The latest releases from Meta focus heavily on enhancing AI perception – the ability for machines to process and interpret sensory information – alongside advancements in language modelling, robotics, and collaborative AI agents.

Meta stated its goal involves creating machines “that are able to acquire, process, and interpret sensory information about the world around us and are able to use this information to make decisions with human-like intelligence and speed.”

The five new releases represent diverse but interconnected efforts towards achieving this ambitious goal.

Perception Encoder: Meta sharpens the ‘vision’ of AI

Central to the new releases is the Perception Encoder, described as a large-scale vision encoder designed to excel across various image and video tasks.

Vision encoders function as the “eyes” for AI systems, allowing them to understand visual data.

Meta highlights the increasing challenge of building encoders that meet the demands of advanced AI, requiring capabilities that bridge vision and language, handle both images and videos effectively, and remain robust under challenging conditions, including potential adversarial attacks.

The ideal encoder, according to Meta, should recognise a wide array of concepts while distinguishing subtle details—citing examples like spotting “a stingray burrowed under the sea floor, identifying a tiny goldfinch in the background of an image, or catching a scampering agouti on a night vision wildlife camera.”

Meta claims the Perception Encoder achieves “exceptional performance on image and video zero-shot classification and retrieval, surpassing all existing open source and proprietary models for such tasks.”

Furthermore, its perceptual strengths reportedly translate well to language tasks. 

When aligned with a large language model (LLM), the encoder is said to outperform other vision encoders in areas like visual question answering (VQA), captioning, document understanding, and grounding (linking text to specific image regions). It also reportedly boosts performance on tasks traditionally difficult for LLMs, such as understanding spatial relationships (e.g., “if one object is behind another”) or camera movement relative to an object.

“As Perception Encoder begins to be integrated into new applications, we’re excited to see how its advanced vision capabilities will enable even more capable AI systems,” Meta said.

Perception Language Model (PLM): Open research in vision-language

Complementing the encoder is the Perception Language Model (PLM), an open and reproducible vision-language model aimed at complex visual recognition tasks. 

PLM was trained using large-scale synthetic data combined with open vision-language datasets, explicitly without distilling knowledge from external proprietary models.

Recognising gaps in existing video understanding data, the FAIR team collected 2.5 million new, human-labelled samples focused on fine-grained video question answering and spatio-temporal captioning. Meta claims this forms the “largest dataset of its kind to date.”

PLM is offered in 1, 3, and 8 billion parameter versions, catering to academic research needs requiring transparency.

Alongside the models, Meta is releasing PLM-VideoBench, a new benchmark specifically designed to test capabilities often missed by existing benchmarks, namely “fine-grained activity understanding and spatiotemporally grounded reasoning.”

Meta hopes the combination of open models, the large dataset, and the challenging benchmark will empower the open-source community.

Meta Locate 3D: Giving robots situational awareness

Bridging the gap between language commands and physical action is Meta Locate 3D. This end-to-end model aims to allow robots to accurately localise objects in a 3D environment based on open-vocabulary natural language queries.

Meta Locate 3D processes 3D point clouds directly from RGB-D sensors (like those found on some robots or depth-sensing cameras). Given a textual prompt, such as “flower vase near TV console,” the system considers spatial relationships and context to pinpoint the correct object instance, distinguishing it from, say, a “vase on the table.”

The system comprises three main parts: a preprocessing step converting 2D features to 3D featurised point clouds; the 3D-JEPA encoder (a pretrained model creating a contextualised 3D world representation); and the Locate 3D decoder, which takes the 3D representation and the language query to output bounding boxes and masks for the specified objects.

Alongside the model, Meta is releasing a substantial new dataset for object localisation based on referring expressions. It includes 130,000 language annotations across 1,346 scenes from the ARKitScenes, ScanNet, and ScanNet++ datasets, effectively doubling existing annotated data in this area.

Meta sees this technology as crucial for developing more capable robotic systems, including its own PARTNR robot project, enabling more natural human-robot interaction and collaboration.

Dynamic Byte Latent Transformer: Efficient and robust language modelling

Following research published in late 2024, Meta is now releasing the model weights for its 8-billion parameter Dynamic Byte Latent Transformer.

This architecture represents a shift away from traditional tokenisation-based language models, operating instead at the byte level. Meta claims this approach achieves comparable performance at scale while offering significant improvements in inference efficiency and robustness.

Traditional LLMs break text into ‘tokens’, which can struggle with misspellings, novel words, or adversarial inputs. Byte-level models process raw bytes, potentially offering greater resilience.

Meta reports that the Dynamic Byte Latent Transformer “outperforms tokeniser-based models across various tasks, with an average robustness advantage of +7 points (on perturbed HellaSwag), and reaching as high as +55 points on tasks from the CUTE token-understanding benchmark.”

By releasing the weights alongside the previously shared codebase, Meta encourages the research community to explore this alternative approach to language modelling.

Collaborative Reasoner: Meta advances socially-intelligent AI agents

The final release, Collaborative Reasoner, tackles the complex challenge of creating AI agents that can effectively collaborate with humans or other AIs.

Meta notes that human collaboration often yields superior results, and aims to imbue AI with similar capabilities for tasks like helping with homework or job interview preparation.

Such collaboration requires not just problem-solving but also social skills like communication, empathy, providing feedback, and understanding others’ mental states (theory-of-mind), often unfolding over multiple conversational turns.

Current LLM training and evaluation methods often neglect these social and collaborative aspects. Furthermore, collecting relevant conversational data is expensive and difficult.

Collaborative Reasoner provides a framework to evaluate and enhance these skills. It includes goal-oriented tasks requiring multi-step reasoning achieved through conversation between two agents. The framework tests abilities like disagreeing constructively, persuading a partner, and reaching a shared best solution.

Meta’s evaluations revealed that current models struggle to consistently leverage collaboration for better outcomes. To address this, they propose a self-improvement technique using synthetic interaction data where an LLM agent collaborates with itself.

Generating this data at scale is enabled by a new high-performance model serving engine called Matrix. Using this approach on maths, scientific, and social reasoning tasks reportedly yielded improvements of up to 29.4% compared to the standard ‘chain-of-thought’ performance of a single LLM.

By open-sourcing the data generation and modelling pipeline, Meta aims to foster further research into creating truly “social agents that can partner with humans and other agents.”

These five releases collectively underscore Meta’s continued heavy investment in fundamental AI research, particularly focusing on building blocks for machines that can perceive, understand, and interact with the world in more human-like ways. 

See also: Meta will train AI models using EU user data

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.

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While large language models popularised the concept of “agents” as tools that answer queries or retrieve information via methods such as Retrieval-Augmented Generation (RAG), Amazon envisions something more robust. The company defines agents not just as responders but as entities capable of performing tangible, multi-step tasks in diverse digital and physical environments.

“Our dream is for agents to perform wide-ranging, complex, multi-step tasks like organising a wedding or handling complex IT tasks to increase business productivity,” said Amazon.

Current market offerings often fall short, with many agents requiring continuous human supervision and their functionality dependent on comprehensive API integration—something not feasible for all tasks. Nova Act is Amazon’s answer to these limitations.

Alongside the model, Amazon is releasing a research preview of the Amazon Nova Act SDK. Using the SDK, developers can create agents capable of automating web tasks like submitting out-of-office notifications, scheduling calendar holds, or enabling automatic email replies.

The SDK aims to break down complex workflows into dependable “atomic commands” such as searching, checking out, or interacting with specific interface elements like dropdowns or popups. Detailed instructions can be added to refine these commands, allowing developers to, for instance, instruct an agent to bypass an insurance upsell during checkout.

To further enhance accuracy, the SDK supports browser manipulation via Playwright, API calls, Python integrations, and parallel threading to overcome web page load delays.

Nova Act: Exceptional performance on benchmarks

Unlike other generative models that showcase middling accuracy on complex tasks, Nova Act prioritises reliability. Amazon highlights its model’s impressive scores of over 90% on internal evaluations for specific capabilities that typically challenge competitors. 

Nova Act achieved a near-perfect 0.939 on the ScreenSpot Web Text benchmark, which measures natural language instructions for text-based interactions, such as adjusting font sizes. Competing models such as Claude 3.7 Sonnet (0.900) and OpenAI’s CUA (0.883) trail behind by significant margins.

Similarly, Nova Act scored 0.879 in the ScreenSpot Web Icon benchmark, which tests interactions with visual elements like rating stars or icons. While the GroundUI Web test, designed to assess an AI’s proficiency in navigating various user interface elements, showed Nova Act slightly trailing competitors, Amazon sees this as an area ripe for improvement as the model evolves.

Amazon stresses its focus on delivering practical reliability. Once an agent built using Nova Act functions as expected, developers can deploy it headlessly, integrate it as an API, or even schedule it to run tasks asynchronously. In one demonstrated use case, an agent automatically orders a salad for delivery every Tuesday evening without requiring ongoing user intervention.

Amazon sets out its vision for scalable and smart AI agents

One of Nova Act’s standout features is its ability to transfer its user interface understanding to new environments with minimal additional training. Amazon shared an instance where Nova Act performed admirably in browser-based games, even though its training had not included video game experiences. This adaptability positions Nova Act as a versatile agent for diverse applications.

This capability is already being leveraged in Amazon’s own ecosystem. Within Alexa+, Nova Act enables self-directed web navigation to complete tasks for users, even when API access is not comprehensive enough. This represents a step towards smarter AI assistants that can function independently, harnessing their skills in more dynamic ways.

Amazon is clear that Nova Act represents the first stage in a broader mission to craft intelligent, reliable AI agents capable of handling increasingly complex, multi-step tasks. 

Expanding beyond simple instructions, Amazon’s focus is on training agents through reinforcement learning across varied, real-world scenarios rather than overly simplistic demonstrations. This foundational model serves as a checkpoint in a long-term training curriculum for Nova models, indicating the company’s ambition to reshape the AI agent landscape.

“The most valuable use cases for agents have yet to be built,” Amazon noted. “The best developers and designers will discover them. This research preview of our Nova Act SDK enables us to iterate alongside these builders through rapid prototyping and iterative feedback.”

Nova Act is a step towards making AI agents truly useful for complex, digital tasks. From rethinking benchmarks to emphasising reliability, its design philosophy is centred around empowering developers to move beyond what’s possible with current-generation tools. 

See also: Anthropic provides insights into the ‘AI biology’ of Claude

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.

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Efficiently managing and coordinating AI inference requests across a fleet of GPUs is a critical endeavour to ensure that AI factories can operate with optimal cost-effectiveness and maximise the generation of token revenue.

As AI reasoning becomes increasingly prevalent, each AI model is expected to generate tens of thousands of tokens with every prompt, essentially representing its “thinking” process. Enhancing inference performance while simultaneously reducing its cost is therefore crucial for accelerating growth and boosting revenue opportunities for service providers.

A new generation of AI inference software

NVIDIA Dynamo, which succeeds the NVIDIA Triton Inference Server, represents a new generation of AI inference software specifically engineered to maximise token revenue generation for AI factories deploying reasoning AI models.

Dynamo orchestrates and accelerates inference communication across potentially thousands of GPUs. It employs disaggregated serving, a technique that separates the processing and generation phases of large language models (LLMs) onto distinct GPUs. This approach allows each phase to be optimised independently, catering to its specific computational needs and ensuring maximum utilisation of GPU resources.

“Industries around the world are training AI models to think and learn in different ways, making them more sophisticated over time,” stated Jensen Huang, founder and CEO of NVIDIA. “To enable a future of custom reasoning AI, NVIDIA Dynamo helps serve these models at scale, driving cost savings and efficiencies across AI factories.”

Using the same number of GPUs, Dynamo has demonstrated the ability to double the performance and revenue of AI factories serving Llama models on NVIDIA’s current Hopper platform. Furthermore, when running the DeepSeek-R1 model on a large cluster of GB200 NVL72 racks, NVIDIA Dynamo’s intelligent inference optimisations have shown to boost the number of tokens generated by over 30 times per GPU.

To achieve these improvements in inference performance, NVIDIA Dynamo incorporates several key features designed to increase throughput and reduce operational costs.

Dynamo can dynamically add, remove, and reallocate GPUs in real-time to adapt to fluctuating request volumes and types. The software can also pinpoint specific GPUs within large clusters that are best suited to minimise response computations and efficiently route queries. Dynamo can also offload inference data to more cost-effective memory and storage devices while retrieving it rapidly when required, thereby minimising overall inference costs.

NVIDIA Dynamo is being released as a fully open-source project, offering broad compatibility with popular frameworks such as PyTorch, SGLang, NVIDIA TensorRT-LLM, and vLLM. This open approach supports enterprises, startups, and researchers in developing and optimising novel methods for serving AI models across disaggregated inference infrastructures.

NVIDIA expects Dynamo to accelerate the adoption of AI inference across a wide range of organisations, including major cloud providers and AI innovators like AWS, Cohere, CoreWeave, Dell, Fireworks, Google Cloud, Lambda, Meta, Microsoft Azure, Nebius, NetApp, OCI, Perplexity, Together AI, and VAST.

NVIDIA Dynamo: Supercharging inference and agentic AI

A key innovation of NVIDIA Dynamo lies in its ability to map the knowledge that inference systems hold in memory from serving previous requests, known as the KV cache, across potentially thousands of GPUs.

The software then intelligently routes new inference requests to the GPUs that possess the best knowledge match, effectively avoiding costly recomputations and freeing up other GPUs to handle new incoming requests. This smart routing mechanism significantly enhances efficiency and reduces latency.

“To handle hundreds of millions of requests monthly, we rely on NVIDIA GPUs and inference software to deliver the performance, reliability and scale our business and users demand,” said Denis Yarats, CTO of Perplexity AI.

“We look forward to leveraging Dynamo, with its enhanced distributed serving capabilities, to drive even more inference-serving efficiencies and meet the compute demands of new AI reasoning models.”

AI platform Cohere is already planning to leverage NVIDIA Dynamo to enhance the agentic AI capabilities within its Command series of models.

“Scaling advanced AI models requires sophisticated multi-GPU scheduling, seamless coordination and low-latency communication libraries that transfer reasoning contexts seamlessly across memory and storage,” explained Saurabh Baji, SVP of engineering at Cohere.

“We expect NVIDIA Dynamo will help us deliver a premier user experience to our enterprise customers.”

Support for disaggregated serving

The NVIDIA Dynamo inference platform also features robust support for disaggregated serving. This advanced technique assigns the different computational phases of LLMs – including the crucial steps of understanding the user query and then generating the most appropriate response – to different GPUs within the infrastructure.

Disaggregated serving is particularly well-suited for reasoning models, such as the new NVIDIA Llama Nemotron model family, which employs advanced inference techniques for improved contextual understanding and response generation. By allowing each phase to be fine-tuned and resourced independently, disaggregated serving improves overall throughput and delivers faster response times to users.

Together AI, a prominent player in the AI Acceleration Cloud space, is also looking to integrate its proprietary Together Inference Engine with NVIDIA Dynamo. This integration aims to enable seamless scaling of inference workloads across multiple GPU nodes. Furthermore, it will allow Together AI to dynamically address traffic bottlenecks that may arise at various stages of the model pipeline.

“Scaling reasoning models cost effectively requires new advanced inference techniques, including disaggregated serving and context-aware routing,” stated Ce Zhang, CTO of Together AI.

“The openness and modularity of NVIDIA Dynamo will allow us to seamlessly plug its components into our engine to serve more requests while optimising resource utilisation—maximising our accelerated computing investment. We’re excited to leverage the platform’s breakthrough capabilities to cost-effectively bring open-source reasoning models to our users.”

Four key innovations of NVIDIA Dynamo

NVIDIA has highlighted four key innovations within Dynamo that contribute to reducing inference serving costs and enhancing the overall user experience:

• GPU Planner: A sophisticated planning engine that dynamically adds and removes GPUs based on fluctuating user demand. This ensures optimal resource allocation, preventing both over-provisioning and under-provisioning of GPU capacity.
• Smart Router: An intelligent, LLM-aware router that directs inference requests across large fleets of GPUs. Its primary function is to minimise costly GPU recomputations of repeat or overlapping requests, thereby freeing up valuable GPU resources to handle new incoming requests more efficiently.
• Low-Latency Communication Library: An inference-optimised library designed to support state-of-the-art GPU-to-GPU communication. It abstracts the complexities of data exchange across heterogeneous devices, significantly accelerating data transfer speeds.
• Memory Manager: An intelligent engine that manages the offloading and reloading of inference data to and from lower-cost memory and storage devices. This process is designed to be seamless, ensuring no negative impact on the user experience.

NVIDIA Dynamo will be made available within NIM microservices and will be supported in a future release of the company’s AI Enterprise software platform. 

See also: LG EXAONE Deep is a maths, science, and coding buff

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.

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The Yokohama platform release features teams of preconfigured AI agents designed to deliver immediate productivity gains. These agents operate on a single, unified platform, ensuring seamless integration and coordination across different business functions. The platform also includes capabilities to build, onboard, and manage the entire AI agent lifecycle, making it easier for enterprises to adopt and scale AI solutions.

Data is the lifeblood of AI, and ServiceNow recognises this by expanding its Knowledge Graph with advancements to its Common Service Data Model (CSDM). This expansion aims to break down barriers among data sources, enabling more connected and intelligent AI agents. By unifying data from various sources, ServiceNow’s platform ensures that AI agents can operate with a comprehensive view of the enterprise, driving more informed decisions and actions.

The growing need for ‘Guardian Agents’

According to Gartner, by 2028, 40% of CIOs will demand ‘Guardian Agents’ to autonomously track, oversee, or contain the results of AI agent actions. This underscores the growing need for a coordinated, enterprise-wide approach to AI deployment and management.

ServiceNow’s Yokohama release addresses this need by serving as the AI agent control tower for enterprises. The platform removes common roadblocks such as data fragmentation, governance gaps, and real-time performance challenges, ensuring seamless data connectivity with Workflow Data Fabric.

Unlike other AI providers that operate in silos or require complex integrations, ServiceNow AI Agents are built on a single, enterprise-wide platform. This ensures seamless data connectivity and provides a single view of all workflows, AI, and automation needs.

Amit Zavery, President, Chief Product Officer, and Chief Operating Officer at ServiceNow, commented: “Agentic AI is the new frontier. Enterprise leaders are no longer just experimenting with AI agents; they’re demanding AI solutions that can help them achieve productivity at scale.

“ServiceNow’s industry‑leading agentic AI framework meets this need by delivering predictability and efficiency from the start. With the combination of agentic AI, data fabric, and workflow automation all on one platform, we’re making it easier for organisations to embed connected AI where work happens and both measure and drive business outcomes faster, smarter, and at scale.”

New AI agents from ServiceNow aim to accelerate productivity

ServiceNow’s new AI Agents are now available to accelerate productivity at scale. These agents are designed to drive real outcomes for enterprise-wide use cases. For example:

• Security Operations (SecOps) expert AI agents: These agents transform security operations by streamlining the entire incident lifecycle, eliminating repetitive tasks, and empowering SecOps teams to focus on stopping real threats quickly.
• Autonomous change management AI agents: Acting like seasoned change managers, these agents generate custom implementation, test, and backout plans by analysing impact, historical data, and similar changes, ensuring seamless execution with minimal risk.
• Proactive network test & repair AI agents: These AI-powered troubleshooters automatically detect, diagnose, and resolve network issues before they impact performance.

ServiceNow AI Agent Orchestrator and AI Agent Studio are now generally available with expanded capabilities to govern the complete AI agent lifecycle.

These tools help to streamline the setup process with guided instructions, making it easier to design and configure new AI agents using natural language descriptions. Their expanded performance management capabilities include an analytics dashboard for visualising AI agent usage, quality, and value—ensuring that AI agent performance and ROI can be easily tracked.

At the core of the ServiceNow Platform is Workflow Data Fabric, enabling AI-powered workflows that integrate with an organisation’s data, regardless of the system or source. This fabric allows businesses to gain deeper insights through AI-driven contextualisation and decision intelligence while automating manual work and creating process efficiencies.

The Yokohama release continues to expand ServiceNow’s Knowledge Graph data capabilities with enhancements to its Common Service Data Model (CSDM). CSDM provides a standardised framework for managing IT and business services to accelerate quick, safe, and compliant technology deployments.

Several customers and partners have already seen the benefits of ServiceNow’s AI solutions. CANCOM, Cognizant, Davies, and Sentara have all praised the platform’s ability to drive efficiency, cost savings, and productivity. These organisations have successfully integrated ServiceNow’s AI agents into their operations.

Jason Wojahn, Global Head of the ServiceNow Business Group at Cognizant, said: “At Cognizant, we are helping companies harness the next phase of AI with agentic AI workflows that could bring unparalleled efficiency. We were the first to bring ServiceNow’s Workflow Data Fabric to market and are working to help our clients to seamlessly connect their data with AI.

“With the Yokohama release and the integration of AI agents onto the Now Platform, clients can now operate their agents virtually effortlessly with connected data, driving productivity and ROI across their entire business.”

Darrell Burnell, Group Head of Technology at Davies, added: “Agility is essential for Davies, given our work with clients in heavily regulated markets. We’ve transformed our agent experience with ServiceNow’s generative AI, deploying Now Assist for ITSM in just six weeks to streamline information retrieval and accelerate resolution times.”

ServiceNow’s Yokohama platform release is a step forward in the evolution of AI for business transformation. By unleashing new AI agents and expanding data capabilities, ServiceNow aims to empower businesses to achieve faster and smarter workflows to maximise impact.

(Image by Thomas Fengler)

See also: Opera introduces browser-integrated AI agent

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.

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Rather than acting as a separate tool, Browser Operator is an extension of the browser itself—designed to empower users by automating repetitive tasks like purchasing products, completing online forms, and gathering web content.

Unlike server-based AI integrations which require sensitive data to be sent to third-party servers, Browser Operator processes tasks locally within the Opera browser.

Opera’s demonstration video showcases how Browser Operator can streamline an everyday task like buying socks. Instead of manually scrolling through product pages or filling out payment forms, users could delegate the entire process to Browser Operator—allowing them to shift focus to activities that matter more to them, such as spending time with loved ones.

Harnessing natural language processing powered by Opera’s AI Composer Engine, Browser Operator interprets written instructions from users and executes corresponding tasks within the browser. All operations occur locally on a user’s device, leveraging the browser’s own infrastructure to safely and swiftly complete commands.  

If Browser Operator encounters a sensitive step in the process, such as entering payment details or approving an order, it pauses and requests the user’s input. You also have the freedom to intervene and take control of the process at any time.  

Every step Browser Operator takes is transparent and fully reviewable, providing users a clear understanding of how tasks are being executed. If mistakes occur – like placing an incorrect order – you can further instruct the AI agent to make amends, such as cancelling the order or adjusting a form.

The key differentiators: Privacy, performance, and precision  

What sets Browser Operator apart from other AI-integrated tools is its localised, privacy-first architecture. Unlike competitors that depend on screenshots or video recordings to understand webpage content, Opera’s approach uses the Document Object Model (DOM) Tree and browser layout data—a textual representation of the webpage.  

This difference offers several key advantages:

• Faster task completion: Browser Operator doesn’t need to “see” and interpret pixels on the screen or emulate mouse movements. Instead, it accesses web page elements directly, avoiding unnecessary overhead and allowing it to process pages holistically without scrolling.

• Enhanced privacy: With all operations conducted on the browser itself, user data – including logins, cookies, and browsing history – remains secure on the local device. No screenshots, keystrokes, or personal information are sent to Opera’s servers.

• Easier interaction with page elements: The AI can engage with elements hidden from the user’s view, such as behind cookie popups or verification dialogs, enabling seamless access to web page content.

By enabling the browser to autonomously perform tasks, Opera is taking a significant step forward in making browsers “agentic”—not just tools for accessing the internet, but assistants that actively enhance productivity.  

See also: You.com ARI: Professional-grade AI research agent for businesses

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.

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ARI (Advanced Research & Insights) delivers comprehensive, accurate, and interactive reports within minutes—potentially shaking up the $250 billion management consulting industry.  

You.com claims ARI completes reports that typically require weeks of labour and cost thousands of dollars in just five minutes, at a fraction of traditional expenses.

With the ability to process over 400 sources simultaneously – a figure set to grow as the technology scales – ARI promises to deliver “verified citations and insights 3X faster than other currently available solutions.”

Bryan McCann, Co-Founder and CTO of You.com, said: “ARI’s breakthrough is its ability to maintain contextual understanding while processing hundreds of sources simultaneously.

“When combined with chain-of-thought reasoning and extended test-time compute, ARI is able to discover and incorporate adjacent research areas dynamically as analysis progresses.”

A powerful AI agent for business research

Traditional AI research tools are typically limited to processing between 30 to 40 data sources at a time. ARI stands out by handling hundreds of public and private data streams, ensuring unparalleled accuracy and scope in its analysis. The system doesn’t just stop at summarising data; it enhances user experience by producing rich, interactive graphs, charts, and visualisations for deeper insights.  

Designed to cater equally to high-level professionals and knowledge workers across industries, ARI combines advanced functionality with user-friendly accessibility. This dual-purpose design allows enterprises to deploy it as a personal assistant or as a replacement for expensive research efforts traditionally carried out by consulting firms.

At the heart of ARI is a series of capabilities:

• Simultaneous source analysis: Processes hundreds of data sources, both public and private.  
• Chain-of-Thought reasoning: Dynamically evolves research parameters as insights emerge.  
• Real-time verification: Provides direct validation for every claim and data point.  
• Interactive visualisation engine: Automatically generates and cites graphs and charts to enhance reporting.  
• Enterprise data integration: Analyses a mix of public and private datasets to deliver actionable insights.  

During its initial deployment phase, ARI has demonstrated its versatility and potential for impact across several industries:

• Consulting: By analysing market reports, competitor financials, patent filings, and social sentiment data in hours rather than weeks, ARI supports due diligence with ease.  
• Financial services: With the ability to integrate real-time data from earnings calls, SEC filings, and industry news, ARI helps support faster and more accurate investment decisions.  
• Healthcare: ARI accelerates the synthesis of clinical trials, medical journals, patient data, and treatment guidelines, providing insights that support evidence-based care.  
• Media: From audience data to trending topics and competitor activity, ARI enables the rapid identification of new story angles and anticipates emerging narratives in key markets.  

Dr Dennis Ballwieser, Managing Director and Editor at Wort & Bild Verlag, commented: “The research time has dropped from a few days to just a few hours, and the accuracy across both German and English content has been remarkable.

“What excites me most is the opportunity to democratise access to professional-grade research. With ARI’s ability to analyse hundreds of verifiable sources simultaneously while maintaining accuracy, we can now offer professional insights to organisations of all sizes at a fraction of the traditional cost.”  

Accelerating access to strategic insights  

The potential for technologies like ARI goes beyond time and cost savings. For companies such as global consultancy firm APCO Worldwide, ARI’s capabilities provide a level of quality and personalisation that aligns with the modern needs of clients.

Philip Fraser, CIO at APCO Worldwide, said: “To us, ARI represents a step-change in the quality and alignment to the needs of our clients. We are very excited about working with You.com to integrate the power of ARI into our award-winning, proprietary Margy AI platform.”  

Through such integrations, ARI has the potential to move organisations away from periodic, resource-intensive research projects towards continuous real-time intelligence that drives better decision-making across all levels.  

Richard Socher, Co-Founder and CEO of You.com, added: “When every employee has instant access to comprehensive, validated insights that previously required teams of consultants and weeks of work, it changes the speed and quality of business decision-making. ARI represents a paradigm shift in how organisations operate.”

ARI is the newest addition to You.com’s expanding AI agent ecosystem, which has already seen the development of over 50,000 custom agents since late 2024. The company has raised $99 million in funding from investors such as Salesforce Ventures, NVIDIA, and Georgian Ventures.

With ARI, You.com aims to set a new standard for an enterprise-grade AI research agent as part of broader decision-making systems.

(Photo by Jeremy Beadle)

See also: Endor Labs: AI transparency vs ‘open-washing’

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.

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Described as a gamechanger for AI accessibility and performance, ASI-1 Mini is heralded for delivering results on par with leading LLMs but at significantly reduced hardware costs—a leap forward in making AI enterprise-ready.

ASI-1 Mini integrates into Web3 ecosystems, enabling secure and autonomous AI interactions. Its release sets the foundation for broader innovation within the AI sector—including the imminent launch of the Cortex suite, which will further enhance the use of large language models and generalised intelligence.

“This launch marks the beginning of ASI-1 Mini’s rollout and a new era of community-owned AI. By decentralising AI’s value chain, we’re empowering the Web3 community to invest in, train, and own foundational AI models,” said Humayun Sheikh, CEO of Fetch.ai and Chairman of the Artificial Superintelligence Alliance.

“We’ll soon introduce advanced agentic tool integration, multi-modal capabilities, and deeper Web3 synergy to enhance ASI-1 Mini’s automation capabilities while keeping AI’s value creation in the hands of its contributors.”

Democratising AI with Web3: Decentralised ownership and shared value  

Key to Fetch.ai’s vision is the democratisation of foundational AI models, allowing the Web3 community to not just use, but also train and own proprietary LLMs like ASI-1 Mini. 

This decentralisation unlocks opportunities for individuals to directly benefit from the economic growth of cutting-edge AI models, which could achieve multi-billion-dollar valuations.  

Through Fetch.ai’s platform, users can invest in curated AI model collections, contribute to their development, and share in generated revenues. For the first time, decentralisation is driving AI model ownership—ensuring financial benefits are more equitably distributed.

Advanced reasoning and tailored performance  

ASI-1 Mini introduces adaptability in decision-making with four dynamic reasoning modes: Multi-Step, Complete, Optimised, and Short Reasoning. This flexibility allows it to balance depth and precision based on the specific task at hand.  

Whether performing intricate, multi-layered problem-solving or delivering concise, actionable insights, ASI-1 Mini adapts dynamically for maximum efficiency. Its Mixture of Models (MoM) and Mixture of Agents (MoA) frameworks further enhance this versatility.  

Mixture of Models (MoM):  

ASI-1 Mini selects relevant models dynamically from a suite of specialised AI models, which are optimised for specific tasks or datasets. This ensures high efficiency and scalability, especially for multi-modal AI and federated learning.  

Mixture of Agents (MoA):  

Independent agents with unique knowledge and reasoning capabilities work collaboratively to solve complex tasks. The system’s coordination mechanism ensures efficient task distribution, paving the way for decentralised AI models that thrive in dynamic, multi-agent systems.  

This sophisticated architecture is built on three interacting layers:  

1. Foundational layer: ASI-1 Mini serves as the core intelligence and orchestration hub.  
2. Specialisation layer (MoM Marketplace): Houses diverse expert models, accessible through the ASI platform.  
3. Action layer (AgentVerse): Features agents capable of managing live databases, integrating APIs, facilitating decentralised workflows, and more.  

By selectively activating only necessary models and agents, the system ensures performance, precision, and scalability in real-time tasks.  

Transforming AI efficiency and accessibility

Unlike traditional LLMs, which come with high computational overheads, ASI-1 Mini is optimised for enterprise-grade performance on just two GPUs, reducing hardware costs by a remarkable eightfold. For businesses, this means reduced infrastructure costs and increased scalability, breaking down financial barriers to high-performance AI integration.  

On benchmark tests like Massive Multitask Language Understanding (MMLU), ASI-1 Mini matches or surpasses leading LLMs in specialised domains such as medicine, history, business, and logical reasoning.  

Rolling out in two phases, ASI-1 Mini will soon process vastly larger datasets with upcoming context window expansions:  

• Up to 1 million tokens: Allows the model to analyse complex documents or technical manuals.
• Up to 10 million tokens: Enables high-stakes applications like legal record review, financial analysis, and enterprise-scale datasets.  

These enhancements will make ASI-1 Mini invaluable for complex and multi-layered tasks.  

Tackling the “black-box” problem  

The AI industry has long faced the challenge of addressing the black-box problem, where deep learning models reach conclusions without clear explanations.

ASI-1 Mini mitigates this issue with continuous multi-step reasoning, facilitating real-time corrections and optimised decision-making. While it doesn’t entirely eliminate opacity, ASI-1 provides more explainable outputs—critical for industries like healthcare and finance.  

Its multi-expert model architecture not only ensures transparency but also optimises complex workflows across diverse sectors. From managing databases to executing real-time business logic, ASI-1 outperforms traditional models in both speed and reliability.  

AgentVerse integration: Building the agentic AI economy

ASI-1 Mini is set to connect with AgentVerse, Fetch.ai’s agent marketplace, providing users with the tools to build and deploy autonomous agents capable of real-world task execution via simple language commands. For example, users could automate trip planning, restaurant reservations, or financial transactions through “micro-agents” hosted on the platform.

This ecosystem enables open-source AI customisation and monetisation, creating an “agentic economy” where developers and businesses thrive symbiotically. Developers can monetise micro-agents, while users gain seamless access to tailored AI solutions.  

As its agentic ecosystem matures, ASI-1 Mini aims to evolve into a multi-modal powerhouse capable of processing structured text, images, and complex datasets with context-aware decision-making.  

See also: Endor Labs: AI transparency vs ‘open-washing’

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.

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Designed around a new model called the Computer-Using Agent (CUA), Operator combines GPT-4o’s vision recognition with advanced reasoning capabilities—allowing it to function as a virtual “human-in-the-browser.” Yet, for all its innovation, industry experts see room for refinement.

Yiannis Antoniou, Head of AI, Data, and Analytics at specialist consultancy Lab49, shared his insights on Operator’s significance and positioning in the competitive landscape of agent AI systems.

Agentic AI through a familiar interface

“OpenAI’s announcement of Operator, its latest foray into the agentic AI wars, is both fascinating and incomplete,” said Antoniou, who has over two decades of experience designing AI systems for financial services firms.

“Clearly influenced by Anthropic Claude’s Computer Use system, introduced back in October, Operator streamlines the experience by removing the need for complex infrastructure and focusing on a familiar interface: the browser.”

By designing Operator to operate within an environment users already understand, the web browser, OpenAI sidesteps the need for bespoke APIs or integrations.

“By leveraging the world’s most popular interface, OpenAI enhances the user experience and captures immediate interest from the general public. This browser-centric approach creates significant potential for widespread adoption, something Anthropic – despite its early-mover advantage – has struggled to achieve.”

Unlike some competing systems that may feel technical or niche in their application, Operator’s browser-focused framework lowers the barrier to entry and is a step forward in OpenAI’s efforts to democratise AI.

Unique take on usability and security

One of the hallmarks of Operator is its emphasis on adaptability and security, implemented through human-in-the-loop protocols. Antoniou acknowledged these thoughtful usability features but noted that more work is needed.

“Architecturally, Operator’s browser integration closely mirrors Claude’s system. Both involve taking screenshots of the user’s browser and sending them for analysis, as well as controlling the screen via virtual keystrokes and mouse movements. However, Operator introduces thoughtful usability touches. 

“Features like custom instructions for specific websites add a layer of personalisation, and the emphasis on human-in-the-loop safeguards against unauthorised actions – such as purchases, sending emails, or applying for jobs – demonstrate OpenAI’s awareness of potential security risks posed by malicious websites, but more work is clearly needed to make this system widely safe across a variety of scenarios.”

OpenAI has implemented a multi-layered safety framework for Operator, including takeover mode for secure inputs, user confirmations prior to significant actions, and monitoring systems to detect adversarial behavior. Furthermore, users can delete browsing data and manage privacy settings directly within the tool.

However, Antoniou emphasised that these measures are still evolving—particularly as Operator encounters complex or sensitive tasks. 

OpenAI Operator further democratises AI

Antoniou also sees the release of Operator as a pivotal moment for the consumer AI landscape, albeit one that is still in its early stages. 

“Overall, this is an excellent first attempt at building an agentic system for everyday users, designed around how they naturally interact with technology. As the system develops – with added capabilities and more robust security controls – this limited rollout, priced at $200/month, will serve as a testing ground. 

“Once matured and extended to lower subscription tiers and the free version, Operator has the potential to usher in the era of consumer-facing agents, further democratising AI and embedding it into daily life.”

Designed initially for Pro users at a premium price point, Operator provides OpenAI with an opportunity to learn from early adopters and refine its capabilities.

Antoniou noted that while $200/month might not yet justify the system’s value for most users, investment in making Operator more powerful and accessible could lead to significant competitive advantages for OpenAI in the long run.

“Is it worth $200/month? Perhaps not yet. But as the system evolves, OpenAI’s moat will grow, making it harder for competitors to catch up. Now, the challenge shifts back to Anthropic and Google – both of whom have demonstrated similar capabilities in niche or engineering-focused products – to respond and stay in the game,” he concludes.

As OpenAI continues to fine-tune Operator, the potential to revolutionise how people interact with technology becomes apparent. From collaborations with companies like Instacart, DoorDash, and Uber to use cases in the public sector, Operator aims to balance innovation with trust and safety.

While early limitations and pricing may deter widespread adoption for now, these hurdles might only be temporary as OpenAI commits to enhancing usability and accessibility over time.

See also: OpenAI argues against ChatGPT data deletion in Indian court

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 Yiannis Antoniou, Lab49: OpenAI Operator kickstarts era of browser AI agents appeared first on AI News.

“AI has been advancing at an incredible pace,” Huang said. “It started with perception AI — understanding images, words, and sounds. Then generative AI — creating text, images, and sound. Now, we’re entering the era of ‘physical AI,’ AI that can perceive, reason, plan, and act.”

With NVIDIA’s platforms and GPUs at the core, Huang explained how the company continues to fuel breakthroughs across multiple industries while unveiling innovations such as the Cosmos platform, next-gen GeForce RTX 50 Series GPUs, and compact AI supercomputer Project DIGITS. 

RTX 50 series: “The GPU is a beast”

One of the most significant announcements during CES 2025 was the introduction of the GeForce RTX 50 Series, powered by NVIDIA Blackwell architecture. Huang debuted the flagship RTX 5090 GPU, boasting 92 billion transistors and achieving an impressive 3,352 trillion AI operations per second (TOPS).

“GeForce enabled AI to reach the masses, and now AI is coming home to GeForce,” said Huang.

Holding the blacked-out GPU, Huang called it “a beast,” highlighting its advanced features, including dual cooling fans and its ability to leverage AI for revolutionary real-time graphics.

Set for a staggered release in early 2025, the RTX 50 Series includes the flagship RTX 5090 and RTX 5080 (available 30 January), followed by the RTX 5070 Ti and RTX 5070 (February). Laptop GPUs join the lineup in March.

In addition, NVIDIA introduced DLSS 4 – featuring ‘Multi-Frame Generation’ technology – which boosts gaming performance up to eightfold by generating three additional frames for every frame rendered.

Other advancements, such as RTX Neural Shaders and RTX Mega Geometry, promise heightened realism in video games, including precise face and hair rendering using generative AI.

Cosmos: Ushering in physical AI

NVIDIA took another step forward with the Cosmos platform at CES 2025, which Huang described as a “game-changer” for robotics, industrial AI, and AVs. Much like the impact of large language models on generative AI, Cosmos represents a new frontier for AI applications in robotics and autonomous systems.

“The ChatGPT moment for general robotics is just around the corner,” Huang declared.

Cosmos integrates generative models, tokenisers, and video processing frameworks to enable robots and vehicles to simulate potential outcomes and predict optimal actions. By ingesting text, image, and video prompts, Cosmos can generate “virtual world states,” tailored for complex robotics and AV use cases involving real-world environments and lighting.

Top robotics and automotive leaders – including XPENG, Hyundai Motor Group, and Uber – are among the first to adopt Cosmos, which is available on GitHub via an open licence.

Pras Velagapudi, CTO at Agility, comments: “Data scarcity and variability are key challenges to successful learning in robot environments. Cosmos’ text-, image- and video-to-world capabilities allow us to generate and augment photorealistic scenarios for a variety of tasks that we can use to train models without needing as much expensive, real-world data capture.”

Empowering developers with AI models

NVIDIA also unveiled new AI foundation models for RTX PCs, which aim to supercharge content creation, productivity, and enterprise applications. These models, presented as NVIDIA NIM (Neural Interaction Model) microservices, are designed to integrate with the RTX 50 Series hardware.

Huang emphasised the accessibility of these tools: “These AI models run in every single cloud because NVIDIA GPUs are now available in every cloud.”

NVIDIA is doubling down on its push to equip developers with advanced tools for building AI-driven solutions. The company introduced AI Blueprints: pre-configured tools for crafting agents tailored to specific enterprise needs, such as content generation, fraud detection, and video management.

“They are completely open source, so you could take it and modify the blueprints,” explains Huang.

Huang also announced the release of Llama Nemotron, designed for developers to build and deploy powerful AI agents.

Ahmad Al-Dahle, VP and Head of GenAI at Meta, said: “Agentic AI is the next frontier of AI development, and delivering on this opportunity requires full-stack optimisation across a system of LLMs to deliver efficient, accurate AI agents.

“Through our collaboration with NVIDIA and our shared commitment to open models, the NVIDIA Llama Nemotron family built on Llama can help enterprises quickly create their own custom AI agents.”

Philipp Herzig, Chief AI Officer at SAP, added: “AI agents that collaborate to solve complex tasks across multiple lines of the business will unlock a whole new level of enterprise productivity beyond today’s generative AI scenarios.

“Through SAP’s Joule, hundreds of millions of enterprise users will interact with these agents to accomplish their goals faster than ever before. NVIDIA’s new open Llama Nemotron model family will foster the development of multiple specialised AI agents to transform business processes.”

Safer and smarter autonomous vehicles

NVIDIA’s announcements extended to the automotive industry, where its DRIVE Hyperion AV platform is fostering a safer and smarter future for AVs. Built on the new NVIDIA AGX Thor system-on-a-chip (SoC), the platform allows vehicles to achieve next-level functional safety and autonomous capabilities using generative AI models.

“The autonomous vehicle revolution is here,” Huang said. “Building autonomous vehicles, like all robots, requires three computers: NVIDIA DGX to train AI models, Omniverse to test-drive and generate synthetic data, and DRIVE AGX, a supercomputer in the car.”

Huang explained that synthetic data is critical for AV development, as it dramatically enhances real-world datasets. NVIDIA’s AI data factories – powered by Omniverse and Cosmos platforms – generate synthetic driving scenarios, increasing the effectiveness of training data exponentially.

Toyota, the world’s largest automaker, is committed to using NVIDIA DRIVE AGX Orin and the safety-certified NVIDIA DriveOS to develop its next-generation vehicles. Heavyweights such as JLR, Mercedes-Benz, and Volvo Cars have also adopted DRIVE Hyperion.

Project DIGITS: Compact AI supercomputer

Huang concluded his NVIDIA keynote at CES 2025 with a final “one more thing” announcement: Project DIGITS, NVIDIA’s smallest yet most powerful AI supercomputer, powered by the cutting-edge GB10 Grace Blackwell Superchip.

“This is NVIDIA’s latest AI supercomputer,” Huang declared, revealing its compact size, claiming it’s portable enough to “practically fit in a pocket.”

Project DIGITS enables developers and engineers to train and deploy AI models directly from their desks, providing the full power of NVIDIA’s AI stack in a compact form.

Set to launch in May, Project DIGITS represents NVIDIA’s push to make AI supercomputing accessible to individuals as well as organisations.

Vision for tomorrow

Reflecting on NVIDIA’s journey since inventing the programmable GPU in 1999, Huang described the past 12 years of AI-driven change as transformative.

“Every single layer of the technology stack has been fundamentally transformed,” he said.

With advancements spanning gaming, AI-driven agents, robotics, and autonomous vehicles, Huang foresees an exciting future.

“All of the enabling technologies I’ve talked about today will lead to surprising breakthroughs in general robotics and AI over the coming years,” Huang concludes.

(Image Credit: NVIDIA)

See also: Sam Altman, OpenAI: ‘Lucky and humbling’ to work towards superintelligence

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

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

The post NVIDIA advances AI frontiers with CES 2025 announcements appeared first on AI News.

The report places the UK 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 UK AI Readiness Index positively highlights that the UK has both the vision and infrastructure to be a powerhouse globally in AI, and lead the current third wave of agentic AI.”

UK AI adoption sets the stage for agentic revolution

The Index details how both the public and private sectors in the UK have embraced AI’s transformative potential. With a readiness score of 65.5, surpassing the G7 average of 61.2, the UK 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 UK industries scoring 52, well above the G7 average of 47.8. SMEs in the UK 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 execute meaningful tasks and decisions.

Investments fuelling AI growth

Salesforce is committing $4 billion to the UK’s AI ecosystem over the next five years. Since establishing its UK 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 lies 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 UK 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 UK 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 UK’s leadership in the global AI economy.”

Agentic AI boosting UK 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 European 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 UK’s AI readiness is testament to the synergy between government, business, and academia. To maintain its leadership, the UK 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: UK set to lead agentic AI revolution appeared first on AI News.