News from the AI & ML world
DeeperML - #llms
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nftjedi@chatgptiseatingtheworld.com
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Apple researchers recently published a study titled "The Illusion of Thinking," suggesting that advanced language models (LLMs) struggle with true reasoning, relying instead on pattern matching. The study presented findings based on tasks like the Tower of Hanoi puzzle, where models purportedly failed when complexity increased, leading to the conclusion that these models possess limited problem-solving abilities. However, these conclusions are now under scrutiny, with critics arguing the experiments were not fairly designed.
Alex Lawsen of Open Philanthropy has published a counter-study challenging the foundations of Apple's claims. Lawsen argues that models like Claude, Gemini, and OpenAI's latest systems weren't failing due to cognitive limits, but rather because the evaluation methods didn't account for key technical constraints. One issue raised was that models were often cut off from providing full answers because they neared their maximum token limit, a built-in cap on output text, which Apple's evaluation counted as a reasoning failure rather than a practical limitation. Another point of contention involved the River Crossing test, where models faced unsolvable problem setups. When the models correctly identified the tasks as impossible and refused to attempt them, they were still marked wrong. Furthermore, the evaluation system strictly judged outputs against exhaustive solutions, failing to credit models for partial but correct answers, pattern recognition, or strategic shortcuts. To illustrate, Lawsen demonstrated that when models were instructed to write a program to solve the Hanoi puzzle, they delivered accurate, scalable solutions even with 15 disks, contradicting Apple's assertion of limitations. Recommended read:
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Emilia David@AI News | VentureBeat
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Google's Gemini 2.5 Pro is making waves in the AI landscape, with claims of superior coding performance compared to leading models like DeepSeek R1 and Grok 3 Beta. The updated Gemini 2.5 Pro, currently in preview, is touted to deliver faster and more creative responses, particularly in coding and reasoning tasks. Google highlighted improvements across key benchmarks such as AIDER Polyglot, GPQA, and HLE, noting a significant Elo score jump since the previous version. This newest iteration, referred to as Gemini 2.5 Pro Preview 06-05, builds upon the I/O edition released earlier in May, promising even better performance and enterprise-scale capabilities.
Google is also planning several enhancements to the Gemini platform. These include upgrades to Canvas, Gemini’s workspace for organizing and presenting ideas, adding the ability to auto-generate infographics, timelines, mindmaps, full presentations, and web pages. There are also plans to integrate Imagen 4, which enhances image generation capabilities, image-to-video functionality, and an Enterprise mode, which offers a dedicated toggle to separate professional and personal workflows. This Enterprise mode aims to provide business users with clearer boundaries and improved data governance within the platform. In addition to its coding prowess, Gemini 2.5 Pro boasts native audio capabilities, enabling developers to build richer and more interactive applications. Google emphasizes its proactive approach to safety and responsibility, embedding SynthID watermarking technology in all audio outputs to ensure transparency and identifiability of AI-generated audio. Developers can explore these native audio features through the Gemini API in Google AI Studio or Vertex AI, experimenting with audio dialog and controllable speech generation. Google DeepMind is also exploring ways for AI to take over mundane email chores, with CEO Demis Hassabis envisioning an AI assistant capable of sorting, organizing, and responding to emails in a user's own voice and style. Recommended read:
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Tulsee Doshi@The Official Google Blog
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Google has launched an upgraded preview of Gemini 2.5 Pro, touting it as their most intelligent model yet. Building upon the version revealed in May, this updated AI demonstrates significant improvements in coding capabilities. One striking example of its advanced functionality is its ability to generate intricate images, such as a "pretty solid pelican riding a bicycle."
The model's enhanced coding proficiency is further highlighted by its ethical safeguards. When prompted to run SnitchBench, a tool designed to test the ethical boundaries of AI models, Gemini 2.5 Pro notably "tipped off both the feds and the WSJ and NYTimes." This self-awareness and alert system underscore the advancements in AI safety protocols integrated into the new model. The rapid development and release of Gemini 2.5 Pro reflect Google's increasing confidence in its AI technology. The company emphasizes that this iteration offers substantial improvements over its predecessors, solidifying its position as a leading AI model. Developers and enthusiasts alike are encouraged to try the latest Gemini 2.5 Pro before its general release to experience its improved capabilities firsthand. Recommended read:
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@www.linkedin.com
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Nvidia's Blackwell GPUs have achieved top rankings in the latest MLPerf Training v5.0 benchmarks, demonstrating breakthrough performance across various AI workloads. The NVIDIA AI platform delivered the highest performance at scale on every benchmark, including the most challenging large language model (LLM) test, Llama 3.1 405B pretraining. Nvidia was the only vendor to submit results on all MLPerf Training v5.0 benchmarks, highlighting the versatility of the NVIDIA platform across a wide array of AI workloads, including LLMs, recommendation systems, multimodal LLMs, object detection, and graph neural networks.
The at-scale submissions used two AI supercomputers powered by the NVIDIA Blackwell platform: Tyche, built using NVIDIA GB200 NVL72 rack-scale systems, and Nyx, based on NVIDIA DGX B200 systems. Nvidia collaborated with CoreWeave and IBM to submit GB200 NVL72 results using a total of 2,496 Blackwell GPUs and 1,248 NVIDIA Grace CPUs. The GB200 NVL72 systems achieved 90% scaling efficiency up to 2,496 GPUs, improving time-to-convergence by up to 2.6x compared to Hopper-generation H100. The new MLPerf Training v5.0 benchmark suite introduces a pretraining benchmark based on the Llama 3.1 405B generative AI system, the largest model to be introduced in the training benchmark suite. On this benchmark, Blackwell delivered 2.2x greater performance compared with the previous-generation architecture at the same scale. Furthermore, on the Llama 2 70B LoRA fine-tuning benchmark, NVIDIA DGX B200 systems, powered by eight Blackwell GPUs, delivered 2.5x more performance compared with a submission using the same number of GPUs in the prior round. These performance gains highlight advancements in the Blackwell architecture and software stack, including high-density liquid-cooled racks, fifth-generation NVLink and NVLink Switch interconnect technologies, and NVIDIA Quantum-2 InfiniBand networking. Recommended read:
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@www.quantamagazine.org
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References:
Quanta Magazine
, www.trails.umd.edu
Researchers are making strides in AI reasoning and efficiency, tackling both complex problem-solving and the energy consumption of these systems. One promising area involves reversible computing, where programs can run backward as easily as forward, theoretically saving energy by avoiding data deletion. Michael Frank, a researcher interested in the physical limits of computation, discovered that reversible computing could keep computational progress going as traditional computing slows due to physical limitations. Christof Teuscher at Portland State University emphasized the potential for significant power savings with this approach.
An evolution of the LLM-as-a-Judge paradigm is emerging. Meta AI has introduced the J1 framework which shifts the paradigm of LLMs from passive generators to active, deliberative evaluators through self-evaluation. This approach, detailed in "J1: Incentivizing Thinking in LLM-as-a-Judge via Reinforcement Learning," addresses the growing need for rigorous and scalable evaluation as AI systems become more capable and widely deployed. By reframing judgment as a structured reasoning task trained through reinforcement learning, J1 aims to create models that perform consistent, interpretable, and high-fidelity evaluations. Soheil Feizi, an associate professor at the University of Maryland, has received a $1 million federal grant to advance foundational research in reasoning AI models. This funding, stemming from a Presidential Early Career Award for Scientists and Engineers (PECASE), will support his work in defending large language models (LLMs) against attacks, identifying weaknesses in how these models learn, encouraging transparent, step-by-step logic, and understanding the "reasoning tokens" that drive decision-making. Feizi plans to explore innovative approaches like live activation probing and novel reinforcement-learning designs, aiming to transform theoretical advancements into practical applications and real-world usages. Recommended read:
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@www.marktechpost.com
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References:
AI News | VentureBeat
, MarkTechPost
Large Language Models (LLMs) are facing significant challenges in handling real-world conversations, particularly those involving multiple turns and underspecified tasks. Researchers from Microsoft and Salesforce have recently revealed a substantial performance drop of 39% in LLMs when confronted with such conversational scenarios. This decline highlights the difficulty these models have in maintaining contextual coherence and delivering accurate outcomes as conversations evolve and new information is incrementally introduced. Instead of flexibly adjusting to changing user inputs, LLMs often make premature assumptions, leading to errors that persist throughout the dialogue.
These findings underscore a critical gap in how LLMs are currently evaluated. Traditional benchmarks often rely on single-turn, fully-specified prompts, which fail to capture the complexities of real-world interactions where information is fragmented and context must be actively constructed from multiple exchanges. This discrepancy between evaluation methods and actual conversational demands contributes to the challenges LLMs face in integrating underspecified inputs and adapting to evolving user needs. The research emphasizes the need for new evaluation frameworks that better reflect the dynamic and iterative nature of real-world conversations. In contrast to these challenges, Google's DeepMind has developed AlphaEvolve, an AI agent designed to optimize code and reclaim computational resources. AlphaEvolve autonomously rewrites critical code, resulting in a 0.7% reduction in Google's overall compute usage. This system not only pays for itself but also demonstrates the potential for AI agents to significantly improve efficiency in complex computational environments. AlphaEvolve's architecture, featuring a controller, fast-draft models, deep-thinking models, automated evaluators, and versioned memory, represents a production-grade approach to agent engineering. This allows for continuous improvement at scale. Recommended read:
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Kevin Okemwa@windowscentral.com
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OpenAI has announced the release of GPT-4.1 and GPT-4.1 mini, the latest iterations of their large language models, now accessible within ChatGPT. This move marks the first time GPT-4.1 is available outside of the API, opening up its capabilities to a broader user base. GPT-4.1 is designed as a specialized model that excels at coding tasks and instruction following, making it a valuable tool for developers and users with coding needs. OpenAI is making the models accessible via the “more models” dropdown selection in the top corner of the chat window within ChatGPT, giving users the flexibility to choose between GPT-4.1, GPT-4.1 mini, and other models.
The GPT-4.1 model is being rolled out to paying subscribers of ChatGPT Plus, Pro, and Team, with Enterprise and Education users expected to gain access in the coming weeks. For free users, OpenAI is introducing GPT-4.1 mini, which replaces GPT-4o mini as the default model once the daily GPT-4o limit is reached. The "mini" version provides a smaller-scale parameter and less powerful version with similar safety standards. OpenAI’s decision to add GPT-4.1 to ChatGPT was driven by popular demand, despite initially planning to keep it exclusive to the API. GPT-4.1 was built prioritizing developer needs and production use cases. The company claims GPT-4.1 delivers a 21.4-point improvement over GPT-4o on the SWE-bench Verified software engineering benchmark, and a 10.5-point gain on instruction-following tasks in Scale’s MultiChallenge benchmark. In addition, it reduces verbosity by 50% compared to other models, a trait enterprise users praised during early testing. The model supports standard context windows for ChatGPT, ranging from 8,000 tokens for free users to 128,000 tokens for Pro users. Recommended read:
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Matthias Bastian@THE DECODER
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OpenAI has announced the integration of GPT-4.1 and GPT-4.1 mini models into ChatGPT, aimed at enhancing coding and web development capabilities. The GPT-4.1 model, designed as a specialized model excelling at coding tasks and instruction following, is now available to ChatGPT Plus, Pro, and Team users. According to OpenAI, GPT-4.1 is faster and a great alternative to OpenAI o3 & o4-mini for everyday coding needs, providing more help to developers creating applications.
OpenAI is also rolling out GPT-4.1 mini, which will be available to all ChatGPT users, including those on the free tier, replacing the previous GPT-4o mini model. This model serves as the fallback option once GPT-4o usage limits are reached. The release notes confirm that GPT 4.1 mini offers various improvements over GPT-4o mini, including instruction-following, coding, and overall intelligence. This initiative is part of OpenAI's effort to make advanced AI tools more accessible and useful for a broader audience, particularly those engaged in programming and web development. Johannes Heidecke, Head of Systems at OpenAI, has emphasized that the new models build upon the safety measures established for GPT-4o, ensuring parity in safety performance. According to Heidecke, no new safety risks have been introduced, as GPT-4.1 doesn’t introduce new modalities or ways of interacting with the AI, and that it doesn’t surpass o3 in intelligence. The rollout marks another step in OpenAI's increasingly rapid model release cadence, significantly expanding access to specialized capabilities in web development and coding. Recommended read:
References :
Kevin Okemwa@windowscentral.com
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OpenAI has launched GPT-4.1 and GPT-4.1 mini, the latest iterations of its language models, now integrated into ChatGPT. This upgrade aims to provide users with enhanced coding and instruction-following capabilities. GPT-4.1, available to paid ChatGPT subscribers including Plus, Pro, and Team users, excels at programming tasks and provides a smarter, faster, and more useful experience, especially for coders. Additionally, Enterprise and Edu users are expected to gain access in the coming weeks.
GPT-4.1 mini, on the other hand, is being introduced to all ChatGPT users, including those on the free tier, replacing the previous GPT-4o mini model. It serves as a fallback option when GPT-4o usage limits are reached. OpenAI says GPT-4.1 mini is a "fast, capable, and efficient small model". This approach democratizes access to improved AI, ensuring that even free users benefit from advancements in language model technology. Both GPT-4.1 and GPT-4.1 mini demonstrate OpenAI's commitment to rapidly advancing its AI model offerings. Initial plans were to release GPT-4.1 via API only for developers, but strong user feedback changed that. The company claims GPT-4.1 excels at following specific instructions, is less "chatty", and is more thorough than older versions of GPT-4o. OpenAI also notes that GPT-4.1's safety performance is at parity with GPT-4o, showing improvements can be delivered without new safety risks. Recommended read:
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@learn.aisingapore.org
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Anthropic's Claude 3.7 model is making waves in the AI community due to its enhanced reasoning capabilities, specifically through a "deep thinking" approach. This method utilizes chain-of-thought (CoT) techniques, enabling Claude 3.7 to tackle complex problems more effectively. This development represents a significant advancement in Large Language Model (LLM) technology, promising improved performance in a variety of demanding applications.
The implications of this enhanced reasoning are already being seen across different sectors. FloQast, for example, is leveraging Anthropic's Claude 3 on Amazon Bedrock to develop an AI-powered accounting transformation solution. The integration of Claude’s capabilities is assisting companies in streamlining their accounting operations, automating reconciliations, and gaining real-time visibility into financial operations. The model’s ability to handle the complexities of large-scale accounting transactions highlights its potential for real-world applications. Furthermore, recent reports highlight the competitive landscape where models like Mistral AI's Medium 3 are being compared to Claude Sonnet 3.7. These comparisons focus on balancing performance, cost-effectiveness, and ease of deployment. Simultaneously, Anthropic is also enhancing Claude's functionality by allowing users to connect more applications, expanding its utility across various domains. These advancements underscore the ongoing research and development efforts aimed at maximizing the potential of LLMs and addressing potential security vulnerabilities. Recommended read:
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@the-decoder.com
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OpenAI is making strides in AI customization and application development with the release of Reinforcement Fine-Tuning (RFT) on its o4-mini reasoning model and the appointment of Fidji Simo as the CEO of Applications. The RFT release allows organizations to tailor their versions of the o4-mini model to specific tasks using custom objectives and reward functions, marking a significant advancement in model optimization. This approach utilizes reinforcement learning principles, where developers provide a task-specific grader that evaluates and scores model outputs based on custom criteria, enabling the model to optimize against a reward signal and align with desired behaviors.
Reinforcement Fine-Tuning is particularly valuable for complex or subjective tasks where ground truth is difficult to define. By using RFT on o4-mini, a compact reasoning model optimized for text and image inputs, developers can fine-tune for high-stakes, domain-specific reasoning tasks while maintaining computational efficiency. Early adopters have demonstrated the practical potential of RFT. This capability allows developers to tweak the model to better fit their needs using OpenAI's platform dashboard, deploy it through OpenAI's API, and connect it to internal systems. In a move to scale its AI products, OpenAI has appointed Fidji Simo, formerly CEO of Instacart, as the CEO of Applications. Simo will oversee the scaling of AI products, leveraging her extensive experience in consumer tech to drive revenue generation from OpenAI's research and development efforts. Previously serving on OpenAI's board of directors, Simo's background in leading development at Facebook suggests a focus on end-users rather than businesses, potentially paving the way for new subscription services and products aimed at a broader audience. OpenAI is also rolling out a new GitHub connector for ChatGPT's deep research agent, allowing users with Plus, Pro, or Team subscriptions to connect their repositories and ask questions about their code. Recommended read:
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@the-decoder.com
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OpenAI is expanding its global reach through strategic partnerships with governments and the introduction of advanced model customization tools. The organization has launched the "OpenAI for Countries" program, an initiative designed to collaborate with governments worldwide on building robust AI infrastructure. This program aims to assist nations in setting up data centers and adapting OpenAI's products to meet local language and specific needs. OpenAI envisions this initiative as part of a broader global strategy to foster cooperation and advance AI capabilities on an international scale.
This expansion also includes technological advancements, with OpenAI releasing Reinforcement Fine-Tuning (RFT) for its o4-mini reasoning model. RFT enables enterprises to fine-tune their own versions of the model using reinforcement learning, tailoring it to their unique data and operational requirements. This allows developers to customize the model to better fit their needs using OpenAI’s platform dashboard, tweaking it for internal terminology, goals, processes and more. Once deployed, if an employee or leader at the company wants to use it through a custom internal chatbot orcustom OpenAI GPTto pull up private, proprietary company knowledge, answer specific questions about company products and policies, or generate new communications and collateral in the company’s voice, they can do so more easily with their RFT version of the model. The "OpenAI for Countries" program is slated to begin with ten international projects, supported by funding from both OpenAI and participating governments. Chris Lehane, OpenAI's vice president of global policy, indicated that the program was inspired by the AI Action Summit in Paris, where several countries expressed interest in establishing their own "Stargate"-style projects. Moreover, the release of RFT on o4-mini signifies a major step forward in custom model optimization, offering developers a powerful new technique for tailoring foundation models to specialized tasks. This allows for fine-grained control over how models improve, by defining custom objectives and reward functions. Recommended read:
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@www.marktechpost.com
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Meta is making significant strides in the AI landscape, highlighted by the release of Llama Prompt Ops, a Python package aimed at streamlining prompt adaptation for Llama models. This open-source tool helps developers enhance prompt effectiveness by transforming inputs to better suit Llama-based LLMs, addressing the challenge of inconsistent performance across different AI models. Llama Prompt Ops facilitates smoother cross-model prompt migration and improves performance and reliability, featuring a transformation pipeline for systematic prompt optimization.
Meanwhile, Meta is expanding its AI strategy with the launch of a standalone Meta AI app, powered by Llama 4, to compete with rivals like Microsoft’s Copilot and ChatGPT. This app is designed to function as a general-purpose chatbot and a replacement for the “Meta View” app used with Meta Ray-Ban glasses, integrating a social component with a public feed showcasing user interactions with the AI. Meta also previewed its Llama API, designed to simplify the integration of its Llama models into third-party products, attracting AI developers with an open-weight model that supports modular, specialized applications. However, Meta's AI advancements are facing legal challenges, as a US judge is questioning the company's claim that training AI on copyrighted books constitutes fair use. The case, focusing on Meta's Llama model, involves training data including works by Sarah Silverman. The judge raised concerns that using copyrighted material to create a product capable of producing an infinite number of competing products could undermine the market for original works, potentially obligating Meta to pay licenses to copyright holders. Recommended read:
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@docs.llamaindex.ai
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References:
Blog on LlamaIndex
, docs.llamaindex.ai
LlamaIndex is advancing agentic systems design by focusing on the optimal blend of autonomy and structure, particularly through its innovative Workflows system. Workflows provide an event-based mechanism for orchestrating agent execution, connecting individual steps implemented as vanilla functions. This approach enables developers to create chains, branches, loops, and collections within their agentic systems, aligning with established design patterns for effective agents. The system, available in both Python and TypeScript frameworks, is fundamentally simple yet powerful, allowing for complex orchestration of agentic tasks.
LlamaIndex Workflows support hybrid systems by allowing decisions about control flow to be made by LLMs, traditional imperative programming, or a combination of both. This flexibility is crucial for building robust and adaptable AI solutions. Furthermore, Workflows not only facilitate the implementation of agents but also enable the use of sub-agents within each step. This hierarchical agent design can be leveraged to decompose complex tasks into smaller, more manageable units, enhancing the overall efficiency and effectiveness of the system. The introduction of Workflows underscores LlamaIndex's commitment to providing developers with the tools they need to build sophisticated knowledge assistants and agentic applications. By offering a system that balances autonomy with structured execution, LlamaIndex is addressing the need for design principles when building agents. The company draws from its experience with LlamaCloud and its collaboration with enterprise customers to offer a system that integrates agents, sub-agents, and flexible decision-making capabilities. Recommended read:
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@the-decoder.com
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References:
composio.dev
, THE DECODER
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OpenAI is actively benchmarking its language models, including o3 and o4-mini, against competitors like Gemini 2.5 Pro, to evaluate their performance in reasoning and tool use efficiency. Benchmarks like the Aider polyglot coding test show that o3 leads in some areas, achieving a new state-of-the-art score of 79.60% compared to Gemini 2.5's 72.90%. However, this performance comes at a higher cost, with o3 being significantly more expensive. O4-mini offers a slightly more balanced price-performance ratio, costing less than o3 while still surpassing Gemini 2.5 on certain tasks. Testing reveals Gemini 2.5 excels in context awareness and iterating on code, making it preferable for real-world use cases, while o4-mini surprisingly excelled in competitive programming.
Open AI have just launched its GPT-Image-1 model for image generation to developers via API. Previously, this model was only accessible through ChatGPT. The versatility of the model means that it can create images across diverse styles, custom guidelines, world knowledge, and accurately render text. The company's blog post said that this unlocks countless practical applications across multiple domains. Several enterprises and startups are already incorporating the model for creative projects, products, and experiences. Image processing with GPT-Image-1 is billed by tokens. Text input tokens, or the prompt text, will cost $5 per 1 million tokens. Image input tokens will be $10 per million tokens, while image output tokens, or the generated image, will be a whopping $40 per million tokens. Depending on the selected image quality,costs typically range from $0.02 to $0.19 per image. Recommended read:
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Michael Nuñez@AI News | VentureBeat
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References:
venturebeat.com
, www.marktechpost.com
Amazon Web Services (AWS) has announced significant advancements in its AI coding and Large Language Model (LLM) infrastructure. A key highlight is the introduction of SWE-PolyBench, a comprehensive multi-language benchmark designed to evaluate the performance of AI coding assistants. This benchmark addresses the limitations of existing evaluation frameworks by assessing AI agents across a diverse range of programming languages like Python, JavaScript, TypeScript, and Java, using real-world scenarios derived from over 2,000 curated coding challenges from GitHub issues. The aim is to provide researchers and developers with a more accurate understanding of how well these tools can navigate complex codebases and solve intricate programming tasks involving multiple files.
The latest Amazon SageMaker Large Model Inference (LMI) container v15, powered by vLLM 0.8.4, further enhances LLM capabilities. This version supports a wider array of open-source models, including Meta’s Llama 4 models and Google’s Gemma 3, providing users with more flexibility in model selection. LMI v15 delivers significant performance improvements through an async mode and support for the vLLM V1 engine, resulting in higher throughput and reduced CPU overhead. This enables seamless deployment and serving of large language models at scale, with expanded API schema support and multimodal capabilities for vision-language models. AWS is also launching new Amazon EC2 Graviton4-based instances with NVMe SSD storage. These compute optimized (C8gd), general purpose (M8gd), and memory optimized (R8gd) instances offer up to 30% better compute performance and 40% higher performance for I/O intensive database workloads compared to Graviton3-based instances. They also include larger instance sizes with up to 3x more vCPUs, memory, and local storage. These instances are ideal for storage intensive Linux-based workloads including containerized and micro-services-based applications built using Amazon Elastic Kubernetes Service(Amazon EKS),Amazon Elastic Container Service(Amazon ECS),Amazon Elastic Container Registry(Amazon ECR), Kubernetes, and Docker, as well as applications written in popular programming languages such as C/C++, Rust, Go, Java, Python, .NET Core, Node.js, Ruby, and PHP. Recommended read:
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@www.microsoft.com
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References:
news.microsoft.com
, www.microsoft.com
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Microsoft Research is delving into the transformative potential of AI as "Tools for Thought," aiming to redefine AI's role in supporting human cognition. At the upcoming CHI 2025 conference, researchers will present four new research papers and co-host a workshop exploring this intersection of AI and human thinking. The research includes a study on how AI is changing the way we think and work along with three prototype systems designed to support different cognitive tasks. The goal is to explore how AI systems can be used as Tools for Thought and reimagine AI’s role in human thinking.
As AI tools become increasingly capable, Microsoft has unveiled new AI agents designed to enhance productivity in various domains. The "Researcher" agent can tackle complex research tasks by analyzing work data, emails, meetings, files, chats, and web information to deliver expertise on demand. Meanwhile, the "Analyst" agent functions as a virtual data scientist, capable of processing raw data from multiple spreadsheets to forecast demand or visualize customer purchasing patterns. The new AI agents unveiled over the past few weeks can help people every day with things like research, cybersecurity and more. Johnson & Johnson has reportedly found that only a small percentage, between 10% and 15%, of AI use cases deliver the vast majority (80%) of the value. After encouraging employees to experiment with AI and tracking the results of nearly 900 use cases over about three years, the company is now focusing resources on the highest-value projects. These high-value applications include a generative AI copilot for sales representatives and an internal chatbot answering employee questions. Other AI tools being developed include one for drug discovery and another for identifying and mitigating supply chain risks. Recommended read:
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@www.searchenginejournal.com
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hackernoon.com
, Search Engine Journal
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Recent advancements are showing language models (LLMs) are expanding past basic writing and are now being used to generate functional code. These models can produce full scripts, browser extensions, and web applications from natural language prompts, opening up opportunities for those without coding skills. Marketers and other professionals can now automate repetitive tasks, build custom tools, and experiment with technical solutions more easily than ever before. This unlocks a new level of efficiency, allowing individuals to create one-off tools for tasks that previously seemed too time-consuming to justify automation.
Advances in AI are also focusing on improving the accuracy of code generated by LLMs. Researchers at MIT have developed a new approach that guides LLMs to generate code that adheres to the rules of the specific programming language. This method allows the LLM to prioritize outputs that are likely to be valid and accurate, improving computational efficiency. This new architecture has enabled smaller LLMs to outperform larger models in generating accurate outputs in fields like molecular biology and robotics. The goal is to allow non-experts to control AI-generated content by ensuring that the outputs are both useful and correct, potentially improving programming assistants, AI-powered data analysis, and scientific discovery tools. New tools are emerging to aid developers, such as Amazon Q Developer and OpenAI Codex CLI. Amazon Q Developer is an AI-powered coding assistant that integrates into IDEs like Visual Studio Code, providing context-aware code recommendations, snippets, and unit test suggestions. The service uses advanced generative AI to understand the context of a project and offers features like intelligent code generation, integrated testing and debugging, seamless documentation and effective code review and refactoring. Similarly, OpenAI Codex CLI is a terminal-based AI assistant that allows developers to interact with OpenAI models using natural language to read, modify, and run code. These tools aim to boost coding productivity by assisting with tasks like bug fixing, refactoring, and prototyping. Recommended read:
References :
@www.quantamagazine.org
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References:
pub.towardsai.net
, Sebastian Raschka, PhD
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Recent developments in the field of large language models (LLMs) are focusing on enhancing reasoning capabilities through reinforcement learning. This approach aims to improve model accuracy and problem-solving, particularly in challenging tasks. While some of the latest LLMs, such as GPT-4.5 and Llama 4, were not explicitly trained using reinforcement learning for reasoning, the release of OpenAI's o3 model shows that strategically investing in compute and tailored reinforcement learning methods can yield significant improvements.
Competitors like xAI and Anthropic have also been incorporating more reasoning features into their models, such as the "thinking" or "extended thinking" button in xAI Grok and Anthropic Claude. The somewhat muted response to GPT-4.5 and Llama 4, which lack explicit reasoning training, suggests that simply scaling model size and data may be reaching its limits. The field is now exploring ways to make language models work better, including the use of reinforcement learning. One of the ways that researchers are making language models work better is to sidestep the requirement for language as an intermediary step. Language isn't always necessary, and that having to turn ideas into language can slow down the thought process. LLMs process information in mathematical spaces, within deep neural networks, however, they must often leave this latent space for the much more constrained one of individual words. Recent papers suggest that deep neural networks can allow language models to continue thinking in mathematical spaces before producing any text. Recommended read:
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Chris McKay@Maginative
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OpenAI has unveiled its latest advancements in AI technology with the launch of the GPT-4.1 family of models. This new suite includes GPT-4.1, GPT-4.1 mini, and GPT-4.1 nano, all accessible via API, and represents a significant leap forward in coding capabilities, instruction following, and context processing. Notably, these models feature an expanded context window of up to 1 million tokens, enabling them to handle larger codebases and extensive documents. The GPT-4.1 family aims to cater to a wide range of developer needs by offering different performance and cost profiles, with the goal of creating more advanced and efficient AI applications.
These models demonstrate superior results on various benchmarks compared to their predecessors, GPT-4o and GPT-4o mini. Specifically, GPT-4.1 showcases a substantial improvement on the SWE-bench Verified coding test with a 54.6% increase, and a 38.3% increase on Scale’s MultiChallenge for instruction following. Each model is designed with a specific purpose in mind: GPT-4.1 excels in high-level cognitive tasks like software development and research, GPT-4.1 mini offers a balanced performance with reduced latency and cost, while GPT-4.1 nano provides the quickest and most affordable option for tasks such as classification. All three models have knowledge updated through June 2024. The introduction of the GPT-4.1 family also brings about changes in OpenAI's existing model offerings. The GPT-4.5 Preview model in the API is set to be deprecated on July 14, 2025, due to GPT-4.1 offering comparable or better utility at a lower cost. In terms of pricing, GPT-4.1 is 26% less expensive than GPT-4o for median queries, along with increased prompt caching discounts. Early testers have already noted positive outcomes, with improvements in code review suggestions and data retrieval from large documents. OpenAI emphasizes that many underlying improvements are being integrated into the current GPT-4o version within ChatGPT. Recommended read:
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