News from the AI & ML world
DeeperML - #enterprises
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Michal Langmajer@Fello AI
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OpenAI has launched its latest AI model, GPT-4.5, described as the company's most advanced language model to date. This new model features substantial enhancements over its predecessors, particularly in advanced reasoning, problem-solving, and contextual understanding. GPT-4.5 is designed to offer a more natural and engaging conversational experience, with improvements including superior capabilities in handling complex reasoning tasks, enhanced creativity, and the ability to manage intricate logic problems while maintaining nuanced conversations with improved contextual recall.
However, the launch of GPT-4.5 is facing challenges due to a shortage of GPUs, according to OpenAI CEO Sam Altman. This limitation is restricting access to the priciest tiers of ChatGPT Pro subscribers and developers initially. Altman stated that OpenAI has "run out of GPUs" due to growing demand, leading to a staggered rollout. The company plans to add tens of thousands of GPUs next week and expand access to Plus, Team, Enterprise, and Edu users in the following weeks. Recommended read:
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Nitika Sharma@Analytics Vidhya
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China's Manus AI, developed by Monica, is generating buzz as an invite-only multi-agent AI product. This AI agent is designed to autonomously tackle complex, real-world tasks by operating as a multi-agent system. It utilizes a planner optimized for strategic reasoning, and an executor driven by Claude 3.5 Sonnet, incorporating code execution, web browsing, and multi-file code management.
The AI agent has sparked considerable global attention, igniting discussions about its technological and ethical implications, as well as its potential impact on the AI landscape. Manus reportedly outperformed OpenAI's o3-powered Deep Research agent on benchmarks, as showcased on the Manus website, leading some to believe it is among the most effective autonomous agents currently available. However, there is some skepticism due to it appearing to be a Claude wrapper with a jailbreak and tools optimized for the GAIA benchmark. Recommended read:
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Matt Marshall@AI News | VentureBeat
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OpenAI has unveiled a new suite of APIs and tools aimed at simplifying the development of AI agents for enterprises. The firm is releasing building blocks designed to assist developers and businesses in creating practical and dependable agents, defined as systems capable of independently accomplishing tasks. These tools are designed to address challenges faced by software developers in building production-ready applications, with the goal of automating and streamlining operations.
The newly launched platform includes the Responses API, which is a superset of the chat completion API, along with built-in tools, the OpenAI Agents SDK, and enhanced Observability features. Nikunj Handa and Romain Huet from OpenAI previewed new Agents APIs such as Responses, Web Search, and Computer Use, and also introduced a new Agents SDK. The Responses API is positioned as a more flexible foundation for developers working with OpenAI models, offering functionalities like Web Search, Computer Use, and File Search. Recommended read:
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@tomshardware.com
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Nvidia has unveiled its next-generation data center GPU, the Blackwell Ultra, at its GTC event in San Jose. Expanding on the Blackwell architecture, the Blackwell Ultra GPU will be integrated into the DGX GB300 and DGX B300 systems. The DGX GB300 system, designed with a rack-scale, liquid-cooled architecture, is powered by the Grace Blackwell Ultra Superchip, combining 36 NVIDIA Grace CPUs and 72 NVIDIA Blackwell Ultra GPUs. Nvidia officially revealed its Blackwell Ultra B300 data center GPU, which packs up to 288GB of HBM3e memory and offers 1.5X the compute potential of the existing B200 solution.
The Blackwell Ultra GPU promises a 70x speedup in AI inference and reasoning compared to the previous Hopper-based generation. This improvement is achieved through hardware and networking advancements in the DGX GB300 system. Blackwell Ultra is designed to meet the demand for test-time scaling inference with a 1.5X increase in the FP4 compute. Nvidia's CEO, Jensen Huang, suggests that the new Blackwell chips render the previous generation obsolete, emphasizing the significant leap forward in AI infrastructure. Recommended read:
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Maximilian Schreiner@THE DECODER
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OpenAI has announced it will adopt Anthropic's Model Context Protocol (MCP) across its product line. This surprising move involves integrating MCP support into the Agents SDK immediately, followed by the ChatGPT desktop app and Responses API. MCP is an open standard introduced last November by Anthropic, designed to enable developers to build secure, two-way connections between their data sources and AI-powered tools. This collaboration between rivals marks a significant shift in the AI landscape, as competitors typically develop proprietary systems.
MCP aims to standardize how AI assistants access, query, and interact with business tools and repositories in real-time, overcoming the limitation of AI being isolated from systems where work happens. It allows AI models like ChatGPT to connect directly to the systems where data lives, eliminating the need for custom integrations for each data source. Other companies, including Block, Apollo, Replit, Codeium, and Sourcegraph, have already added MCP support, and Anthropic's Chief Product Officer Mike Krieger welcomes OpenAI's adoption, highlighting MCP as a thriving open standard with growing integrations. Recommended read:
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@techstrong.ai
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Advancements in AI agents are rapidly transforming how businesses operate and strategize, shifting AI from a mere tool to a foundational element of enterprise operations. AI agents are autonomous systems that combine language and multimodal understanding with the decision-making of foundation models. These systems can now interpret complex inputs, reason through multifaceted scenarios, and autonomously execute tasks. This progression is enabling businesses to realize significant improvements in operational efficiency, customer engagement, and data-driven decision-making, which is driving substantial market investments.
These advancements, however, present challenges, notably in transitioning AI agents from controlled testing environments to real-world applications. Issues such as the opacity of AI models, the limitations of conventional evaluation frameworks, and the difficulties in integrating with diverse APIs must be addressed. Businesses that can successfully navigate these challenges stand to gain a competitive advantage by fully embedding and optimizing AI to drive sustained competitive advantage. One such agent, Manus, developed in China, is gaining attention for its autonomous capabilities. Manus AI is designed as a multi-agent system that combines several AI models to handle tasks independently, like generating reports and managing social media accounts. Build.inc is also pushing the boundaries of agentic systems to automate labor intensive workflows. They created a network of specialized agents performing specific, smaller tasks for data center development which previously took humans four weeks, can now be accomplished in 75 minutes. Recommended read:
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Matt Marshall@AI News | VentureBeat
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OpenAI has unveiled its Agents SDK, along with a revamped Responses API, built-in tools, and an open-source SDK. These tools simplify the development of AI agents for enterprise use by consolidating the complex ecosystem into a unified framework. This platform allows developers to create AI agents capable of performing tasks autonomously. The Responses API integrates with OpenAI’s existing Chat Completions API and Assistants API to assist in agent construction, while the Agents SDK helps users orchestrate both single and multi-agent workflows.
This initiative addresses AI agent reliability issues, recognizing that external developers can offer innovative solutions. The SDK reduces the complexity of AI agent development, enabling projects that previously required multiple frameworks and specialized databases to be achieved through a single, standardized platform. This marks a critical turning point as OpenAI recognizes the value of external contributions to the advancement of AI agent technology. With web search, file search, and computer use integrated, the Responses API enables agents to interact with real-world data and internal proprietary business contexts more effectively. Recommended read:
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Salesforce Newsroom@Salesforce
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Salesforce has launched Agentforce 2dx, embedding proactive agentic AI into workflows and introducing AgentExchange, a marketplace for AI agents. This move positions Salesforce at the center of the projected $6 trillion "digital labor" market. Agentforce 2dx empowers AI agents to proactively engage with users, triggered by data changes, operate autonomously in business processes, and interact through various interfaces, while AgentExchange provides a trusted platform for developing and monetizing AI components.
AgentExchange launches with over 200 partners, including Google Cloud, DocuSign, Box, and Workday, offering pre-packaged agent solutions that streamline implementation. For example, Google Cloud leverages Google Search and Vertex AI to provide real-time data insights, while Workday streamlines employee self-service workflows. Salesforce's push into AI agents aims to transform business operations by automating administrative tasks and boosting productivity. Recommended read:
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The Daily@The Daily Upside
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ServiceNow has announced its acquisition of Moveworks, an AI startup, for $2.85 billion in a cash-and-stock transaction expected to close in the latter half of 2025. This acquisition, the largest in ServiceNow's history, is aimed at boosting the company's agentic AI capabilities by integrating Moveworks' AI-driven platform into the ServiceNow Platform. The combination will create a unified, end-to-end search and self-service experience for employees across various workflows.
The move will see more than 500 Moveworks employees join ServiceNow, significantly expanding its AI team. Moveworks specializes in front-end agentic AI tools designed to enhance workplace efficiency through conversational AI assistants that automate employee support. ServiceNow plans to leverage Moveworks' technology to accelerate enterprise-wide AI adoption and innovation, with no layoffs anticipated as a result of the acquisition. Recommended read:
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Ryan Daws@AI News
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ServiceNow has announced the release of its Yokohama platform, marking a significant advancement in the integration of AI agents within enterprise workflows. The platform introduces preconfigured AI agents designed to enhance productivity across various sectors, offering seamless integration and immediate benefits. New features facilitate the building, onboarding, and management of AI agents, aiming to broaden the adoption of AI-driven solutions throughout organizations. This release is part of ServiceNow's strategy to double down on AI investments, particularly in agentic AI capabilities, which are designed to automate tasks and improve workflows across CRM, HR, IT, and other departments.
The Yokohama platform features ServiceNow Studio, a centralized environment for no-code, low-code, and pro-code developers to create and manage agentic applications. This tool aims to streamline enterprise automation and reduce adoption barriers. New AI agents have been added, including a SecOps agent for security operations, autonomous change management agents, and a network test and repair agent. These agents aim to automate repetitive tasks, improve network performance, and free up human employees to focus on more strategic work. ServiceNow also acquired Moveworks to expand its AI capabilities into enterprise search, improving information access for employees. Recommended read:
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staff@insideAI News
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IBM has announced the release of the Granite 3.2 family of large language models (LLMs), designed to provide efficient AI solutions for enterprises. The new Granite 3.2 models include a vision language model (VLM) that excels in document understanding tasks, rivaling the performance of significantly larger models like Llama 3.211B and Pixtral12B on benchmarks such as DocVQA, ChartQA, AI2D, and OCRBench. IBM also employed its open-source Docling toolkit to process millions of PDFs and generate question-answer pairs, enhancing the VLM's ability to handle document-heavy workflows.
IBM is incorporating conditional reasoning into its Granite 3.2 LLMs, allowing for the optimization of efficiency by enabling users to switch reasoning capabilities on or off. This approach provides flexibility for users to manage intensive processing needs. Additionally, IBM is releasing a new vision model optimized for document processing, aiding in the digitization of legacy documents, and time series forecasting models that apply transformer technology to predict future values from time-based data. All Granite 3.2 models are available under the Apache 2.0 license on Hugging Face, with select models also available on IBM watsonx.ai, Ollama, Replicate, and LM Studio, and expected soon in RHEL AI 1.5. Recommended read:
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staff@insideAI News
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Penguin Solutions, Inc. (Nasdaq: PENG) announced the expansion of its ICE ClusterWare software platform on March 4, 2025. This update includes multi-tenancy support, streamlined workflows, and enhanced controls, aiming to help enterprises construct optimized AI ecosystems, known as Intelligent Compute Environments. The ICE ClusterWare platform, formerly Scyld ClusterWare, is designed to scale AI infrastructure seamlessly, addressing the increasing demands of AI computing.
Penguin Solutions also introduced the ICE ClusterWare AIM service, an advanced optimization service to maximize performance, availability, and operational efficiency of AI infrastructure through predictive automation. According to Trey Layton, vice president of software and product management at Penguin Solutions, this offering enables enterprises to build intelligent compute environments that optimize efficiency, scalability, and cost-effectiveness, ensuring AI workloads run at peak performance while minimizing operational complexity. The updated ICE ClusterWare software platform now includes multi-tenancy foundational support, enhanced orchestration controls, and streamlined workflows. Recommended read:
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Eira May@Stack Overflow Blog
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AI agents are rapidly transforming business operations across various sectors, promising to automate tasks, enhance efficiency, and streamline workflows. Companies are integrating these intelligent systems to modernize customer experiences and unlock enterprise value. To fully leverage the potential of AI agents, businesses need to ensure they have real-time and seamless connections to company databases, internal communication tools, and documents. This integration is crucial for the agents to provide contextually aware and valuable assistance.
Saltbox Mgmt, a Salesforce consulting company, has successfully implemented Agentforce to modernize the buying experience, resulting in improved efficiency and enhanced personalization. Moreover, the integration of AI in real estate technology presents opportunities for strategic transformation, boosting efficiency, value, and decision-making capabilities. However, AI assistants are only as effective as the knowledge base they are connected to, highlighting the importance of comprehensive and up-to-date internal data. Recommended read:
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Sean Michael@AI News | VentureBeat
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AI News | VentureBeat
, www.computerworld.com
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Gartner, an analyst firm, released a report forecasting that global generative AI spending will reach $644 billion in 2025. This figure represents a 76.4% year-over-year increase from 2024. Despite high failure rates among early generative AI projects, organizations are still expected to invest heavily, with the lion's share of spending going towards services. GenAI services are projected to grow by 162% this year, following a 177% increase last year. According to Gartner Analyst John-David Lovelock, the shift from software to generative AI is becoming a "tidal wave of money."
The surge in spending is primarily driven by vendor investments in the technology. Hyperscalers are making massive capital expenditures on GPU infrastructure, and software vendors are rushing to deploy generative AI tools. Enterprises, however, are pulling back on in-house AI projects and increasingly opting for off-the-shelf solutions. "CIOs are no longer building generative AI tools, they’re being sold technology," Lovelock stated, emphasizing that vendors are offering solutions that meet enterprise needs. Recommended read:
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Lindsey Wilkinson@CIO Dive - Latest News
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AI News | VentureBeat
, CIO Dive - Latest News
Enterprises are rapidly adopting AI agents, driven by the expectation of high returns on investment. A recent PagerDuty report, surveying 1,000 IT and business executives, revealed that over 60% anticipate a return of over 100% on their agentic AI investments, with an average expected return of around 171%. Optimism is even higher among U.S.-based companies, where decision-makers project returns closer to 192%. This enthusiasm is fueling a faster adoption rate for AI agents compared to generative AI, with over 90% of those surveyed believing agents will be implemented more quickly.
While excitement surrounds agentic AI, enterprises are also mindful of lessons learned from initial generative AI deployments. Challenges with realizing ROI due to rushing implementations, overspending, and lacking proper infrastructure have prompted a more cautious and strategic approach to agentic AI. According to a Gartner report, global generative AI spending is projected to reach $644 billion in 2025, with hardware accounting for a significant portion of this investment. Despite the potential benefits, decision-makers express concerns about data security, privacy, and integration with existing systems, highlighting the importance of establishing robust security measures and governance frameworks for agentic AI deployments. Recommended read:
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Sam Pearcy@hiddenlayer.com
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BigDATAwire
, www.computerworld.com
AI agentic systems are rapidly transforming enterprise workflows, offering the promise of automating complex tasks and boosting productivity. Gartner Research reports that 64% of respondents in a recent poll plan to pursue agentic AI initiatives within the next year, signaling widespread adoption. These agents, unlike traditional AI, possess agency, enabling them to autonomously pursue goals, make decisions, and adapt based on feedback, expanding the capabilities of large language models (LLMs) with memory, tool access, and task management. Model Context Protocol (MCP) is emerging as a potential standard for connecting AI agents with data and tools, aiming to streamline the integration process with a lightweight architecture.
Challenges and risks accompany the deployment of AI agents, including ensuring their security and trustworthiness. Security vulnerabilities that allow AI agents to be manipulated or weaponized are already emerging, which is why developers are focusing on transparency, access controls, and auditing agent behavior to detect anomalies. The agents can be scammed because they are independent-acting and can use APIs or be embedded with standard apps and automate all kinds of business processes. Ethical considerations and the implementation of responsible AI practices are also vital aspects that organizations must address during the integration of these new AI systems. Recommended read:
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Alex Woodie@AIwire
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AIwire
, BigDATAwire
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VAST Data has unveiled enhancements to its data platform, positioning it as a unified solution for structured and unstructured data, scaling linearly to hyperscale. According to VAST, this makes their platform the first in the market capable of such unification. The enhanced platform aims to redefine enterprise AI and analytics by integrating real-time vector search, fine-grained security, and event-driven processing. This integration creates a high-performance data ecosystem designed to power the VAST InsightEngine, which transforms raw data into AI-ready insights via intelligent automation, enabling the development of advanced AI applications.
These new capabilities address the challenges organizations face in scaling enterprise AI deployments. The VAST Data Platform now includes vector search and retrieval, enabling trillion-vector scale searches with constant time access. It also includes serverless triggers and functions for real-time workflows, and fine-grained access control for enterprise-grade security. These additions are designed to help enterprises unlock their data for agentic querying and chatbot interactions, streamlining data access without compromising security. Recommended read:
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mpesce@Windows Copilot News
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AI News | VentureBeat
, Windows Copilot News
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Google is advancing its AI capabilities on multiple fronts, emphasizing both security and performance. The company is integrating Google Cloud Champion Innovators into the Google Developer Experts (GDE) program, creating a unified community of over 1,400 members. This consolidation aims to enhance collaboration, streamline resources, and amplify the impact of passionate experts, providing a stronger voice for developers within Google and the broader industry.
Google is also pushing forward with its Gemini AI model, with the plan for Gemini 2.0 to be implemented across Google's products. Researchers from Google and UC Berkeley have found that a simple test-time scaling approach, based on sampling-based search, can significantly boost the reasoning abilities of large language models (LLMs). This method uses random sampling and self-verification to improve model performance, potentially outperforming more complex and specialized training methods. Recommended read:
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@www.marktechpost.com
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Windows Copilot News
, www.marktechpost.com
A new wave of AI-powered browser-use agents is emerging, with companies like OpenAI, Convergence, Google, Anthropic, and Microsoft developing solutions. These agents aim to transform how enterprises interact with the web by autonomously navigating websites, retrieving information, and completing tasks. For example, OpenAI's Operator focuses on consumer-friendly web automation, while Convergence's Proxy offers free limited use and a paid unlimited access option.
However, early testing reveals significant gaps between promise and performance. While consumer-focused applications like ordering pizza or booking game tickets have garnered attention, the primary developer and enterprise use cases are still being determined. Experts suggest that these agents may find their niche in time-consuming web-based tasks like price comparisons and hotel booking or be used in combination with other tools like Deep Research, where companies can then do even more sophisticated research plus execution of tasks around the web. AI agents are autonomous software entities that perceive their surroundings, process data, and take action to achieve specified goals. They differ from traditional software by employing machine learning and natural language processing for decision-making, allowing them to evolve over time. Key characteristics include autonomy, adaptability, interactivity, and context awareness. The evolution of AI agents represents a shift from rule-based systems to systems that learn and adapt. Recommended read:
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