In its recent UK copyright case, Getty Images asserts that the lawsuit does not pose a threat to artificial intelligence (AI) development. The case centers on Getty’s claims against various AI training programs that utilize its copyrighted images without permission, which Getty argues infringes its intellectual property rights. Despite concerns from the tech industry about potential implications for AI innovation, Getty maintains that its actions are aimed at safeguarding content creators’ rights while still allowing for legitimate usage of images. The company emphasizes that the case is focused on commercial use of copyrighted material rather than stifling technological advancement. As the debate continues, Getty encourages a balance between protecting creative rights and fostering innovation in AI. The outcome of this case could have significant ramifications within both the copyright and technological sectors, but Getty insists that its primary goal is the enforcement of copyright law.
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Getty Asserts Landmark UK Copyright Case Won’t Impact AI Development – Reuters
Navigating the Tech Landscape: Defining AI Agents, Apple Reveals LLM Reasoning Gaps, and a CEO Challenges Return-to-Office Policies
Ian Thomas highlights the need for clear definitions of enterprise AI agents amid vendor exaggeration and confusion. He critiques a Carnegie Mellon experiment where autonomous agents failed to work cohesively, leading to chaotic outcomes. Thomas argues for a simplified framework based on two key axes: agency (initiative) and coordination (collaboration), with a proposed taxonomy dividing agents into four categories: Instruction, Orchestration, Autonomy, and Choreography. Each category carries distinct risk levels, emphasizing the importance of monitoring and designing processes to mitigate these risks effectively. He notes that many implementations still depend heavily on human oversight. Additionally, the article covers other significant developments, including Moderna’s integration of HR and IT with AI and case studies on AI chatbots improving customer engagement. Overall, this discussion emphasizes the necessity for businesses to approach AI agents thoughtfully, balancing risk management with effective implementation to enhance productivity.
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VeriSilicon’s Ultra-Low Energy NPU Delivers 40+ TOPS for On-Device LLM Inference in Mobile Applications
VeriSilicon has unveiled its ultra-low energy Neural Network Processing Unit (NPU) IP, designed for on-device inference of large language models (LLMs) with an impressive performance exceeding 40 TOPS. This energy-efficient architecture addresses the growing demand for generative AI capabilities on mobile platforms like AI phones and PCs while meeting stringent energy efficiency standards. The NPU supports mixed-precision computation, advanced sparsity optimization, and parallel processing, enhancing memory management and reducing latency for responsive AI applications. It is compatible with a range of AI algorithms and models, including Stable Diffusion and LLaMA-7B, and integrates seamlessly with other VeriSilicon processing IPs for comprehensive AI solutions. Furthermore, the NPU supports popular AI frameworks like TensorFlow Lite, ONNX, and PyTorch, facilitating ease of deployment. VeriSilicon’s commitment to ultra-low energy NPU development positions it as a pivotal player in the evolution of mobile devices into personal AI servers amid rapidly advancing AI technologies.
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AI Adoption Among Small Businesses Drops to a Low 28%
A recent survey by NEXT reveals a decline in artificial intelligence (AI) tool adoption among small businesses, dropping from 42% in 2024 to just 28% in 2025. Small business owners express optimism about AI’s efficiency but face barriers such as high costs and complexity, leading many to reconsider its implementation. While approximately 19% of respondents might increase AI usage, 58% have no plans to adopt it. Those still using AI primarily leverage it for marketing, customer service, product recommendations, and financial management, finding value in tools that streamline operations, like chatbots and accounting software. However, skepticism persists, with only a minority believing AI will enhance revenue or operational efficiency. Experts stress the importance of using AI as a supportive tool rather than a replacement for human expertise, particularly in tasks requiring context and nuance. Key obstacles to adoption include implementation costs, lack of understanding, and challenges in data management.
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Google Gemini’s Latest Tool Enhances Its Competitive Edge Against ChatGPT’s Assistant Features
Google is enhancing its Gemini AI assistant with a new feature called Scheduled Actions, aimed at increasing user productivity through task automation and personalized updates. Announced at Google I/O, this feature allows users to set specific tasks for Gemini to perform at designated times, such as sending daily news summaries or updating grocery lists. Unlike OpenAI’s ChatGPT, Gemini’s deep integration with Google services like Gmail and Calendar enables it to streamline tasks and provide contextually relevant updates. Currently, Scheduled Actions are available only to select users, including those on Google’s AI Pro or Ultra subscriptions. Google has not yet revealed how many tasks can be scheduled. Alongside this feature, Google is refining the Gemini experience with the upcoming Gemini 2.5 Pro update, which promises improved performance in coding and reasoning. Interface changes, such as a new swipe gesture, enhance user interaction, positioning Gemini as a proactive productivity tool.
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Unlocking Unicorn Ideas: Insights from Superblocks CEO on Harnessing AI System Prompts
Brad Menezes, CEO of Superblocks, sees potential billion-dollar startup ideas within the system prompts utilized by AI startups to guide foundational models from companies like OpenAI. These detailed prompts, often over 5,000-6,000 words, instruct models to generate domain-specific applications. Despite being accessible through various AI tools, most prompts remain private. In announcing an enterprise coding AI agent named Clark, Superblocks shared 19 system prompts from popular coding AI products. Menezes emphasizes that the system prompt constitutes only 20% of the success formula; the remaining 80% involves “prompt enrichment,” which includes context and user instructions. He identifies three components to study: role prompting for consistency, contextual prompting for clarity, and tool use for operational tasks. By analyzing these prompts, Superblocks aims to empower non-programmers to create applications while already servicing notable clients like Instacart and PayPal. Internally, the team uses their product exclusively for building tools.
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Build a Robust Generative AI Framework on AWS
Generative AI applications, while appearing straightforward, involve complex workflows integrating foundation models (FMs), APIs, and domain-specific data, necessitating robust safety controls and operational elements like CI/CD. Organizations often face fragmentation from siloed generative AI initiatives, leading to inefficiencies and inconsistent governance. To counter this, a centralized, unified approach is increasingly adopted, allowing departments to utilize foundational services efficiently, enhancing scalability and innovation while reducing costs. A generative AI foundation comprises crucial elements such as model and tool hubs, gateways for secure APIs, orchestration of workflows, model customization, data management, and observability tools. Key practices like GenAIOps focus on managing generative AI operations, emphasizing model governance and operationalization of training processes. Establishing a generative AI foundation enables organizations to navigate unique challenges effectively, paving the way for successful AI adoption across various scenarios. This maturity model allows assessment of development progression, ultimately facilitating a well-structured generative AI landscape within enterprises.
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The Impact of Meta’s AI-Driven Ad Tools on Creative Agencies
Meta’s goal to fully automate ads with AI by 2026 poses challenges for creative agencies, making client retention and new business acquisition more difficult. The plan allows brands to create and target ads with AI, reflecting broader industry trends among platforms like Google and TikTok that are embracing AI-driven advertising. While executives recognize the need for adaptation, they emphasize that AI still requires human oversight, as it may not replicate the strategic depth and storytelling that agencies provide. Many agencies are integrating their own AI tools to enhance offerings, positioning themselves as valuable intermediaries between brands and automated systems. However, concerns remain about the quality of AI-generated content and the risk of brand differentiation fading into “ad slop.” Ultimately, while AI will change the creative landscape, it does not eliminate the role of agencies; rather, it compels them to redefine their value and focus on high-level strategy and narrative creation.
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The Frightening Truth About AI: Companies Lack Full Understanding of Their Models – Axios
In a recent report by Axios, concerns are raised about companies’ lack of understanding regarding their artificial intelligence (AI) models. Many organizations leverage AI for various purposes but often do not grasp how these models function, leading to potential risks and misapplications. This lack of transparency can create issues, including biased outcomes and unintended consequences, as decision-making is increasingly delegated to AI systems. The report emphasizes that as AI technology evolves, it is crucial for companies to improve their understanding and oversight of these systems. Failure to do so may result in ethical dilemmas, compliance issues, and damage to public trust. In light of these challenges, experts advocate for greater accountability and regulatory frameworks to ensure that AI is used responsibly and effectively. Overall, the report highlights the need for organizations to bridge the knowledge gap in AI to mitigate risks and harness its benefits effectively.
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Former Apple Employees Discuss Why LLM Siri Still Isn’t Ready Ahead of WWDC 2025
Just before Apple’s WWDC 2025 event, insights from former employees reveal why the new AI-based Siri isn’t ready for release. Apple is attempting to enhance Siri using large language models (LLMs), but the process has proven challenging. Insiders report that integrating these newer AI features with the existing Siri system, which is operational on millions of devices, has led to bugs and slowed progress. Unlike competitors like OpenAI, which started fresh, Apple’s strategy of layering upgrades on an outdated system has not met expectations. Some leaders within Apple have characterized the approach as “climbing the hill,” indicating a desire for gradual improvement rather than a complete overhaul. As of now, it remains uncertain when the revamped Siri will launch; with speculation pointing to a possible release by 2027. Meanwhile, the upcoming WWDC is expected to highlight advancements across Apple’s various operating systems and AI tools.
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