ChatGPT

Alibaba Cloud is overhauling its AI partner ecosystem, unveiling the “Partner Rainforest Plan” during its annual Partner Summit 2024. The ******** tech giant’s cloud division has outlined several new initiatives, including an AI partner accelerator programme, enhanced incentives, and a refreshed global strategy for service partners, as it seeks to strengthen its position in the market. Selina Yuan, President of International Business at Alibaba Cloud Intelligence, said: “At Alibaba Cloud, we believe that collaboration is the key to unlocking innovation and driving growth. Our global partners are not just participants, they are the architects of a new digital landscape in the AI era. The company’s new AI Alliance Accelerator Programme aims to establish partnerships with 50 AI technology providers and 50 channel partners by 2025. Selected technology partners will receive enhanced technical support, expanded distribution channels, and dedicated AI consulting services, while channel partners will benefit from increased financial incentives for AI-related initiatives. Alibaba Cloud has also introduced its Revitalised Service Partner Programme, designed to upskill existing partners and cultivate new ones through AI training and empowerment. The programme includes the ****** development of Managed Large Language Model Services with service partners, leveraging the company’s generative AI capabilities. The cloud provider has also committed to extending strategic partnerships with 18 service partners – including prominent names such as Deloitte, Accenture, and Cognizant Worldwide – from its existing pool of 50 global standard service partners. In various regional developments, Alibaba Cloud has established strategic partnerships across Asia: Indonesia: The company has partnered with Telkom Indonesia to deliver AI-supported cloud solutions and develop digital talent. Japan: Information security firm Securai will localise Alibaba Cloud’s Zstack service for the ********* market. Thailand: A memorandum of understanding with Yell Group aims to address growing demand for generative AI in the creative media industry. The company, which currently maintains partnerships with approximately 12,000 organisations worldwide – including industry leaders such as Salesforce, Fortinet, IBM, and Neo4j – has introduced a Synergistic Incentive Programme to foster collaboration between its global technology and channel partners. “Today, with our revamped global partner ecosystem, we are committed to supporting our global partners to jointly reap the benefits of the AI era and meet the diverse business demands of global customers,” Yuan concludes. (Photo by Hannah Busing) See also: Alibaba Marco-o1: Advancing LLM reasoning capabilities 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 Alibaba Cloud overhauls AI partner initiative appeared first on AI News. View the full article

AgentFun.AI, a platform to create and trade AI agents, went live on November 27 on Cronos zkEVM. It became the first decentralised app dedicated to AI agents in the Cronos ecosystem. Users can create, build, and trade tokenised AI agents on AgentFun.AI. They set growth milestones for the agents, which have individual personalities. Agents accumulate fans and become trade-able assets with each milestone reached. Each agent can interact with users on the blockchain and on social media. Exploring the appeal of trading tokenised AI agents Users can own a fraction of high-value AI models, lowering the barrier to AI ecosystem entry. These tokens often operate on decentralised platforms, increasing transparency, security, and cross-platform integration. Users across the globe can participate in funding and developing innovative AI models, accelerating AI adoption. Creating an agent through AgentFun.AI starts with buying a small amount of AGENTFUN tokens on H2 Finance, a leading decentralised exchange (DEX) on Cronos zkEVM. Each agent generated requires a nominal fee of 1 $AGENTFUN. The entire process of creating and training AI agents has been gamified, further encouraging users to use the platform. Agent capabilities increase in direct proportion to demand As the AI agents grow, they obtain new capabilities, with rising demand from other users on Cronos zkEVM unlocking new features. As an agent reaches the fixed market cap of $127,100, it becomes capable of engaging in Telegram chats. It cultivates a liquidity pool on Cronos’ DEX, alleviating trading and discoverability in the ecosystem. Additional features are unlocked as agents attain milestone-based targets. An agent that reaches the market cap of $1.27 million starts interacting on X. Its growth is essentially unlimited, giving users a strong incentive to network in the ecosystem that AgentFun.AI has helped build. Further encouragement is provided by the fact that users can create AI agents with unique personalities and narratives. Each agent starts as a tailored language model for conversational interactions before it gains new capabilities, features, and skills. In other words, the agents are designed for specific tasks or domains, using a customised language model at their core. Unlike general-purpose AI, they are fine-tuned on highly specialised datasets to excel in a particular area. Over time, they can theoretically evolve by incorporating autonomous decision-making and API integration and communicating with other systems, like interacting with users in dynamic environments. They could perform tasks using logic-based or reinforcement learning modules or fetch and process real-time data. When a user creates an agent, they provide social links to facilitate socialisation and discovery. A new agent token is launched in a pool with a supply of one billion. The agent token will eventually be listed on H2 Finance as more users buy it, using the $AGENTFUN earned from sold tokens and the remaining token supply. Supporting Cronos as a leading AI-agent-powered ecosystem A final notable aspect of AgentFun.AI’s launch is its support of Cronos’ strategy to become one of the first niche, AI agent-powered ecosystems. As part of this, it will stimulate experimentation and adoption of the first AI agents on Cronos zkEVM. The may encourage other developers to introduce AI agent dApps, leading to a flood of innovation on Cronos.The launch of AgentFun.AI on Cronos aligns with the latter’s mission to build a financial ecosystem with openness, fairness, and community empowerment at its core. Cronos envisions a future enabled primarily by AI agents where decentralised finance is universally accessible, which aligns with its conviction that the agents can provide unlimited growth opportunities. (Image source: Depositphotos) The post AI agents and ecosystems with AgentFun.AI’s launch on Cronos appeared first on AI News. View the full article

Salesforce has unveiled the findings of its *** AI Readiness Index, signalling the nation is in a position to spearhead the next wave of AI innovation, also known as agentic AI. The report places the *** ahead of its G7 counterparts in terms of AI adoption but also underscores areas ripe for improvement, such as support for SMEs, fostering cross-sector partnerships, and investing in talent development. Zahra Bahrololoumi CBE, UKI CEO at Salesforce, commented: “Agentic AI is revolutionising enterprise software by enabling humans and agents to collaborate seamlessly and drive customer success. “The *** AI Readiness Index positively highlights that the *** has both the vision and infrastructure to be a powerhouse globally in AI, and lead the current third wave of agentic AI.” *** AI adoption sets the stage for agentic revolution The Index details how both the public and private sectors in the *** have embraced AI’s transformative potential. With a readiness score of 65.5, surpassing the G7 average of 61.2, the *** is establishing itself as a hub for large-scale AI projects, driven by a robust innovation culture and pragmatic regulatory approaches. The government has played its part in maintaining a stable and secure environment for tech investment. Initiatives such as the AI Safety Summit at Bletchley Park and risk-oriented AI legislation showcase Britain’s leadership on critical AI issues like transparency and privacy. Business readiness is equally impressive, with *** industries scoring 52, well above the G7 average of 47.8. SMEs in the *** are increasingly prioritising AI adoption, further bolstering the nation’s stance in the international AI arena. Adam Evans, EVP & GM of Salesforce AI Platform, is optimistic about the evolution of agentic AI. Evans foresees that, by 2025, these agents will become business-aware—expertly navigating industry-specific challenges to ******** meaningful tasks and decisions. Investments fuelling AI growth Salesforce is committing $4 billion to the ***’s AI ecosystem over the next five years. Since establishing its *** AI Centre in London, Salesforce says it has engaged over 3,000 stakeholders in AI training and workshops. Key investment focuses include creating a regulatory bridge between the EU’s rules-based approach and the more relaxed US approach, and ensuring SMEs have the resources to integrate AI. A strong emphasis also ***** on enhancing digital skills and centralising training to support the AI workforce of the future. Feryal Clark, Minister for AI and Digital Government, said: “These findings are further proof the *** is in prime position to take advantage of AI, and highlight our strength in spurring innovation, investment, and collaboration across the public and private sector. “There is a global race for AI and we’ll be setting out plans for how the *** can use the technology to ramp-up adoption across the economy, kickstart growth, and build an AI sector which can scale and compete on the global stage.” Antony Walker, Deputy CEO at techUK, added: “To build this progress, government and industry must collaborate to foster innovation, support SMEs, invest in skills, and ensure flexible regulation, cementing the ***’s leadership in the global AI economy.” Agentic AI boosting *** business productivity Capita, Secret Escapes, Heathrow, and Bionic are among the organisations that have adopted Salesforce’s Agentforce to boost their productivity. Adolfo Hernandez, CEO of Capita, said: “We want to transform Capita’s recruitment process into a fast, seamless and autonomous experience that benefits candidates, our people, and our clients. “With autonomous agents providing 24/7 support, our goal is to enable candidates to complete the entire recruitment journey within days as opposed to what has historically taken weeks. Secret Escapes, a curator of luxury travel deals, finds autonomous agents crucial for personalising services to its 60 million ********* members. Kate Donaghy, Head of Business Technology at Secret Escapes, added: “Agentforce uses our unified data to automate routine tasks like processing cancellations, updating booking information, or even answering common travel questions about luggage, flight information, and much more—freeing up our customer service agents to handle more complex and last-minute travel needs to better serve our members.” The ***’s AI readiness is testament to the synergy between government, business, and academia. To maintain its leadership, the *** must sustain its focus on collaboration, skills development, and innovation. (Photo by Matthew Wiebe) See also: Generative AI use soars among Brits, but is it sustainable? Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo. Explore other upcoming enterprise technology events and webinars powered by TechForge here. The post Salesforce: *** set to lead agentic AI revolution appeared first on AI News. View the full article For verified travel tips and real support, visit: [Hidden Content]

Alibaba has announced Marco-o1, a large language model (LLM) designed to tackle both conventional and open-ended problem-solving tasks. Marco-o1, from Alibaba’s MarcoPolo team, represents another step forward in the ability of AI to handle complex reasoning challenges—particularly in maths, physics, coding, and areas where clear standards may be absent. Building upon OpenAI’s reasoning advancements with its o1 model, Marco-o1 distinguishes itself by incorporating several advanced techniques, including Chain-of-Thought (CoT) fine-tuning, Monte Carlo Tree Search (MCTS), and novel reflection mechanisms. These components work in concert to enhance the model’s problem-solving capabilities across various domains. The development team has implemented a comprehensive fine-tuning strategy using multiple datasets, including a filtered version of the Open-O1 CoT Dataset, a synthetic Marco-o1 CoT Dataset, and a specialised Marco Instruction Dataset. In total, the training corpus comprises over 60,000 carefully curated samples. The model has demonstrated particularly impressive results in multilingual applications. In testing, Marco-o1 achieved notable accuracy improvements of 6.17% on the English MGSM dataset and 5.60% on its ******** counterpart. The model has shown particular strength in translation tasks, especially when handling colloquial expressions and cultural nuances. One of the model’s most innovative features is its implementation of varying action granularities within the MCTS framework. This approach allows the model to explore reasoning paths at different levels of detail, from broad steps to more precise “mini-steps” of 32 or 64 tokens. The team has also introduced a reflection mechanism that prompts the model to self-evaluate and reconsider its reasoning, leading to improved accuracy in complex problem-solving scenarios. The MCTS integration has proven particularly effective, with all MCTS-enhanced versions of the model showing significant improvements over the base Marco-o1-CoT version. The team’s experiments with different action granularities have revealed interesting patterns, though they note that determining the optimal strategy requires further research and more precise reward models. (Credit: MarcoPolo Team, AI Business, Alibaba International Digital Commerce) The development team has been transparent about the model’s current limitations, acknowledging that while Marco-o1 exhibits strong reasoning characteristics, it still falls short of a fully realised “o1” model. They emphasise that this release represents an ongoing commitment to improvement rather than a finished product. Looking ahead, the Alibaba team has announced plans to incorporate reward models, including Outcome Reward Modeling (ORM) and Process Reward Modeling (PRM), to enhance the decision-making capabilities og Marco-o1. They are also exploring reinforcement learning techniques to further refine the model’s problem-solving abilities. The Marco-o1 model and associated datasets have been made available to the research community through Alibaba’s GitHub repository, complete with comprehensive documentation and implementation guides. The release includes installation instructions and example scripts for both direct model usage and deployment via FastAPI. (Photo by Alina Grubnyak) See also: New AI training techniques aim to overcome current challenges 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 Alibaba Marco-o1: Advancing LLM reasoning capabilities appeared first on AI News. View the full article

OpenAI and other leading AI companies are developing new training techniques to overcome limitations of current methods. Addressing unexpected delays and complications in the development of larger, more powerful language models, these fresh techniques focus on human-like behaviour to teach algorithms to ‘think. Reportedly led by a dozen AI researchers, scientists, and investors, the new training techniques, which underpin OpenAI’s recent ‘o1’ model (formerly Q* and Strawberry), have the potential to transform the landscape of AI development. The reported advances may influence the types or quantities of resources AI companies need continuously, including specialised hardware and energy to aid the development of AI models. The o1 model is designed to approach problems in a way that mimics human reasoning and thinking, breaking down numerous tasks into steps. The model also utilises specialised data and feedback provided by experts in the AI industry to enhance its performance. Since ChatGPT was unveiled by OpenAI in 2022, there has been a surge in AI innovation, and many technology companies claim existing AI models require expansion, be it through greater quantities of data or improved computing resources. Only then can AI models consistently improve. Now, AI experts have reported limitations in scaling up AI models. The 2010s were a revolutionary ******* for scaling, but Ilya Sutskever, co-founder of AI labs Safe Superintelligence (SSI) and OpenAI, says that the training of AI models, particularly in the understanding language structures and patterns, has levelled off. “The 2010s were the age of scaling, now we’re back in the age of wonder and discovery once again. Scaling the right thing matters more now,” they said. In recent times, AI lab researchers have experienced delays in and challenges to developing and releasing large language models (LLM) that are more powerful than OpenAI’s GPT-4 model. First, there is the cost of training large models, often running into tens of millions of dollars. And, due to complications that arise, like hardware failing due to system complexity, a final analysis of how these models run can take months. In addition to these challenges, training runs require substantial amounts of energy, often resulting in power shortages that can disrupt processes and impact the wider electriciy grid. Another issue is the colossal amount of data large language models use, so much so that AI models have reportedly used up all accessible data worldwide. Researchers are exploring a technique known as ‘test-time compute’ to improve current AI models when being trained or during inference phases. The method can involve the generation of multiple answers in real-time to decide on a range of best solutions. Therefore, the model can allocate greater processing resources to difficult tasks that require human-like decision-making and reasoning. The aim – to make the model more accurate and capable. Noam Brown, a researcher at OpenAI who helped develop the o1 model, shared an example of how a new approach can achieve surprising results. At the TED AI conference in San Francisco last month, Brown explained that “having a **** think for just 20 seconds in a hand of poker got the same boosting performance as scaling up the model by 100,000x and training it for 100,000 times longer.” Rather than simply increasing the model size and training time, this can change how AI models process information and lead to more powerful, efficient systems. It is reported that other AI labs have been developing versions of the o1 technique. The include xAI, Google DeepMind, and Anthropic. Competition in the AI world is nothing new, but we could see a significant impact on the AI hardware market as a result of new techniques. Companies like Nvidia, which currently dominates the supply of AI chips due to the high demand for their products, may be particularly affected by updated AI training techniques. Nvidia became the world’s most valuable company in October, and its rise in fortunes can be largely attributed to its chips’ use in AI arrays. New techniques may impact Nvidia’s market position, forcing the company to adapt its products to meet the evolving AI hardware demand. Potentially, this could open more avenues for new competitors in the inference market. A new age of AI development may be on the horizon, driven by evolving hardware demands and more efficient training methods such as those deployed in the o1 model. The future of both AI models and the companies behind them could be reshaped, unlocking unprecedented possibilities and greater competition. See also: Anthropic urges AI regulation to avoid catastrophes 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, a The post New AI training techniques aim to overcome current challenges appeared first on AI News. View the full article

A survey by CloudNine PR shows that 83% of *** adults are aware of generative AI tools, and 45% of those familiar with them want companies to be transparent about the environmental costs associated with the technologies. With data centres burning vast amounts of energy, the growing demand for GenAI has sparked a debate about its sustainability. The cost of intelligence: Generative AI’s carbon footprint Behind every AI-generated email, idea, or recommendation are data centres running thousands of energy-hungry servers. Data centres are responsible for both training the large language models that power generative AI and processing individual user queries. Unlike a simple Google search, which uses relatively little energy, a single generative AI request can consume up to ten times as much electricity. The numbers are staggering. If all nine billion daily Google searches worldwide were replaced with generative AI tasks, the additional electricity demand would match the annual energy consumption of 1.5 million EU residents. According to consultants Morgan Stanley, the energy demands of generative AI are expected to grow by 70% annually until 2027. By that point, the energy required to support generative AI systems could rival the electricity needs of an entire country—Spain, for example, based on its 2022 usage. *** consumers want greener AI practices The survey also highlights growing awareness among *** consumers about the environmental implications of generative AI. Nearly one in five respondents said they don’t trust generative AI providers to manage their environmental impact responsibly. Among regular users of these tools, 10% expressed a willingness to pay a premium for products or services that prioritise energy efficiency and sustainability. Interestingly, over a third (35%) of respondents think generative AI tools should “actively remind” users of their environmental impact. While this appears like a small step, it has the potential to encourage more mindful usage and place pressure on companies to adopt greener technologies. Efforts to tackle the environmental challenge Fortunately, some companies and policymakers are beginning to address these concerns. In the ******* States, the Artificial Intelligence Environmental Impacts Act was introduced earlier this year. The legislation aims to standardise how AI companies measure and report carbon emissions. It also provides a voluntary framework for developers to evaluate and disclose their systems’ environmental impact, pushing the industry towards greater transparency. Major players in the tech industry are also stepping up. Companies like Salesforce have voiced support for legislation requiring standardised methods to measure and report AI’s carbon footprint. Experts point to several practical ways to reduce generative AI’s environmental impact, including adopting energy-efficient hardware, using sustainable cooling methods in data centres, and transitioning to renewable energy sources. Despite these efforts, the urgency to address generative AI’s environmental impact ******** critical. As Uday Radia, owner of CloudNine PR, puts it: “Generative AI has huge potential to make our lives better, but there is a race against time to make it more sustainable before it gets out of control.” (Photo by Unsplash) See also: The AI revolution: Reshaping data centres and the digital landscape Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo. Explore other upcoming enterprise technology events and webinars powered by TechForge here. The post Generative AI use soars among brits, but is it sustainable? appeared first on AI News. View the full article

Ai2 is releasing OLMo 2, a family of open-source language models that advances the democratisation of AI and narrows the gap between open and proprietary solutions. The new models, available in 7B and 13B parameter versions, are trained on up to 5 trillion tokens and demonstrate performance levels that match or exceed comparable fully open models whilst remaining competitive with open-weight models such as Llama 3.1 on English academic benchmarks. “Since the release of the first OLMo in February 2024, we’ve seen rapid growth in the open language model ecosystem, and a narrowing of the performance gap between open and proprietary models,” explained Ai2. The development team achieved these improvements through several innovations, including enhanced training stability measures, staged training approaches, and state-of-the-art post-training methodologies derived from their Tülu 3 framework. Notable technical improvements include the switch from nonparametric layer norm to RMSNorm and the implementation of rotary positional embedding. OLMo 2 model training breakthrough The training process employed a sophisticated two-stage approach. The initial stage utilised the OLMo-Mix-1124 dataset of approximately 3.9 trillion tokens, sourced from DCLM, Dolma, Starcoder, and Proof Pile II. The second stage incorporated a carefully curated mixture of high-quality web data and domain-specific content through the Dolmino-Mix-1124 dataset. Particularly noteworthy is the OLMo 2-Instruct-13B variant, which is the most capable model in the series. The model demonstrates superior performance compared to Qwen 2.5 14B instruct, Tülu 3 8B, and Llama 3.1 8B instruct models across various benchmarks. (Credit: Ai2) Commiting to open science Reinforcing its commitment to open science, Ai2 has released comprehensive documentation including weights, data, code, recipes, intermediate checkpoints, and instruction-tuned models. This transparency allows for full inspection and reproduction of results by the wider AI community. The release also introduces an evaluation framework called OLMES (Open Language Modeling Evaluation System), comprising 20 benchmarks designed to assess core capabilities such as knowledge recall, commonsense reasoning, and mathematical reasoning. OLMo 2 raises the bar in open-source AI development, potentially accelerating the pace of innovation in the field whilst maintaining transparency and accessibility. (Photo by Rick Barrett) See also: OpenAI enhances AI safety with new red teaming methods 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 Ai2 OLMo 2: Raising the bar for open language models appeared first on AI News. View the full article

Blockchain AI research lab YeagerAI has announced the launch of the Intelligent Oracle, an AI-powered oracle that aims to provide decentralised applications (DApps) with online data on-chain. The Oracle can change how data is collected, offering new possibilities and use cases for blockchain DApps. It is built on the GenLayer blockchain, also a brainchild of YeagerAI, and designed to support a new generation of DApps. It can fetch any type of online data and deliver it on-chain. The Intelligent Oracle will initially launch on a permissioned local network, with the GenLayer Testnet expected operational by the end of 2024. By removing the dependency on human-powered resolution systems and offering cross-chain compatibility, the Intelligent Oracle aims to provide a scalable, efficient, and future-proof solution for decision-making. The Intelligent Oracle is powered by LLMs integrated in GenLayer’s Optimistic Democracy consensus mechanism. The consensus mechanism is ‘governed’ by validators that connect to LLMs, verifying and securing the data that the Oracle fetches from on- and off-chain sources. The validators enable the network to process non-deterministic transactions by fetching data from the internet. When a query is made, a lead validator generates a proposed result, while other validators independently verify the output against the pre-set equivalence criteria. Optimistic Democracy ensures all decisions are accurate, reliable, and secure. While blockchain oracles have evolved rapidly in the past few years, there remain several pertinent unresolved issues. Among them are the inability of blockchains to access external data, and blockchains only able to access what is available on-chain. The emerging uses of blockchains are impacted by the lack of broader oracles, with most futuristic DApps requiring immediate, accurate, and sometimes subjective data from the internet. To date, the solution has been to use traditional oracles, which only provide pre-defined datasets or require manual intervention, making them slow, costly, and inflexible. The Intelligent Oracle offers an autonomous solution, offering a virtually unlimited range of data types to dApp builders. Welcoming Intelligent Oracle: A new world of blockchain use cases The Intelligent Oracle is based on Intelligent Contracts operating on the GenLayer blockchain. The oracle operates in the GenLayer ecosystem, allowing users to fetch decentralised, transparent and secure data for their DApps or platforms. It offers cross-chain compatibility, allowing it to integrate with multiple blockchain ecosystems. Following the launch, blockchain DApp developers have significantly more possibilities open to them. The launch of the Intelligent Oracle could be a step forward for decentralised applications in prediction markets, insurance, and financial derivatives, for example. The Oracle enables cost-effective and fast data resolution. While traditional oracles can take days to resolve prediction markets – incurring delays and costs – the Intelligent Oracle achieves transaction finality in less than an hour at a cost of under $1 per market. YeagerAI has seen rapid adoption of its new Oracle service with several partners, and some platforms already committed to integrating Intelligent Oracle. Early partners committed to building with the technology include Radix DLT, Etherisc, PredX, Delphi Bets, and Provably. The post YeagerAI’s Intelligent Oracle: Built on GenLayer blockchain for real-time data access appeared first on AI News. View the full article

The *** is establishing the Laboratory for AI Security Research (LASR) to help protect Britain and its allies against emerging threats in what officials describe as an “AI arms race.” The laboratory – which will receive an initial government funding of £8.22 million – aims to bring together experts from industry, academia, and government to assess AI’s impact on national security. The announcement comes as part of a broader strategy to strengthen the ***’s cyber defence capabilities. Speaking at the NATO Cyber Defence Conference at Lancaster House, the Chancellor of the Duchy of Lancaster said: “NATO needs to continue to adapt to the world of AI, because as the tech evolves, the threat evolves. “NATO has stayed relevant over the last seven decades by constantly adapting to new threats. It has navigated the worlds of nuclear proliferation and militant nationalism. The move from cold warfare to drone warfare.” The Chancellor painted a stark picture of the current cyber security landscape, stating: “Cyber war is now a daily reality. One where our defences are constantly being tested. The extent of the threat must be matched by the strength of our resolve to combat it and to protect our citizens and systems.” The new laboratory will operate under a ‘catalytic’ model, designed to attract additional investment and collaboration from industry partners. Key stakeholders in the new lab include GCHQ, the National Cyber Security Centre, the MOD’s Defence Science and Technology Laboratory, and prestigious academic institutions such as the University of Oxford and Queen’s University Belfast. In a direct warning about Russia’s activities, the Chancellor declared: “Be in no doubt: the ******* Kingdom and others in this room are watching Russia. We know exactly what they are doing, and we are countering their attacks both publicly and behind the scenes. “We know from history that appeasing dictators engaged in aggression against their neighbours only encourages them. Britain learned long ago the importance of standing strong in the face of such actions.” Reaffirming support for Ukraine, he added, “****** is a man who wants destruction, not peace. He is trying to deter our support for Ukraine with his threats. He will not be successful.” The new lab follows recent concerns about state actors using AI to bolster existing security threats. “Last year, we saw the US for the first time publicly call out a state for using AI to aid its malicious cyber activity,” the Chancellor noted, referring to North Korea’s attempts to use AI for malware development and vulnerability scanning. Stephen Doughty, Minister for Europe, North America and *** Overseas Territories, highlighted the dual nature of AI technology: “AI has enormous potential. To ensure it ******** a force for good in the world, we need to understand its threats and its opportunities.” Alongside LASR, the government announced a new £1 million incident response project to enhance collaborative cyber defence capabilities among allies. The laboratory will prioritise collaboration with Five Eyes countries and NATO allies, building on the ***’s historical strength in computing, dating back to Alan Turing’s groundbreaking work. The initiative forms part of the government’s comprehensive approach to cybersecurity, which includes the upcoming Cyber Security and Resilience Bill and the recent classification of data centres as critical national infrastructure. (Photo by Erik Mclean) See also: Anthropic urges AI regulation to avoid catastrophes 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 *** establishes LASR to counter AI security threats appeared first on AI News. View the full article

A critical part of OpenAI’s safeguarding process is “red teaming” — a structured methodology using both human and AI participants to explore potential risks and vulnerabilities in new systems. Historically, OpenAI has engaged in red teaming efforts predominantly through manual testing, which involves individuals probing for weaknesses. This was notably employed during the testing of their DALL·E 2 image generation model in early 2022, where external experts were invited to identify potential risks. Since then, OpenAI has expanded and refined its methodologies, incorporating automated and mixed approaches for a more comprehensive risk assessment. “We are optimistic that we can use more powerful AI to scale the discovery of model mistakes,” OpenAI stated. This optimism is rooted in the idea that automated processes can help evaluate models and train them to be safer by recognising patterns and errors on a larger scale. In their latest push for advancement, OpenAI is sharing two important documents on red teaming — a white paper detailing external engagement strategies and a research study introducing a novel method for automated red teaming. These contributions aim to strengthen the process and outcomes of red teaming, ultimately leading to safer and more responsible AI implementations. As AI continues to evolve, understanding user experiences and identifying risks such as ****** and misuse are crucial for researchers and developers. Red teaming provides a proactive method for evaluating these risks, especially when supplemented by insights from a range of independent external experts. This approach not only helps establish benchmarks but also facilitates the enhancement of safety evaluations over time. The human touch OpenAI has shared four fundamental steps in their white paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” to design effective red teaming campaigns: Composition of red teams: The selection of team members is based on the objectives of the campaign. This often involves individuals with diverse perspectives, such as expertise in natural sciences, cybersecurity, and regional politics, ensuring assessments cover the necessary breadth. Access to model versions: Clarifying which versions of a model red teamers will access can influence the outcomes. Early-stage models may reveal inherent risks, while more developed versions can help identify gaps in planned safety mitigations. Guidance and documentation: Effective interactions during campaigns rely on clear instructions, suitable interfaces, and structured documentation. This involves describing the models, existing safeguards, testing interfaces, and guidelines for recording results. Data synthesis and evaluation: Post-campaign, the data is assessed to determine if examples align with existing policies or require new behavioural modifications. The assessed data then informs repeatable evaluations for future updates. A recent application of this methodology involved preparing the OpenAI o1 family of models for public use—testing their resistance to potential misuse and evaluating their application across various fields such as real-world ******* planning, natural sciences, and AI research. Automated red teaming Automated red teaming seeks to identify instances where AI may fail, particularly regarding safety-related issues. This method excels at scale, generating numerous examples of potential errors quickly. However, traditional automated approaches have struggled with producing diverse, successful ******* strategies. OpenAI’s research introduces “Diverse And Effective Red Teaming With Auto-Generated Rewards And Multi-Step Reinforcement Learning,” a method which encourages greater diversity in ******* strategies while maintaining effectiveness. This method involves using AI to generate different scenarios, such as illicit advice, and training red teaming models to evaluate these scenarios critically. The process rewards diversity and efficacy, promoting more varied and comprehensive safety evaluations. Despite its benefits, red teaming does have limitations. It captures risks at a specific point in time, which may evolve as AI models develop. Additionally, the red teaming process can inadvertently create information hazards, potentially alerting malicious actors to vulnerabilities not yet widely known. Managing these risks requires stringent protocols and responsible disclosures. While red teaming continues to be pivotal in risk discovery and evaluation, OpenAI acknowledges the necessity of incorporating broader public perspectives on AI’s ideal behaviours and policies to ensure the technology aligns with societal values and expectations. See also: EU introduces draft regulatory guidance for AI models 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 enhances AI safety with new red teaming methods appeared first on AI News. View the full article For verified travel tips and real support, visit: [Hidden Content]

In 2024, Big Tech is all-in on artificial intelligence, with companies like Microsoft, Amazon, Alphabet, and Meta leading the way. Their combined spending on AI is projected to exceed a jaw-dropping $240 billion. Why? Because AI isn’t just the future—it’s the present, and the demand for AI-powered tools and infrastructure has never been higher. The companies aren’t just keeping up; they’re setting the pace for the industry. The scale of their investment is hard to ignore. In the first half of 2023, tech giants poured $74 billion into capital expenditure. By Q3, that number had jumped to $109 billion. In mid-2024, spending reached $104 billion, a remarkable 47% rise over the same ******* a year earlier. By Q3, the total hit $171 billion. If this pattern continues, Q4 might add another $70 billion, bringing the total to a truly staggering $240 billion for the year. Why so much spending? AI’s potential is immense, and companies are making sure they’re positioned to reap the rewards. A growing market: AI is projected to create $20 trillion in global economic impact by 2030. In countries like India, AI could contribute $500 billion to GDP by 2025. With stakes this high, big tech isn’t hesitating to invest heavily. Infrastructure demands: Training and running AI models require massive investment in infrastructure, from data centres to high-performance GPUs. Alphabet increased its capital expenditures by 62% last quarter compared to the previous year, even as it cut its workforce by 9,000 employees to manage costs. Revenue potential: AI is already proving its value. Microsoft’s AI products are expected to generate $10 billion annually—the fastest-growing segment in the company’s history. Alphabet, meanwhile, uses AI to write over 25% of its new code, streamlining operations. Amazon is also ramping up, with plans to spend $75 billion on capital expenditure in 2024. Meta’s forecast is not far behind, with estimates between $38 and $40 billion. Across the board, organisations recognise that maintaining their edge in AI requires sustained and significant investment. Supporting revenue streams What keeps the massive investments keep on coming is the strength of big tech’s core businesses. Last quarter, Alphabet’s digital advertising machine, which is powered by Google’s search engine, generated $49.39 billion in ad revenue, a 12% year-over-year increase. This as a solid foundation that allows Alphabet to pour resources into building out its AI arsenal without destabilising the bottom line. Microsoft’s diversified revenue streams are another example. While the company spent $20 billion on AI and cloud infrastructure last quarter, its productivity segment, which includes Office, grew by 12% to $28.3 billion, and its personal computing business, boosted by Xbox and the Activision Blizzard acquisition, grew 17% to $13.2 billion. These successes demonstrate how AI investments can support broader growth strategies. The financial payoff Big tech is already seeing the benefits of its heavy spending. Microsoft’s Azure platform has seen substantial growth, with its AI income approaching $6 billion. Amazon’s AI business is growing at triple-digit rates, and Alphabet reported a 34% jump in profits last quarter, with cloud revenue playing a major role. Meta, while primarily focused on advertising, is leveraging AI to make its platforms more engaging. AI-driven tools, such as improved feeds and search features keep users on its platforms longer, resulting in new revenue growth. AI spending shows no signs of slowing down. Tech leaders at Microsoft and Alphabet view AI as a long-term investment critical to their future success. And the results speak for themselves: Alphabet’s cloud revenue is up 35%, while Microsoft’s cloud business grew 20% last quarter. For the time being, the focus is on scaling up infrastructure and meeting demand. However, the real transformation will come when big tech unlocks AI’s full potential, transforming industries and redefining how we work and live. By investing in high-quality, centralised data strategies, businesses can ensure trustworthy and accurate AI implementations, and unlock AI’s full potential to drive innovation, improve decision-making, and gain competitive edge. AI’s revolutionary promise is within reach—but only for companies prepared to lay the groundwork for sustainable growth and long-term results. (Photo by Unsplash) See also: Microsoft tries to convert Google Chrome users Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo. Explore other upcoming enterprise technology events and webinars powered by TechForge here. The post Big tech’s AI spending hits new heights appeared first on AI News. View the full article

Samsung has revealed Gauss2, the second-generation proprietary AI model set to significantly enhance user experiences. Paul Kyungwhoon Cheun, President and CTO of the Device eXperience (DX) Division, commented: “Samsung Electronics is committed to developing cutting-edge software, including AI and data analytics, to enhance user experiences. “With three distinct models, Samsung Gauss2 is already boosting our internal productivity, and we plan to integrate it into products to deliver higher levels of convenience and personalisation.” Samsung Gauss2: Multimodal AI Gauss2 builds upon last year’s internal generative AI model, showcasing advancements in handling language, code, and images. It comes in three variants: Compact, Balanced, and Supreme, tailored to different computational needs. Compact: Optimised for environments with limited computing resources, ensuring effective performance even on-device. Balanced: Strikes a balance among performance, speed, and efficiency; suitable for a variety of tasks. Supreme: Incorporates Mixture of Experts (MoE) technology to minimise computational costs while maximising efficiency and performance. Gauss2 is designed to support between nine and fourteen languages, depending on the model, and a range of programming languages. Samsung’s custom training techniques and tokeniser aim to deliver peak efficiency across supported languages. The new models promise response generation speeds 1.5 to 3 times faster than leading open-source alternatives, facilitating prompt AI interaction and minimising wait times. Customisation and deployment Samsung’s in-house generative AI model allows easier customisation for specific applications. Gauss2 supports diverse productivity tasks, with widespread adoption among Samsung developers. The coding assistant ‘code.i’ – enhanced by Gauss2 – is utilised extensively within the DX Division and by international research teams, with up to 60% of developers now engaging regularly. The Gauss Portal, another Gauss-powered AI service, enhances productivity through features such as document summarisation and translation. Since its launch, this AI service has expanded internationally, aiding various office tasks. As of August, call centre operations also benefit from AI-driven categorisation and summarisation. Going forward, Samsung aims to further boost internal productivity, improving services like code.i and enhancing the Gauss Portal’s natural language question-and-answer capabilities. Future functionalities will include multimodal operations, like chart analysis and image creation. ‘AI for All’ Samsung’s strategy, “AI for All,” envisions widespread incorporation of AI-based services across its product lines. By combining AI with knowledge graph technology, Samsung anticipates delivering even greater personalisation. Besides Gauss2, the conference featured presentations on software development, including the SmartThings platform’s customer experience improvements. Attendees engaged in 29 technical sessions covering: The future of healthcare developments within Samsung’s health ecosystem. Enhancements in the SmartThings experience through generative AI. Insights into the code.i AI coding assistant. Lifestyle content innovations for TV. AI solutions for Samsung’s home appliances. SDC24 Korea underscored Samsung’s vision to integrate AI across its ecosystem. (Image Credit: Samsung) See also: EU introduces draft regulatory guidance for AI models Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo. Explore other upcoming enterprise technology events and webinars powered by TechForge here. The post Samsung unveils Gauss2 AI model at SDC24 Korea appeared first on AI News. View the full article

Discover how chatbots for marketing can boost your ROI with enhanced engagement and instant customer responses. What are chatbots? Chatbots are automated software applications designed to simulate human conversation. They interact with users through text or voice, providing immediate responses and performing various tasks. AI chatbots can understand and process natural language, enabling them to handle complex queries and provide relevant information or services. Chatbots come in various forms, including: Rule-based chatbots: Respond to specific commands predetermined by developers, AI-driven chatbots: Use machine learning and natural language processing (NLP) to understand and adapt to user queries. The importance of chatbots in marketing Chatbots have become an essential component in modern marketing strategies. They offer marketers a way to connect with consumers efficiently, enhance customer experience, and streamline interactions. Enhanced customer engagement: Chatbots engage customers by providing instant responses and personalised interactions, 24/7 availability: They operate around the clock, ensuring customer inquiries are addressed at any time, Cost-effectiveness: Reduce the need for extensive human customer support, lowering operational costs, Data collection: Gather valuable customer data and insights for better marketing strategies, Increased lead generation: Facilitate the collection of leads by interacting with potential customers and capturing their information. Chatbots play a crucial role in improving customer engagement. They provide a dynamic and interactive way for businesses to communicate with their audience, fostering stronger relationships and increasing satisfaction. Chatbots can quickly address common questions, offer recommendations, and guide customers through the purchasing process, creating a more personalised experience. One of the most valuable features of chatbots is their ability to operate around the clock. Unlike human support teams, chatbots are available 24/7, ensuring customers receive immediate assistance regardless of the time of day. Continuous availability can significantly enhance the customer experience, leading to higher levels of satisfaction and loyalty. The implementation of chatbots in marketing strategies not only streamlines operations but also delivers a more consistent and fulfilling customer experience. The result is an increase in engagement, satisfaction, and overall effectiveness in marketing campaigns. Implementing chatbots in marketing strategies Integrating chatbots into marketing strategies can significantly enhance customer engagement and streamline overall marketing efforts. This section delves into how chatbots can be used for personalised customer interactions and how they facilitate data collection and analysis. Chatbots offer a unique opportunity to create personalised interactions with customers. By using AI and machine learning, chatbots can tailor responses based on user behaviours, preferences, and past interactions. The personalised approach can make customers feel valued and understood, increasing their satisfaction and loyalty. Key aspects of personalised customer interactions: Greeting users by name: Addressing customers by their names makes interactions feel more personal and engaging, Tailored recommendations: Based on previous interactions, chatbots can recommend products, content, or services that align with individual preferences, Customising responses: Chatbots can adjust their responses based on the user’s mood, choices, and patterns. Chatbots are important in gathering and analysing customer data. The interactions between users and chatbots generate valuable insights that can be used to optimise marketing strategies. Collecting this data can help businesses understand customer needs, behaviour patterns, and preferences. Key areas where chatbots aid in data collection and analysis: User interaction history: Chatbots store conversation logs, providing insights into common customer queries and issues, Demographic data: Collecting information like age, location, and interests helps in segmenting the audience for targeted marketing, Feedback mechanisms: Gathering feedback directly through chatbot interactions allows businesses to gauge customer satisfaction and areas for improvement. In the realm of marketing, using chatbots can significantly boost return on investment (ROI). Two of the primary factors contributing to this increase are cost-effectiveness and enhanced lead generation and conversions. Chatbots offer substantial cost savings for businesses. By automating customer interactions, businesses can reduce the need for a large customer service team. This not only lowers operational costs but also streamlines processes. The initial investment in chatbot technology is often offset by the long-term savings achieved through decreased labour costs and increased efficiency. Also, chatbots can play a critical role in lead generation and conversion rates. By engaging users in real-time, chatbots can qualify leads, provide personalised recommendations, and guide users through the sales funnel. Immediate interaction can improve user experience and lead to higher conversion rates. Utilising chatbots for marketing can lead to higher efficiency, greater customer interaction, and ultimately a better ROI. By understanding and implementing these technologies, businesses can enhance their marketing strategies and achieve substantial financial benefits. The post Boost your ROI: The impact of chatbots on marketing appeared first on AI News. View the full article

AI is rapidly becoming ubiquitous across business systems and IT ecosystems, with adoption and development racing faster than anyone could have expected. Today it seems that everywhere we turn, software engineers are building custom models and integrating AI into their products, as business leaders incorporate AI-powered solutions in their working environments. However, uncertainty about the best way to implement AI is stopping some companies from taking action. Boston Consulting Group’s latest Digital Acceleration Index (DAI), a global survey of 2,700 executives, revealed that only 28% say their organisation is fully prepared for new AI regulation. Their uncertainty is exacerbated by AI regulations arriving thick and fast: the EU AI act is on the way; Argentina released a draft AI plan; Canada has the AI and Data Act; China has enacted a slew of AI regulations; and the G7 nations launched the “Hiroshima AI process.” Guidelines abound, with the OECD developing AI principles, the UN proposing a new UN AI advisory body, and the Biden administration releasing a blueprint for an AI Bill of Rights (although that could quickly change with the second Trump administration). Legislation is also coming in individual US states, and is appearing in many industry frameworks. To date, 21 states have enacted laws to regulate AI use in some manner, including the Colourado AI Act, and clauses in California’s CCPA, plus a further 14 states have legislation awaiting approval. Meanwhile, there are loud voices on both sides of the AI regulation debate. A new survey from SolarWinds shows 88% of IT professionals advocate for stronger regulation, and separate research reveals that 91% of British people want the government to do more to hold businesses accountable for their AI systems. On the other hand, the leaders of over 50 tech companies recently wrote an open letter calling for urgent reform of the EU’s heavy AI regulations, arguing that they stifle innovation. It’s certainly a tricky ******* for business leaders and software developers, as regulators scramble to catch up with tech. Of course you want to take advantage of the benefits AI can provide, you can do so in a way that sets you up for compliance with whatever regulatory requirements are coming, and don’t handicap your AI use unnecessarily while your rivals speed ahead. We don’t have a crystal ball, so we can’t predict the future. But we can share some best practices for setting up systems and procedures that will prepare the ground for AI regulatory compliance. Map out AI usage in your wider ecosystem You can’t manage your team’s AI use unless you know about it, but that alone can be a significant challenge. Shadow IT is already the scourge of cybersecurity teams: Employees sign up for SaaS tools without the knowledge of IT departments, leaving an unknown number of solutions and platforms with access to business data and/or systems. Now security teams also have to grapple with shadow AI. Many apps, chatbots, and other tools incorporate AI, machine learning (ML), or natural language programming (NLP), without such solutions necessarily being obvious AI solutions. When employees log into these solutions without official approval, they bring AI into your systems without your knowledge. As Opice Blum’s data privacy expert Henrique Fabretti Moraes explained, “Mapping the tools in use – or those intended for use – is crucial for understanding and fine-tuning acceptable use policies and potential mitigation measures to decrease the risks involved in their utilisation.” Some regulations hold you responsible for AI use by vendors. To take full control of the situation, you need to map all the AI in your, and your partner organisations’ environments. In this regard, using a tool like Harmonic can be instrumental in detecting AI use across the supply chain. Verify data governance Data privacy and security are core concerns for all AI regulations, both those already in place and those on the brink of approval. Your AI use already needs to comply with existing privacy laws like GDPR and CCPR, which require you to know what data your AI can access and what it does with the data, and for you to demonstrate guardrails to protect the data AI uses. To ensure compliance, you need to put robust data governance rules into place in your organisation, managed by a defined team, and backed up by regular audits. Your policies should include due diligence to evaluate data security and sources of all your tools, including those that use AI, to identify areas of potential bias and privacy risk. “It is incumbent on organisations to take proactive measures by enhancing data hygiene, enforcing robust AI ethics and assembling the right teams to lead these efforts,” said Rob Johnson, VP and Global Head of Solutions Engineering at SolarWinds. “This proactive stance not only helps with compliance with evolving regulations but also maximises the potential of AI.” Establish continuous monitoring for your AI systems Effective monitoring is crucial for managing any area of your business. When it comes to AI, as with other areas of cybersecurity, you need continuous monitoring to ensure that you know what your AI tools are doing, how they are behaving, and what data they are accessing. You also need to audit them regularly to keep on top of AI use in your organisation. “The idea of using AI to monitor and regulate other AI systems is a crucial development in ensuring these systems are both effective and ethical,” said Cache Merrill, founder of software development company Zibtek. “Currently, techniques like machine learning models that predict other models’ behaviours (meta-models) are employed to monitor AI. The systems analyse patterns and outputs of operational AI to detect anomalies, biases or potential failures before they become critical.” Cyber GRC automation platform Cypago allows you to run continuous monitoring and regulatory audit evidence collection in the background. The no-code automation allows you to set custom workflow capabilities without technical expertise, so alerts and mitigation actions are triggered instantly according to the controls and thresholds you set up. Cypago can connect with your various digital platforms, synchronise with virtually any regulatory framework, and turn all relevant controls into automated workflows. Once your integrations and regulatory frameworks are set up, creating custom workflows on the platform is as simple as uploading a spreadsheet. Use risk assessments as your guidelines It’s vital to know which of your AI tools are high risk, medium risk, and low risk – for compliance with external regulations, for internal business risk management, and for improving software development workflows. High risk use cases will need more safeguards and evaluation before deployment. “While AI risk management can be started at any point in the project development,” Ayesha Gulley, an AI policy expert from Holistic AI, said. “Implementing a risk management framework sooner than later can help enterprises increase trust and scale with confidence.” When you know the risks posed by different AI solutions, you can choose the level of access you’ll grant them to data and critical business systems. In terms of regulations, the EU AI Act already distinguishes between AI systems with different risk levels, and NIST recommends assessing AI tools based on trustworthiness, social impact, and how humans interact with the system. Proactively set AI ethics governance You don’t need to wait for AI regulations to set up ethical AI policies. Allocate responsibility for ethical AI considerations, put together teams, and draw up policies for ethical AI use that include cybersecurity, model validation, transparency, data privacy, and incident reporting. Plenty of existing frameworks like NIST’s AI RMF and ISO/IEC 42001 recommend AI best practices that you can incorporate into your policies. “Regulating AI is both necessary and inevitable to ensure ethical and responsible use. While this may introduce complexities, it need not hinder innovation,” said Arik Solomon, CEO and co-founder of Cypago. “By integrating compliance into their internal frameworks and developing policies and processes aligned with regulatory principles, companies in regulated industries can continue to grow and innovate effectively.” Companies that can demonstrate a proactive approach to ethical AI will be better positioned for compliance. AI regulations aim to ensure transparency and data privacy, so if your goals align with these principles, you’ll be more likely to have policies in place that comply with future regulation. The FairNow platform can help with this process, with tools for managing AI governance, bias checks, and risk assessments in a single location. Don’t let ***** of AI regulation hold you back AI regulations are still evolving and emerging, creating uncertainty for businesses and developers. But don’t let the fluid situation stop you from benefiting from AI. By proactively implementing policies, workflows, and tools that align with the principles of data privacy, transparency, and ethical use, you can prepare for AI regulations and take advantage of AI-powered possibilities. The post Preparing today for tomorrow’s AI regulations appeared first on AI News. View the full article

Business Insider’s “CXO AI Playbook” looks at how firms are utilising AI to tackle challenges, scale operations, and plan for the future. The Playbook looks at stories from various industries to see what problems AI is solving, who’s driving these initiatives, and how it’s reshaping strategies. Salesforce, well known for its CRM software used by over 150,000 companies like Amazon and Walmart, is no stranger to innovation. It also owns Slack, the popular workplace communication app. Salesforce is now stepping up its AI game with Agentforce, a platform that lets businesses to build and deploy digital agents to automate tasks such as creating sales reports and summarising Slack conversations. What problem is it solving? Salesforce has been working with AI for years. In 2016, it launched Einstein, an AI feature baked into its CRM platform. Einstein handled basic scriptable tasks, but the rise of generative AI brought a chance to do more. Smarter tools could now make better decisions and understand natural language. This sparked a transformation. First came Einstein GPT, then Einstein Copilot, and now Agentforce—a platform designed for flexibility with prebuilt and customisable agents to handle diverse business needs. “Our customers wanted more. Some wanted to tweak the agents we offer, while others wanted to create their own,” said Tyler Carlson, Salesforce’s VP of Business Development. The tech behind it Agentforce is powered by Salesforce’s Atlas Reasoning Engine, developed in-house. The platform connects with AI models from major players like OpenAI, Anthropic, Amazon, and Google, giving businesses access to a variety of tools. Slack has become a testing ground for these AI agents. Currently in beta, Agentforce’s Slack integration puts automations where employees already spend their time. “Slack makes these tools easy to use and accessible,” Carlson added. Smarter, more flexible AI Agentforce uses ReAct prompting, a technique that helps agents break down problems into smaller steps and adjust their approach as they go. This leads to more accurate responses and hands-off task management, from answering questions to scheduling meetings. Agentforce works with Salesforce’s proprietary LLMs and third-party models, giving clients plenty of options. To ensure security, Salesforce enforces strict data privacy policies, including limits on data retention. Making it work for businesses With tools like Agentbuilder, companies can design AI agents tailored to their needs. For example, an agent could sort emails or answer specific HR questions using internal data. One example is Salesforce’s collaboration with Workday to create an AI service agent for employee queries. Salesforce is already seeing results, with Agentforce resolving 90% of customer inquiries in early trials. The goal? Broader adoption, more capabilities, and higher workloads handled by these agents. “We’re building a ******* ecosystem of partners and skills,” Carlson said. “By next year, we want Agentforce to be a must-have for businesses.” (Photo by Unsplash) See also: Paul O’Sullivan, Salesforce: Transforming work in the GenAI era Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo. Explore other upcoming enterprise technology events and webinars powered by TechForge here. The post Salesforce launches AI platform for automated task management appeared first on AI News. View the full article

A report by Publicis Sapient sheds light on the disparities between the C-suite and practitioners, dubbed the “V-suite,” in their perceptions and adoption of generative AI. The report reveals a stark contrast in how the C-suite and V-suite view the potential of generative AI. While the C-suite focuses on visible use cases such as customer experience, service, and sales, the V-suite sees opportunities across various functional areas, including operations, HR, and finance. Risk perception The divide extends to risk perception as well. Fifty-one percent of C-level respondents expressed more concern about the risk and ethics of generative AI than other emerging technologies. In contrast, only 23 percent of the V-suite shared these worries. Simon James, Managing Director of Data & AI at Publicis Sapient, said: “It’s likely the C-suite is more worried about abstract, big-picture dangers – such as Hollywood-style scenarios of a rapidly-evolving superintelligence – than the V-suite.” The report also highlights the uncertainty surrounding generative AI maturity. Organisations can be at various stages of maturity simultaneously, with many struggling to define what success looks like. More than two-thirds of respondents lack a way to measure the success of their generative AI projects. Navigating the generative AI landscape Despite the C-suite’s focus on high-visibility use cases, generative AI is quietly transforming back-office functions. More than half of the V-suite respondents ranked generative AI as extremely important in areas like finance and operations over the next three years, compared to a smaller percentage of the C-suite. To harness the full potential of generative AI, the report recommends a portfolio approach to innovation projects. Leaders should focus on delivering projects, controlling shadow IT, avoiding duplication, empowering domain experts, connecting business units with the CIO’s office, and engaging the risk office early and often. Daniel Liebermann, Managing Director at Publicis Sapient, commented: “It’s as hard for leaders to learn how individuals within their organisation are using ChatGPT or Microsoft Copilot as it is to understand how they’re using the internet.” The path forward The report concludes with five steps to maximise innovation: adopting a portfolio approach, improving communication between the CIO’s office and the risk office, seeking out innovators within the organisation, using generative AI to manage information, and empowering team members through company culture and upskilling. As generative AI continues to evolve, organisations must bridge the gap between the C-suite and V-suite to unlock its full potential. The future of business transformation ***** in harnessing the power of a decentralised, bottom-up approach to innovation. See also: EU introduces draft regulatory guidance for AI models Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo. Explore other upcoming enterprise technology events and webinars powered by TechForge here. The post Generative AI: Disparities between C-suite and practitioners appeared first on AI News. View the full article

The release of the “First Draft General-Purpose AI Code of Practice” marks the EU’s effort to create comprehensive regulatory guidance for general-purpose AI models. The development of this draft has been a collaborative effort, involving input from diverse sectors including industry, academia, and civil society. The initiative was led by four specialised Working Groups, each addressing specific aspects of AI governance and risk mitigation: Working Group 1: Transparency and copyright-related rules Working Group 2: Risk identification and assessment for systemic risk Working Group 3: Technical risk mitigation for systemic risk Working Group 4: Governance risk mitigation for systemic risk The draft is aligned with existing laws such as the Charter of Fundamental Rights of the ********* Union. It takes into account international approaches, striving for proportionality to risks, and aims to be future-proof by contemplating rapid technological changes. Key objectives outlined in the draft include: Clarifying compliance methods for providers of general-purpose AI models Facilitating understanding across the AI value chain, ensuring seamless integration of AI models into downstream products Ensuring compliance with Union law on copyrights, especially concerning the use of copyrighted material for model training Continuously assessing and mitigating systemic risks associated with AI models Recognising and mitigating systemic risks A core feature of the draft is its taxonomy of systemic risks, which includes types, natures, and sources of such risks. The document outlines various threats such as cyber offences, biological risks, loss of control over autonomous AI models, and large-scale disinformation. By acknowledging the continuously evolving nature of AI technology, the draft recognises that this taxonomy will need updates to remain relevant. As AI models with systemic risks become more common, the draft emphasises the need for robust safety and security frameworks (SSFs). It proposes a hierarchy of measures, sub-measures, and key performance indicators (KPIs) to ensure appropriate risk identification, analysis, and mitigation throughout a model’s lifecycle. The draft suggests that providers establish processes to identify and report serious incidents associated with their AI models, offering detailed assessments and corrections as needed. It also encourages collaboration with independent experts for risk assessment, especially for models posing significant systemic risks. Taking a proactive stance to AI regulatory guidance The EU AI Act, which came into force on 1 August 2024, mandates that the final version of this Code be ready by 1 May 2025. This initiative underscores the EU’s proactive stance towards AI regulation, emphasising the need for AI safety, transparency, and accountability. As the draft continues to evolve, the working groups invite stakeholders to participate actively in refining the document. Their collaborative input will shape a regulatory framework aimed at safeguarding innovation while protecting society from the potential pitfalls of AI technology. While still in draft form, the EU’s Code of Practice for general-purpose AI models could set a benchmark for responsible AI development and deployment globally. By addressing key issues such as transparency, risk management, and copyright compliance, the Code aims to create a regulatory environment that fosters innovation, upholds fundamental rights, and ensures a high level of consumer protection. This draft is open for written feedback until 28 November 2024. See also: Anthropic urges AI regulation to avoid catastrophes 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 EU introduces draft regulatory guidance for AI models appeared first on AI News. View the full article

Microsoft Edge has evolved into more than simply a browser; it is a critical component of Microsoft’s ecosystem, meant to integrate smoothly with Windows and highlight the company’s latest innovations, such as its AI assistant, Copilot. While these interconnections make Edge a viable choice, Microsoft’s methods for persuading consumers to choose it have been far from covert. From default settings that prioritise Edge to persistent prompts at startup, Microsoft has made it clear they want Edge to be the go-to for Windows users. And lately, it’s upped the ante: now, Edge can launch automatically when your computer boots up, instantly nudging you to bring over your data from other browsers. The most recent update includes an auto-checked option to import browsing data from Chrome, such as history, bookmarks, and open tabs, in the name of users leveraging the features of AI assistant, Copilot. Although AI features may be appealing to some, the aggressive approach has left many users feeling annoyed rather than tempted. The Verge recently noticed that when you start up your PC, Edge might decide to open on its own, promptly displaying a pop-up for its AI assistant, Copilot. Right next to Copilot, there’s a conveniently checked box allowing Edge to import data from other browsers automatically. For some users, this seems like an overreach, raising doubts about how far Microsoft is ready to go to make Edge the browser of choice. Microsoft has confirmed this setup and stated that customers have the option to opt-out. Still, with default settings that favour data imports and an eye-catching import button, it’s easy for users to unintentionally make the switch, especially if they’re not paying attention. For those who prefer sticking with their existing browsers without interruption, the approach can feel unwelcome. But even if users dodge the pop-ups, Edge isn’t exactly shy. Uninstalling it is a complex process, and it often gets reinstalled by Windows updates, much to the frustration of users who would rather go without. For many, this persistence feels more like a forceful sales pitch rather than a friendly suggestion. Interestingly, this isn’t the first time Microsoft has tried this type of strategy. A similar message appeared to users earlier this year but was pulled back after strong objections. Now, it’s back, with Microsoft’s Caitlin Roulston stating the notification is meant to “give users the choice to import data from other browsers.” In fact, Microsoft’s bold tactics go back some years. In 2022, it introduced a feature that could automatically pull data from Chrome into Edge – although users had the option to decline. In 2021, the company made it practically impossible to set any browser other than Edge as the default, resulting in enough outcry for Microsoft to back down. While Microsoft promotes its intrusive pop-ups as a way to give users more control, others who value choice without constant nudges. The relentless push for Edge usage could actually be detrimental, as the company’s persistence may drive users toward other browsers rather than away. To truly compete, Microsoft might benefit from letting Edge’s strengths speak for themselves rather than relying on aggressive prompts to change hearts and minds. (Photo by Surface) See also: EU probes Microsoft-OpenAI and Google-Samsung AI deals Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo. Explore other upcoming enterprise technology events and webinars powered by TechForge here. The post Microsoft tries to convert Google Chrome users appeared first on AI News. View the full article For verified travel tips and real support, visit: [Hidden Content]

Did you know that effective asset management practices pose challenges for almost half of small businesses? According to the latest research, 43% of businesses either manually report their inventory or in a few cases, do not record assets in any manner. However, asset management is not immune to the disruptive pressure of artificial intelligence (AI) currently revolutionising numerous industries. The manner in which corporations manage their tangible and intangible assets is undergoing a profound transformation due to the evolving technology of AI. This blog will discover how AI-driven fixed asset software softwares transform asset management and what the future holds for businesses embedding those innovations. Introduction to fixed asset management and AI Fixed asset management is a critical feature for organisations to manage, control, and optimise the value of their physical assets. Assets can include everything from equipment and vehicles to home computer systems. Traditionally, manual asset management systems entail manual report maintenance and periodic audits, which can be time-consuming and susceptible to human error. AI-driven fixed assets software offers a modern solution by automating diverse asset control factors. This guarantees accuracy, reduces administrative overhead, and increases an asset’s useful life, ultimately contributing to significant cost savings. AI, blended with the Internet of Things (IoT), machine learning (ML), and predictive analytics, is the primary method to develop smart, efficient, and scalable asset management solutions. The predictive capacities of AI revolutionise proactive asset management. AI can predict when a piece of hardware is likely to fail or spot chances for optimisation by evaluating patterns and trends in data. The proactive strategy not only helps with strategic planning but also ensures the reliability of operations by preventing system outages that can cause serious disruptions to business operations and financial losses. Businesses may use AI to ensure their assets operate at peak efficiency, quickly adopt new technologies, and match operations to corporate goals. AI’s advantages for fixed asset software AI-driven fixed asset software has numerous advantages for businesses, particularly in sectors where asset management is vital to daily operations, like production, healthcare, and logistics. Greater effectiveness: Automation significantly speeds up asset tracking, control, and upkeep. As AI can assess huge amounts of information in real time, managers can respond immediately to determine the state of their assets. Cost savings: Ongoing asset utilisation and predictive analysis can result in lower operating costs. AI is capable of identifying underutilised or poorly functioning items, which may assist corporations in saving money by reallocating or disposal schedules. Enhanced compliance and reporting: Staying compliant can be challenging with increasingly stringent regulatory governance. AI ensures that compliance reports are generated accurately and on time. Moreover, the software can routinely modify asset data to mirror regulatory changes, ensuring that companies consistently comply with laws. Improved decision-making: With AI’s analytics capabilities, managers can make better choices about which assets to invest in, when to repair, and when to retire an asset. Selections are based on real-time information and predictive models instead of guesswork or manual calculations. Case study: Predictive portfolio management precision issue: Predicting market trends and real-time portfolio optimisation was complicated for a top asset management company. Conventional approaches could not keep up with market demands, resulting in lost opportunities and less-than-ideal results. Solution: The company was able to quickly evaluate large datasets by implementing an AI-powered predictive analytics system. The AI algorithms examined market patterns, assessed risk factors, and dynamically altered the portfolio. The end result was a notable improvement in portfolio performance and increased forecasting accuracy. Findings: A 20% boost in portfolio returns was attained. Real-time market trend information improved decision-making. The future of AI in asset management The future of asset management will revolutionise customer satisfaction, operational effectiveness, and decision-making. Below are the important elements that will transform asset management operations: 1) Elevated decision making By revealing hidden patterns from huge datasets, AI will permit asset managers to make better decisions. AI can evaluate the whole portfolio, compiling financial statistics and market news, which together will improve risk posture and portfolio formulation. AI will also make real-time adaptation feasible, preparing managers for future predictions and staying ahead of marketplace swings. 2) Automation and operational efficiency Robo-advisors will become necessary tools, autonomously managing tasks like portfolio rebalancing and standard operations. AI’s algorithmic training will ******** decisions quickly, decreasing human intervention and cutting costs. AI will automate tedious back-office operations, including data entry and regulatory compliance procedures, ensuring smooth, streamlined workflows. 3) Client experience transformation In the future, client interactions will become customised and more responsive. AI will analyse purchaser information to provide tailored funding recommendations, and AI-powered chatbots will be available 24/7 to answer queries. The technology can even simplify reporting, turning complex economic information into easily digestible, jargon-free insights, building trust and transparency in customer relationships. Conclusion: The future of asset management is undeniably tied to improvements in AI technology. AI-driven fixed asset software is already impacting asset monitoring, predictive analytics, and risk management by optimisation and automation. As hyper automation and IoT continue to adapt, the possibilities for remodeling asset management are limitless. (Photo source) The post Using AI technologies for future asset management appeared first on AI News. View the full article For verified travel tips and real support, visit: [Hidden Content]

Japan is on a mission to become a global AI powerhouse, and it’s starting with some impressive advances in AI-driven language models. ********* technology experts are developing advanced models that grasp the unique nuances of the ********* language and culture—essential for industries such as healthcare, finance, and manufacturing – where precision is key. But this effort isn’t Japan’s alone. Consulting giants like Accenture, Deloitte, EY Japan, FPT, Kyndryl, and TCS Japan are partnering with NVIDIA to create AI innovation hubs across the country. The centres are using NVIDIA’s AI software and specialised ********* language models to build tailored AI solutions, helping industries boost productivity in a digital workforce. The goal? To get ********* companies fully on board with enterprise and physical AI. One standout technology supporting the drive is NVIDIA’s Omniverse platform. With Omniverse, ********* companies can create digital twins—virtual replicas of real-world assets—and test complex AI systems safely before implementing them. This is a game-changer for industries such as manufacturing and robotics, allowing businesses to fine-tune processes without the risk of real-world trial and error. This use of AI is more than just innovation; it represents Japan’s plan for addressing some major challenges ahead. Japan faces a shrinking workforce presence as its population ages. With its strengths in robotics and automation, Japan is well-positioned to use AI solutions to bridge the gap. In fact, Japan’s government recently shared its vision of becoming “the world’s most AI-friendly country,” underscoring the perceived role AI will play in the nation’s future. Supporting this commitment, Japan’s AI market hit $5.9 billion in value this year; a 31.2% growth rate according to IDC. New AI-focused consulting centres in Tokyo and Kansai give ********* businesses hands-on access to NVIDIA’s latest technologies, equipping them to solve social challenges and aid economic growth. Top cloud providers like SoftBank, GMO Internet Group, KDDI, Highreso, Rutilea, and SAKURA Internet are also involved, working with NVIDIA to build AI infrastructure. Backed by Japan’s Ministry of Economy, Trade and Industry, they’re establishing AI data centres across Japan to accelerate growth in robotics, automotive, healthcare, and telecoms. NVIDIA and SoftBank have also formed a remarkable partnership to build Japan’s most powerful AI supercomputer using NVIDIA’s Blackwell platform. Additionally, SoftBank has tested the world’s first AI and 5G hybrid telecoms network with NVIDIA’s AI Aerial platform, allowing Japan to set a worldwide standard. With these developments, Japan is taking big strides toward establishing itself as a leader in the AI-powered industrial revolution. (Photo by Andrey Matveev) See also: NVIDIA’s share price nosedives as antitrust clouds gather Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo. Explore other upcoming enterprise technology events and webinars powered by TechForge here. The post NVIDIA AI Summit Japan: NVIDIA’s role in Japan’s big AI ambitions appeared first on AI News. View the full article

AI continues to transform industries, and having the right skills can make a significant difference to your career. Professionals wishing to get into this evolving field can take advantage of a variety of specialised courses that teach how to use AI in business, creativity, and data analysis. Artificial Intelligence: Preparing Your Career for AI Artificial Intelligence: Preparing Your Career for AI is an option for those wanting to future-proof their careers in an AI-centric workplace. The course outlines five essential steps for preparing for AI’s impact on job roles and skill requirements. Participants learn the basics of AI, strategies for aligning their career paths with AI advancements, and how to use AI responsibly. The course is ideal for individuals at any career stage who wish to understand AI’s impact on the job market and adapt proactively. Generative AI for Leaders For business leaders, Generative AI for Leaders focuses on integrating AI into organisation’s strategy. AI’s rapid advancement offers both opportunities and challenges for business leaders, who must balance innovation with ethical and operational concerns. In this course, participants learn strategies for building AI-driven business initiatives and fostering collaboration, and learn how to address compliance and ethical considerations. With a practical look at AI trends, this course prepares leaders to develop a culture that supports AI adoption and equips them with the tools needed to make informed decisions. Business Analyst: Digital Director for AI and Data Science Business Analyst: Digital Director for AI and Data Science is a course designed for business analysts and professionals explaining how to define requirements for data science and artificial intelligence projects. The course covers the requirements elicitation process for AI applications and teaches participants how to work closely with data scientists and machine learning engineers to ensure that AI projects meet business goals. Learners gain insights into conversational AI tools, the differences between Natural Language Understanding (NLU) bots and rule-based bots, and best practices in conversation flow analysis. For business analysts, the course provides essential skills to guide AI initiatives that deliver real business value. Prompt Engineering+: Master Speaking to AI One valuable course is Prompt Engineering+: Master Speaking to AI, which teaches the art of creating precise instructions for generative AI models. ‘Prompt engineering’ is essential for situations in which human intent must be accurately translated into AI output. The course covers prompt structure, including one-shot, few-shot, and zero-shot learning, as well as fundamental skills like natural language processing and Python programming. Students work with leading models including ChatGPT, Google Gemini, and DALL-E, and learn practical methods to refine and test prompts, control model output, and tackle inaccuracies. For those looking to work directly with generative AI, this course provides a foundational skill set to optimise AI interactions. Canva AI: Master Canva AI Tools and Apps 2024 Content creators can benefit from Canva AI: Master Canva AI Tools and Apps 2024, a course focused on using Canva’s AI-driven tools to streamline and enhance content production. This course introduces participants to Canva’s Magic Studio, where they explore tools for creating engaging social media posts, PDFs, videos, and presentations. From text-to-image conversions to speaking avatars, the course delves into AI tools that help creators produce content efficiently. Through hands-on projects, learners experience Canva AI’s capabilities, enabling them to produce a wide variety of content quickly and effectively—a valuable skill for social media, marketing, and creative professionals. Conclusion These courses offer a comprehensive toolkit for mastering AI skills in various fields. Embracing these opportunities can empower professionals to lead, create, and adapt in an AI-driven organisations. Whether you’re a business leader, a content creator, or a data professional, investing in AI skills prepares you to navigate the future with confidence and purpose. See also: Understanding AI’s impact on the workforce 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 courses to boost your skills and stay ahead appeared first on AI News. View the full article For verified travel tips and real support, visit: [Hidden Content]

OpenAI is facing diminishing returns with its latest AI model while navigating the pressures of recent investments. According to The Information, OpenAI’s next AI model – codenamed Orion – is delivering smaller performance gains compared to its predecessors. In employee testing, Orion reportedly achieved the performance level of GPT-4 after completing just 20% of its training. However, the transition from GPT-4 to the anticipated GPT-5 is said to exhibit smaller quality improvements than the leap from GPT-3 to GPT-4. “Some researchers at the company believe Orion isn’t reliably better than its predecessor in handling certain tasks,” stated employees in the report. “Orion performs better at language tasks but may not outperform previous models at tasks such as coding, according to an OpenAI employee.” Early stages of AI training usually yield the most significant improvements, while subsequent phases typically result in smaller performance gains. Consequently, the remaining 80% of training is unlikely to deliver advancements on par with previous generational improvements. This situation with its latest AI model emerges at a pivotal time for OpenAI, following a recent funding round that saw the company raise $6.6 billion. With this financial backing comes increased expectations from investors, as well as technical challenges that complicate traditional scaling methodologies in AI development. If these early versions do not meet expectations, OpenAI’s future fundraising prospects may not attract the same level of interest. The limitations highlighted in the report underline a significant challenge confronting the entire AI industry: the diminishing availability of high-quality training data and the necessity to maintain relevance in an increasingly competitive field. According to a paper (PDF) that was published in June, AI firms will deplete the pool of publicly available human-generated text data between 2026 and 2032. The Information notes that developers have “”largely squeezed as much out of” the data that has been used for enabling the rapid AI advancements we’ve seen in recent years. To address these challenges, OpenAI is fundamentally rethinking its AI development strategy. “In response to the recent challenge to training-based scaling laws posed by slowing GPT improvements, the industry appears to be shifting its effort to improving models after their initial training, potentially yielding a different type of scaling law,” explains The Information. As OpenAI navigates these challenges, the company must balance innovation with practical application and investor expectations. However, the ongoing exodus of leading figures from the company won’t help matters. (Photo by Jukan Tateisi) See also: ASI Alliance launches AIRIS that ‘learns’ in Minecraft 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 faces diminishing returns with latest AI model appeared first on AI News. View the full article

The Tony Blair Institute (TBI) has examined AI’s impact on the workforce. The report outlines AI’s potential to reshape work environments, boost productivity, and create opportunities—while warning of potential challenges ahead. “Technology has a long history of profoundly reshaping the world of work,” the report begins. From the agricultural revolution to the digital age, each wave of innovation has redefined labour markets. Today, AI presents a seismic shift, advancing rapidly and prompting policymakers to prepare for change. Economic opportunities The TBI report estimates that AI, when fully adopted by *** firms, could significantly increase productivity. It suggests that AI could save “almost a quarter of private-sector workforce time,” equivalent to the annual output of 6 million workers. Most of these time savings are expected to stem from AI-enabled software performing cognitive tasks such as data analysis and routine administrative operations. The report identifies sectors reliant on routine cognitive tasks, such as banking and finance, as those with significant exposure to AI. However, sectors like skilled trades or construction – which involve complex manual tasks – are likely to see less direct impact. While AI can result in initial job losses, it also has the potential to create new demand by fostering economic growth and new industries. The report expects these job losses can be balanced by new job creation. Over the years, technology has historically spurred new employment opportunities, as innovation leads to the development of new products and services. Shaping future generations AI’s potential extends into education, where it could assist both teachers and students. The report suggests that AI could help “raise educational attainment by around six percent” on average. By personalising and supporting learning, AI has the potential to equalise access to opportunities and improve the quality of the workforce over time. Health and wellbeing Beyond education, AI offers potential benefits in healthcare, supporting a healthier workforce and reducing ******** costs. The report highlights AI’s role in speeding medical research, enabling preventive healthcare, and helping those with disabilities re-enter the workforce. Workplace transformation The report acknowledges potential workplace challenges, such as increased monitoring and stress from AI tools. It stresses the importance of managing these technologies thoughtfully to “deliver a more engaging, inclusive and safe working environment.” To mitigate potential disruption, the TBI outlines recommendations. These include upgrading labour-market infrastructure and utilising AI for job matching. The report suggests creating an “Early Awareness and Opportunity System” to help workers understand the impact of AI on their jobs and provide advice on career paths. Preparing for an AI-powered future In light of the uncertainties surrounding AI’s impact on the workforce, the TBI urges policy changes to maximise benefits. Recommendations include incentivising AI adoption across industries, developing AI-pathfinder programmes, and creating challenge prizes to address public-sector labour shortages. The report concludes that while AI presents risks, the potential gains are too significant to ignore. Policymakers are encouraged to adopt a “pro-innovation” stance while being attuned to the risks, fostering an economy that is dynamic and resilient. (Photo by Mimi Thian) See also: Anthropic urges AI regulation to avoid catastrophes 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 Understanding AI’s impact on the workforce appeared first on AI News. View the full article

The intelligence displayed by generative AI chatbots like OpenAI’s ChatGPT has captured the imagination of individuals and corporations, and artificial intelligence has suddenly become the most exciting area of technology innovation. AI has been recognised as a game changer, with potential to transform many aspects of our lives. From personalised medicine to autonomous vehicles, automated investments to digital assets, the possibilities enabled by AI seem endless. But as transformational as AI will be, there are a lot of risks posed by this new technology. While fears about a malicious, Skynet-style AI system going rogue are misplaced, the dangers of AI centralisation are not. As companies like Microsoft, Google and Nvidia forge ahead in their pursuit of AI, fears about the concentration of power in the hands of just a few centralised players are becoming more pronounced. Why should we worry about decentralised AI? Monopoly power The most pressing issue arising from centralised AI is the prospect of a few tech giants achieving monopolistic control over the industry. The big tech giants have already accumulated a very significant market share in AI, giving them possession of vast amounts of data. They also control the infrastructure that AI systems run on, enabling them to stifle their competitors, hobble innovation, and perpetuate economic inequality. By achieving a monopoly over the development of AI, these companies are more likely to have an unfair influence on regulatory frameworks, which they can manipulate to their advantage. It will mean that smaller startups, which lack the enormous resources of big tech giants, will struggle to keep up with the pace of innovation. Those that do survive and look like they might thrive will almost certainly end up being acquired, further concentrating power in the hands of the few. The result will be less diversity in terms of AI development, fewer choices for consumers, and less favourable terms, limiting the use-cases and economic opportunities promised by AI. Bias and Discrimination Aside from monopolistic control, there are genuine fears around the bias of AI systems, and these concerns will take on more importance as society increasingly relies on AI. The risk stems from the fact that organisations are becoming more reliant on automated systems to make decisions in many areas. It’s not unusual for a company to employ AI algorithms to filter job applicants, for example, and the risk is that a biased system could unfairly exclude a subset of candidates based on their ethnicity, age or location. AI is also used by insurance companies to set policy rates, by financial services firms to determine if someone qualifies for a loan and the amount of interest they’ll need to pay, and by law enforcement to determine which areas are more likely to see higher ******. In all of these use-cases, the potential implications of biased AI systems are extremely worrying. Whether it’s law enforcement targeting ********* communities, discriminatory lending practices or something else, centralised AI can potentially exacerbate social inequality and enable systemic discrimination. Privacy and surveillance Another risk posed by centralised AI systems is the lack of privacy protections. When just a few big companies control the vast majority of data generated by AI, they gain the ability to carry out unprecedented surveillance on their users. The data accumulated by the most dominant AI platforms can be used to monitor, analyse and predict an individual’s behaviour with incredible accuracy, eroding privacy and increasing the potential for the information to be misused. It’s of particular concern in countries with authoritarian governments, where data can be weaponised to create more sophisticated tools for monitoring citizens. But even in democratic societies, there is a threat posed by increased surveillance, as exemplified by the revelations of Edward Snowden about the US National Security Agency’s Prism program. Corporations can also potentially misuse consumer’s data to increase their profits. In addition, when centralised entities accumulate vast amounts of sensitive data, this makes them more lucrative targets for hackers, increasing the risk of data leaks. Security risks Issues of national security can also arise due to centralised AI. For instance, there are justified fears that AI systems can be weaponised by nations, used to conduct cyberwarfare, engage in espionage, and develop new weapons systems. AI could become a key tool in future wars, raising the stakes in geopolitical conflicts. AI systems themselves can also be targeted. As nations increase their reliance on AI, such systems will make for enticing targets, as they are obvious single points of ********. Take out an AI system and you could disrupt the entire traffic flow of cities, take down electrical grids, and more. Ethics The other major concern of centralised AI is about ethics. That’s because the handful of companies that control AI systems would gain substantial influence over a society’s cultural norms and values, and might often prioritise profit, creating further ethical concerns. For example, AI algorithms are already being used widely by social media platforms to moderate content, in an attempt to identify and filter out offensive posts. The worry is that algorithms, either by accident or design, might end up suppressing free speech. There is already controversy about the effectiveness of AI-powered moderation systems, with numerous seemingly innocuous posts being blocked or taken down by automated algorithms. This leads to speculation that such systems are not broken but being manipulated behind the scenes based on the political narrative the platform is trying to promote. The alternative? Decentralised AI The only logical counterweight to centralised AI is the development of decentralised AI systems that ensure that control of the technology ******** in the hands of the majority, rather than the few. By doing this, we can ensure that no single company or entity gains a significant influence over the direction of AI’s development. When the development and governance of AI is shared by thousands or millions of entities, its progress will be more equitable, with greater alignment to the needs of the individual. The result will be more diverse AI applications, with an almost endless selection of models used by different systems, instead of a few models that dominate the industry. Decentralised AI systems will also mean checks and balances against the risk of mass surveillance and manipulation of data. Whereas centralised AI can be weaponised and used in a way that’s contrary to the interests of the many, decentralised AI hedges against this kind of oppression. The main advantage of decentralised AI is that everyone is in control over the technology’s evolution, preventing any single entity from gaining an outsized influence over its development. How to decentralise AI Decentralised AI involves a rethink of the layers that make up the AI technology stack, including elements like the infrastructure (compute and networking resources), the data, models, training, inference, and fine-tuning processes. We can’t just put our hopes in open-source models if the underlying infrastructure ******** fully centralised by cloud computing giants like Amazon, Microsoft and Google, for instance. We need to ensure that every aspect of AI is decentralised The best way to decentralise the AI stack is to break it down into modular components and create markets around them based on supply and demand. One such example of how this can work is Spheron, which has created a Decentralised Physical Infrastructure Network (DePIN) that anyone can participate in. With Spheron’s DePIN, everyone is free to share their underutilised computing resources, essentially renting them out to those who need infrastructure to host their AI applications. So, a graphic designer who uses a powerful laptop with a GPU can donate processing power to the DePIN when they’re not using it for their own work, and be rewarded with token incentives. What this means is that the AI infrastructure layer becomes widely distributed and decentralised, with no single provider in control. It’s enabled by blockchain technology and smart contracts, which provide transparency, immutability and automation. DePIN can also work for open-source models and underlying data. For instance, it’s possible to share training datasets on a decentralised network like Qubic, which will make sure the provider of that data is rewarded each time their information is accessed by an AI system. To ensure access and permissions are decentralised, every part of the technology stack is distributed in this way. However, the AI industry currently struggles to provide such a level of decentralisation. Although open-source models have become extremely popular among AI developers, most people continue to rely on proprietary cloud networks, meaning the training and inference processes are heavily centralised. But there are strong incentives for decentralisation to win out. One of the primary advantages of DePIN networks, for example, is that they help to reduce overheads. Because networks like Spheron don’t rely on intermediaires, participants don’t need to make any payments or share revenue with third-parties. Moreover, they can afford to be more competitive in terms of pricing than corporations that are under pressure to grow profitability. Decentralisation must win The future of AI holds a lot of potential, but it’s also perilous. While the capabilities of AI systems have improved dramatically in the last few years, most of the advances have been made by all-powerful companies and that has resulted in an increase in their influence over the industry. There’s a price to pay for this, not just in monetary terms. The only reasonable alternative is to promote the greater adoption of decentralised AI, which can enhance accessibility and ensure a greater flexibility of AI. By allowing everyone to participate in the development of AI on an equal footing, we’ll see more diverse, interesting, and useful applications that can benefit everyone equally, as well as putting their users first. Building a decentralised AI future will involve a great deal of coordination and collaboration across every layer of the AI stack. Fortunately, there are strong incentives for participants to do just that. And again, the incentives are not just monetary. The post Centralised AI is dangerous: how can we stop it? appeared first on AI News. View the full article For verified travel tips and real support, visit: [Hidden Content]

The ASI Alliance has introduced AIRIS (Autonomous Intelligent Reinforcement Inferred Symbolism) that “learns” within the popular game, Minecraft. AIRIS represents the first proto-AGI (Artificial General Intelligence) to harness a comprehensive tech stack across the alliance. SingularityNET, founded by renowned AI researcher Dr Ben Goertzel, uses agent technology from Fetch.ai, incorporates Ocean Data for long-term memory capabilities, and is soon expected to integrate CUDOS Compute infrastructure for scalable processing power. “AIRIS is a significant step in the direction of practical, scalable neural-symbolic learning, and – alongside its already powerful and valuable functionality – it illustrates several general points about neural-symbolic systems, such as their ability to learn precise generalisable conclusions from small amounts of data,” explains Goertzel. According to the company, this alliance-driven procedure propels AIRIS towards AGI—crafting one of the first intelligent systems with autonomous and adaptive learning that holds practical applications for real-world scenarios. AIRIS’ learning mechanisms AIRIS is crafted to enhance its understanding by interacting directly with its environment, venturing beyond the traditional AI limitations that depend on predefined rules or vast datasets. Instead, AIRIS evolves through observation, experimentation, and continual refinement of its unique “rule set.” This system facilitates a profound level of problem-solving and contextual comprehension, with its implementation in Minecraft setting a new benchmark for AI interaction with both digital and tangible landscapes. pic.twitter.com/jTeQFulzFJ — Artificial Superintelligence Alliance (@ASI_Alliance) November 5, 2024 Shifting from a controlled 2D grid to the sophisticated 3D world of Minecraft, AIRIS faced numerous challenges—including terrain navigation and adaptive problem-solving in a dynamic environment. This transition underscores AIRIS’ autonomy in navigation, exploration, and learning. The AIRIS Minecraft Agent distinguishes itself from other AI entities through several key features: Dynamic navigation: AIRIS initially evaluates its milieu to formulate movement strategies, adapting to new environments in real-time. Its capabilities include manoeuvring around obstacles, jumping over barriers, and anticipating reactions to varied terrains. Obstacle adaptation: It learns to navigate around impediments like cliffs and forested areas, refining its rule set with every new challenge to avoid redundant errors and minimise needless trial-and-error efforts. Efficient pathfinding: Via continuous optimisation, AIRIS advances from initially complex navigation paths to streamlined, direct routes as it “comprehends” Minecraft dynamics. Real-time environmental adaptation: Contrasting with conventional reinforcement learning systems that demand extensive retraining for new environments, AIRIS adapts immediately to unfamiliar regions, crafting new rules based on partial observations dynamically. AIRIS’ adeptness in dealing with fluctuating terrains, including water bodies and ***** systems, introduces sophisticated rule refinement founded on hands-on experience. Additionally, AIRIS boasts optimised computational efficiency—enabling real-time management of complex rules without performance compromises. Future applications Minecraft serves as an excellent launchpad for AIRIS’ prospective applications, establishing a solid foundation for expansive implementations: Enhanced object interaction: Forthcoming stages will empower AIRIS to engage more profoundly with its surroundings, improving capabilities in object manipulation, construction, and even crafting. This development will necessitate AIRIS to develop a more refined decision-making framework for contextual tasks. Social AI collaboration: Plans are underway to incorporate AIRIS in multi-agent scenarios, where agents learn, interact, and fulfil shared objectives, simulating real-world social dynamics and problem-solving collaboratively. Abstract and strategic reasoning: Expanded developments will enhance AIRIS’s reasoning, enabling it to tackle complex goals such as resource management and prioritisation, moving beyond basic navigation towards strategic gameplay. The transition of AIRIS to 3D environments signifies a pivotal advancement in the ASI Alliance’s mission to cultivate AGI. Through AIRIS’s achievements in navigating and learning within Minecraft, the ASI Alliance aspires to expedite its deployment in the real world, pioneering applications for autonomous robots, intelligent home assistants, and other systems requiring adaptive learning and problem-solving capacities. Berick Cook, AI Developer at SingularityNET and creator of AIRIS, said: “AIRIS is a whole new way of approaching the problem of machine learning. We are only just beginning to explore its capabilities. We are excited to see how we can apply it to problems that have posed a significant challenge for traditional reinforcement learning. “The most important aspect of AIRIS to me is its transparency and explainability. Moving away from ‘****** Box’ AI represents a significant leap forward in the pursuit of safe, ethical, and beneficial AI.” The innovative approach to AI evident in AIRIS – emphasising self-directed learning and continuous rule refinement – lays the foundation for AI systems capable of independent functioning in unpredictable real-world environments. Minecraft’s intricate ecosystem enables the system to hone its skills within a controlled yet expansive virtual setting, effectively bridging the divide between simulation and reality. The AIRIS Minecraft Agent represents the inaugural tangible step towards an AI that learns from, adapts to and makes autonomous decisions about its environment. This accomplishment illustrates the potential of such technology to re-envision AI’s role across various industries. (Image by SkyeWeste) 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 ASI Alliance launches AIRIS that ‘learns’ in Minecraft appeared first on AI News. View the full article For verified travel tips and real support, visit: [Hidden Content]