News/AI Models
New AI Platform TCS Simplifies LLM Adoption for Businesses
The growing adoption of AI and large language models (LLMs) by businesses presents both opportunities and challenges, but a new platform aims to simplify the process and drive innovation. Enhancing workflows with AI: TCS' new AI platform, WisdomNext, is designed to enhance business workflows and processes by aggregating multiple Generative AI services into a single interface: The platform enables rapid adoption of next-gen technologies at scale and lower costs within regulatory frameworks, allowing for real-time experimentation across vendor, internal, and open-source LLMs. It provides industry-specific business solution templates and productivity enhancers to assist with automating routine tasks, enhancing decision-making, streamlining...
read Aug 2, 2024DeepMind’s Gemma Scope Helps Demystify How LLMs Work
DeepMind introduces Gemma Scope, a new toolset for understanding the inner workings of large language models and addressing interpretability challenges, with the potential to enable more robust and transparent AI systems. Interpreting LLM activations is crucial but challenging: Understanding the decision-making process of large language models (LLMs) is essential for their safe and transparent deployment in critical applications. However, interpreting the billions of neuron activations generated during LLM inferences is a major challenge. LLMs process inputs through a complex network of artificial neurons, and the values emitted by these neurons, known as "activations," guide the model's response and represent its...
read Aug 2, 2024How to Prevent the Misuse of Open-Source AI Models
The rise of open source AI models and the need for tamperproofing safeguards to prevent misuse are highlighting the complex challenges and opportunities surrounding the development and deployment of powerful AI systems. Key Takeaways: Researchers have developed a new training technique that could make it harder to remove safety restrictions from open source AI models like Meta's Llama 3, which are designed to prevent the models from generating harmful or inappropriate content. The technique involves replicating the modification process used to remove safeguards and then altering the model's parameters to ensure that attempts to make the model respond to problematic...
read Aug 2, 2024GitHub Just Launched an AI Model Playground for Developers
GitHub Models is a new feature that tightly integrates generative AI models into existing developer tools and workflows, aiming to make AI more accessible and accelerate the development of AI applications. Seamless integration with developer tools; GitHub Models allows developers to explore, test, and compare various AI models directly within the GitHub web interface, GitHub Codespaces, or Visual Studio Code, streamlining the process of experimenting with AI and incorporating it into their projects: The feature provides a robust playground for developers to interact with leading models like Meta's Llama 3.1, OpenAI's GPT-4o and GPT-4o mini, Cohere's Command, and Mistral AI's...
read Aug 1, 2024Black Forest Labs’ ‘Flux’ Is the Lates Open-Source Text-to-Image Model
Flux by Black Forest Labs sets a new standard for open-source text-to-image models with its impressive 12B parameters and Midjourney-like aesthetics. Introducing Flux: Black Forest Labs, the team behind Stable Diffusion, has released Flux, the largest state-of-the-art open-source text-to-image model to date: Flux pushes the boundaries of creativity and performance, delivering high-quality visuals reminiscent of Midjourney. The model boasts an impressive 12B parameters, setting a new standard for open-source text-to-image models. Three Variations of Flux: BFL has released three versions of the model, each catering to different user needs and licensing requirements: FLUX.1 [dev]: The base model, open-sourced with a...
read Aug 1, 2024Gary Marcus: How Outliers Expose the AI Industry’s Fragile Future
The rapid rise and potential fall of the current AI industry can be largely explained by one crucial fact: AI struggles with outliers, leading to absurd outputs when faced with unusual situations. The outlier problem: Current machine learning approaches, which underpin most of today's AI, perform poorly when encountering circumstances that deviate from their training examples: A Carnegie Mellon computer scientist, Phil Koopman, illustrates this issue using the example of a driverless car accident involving an overturned double trailer, which the AI system failed to recognize due to its unfamiliarity with the situation. This limitation, also known as the problem...
read Aug 1, 2024Why CIOs Are Considering Small Language Models
The integration of generative AI in enterprise products is prompting CIOs to carefully evaluate the technology's potential and risks, with a focus on sustainability, governance, and building internal AI expertise. Balancing technological excitement and commercial propositions: CIOs are studying generative AI to stay current while maintaining a pragmatic approach: The rapid advancements in AI, such as GPT-4 passing the Turing test and the availability of AI assistants like Microsoft Copilot and Gemini Advanced, are driving CIOs to closely monitor the technology. However, CIOs are cautious about adopting generative AI without proper considerations, recognizing the risks of overestimating its capabilities and...
read Aug 1, 2024Google’s Gemma 2 2B is a Small Model with Big Impact
Google's Gemma 2 2B represents a significant breakthrough in AI efficiency, offering performance on par with industry leaders despite its compact size. This development could potentially reshape the AI landscape, making advanced capabilities more accessible and deployable. Gemma 2 2B challenges the notion that bigger is better in AI: With just 2.6 billion parameters, Google's new language model achieves results comparable to or surpassing models like GPT-3.5 and Mistral 8x7B, which have around ten times more parameters. Independent testing by LMSYS saw Gemma 2 2B score 1130 in their evaluation arena, slightly outperforming GPT-3.5-Turbo-0613 (1117) and Mixtral-8x7B (1114). The model...
read Jul 31, 2024Writer’s Specialized AI Models for Healthcare, Finance Are Very Good and Open-Source
The launch of specialized AI models by Writer aims to revolutionize the adoption of artificial intelligence in the highly regulated healthcare and financial services industries, addressing concerns about accuracy, safety, and compliance. Breaking new ground in domain-specific AI: Writer's Palmyra-Med-70b and Palmyra-Fin-70b models are tailored specifically for healthcare and finance, respectively, and claim to outperform larger, general-purpose AI models in domain-specific tasks: The healthcare model, Palmyra-Med-70b, achieved an average accuracy of 85.9% across medical benchmarks in zero-shot attempts, surpassing competitors like Med-PaLM-2. Palmyra-Fin-70b is the first AI model to pass the challenging CFA Level III exam, demonstrating its advanced capabilities...
read Jul 31, 2024Galileo Benchmark Shows Open-Source AI Models Challenging Proprietary Dominance
Galileo's latest benchmark reveals open-source AI models are rapidly catching up to their proprietary counterparts, potentially democratizing advanced AI and accelerating innovation across industries. Shifting AI landscape: The second annual Hallucination Index from Galileo evaluated 22 leading large language models, revealing that the performance gap between open-source and proprietary models has narrowed significantly in just eight months: Anthropic's Claude 3.5 Sonnet outperformed offerings from OpenAI, which dominated last year's rankings, indicating a changing of the guard in the AI arms race. Google's Gemini 1.5 Flash emerged as the most cost-effective option, delivering strong results at a fraction of the price...
read Jul 31, 2024Zyphra Launches Open-Source Small Language Model Zamba2
The recently released Zamba2-2.7B is a cutting-edge small language model that delivers superior performance and efficiency, underscoring the growing prominence and potential of SLMs in the AI landscape. Key Takeaways from Zamba2-2.7B Release: Zyphra's latest model, Zamba2-2.7B, sets a new standard for small language models, offering impressive capabilities and efficiency: Zamba2-2.7B achieves twice the speed and 27% reduced memory overhead compared to other models, matching the performance of larger 7B models. The model is trained on a substantial dataset of approximately 3 trillion tokens derived from Zyphra's proprietary datasets, ensuring high-quality and relevant training data. Advanced techniques such as interleaved...
read Jul 31, 2024Baidu’s Self-Reasoning AI Holds Promise for Combating Hallucinations
Baidu unveils self-reasoning AI framework to enhance language model accuracy: Baidu's innovative approach aims to tackle the issue of hallucination in large language models by enabling AI systems to critically evaluate their own knowledge and decision-making processes. The multi-step framework involves assessing the relevance of retrieved information, selecting pertinent documents, and analyzing the reasoning path to generate well-supported answers. By being more discerning about the information it uses, the AI system can improve accuracy and transparency, which is crucial for building trust in AI-generated content. Impressive performance with limited training data: Baidu's self-reasoning AI framework has demonstrated remarkable results, outperforming...
read Jul 31, 2024AI Startup Not Diamond Sends Your Chat Queries to Different LLMs
The startup Not Diamond has emerged with a novel solution for enterprises looking to deploy the best large language models (LLMs) for their applications. By using smart routing, the company aims to enable a multi-model future where queries are directed to the most suitable model based on accuracy, latency, and cost considerations. Key innovation: an LLM router for a multi-model future; Not Diamond's router uses a 'meta-model' to understand incoming queries and automatically route them to the most appropriate model, optimizing for accuracy, latency, and cost: The company believes that the future will involve many foundation models, fine-tuned variants, and...
read Jul 31, 2024Anthropic’s Aggressive Data Scraping Is Causing Problems for Sites It Targets
Anthropic's aggressive data scraping practices raise ethical concerns as the company disregards website permissions in its quest to train its Claude language model. Anthropic's data scraping tactics: The artificial intelligence company Anthropic has been engaging in aggressive data scraping practices to gather training data for its Claude language model, often disregarding website permissions and robots.txt files: Ifixit.com reported that Anthropic's ClaudeBot hit their site a million times in a single day, highlighting the intensity of the company's data scraping efforts. Freelancer experienced 3.5 million hits from ClaudeBot in just four hours, with the bot's activities triggering alarms and waking up...
read Jul 31, 2024OpenAI’s “GPT-4o Long Output” Produces 16x Longer Responses
OpenAI announces GPT-4o Long Output, a variation of its GPT-4o model that significantly extends output capabilities, allowing for responses up to 64,000 tokens in length. Key features and improvements: The new GPT-4o Long Output model offers a substantial upgrade in output length while maintaining the same overall context window as the original GPT-4o: GPT-4o Long Output can generate outputs up to 64,000 tokens, a 16-fold increase from the original GPT-4o's 4,000-token limit, enabling the model to produce responses as long as a 200-page novel. The maximum context window, which includes both input and output tokens, remains at 128,000 tokens for...
read Jul 31, 2024MIT’s Thermometer Method Efficiently Calibrates Large Language Models
Researchers from MIT and the MIT-IBM Watson AI Lab have developed a new method called Thermometer to efficiently calibrate large language models (LLMs) and improve their reliability across diverse tasks. Key Takeaways: Thermometer addresses the challenges of calibrating LLMs, which can generate inaccurate responses and exhibit misaligned confidence levels: The method involves building a smaller, auxiliary model that runs on top of an LLM to calibrate it, enabling better-calibrated responses on unseen tasks. Thermometer is more efficient than other approaches, requiring less power-hungry computation while preserving the model's accuracy. Understanding the Problem: LLMs can be applied to a wide range...
read Jul 30, 2024Meta’s SAM 2 Model A Big Step for Object Segmentation in Videos
The Meta Segment Anything Model 2 (SAM 2) represents a significant advancement in object segmentation for both images and videos, with the potential to revolutionize video segmentation and enable seamless application across various image and video use cases. Key features and capabilities: SAM 2 is the first unified model for real-time, promptable object segmentation in images and videos, offering improved accuracy and performance compared to existing solutions: SAM 2 achieves better video segmentation performance than current methods while requiring three times less interaction time. The model can segment any object in any video or image without the need for custom...
read Jul 30, 2024Apple Paper Suggests Apple Intelligence Was Trained with Google
Key revelations from Apple's research paper: A quote buried in Apple's "Apple Intelligence Foundation Language Models" paper suggests that the company initially relied on Google's hardware for training its AI models: The paper states that Apple's Foundation Model (AFM) and its underlying server technology were initially built on "v4 and v5P Cloud TPU clusters" using Apple software. While a CNBC report suggested that Apple rented time on Google-hosted clusters, the research paper does not explicitly mention Google or Nvidia, implying that Apple likely purchased the hardware directly and used it within its own data centers. Apple's evolving AI infrastructure: As...
read Jul 30, 2024How to Build a YouTube Summarizer with LLM and yt-dlp
The rapid development of artificial intelligence tools for analyzing and summarizing YouTube videos is transforming how we consume and understand online content, with far-reaching implications for education, research, and public discourse. In a recent blog post, Shekhar Gulati provides instructions to developers for how to summarize any YouTube video using the power of LLMs and Python's yt-dlp tool. Harnessing AI for video summarization: A new utility combines large language models (LLMs) and Python's yt-dlp tool to generate concise summaries of YouTube videos by extracting key points from subtitles: The script utilizes the llm command-line interface to interact with powerful language...
read Jul 29, 2024Stanford Study: How Culture Impacts What We Want from AI
China and the U.S. have differing views on what people want from AI, with cultural models of agency shaping these ideal preferences, according to new research from Stanford University that aims to foster more inclusive AI development. Cultural models of agency influence AI preferences: The study found clear associations between the cultural models of agency prevalent in different contexts and the type of AI considered ideal: In many European American middle-class cultural contexts, people often desire control over AI, treating it as a tool in service of individual goals and concerns in an impersonal, hierarchical relationship. In contrast, some cultures...
read Jul 29, 2024How to Navigate the Perils of Over-reliance on LLMs and Chatbots
The power and peril of LLMs: The advent of large language models (LLMs) has revolutionized information access, providing accurate and comprehensive answers to a wide range of queries. However, the convenience of LLMs can lead to dependency, potentially eroding cognitive abilities and self-confidence: Over-reliance on LLMs for even minor tasks can impede critical thinking skills, as the brain becomes accustomed to taking the easier route suggested by AI. The availability of precise, tailored answers can exacerbate "imposter syndrome," causing individuals to doubt their own abilities and curbing natural curiosity. LLMs may summarize incorrect information based on the context of the...
read Jul 27, 2024Salesforce’s New Trillion-Token AI Dataset Could Revolutionize Machine Learning
Salesforce's MINT-1T dataset, containing one trillion text tokens and 3.4 billion images, has the potential to significantly impact the AI industry by enabling breakthroughs in multimodal learning and leveling the playing field for researchers. Massive AI dataset: Bridging the gap in machine learning; The scale and diversity of MINT-1T, drawing from a wide range of sources like web pages and scientific papers, provides AI models with a broad view of human knowledge, which is crucial for developing AI systems that can work across different fields and tasks: The release of MINT-1T breaks down barriers in AI research, allowing small labs...
read Jul 27, 2024X Introduces User Opt-Out for Grok AI Training Data
X allows users to opt out of AI training data for Grok chatbot. The social media platform X now provides a setting for users to prevent their posts and interactions from being used to train and fine-tune the company's Grok AI assistant. Key details of the opt-out feature: The setting is accessible on the web and will soon be available on mobile. Users can uncheck a box to opt out of allowing their posts, interactions, inputs, and results with Grok to be used for training and fine-tuning purposes. Private accounts are automatically excluded from having their posts used to train...
read Jul 26, 2024DeepMind’s JumpReLU Architecture Sheds Light on the Inner Workings of Language Models
DeepMind has made significant progress in interpreting large language models (LLMs) with the introduction of JumpReLU sparse autoencoder (SAE), a deep learning architecture that decomposes the complex activations of LLMs into smaller, more understandable components. The challenge of interpreting LLMs: Understanding how the billions of neurons in LLMs work together to process and generate language is extremely difficult due to the complex activation patterns across the network: Individual neurons don't necessarily correspond to specific concepts, with a single neuron potentially activating for thousands of different concepts, and a single concept activating a broad range of neurons. The massive scale of...
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