semantics of a sentence can all be examined using it. This helps in deciphering meaning and creating suitable answers. Predictive analytics uses machine learning, data mining, and statistical analysis techniques to analyse data and identify relationships, patterns, and trends. One can create a predictive model using such data. It forecasts the possibility of a given thing happening, the tendency to do something, and their possible consequences. How does speech analytics work in contact centres? Software for speech analytics gathers and examines data from conversations with customers. Transcripts of phone conversations, dashboards, and reports can all be created using the gathered data. Agent productivity, customer satisfaction, call volume, and other metrics are all shown in real time to contact centre management through dashboards. Call transcripts are recordings of conversations in text format used for training and quality control of service. Speech analysis is most often carried out in the following stages: #1 Interaction recording A recording of a conversation that needs to be analysed. #2 Separating the audio tracks of interlocutors It enables you to more clearly pinpoint issues. For example, if the paths intersect in a conversation between a manager and a client, one interlocutor interrupts the other. #3 Converting speech to text This step helps to obtain a text version of the conversation that will be used for subsequent research. #4 Text transcript Different text processing techniques are applied to the resultant text to examine it. These include of finding tags and themes, marking words and phrases, and assessing the tone of the text. The program also processes terms, dialogues, and discussion. #5 Data classification By terms, topic, tone of emotion, or other parameters. #6 Data visualisation By charts, graphs, heat maps, and other visuals. The program will clearly show the results achieved. #7 Data analytics During this phase, judgments are made, trends are found, important discoveries are highlighted, and data is interpreted. The system allows you to record calls and create detailed, complete reports, which will allow you to identify errors in work and find additional points of growth. This information will help develop the project and increase the average bill with the right choice of promotion tools and budget savings. How can AI-driven speech analytics help businesses? Depending on the company size, industry, size of the contact centre, and other factors, different benefits of speech analytics will come to the *****. The universal advantages are the following: Increasing the number of verified calls Quality control teams in call centres check an average of two to four operator calls per month. Businesses may quickly validate up to 100% of calls with speech analytics. KPI fulfilment tracking Various interaction metrics can be analysed with the use of speech analytics: Request escalation rates Out-of-script behaviour Customer satisfaction Average call handling time, etc. Speech analytics tools are able to pinpoint the areas in which agents’ quality scores are lagging. Following that, it offers useful data to boost productivity. Instant feedback Supervisors may provide agents individualised feedback more quickly with faster analysis and 100% call coverage. Many contact centres have begun implementing AI assistants to give agents real-time suggestions. Improved operational efficiency Speech analytics reduces the time for verification processes. Contact centres can handle large call volumes and enhance operational efficiency with its help. Large-scale customer self-service capabilities for common queries are provided by speech-to-text and text-to-speech voice assistants. Resources for agents to handle more complicated scenarios are freed up. Personalised learning Programs for individualised agent training can be developed by managers and workforce development teams. Because each agent’s call performance and attributes are advanced assessed, it becomes feasible. Higher customer service quality Speech analytics offers thorough insight into the requirements of the consumer. Teams can find elements of a satisfying customer experience by using sentiment analysis. Or indicators of a negative customer experience to influence the customer experience and lifecycle. Problem identification and management Words and phrases used in consumer interactions can be found via speech analytics. Problem-call information can be instantly sent to supervisors by email or instant messenger. Managers are able to address challenging issues in a timely manner because of notifications. After that, they use reports and dashboards to evaluate the effectiveness of their decisions. Customer sentiment analysis Speech analytics can determine a speaker’s emotions at a given moment by considering speech characteristics such as voice volume and pitch. Contact centres can use this information to determine a customer’s general opinion of the business. What difficulties could you expect when using AI-based speech analytics? Data privacy and security Contact centres handle a large amount of personal and financial information. There is a risk of data breaches, unauthorised access, and misuse of customer information, which can lead to regulatory penalties and a loss of customer trust. How to address: Contact centres need to put strong data security procedures in place. These are the following: Data encryption Strict access controls Regular security audits, etc. It helps identify and address vulnerabilities. Also, you can employ solutions with built-in security features. Cost of implementation AI-based voice analytics implementation can need a large financial outlay. Such costs include the following: Purchasing software Integrating new systems with existing infrastructure Training staff Ongoing maintenance and support How to address: Contact centres should start with an ROI analysis. They ought to project possible cost reductions as well as increased income. Phased implementing modifications can assist in distributing costs. It lessens the financial load in the short term. You can also implement cloud-based solutions—it lowers up-front expenses because these are usually pay-as-you-go. Technological complexity Deploying advanced AI technologies and their integration with existing systems can be technically demanding and require specialised knowledge. How to address: Implementation complexity can be decreased by collaborating with seasoned suppliers that have a solid track record. These vendors can provide end-to-end services, including integration, training, and ongoing support. The bottom line Statistics show that mundane duties take up almost half of a contact centre agent’s working hours. The introduction of modern speech analytics services significantly optimises processes and allows you to obtain analytical data. Based on this data, you can develop a strategy for the further development of the company and improve relationships with customers, forming their loyalty. The post How to use AI-driven speech analytics in contact centres appeared first on AI News. View the full article

As the EU’s AI Act prepares to come into force tomorrow, industry experts are weighing in on its potential impact, highlighting its role in building trust and encouraging responsible AI adoption. Curtis Wilson, Staff Data Engineer at Synopsys’ Software Integrity Group, believes the new regulation could be a crucial step in addressing the AI industry’s most pressing challenge: building trust. “The greatest problem facing AI developers is not regulation, but a lack of trust in AI,” Wilson stated. “For an AI system to reach its full potential, it needs to be trusted by the people who use it.” This sentiment is echoed by Paul Cardno, Global Digital Automation & Innovation Senior Manager at 3M, who noted, “With nearly 80% of *** adults now believing AI needs to be heavily regulated, the introduction of the EU’s AI Act is something that businesses have been long-waiting for.” Both experts emphasise the Act’s potential to foster confidence in AI technologies. Wilson explained that while his company has implemented internal measures to build trust, external regulation is equally important. “I see regulatory frameworks like the EU AI Act as an essential component to building trust in AI,” Wilson said. “The strict rules and punishing fines will deter careless developers and help customers feel more confident in trusting and using AI systems.” Cardno added, “We know that AI is shaping the future, but companies will only be able to reap the rewards if they have the confidence to rethink existing processes and break away from entrenched structures.” The EU AI Act primarily focuses on high-risk systems and foundational models. Wilson noted that many of its requirements align with existing best practices in data science, such as risk management, testing procedures, and comprehensive documentation. For *** businesses, the impact of the EU AI Act extends beyond those directly selling to EU markets. Wilson pointed out that certain aspects of the Act may apply to Northern Ireland due to the Windsor Framework. Additionally, the *** government is developing its own AI regulations, with a recent whitepaper emphasising interoperability with EU and US regulations. “While the EU Act isn’t perfect, and needs to be assessed in relation to other global regulations, having a clear framework and guidance on AI from one of the world’s major economies will help encourage those who remain on the fence to tap into the AI revolution,” Cardno explained. While acknowledging that the new regulations may create some friction, particularly around registration and certification, Wilson emphasised that many of the Act’s obligations are already standard practice for responsible companies. However, he recognised that small companies and startups might face greater challenges. “Small companies and start-ups will experience issues more strongly,” Wilson said. “The regulation acknowledges this and has included provisions for sandboxes to foster AI innovation for these smaller businesses.” However, Wilson notes that these sandboxes will be established at the national level by individual EU member states, potentially limiting access for *** businesses. As the AI landscape continues to evolve, the EU AI Act represents a significant step towards establishing a framework for responsible AI development and deployment. “Having a clear framework and guidance on AI from one of the world’s major economies will help encourage those who remain on the fence to tap into the AI revolution, ensuring it has a safe, positive ongoing influence for all organisations operating across the EU, which can only be a promising step forwards for the industry,” concludes Cardno. (Photo by Guillaume Périgois) See also: UAE blocks US congressional meetings with G42 amid AI transfer concerns 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 Balancing innovation and trust: Experts assess the EU’s AI Act appeared first on AI News. View the full article

There have been reports that the ******* ***** Emirates (UAE) has “suddenly cancelled” the ongoing series of meetings between a group of US congressional staffers and Emirati AI firm G42, after some US lawmakers raised concerns that this practice may lead to the transfer of advanced ********* AI technology to China. However, a congressional spokesperson, who provided this information, chose to remain anonymous due to internal committee policy, as reported by Reuters. The order was given directly by the UAE’s ambassador to the US, who halted the meetings between staffers from the House Select Committee on China and G42, as well as various Emirati government officials. This development only adds fuel to the ***** of high tensions surrounding the scrutiny of G42 amid a $1.5 billion agreement with Microsoft. Some US congresspeople are already worried about sensitive technology getting into the hands of a UAE firm that reportedly has ******** ties. The committee’s spokesperson expressed increased concerns regarding the G42-Microsoft deal due to the UAE’s unwillingness to engage in talks. “Expect Congress to become more involved in overseeing these negotiations,” the spokesperson said. The cancelled meetings may signal a diplomatic crisis due to the increased attention of China hawks in Congress. The efforts of these lawmakers to closely scrutinise the G42-Microsoft deal have particularly sparked controversies. Members of Congress are focused on ensuring that sensitive AI developments and products resulting from the agreement will not be diverted by the Emiratis to China. The State Department gave no comment, whereas G42 directed the media to the Emirati government. The UAE embassy spokesperson announced that the situation resulted from a “miscommunication,” as they were notified of the staff delegation just the day before their planned arrival. The embassy emphasised its regular engagement with committee members and staffers in recent months, asserting that the committee has been kept informed about ****** UAE-US efforts to strengthen control over critical advanced technologies. The congressional staffers had planned these meetings as part of a regional visit from July 16-19. Their agenda included discussions on the transfer of sophisticated chips from companies like Nvidia to the UAE and Saudi Arabia, as well as US-China tech competition. Ambassador Yousef Al Otaiba cited a July 11 letter from committee chairman John Moolenaar to US National Security Advisor Jake Sullivan as the reason for the cancellations. This letter, co-signed by House Foreign Affairs chair Michael McCaul, requested a White House intelligence briefing on Microsoft’s investment in G42 before the deal could progress to its second phase, which would involve transferring export-restricted semiconductor chips from Nvidia and sophisticated AI model weights. The Biden administration has taken a positive view of the G42-Microsoft deal, stating that G42’s severance from China’s Huawei has been a major positive factor for the deal. However, last year, the administration also imposed sweeping curbs on AI chip exports, requiring licenses for shipments under a more restrictive policy than the previous Trump administration. Additionally, the policy of restricting exports to China requires licenses for exports to the UAE and some other Middle Eastern countries. It is noted that a regional visit by a congressional delegation took place, during which they met with Saudi officials who expressed a ******* to alleviate US companies’ concerns about the activities of the ******** government in Saudi Arabia. Their goal was to obtain permission to import advanced ********* chips. The level of interaction between US and other countries’ authorities illustrates the link between technological innovation, international political relationships, and national security issues. See also: UAE unveils new AI model to rival big tech giants 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 UAE blocks US congressional meetings with G42 amid AI transfer concerns appeared first on AI News. View the full article For verified travel tips and real support, visit: [Hidden Content]