News/AI Models
Using LLMs? Here’s where you may be wasting the most money
The big picture: Large Language Models (LLMs) like GPT, Claude, and Mistral have significantly boosted productivity in content creation, but inefficiencies arise when making small changes to large documents, leading to wasted time and resources. The Pareto Principle in AI-generated content: As content grows in size, even minor modifications become increasingly tedious, inefficient, and costly in terms of time and resources. This pattern is particularly noticeable in code generation and text creation. The issue becomes more pronounced as the content length increases, making small changes disproportionately challenging. A real-world example: Creating and modifying a landing page using an LLM illustrates...
read Oct 15, 2024New algorithm could reduce AI’s energy needs by 95% — but there’s a catch
Breakthrough in AI energy efficiency: A team of engineers at BitEnergy AI has developed a new method that could potentially reduce the energy consumption of AI applications by 95%, addressing growing concerns about the environmental impact of artificial intelligence. The research team has published their findings in a paper on the arXiv preprint server, detailing a novel approach to AI computation. This development comes at a crucial time as AI applications, particularly large language models (LLMs) like ChatGPT, are facing scrutiny for their substantial energy requirements. The current energy challenge: The rapid adoption and increasing complexity of AI systems have...
read Oct 14, 2024Adobe’s new AI video model emphasizes responsibly trained AI
Adobe Unveils Firefly Video Model with Responsible AI Training Approach: Adobe has introduced its Firefly Video Model at the annual Max conference, emphasizing a commitment to ethical AI training practices and creator-friendly policies. Key features of Adobe Firefly: Generative Extend: Allows users to extend video clips and audio by small increments Text-to-video: Generates videos based on detailed text descriptions Image-to-video: Creates short clips using reference images and accompanying text Firefly Image 3 model: Claimed to be four times faster than previous versions Responsible AI training practices: Adobe compensates creators for training data Does not train on customer content or scrape...
read Oct 14, 2024AI model DeepSeek uses synthetic data to prove complex theorems
Breakthrough in AI-driven theorem proving: DeepSeek-Prover, a new large language model (LLM), has achieved significant advancements in formal theorem proving, outperforming previous models and demonstrating the potential of synthetic data in enhancing mathematical reasoning capabilities. Key innovation - Synthetic data generation: The researchers addressed the lack of training data for theorem proving by developing a novel approach to generate extensive Lean 4 proof data. The synthetic data is derived from high-school and undergraduate-level mathematical competition problems. The process involves translating natural language problems into formal statements, filtering out low-quality content, and generating proofs. This approach resulted in a dataset of...
read Oct 14, 2024When it comes to coding, AlphaCodium outperforms OpenAI’s best model
Advancing AI problem-solving capabilities: OpenAI's o1 model shows improved performance on complex coding tasks when paired with Qodo's AlphaCodium tool, demonstrating potential for more sophisticated AI reasoning. Researchers from Qodo tested OpenAI's o1 model using their AlphaCodium tool to enhance its performance on coding problems, exploring the potential for more advanced AI reasoning capabilities. The experiment aimed to push o1 beyond its default "System 1" (fast, intuitive) thinking towards "System 2" (deliberate, reasoned) problem-solving approaches. Results showed that AlphaCodium significantly improved o1's performance on the Codeforces coding benchmark compared to direct prompting alone. Understanding AlphaCodium: The tool employs a novel...
read Oct 14, 2024The Gemini Nano AI model will run on your phone — here’s what’s inside
Gemini Nano: Google's AI model for smartphones: Google has introduced Gemini Nano, its smallest AI model designed to run directly on smartphones, offering enhanced text-based tasks and the ability to understand images and audio. Gemini Nano operates on-device, providing faster response times and improved privacy compared to cloud-based AI models. The model excels at various text-based tasks and can process both visual and audio inputs, making it versatile for multiple smartphone applications. Key features and functionalities: Gemini Nano brings a range of AI-powered capabilities to supported devices, enhancing user experience across various applications. Gboard Smart Reply utilizes AI to generate...
read Oct 14, 2024New AI model is predicting Hurricane Milton’s path with extreme accuracy
Groundbreaking AI weather model revolutionizes hurricane prediction: A new artificial intelligence-driven weather model called AIFS has demonstrated unprecedented accuracy in forecasting Hurricane Milton's path, outperforming traditional models by a significant margin. The big picture: AIFS, developed by the European Centre for Medium-Range Weather Forecasts (ECMWF), predicted Hurricane Milton's landfall location within just 13 miles of the actual point, with an average error of about 7 miles. Traditional weather models had maximum errors exceeding 100 miles in their forecasts for Hurricane Milton. The AI model accurately projected the storm's trajectory five days before landfall, dismissing other potential sites along the Florida...
read Oct 14, 2024Adobe just announced its Firefly AI video generator — here’s how to sign up
Adobe's Firefly Video Model: A leap in AI-powered content creation: Adobe has unveiled its groundbreaking Firefly Video Model at the Max conference, marking a significant advancement in AI-driven video generation and editing capabilities. The new model supports both text-to-video and image-to-video generation, offering creators unprecedented flexibility in producing visual content. Integrated into the standalone Firefly web application, the model provides a user-friendly interface for content creators of all skill levels. Adobe has trained the model on hundreds of millions of high-quality assets, ensuring a diverse range of outputs and styles. Join the waitlist here Key features and functionalities: The Firefly...
read Oct 14, 2024Between chaos and control: Where AI models find brilliance
Balancing chaos and control: The key to LLM intelligence: A recent study titled "Intelligence at the Edge of Chaos" suggests that the intelligence of Large Language Models (LLMs) emerges from a delicate balance between order and randomness, rather than from pure order or chaos alone. Understanding the edge of chaos: Researchers used elementary cellular automata (ECA) to model intelligence, finding that systems operating between order and chaos performed best in tasks requiring intelligent behavior. ECAs at the "edge of chaos" demonstrated improved pattern recognition and adaptability compared to those in highly ordered or chaotic states. This concept was then extended...
read Oct 13, 2024LLMs don’t outperform a 1970s technique, but they’re still worth using
LLMs show promise in anomaly detection despite performance gaps: A recent study by MIT's Data to AI Lab explored the use of large language models (LLMs) for anomaly detection in time series data, revealing both limitations and unexpected advantages compared to traditional methods. Key findings and implications: The study compared LLMs to 10 other anomaly detection methods, including state-of-the-art deep learning tools and the decades-old ARIMA model. LLMs were outperformed by most other models, including ARIMA, which surpassed LLMs on 7 out of 11 datasets. Surprisingly, LLMs managed to outperform some models, including certain transformer-based deep learning methods. LLMs achieved...
read Oct 13, 2024New breakthrough enables LLMs to run efficiently on low-resource edge devices
Advancing edge AI with TPI-LLM: Researchers have developed a new system called TPI-LLM that enables large language models (LLMs) to run efficiently on low-resource edge devices, addressing privacy concerns and resource limitations. The shift towards edge computing for LLM inference is driven by growing privacy concerns surrounding user interaction data. Edge devices typically face constraints in computing power, memory, and bandwidth, necessitating collaboration across multiple devices to run and accelerate LLM inference. Existing solutions like pipeline parallelism and tensor parallelism have limitations in single-user scenarios and communication efficiency, respectively. Key innovations of TPI-LLM: The system introduces novel approaches to overcome...
read Oct 13, 2024Why powerful generative AI models are bad at simple math like counting
AI's Unexpected Stumbling Block: Large Language Models (LLMs) like ChatGPT and Claude, despite their advanced capabilities, struggle with simple tasks such as counting letters in words, revealing fundamental limitations in their processing methods. The Irony of AI Capabilities: While concerns about AI replacing human jobs are widespread, these sophisticated systems falter at basic tasks that humans find trivial. LLMs fail to accurately count the number of "r"s in "strawberry," "m"s in "mammal," or "p"s in "hippopotamus." This limitation highlights the difference between AI's pattern recognition abilities and human-like reasoning. Understanding LLM Architecture: The root of this counting problem lies in...
read Oct 13, 2024Apple research reveals key reasoning flaws in AI language models
AI Models Struggle with Basic Reasoning: Apple Study Reveals Flaws in LLMs A recent study conducted by Apple's artificial intelligence scientists has uncovered significant limitations in the reasoning abilities of large language models (LLMs), including those developed by industry leaders like Meta and OpenAI. The research highlights the fragility of these AI systems when faced with tasks requiring genuine understanding and critical thinking. Key findings: LLMs lack robust reasoning skills Apple researchers developed a new benchmark called GSM-Symbolic to evaluate the reasoning capabilities of various LLMs. Initial testing showed that minor changes in query wording can lead to dramatically different...
read Oct 12, 2024Fine-tuning fundamentals for founders
The evolution of AI applications: Fine-tuning AI models has emerged as a powerful technique for founders and engineers looking to enhance their AI-powered features beyond the capabilities of off-the-shelf models. Fine-tuning involves updating a model's weights to steer its behavior, offering deeper control than prompt engineering alone. This process can significantly improve model performance in terms of reliability, cost-effectiveness, and latency for specific tasks. However, fine-tuning is not a one-size-fits-all solution and should be approached strategically based on a project's stage and needs. Understanding fine-tuning: The technique allows developers to customize AI models for specific use cases, potentially solving common...
read Oct 12, 2024AI21 CEO thinks unlocking AI agents means outgrowing the Transformer model
The rise of alternative AI architectures: AI21 CEO Ari Goshen argues that Transformer models, while popular, may not be the best choice for developing efficient AI agents due to their limitations and high costs. Goshen believes that alternative architectures, such as Mamba and AI21's JAMBA, offer better performance and efficiency for AI agents. These architectures can provide faster inference times, longer context, and improved memory performance compared to Transformer models. AI21 is developing foundation models using its JAMBA architecture, which combines elements of Joint Attention and Mamba. Challenges with Transformer models: The reliance on Large Language Models (LLMs) built with...
read Oct 12, 2024Prime Intellect launches initiative to train open model with decentralized computing
Pioneering decentralized AI training: Prime Intellect is launching INTELLECT-1, a groundbreaking initiative to train a 10-billion-parameter AI model using decentralized computing resources. INTELLECT-1 builds upon Prime Intellect's previous OpenDiLoCo work, which implemented DeepMind's Distributed Low-Communication (DiLoCo) method for distributed AI training. The project aims to enable open-source, decentralized training of large AI models, challenging the current paradigm of centralized control in AI development. Key partners contributing computing power include Hugging Face, SemiAnalysis, and Arcee, among others. Prime Intellect has opened the platform for anyone to contribute their computing resources to the project. Technological advancements: The INTELLECT-1 project incorporates several algorithmic...
read Oct 11, 2024LLMs don’t reason — new Apple research shows why that’s a big problem
Apple researchers challenge LLM reasoning capabilities: A new study from Apple's AI researchers has cast doubt on the formal reasoning abilities of large language models (LLMs), suggesting their performance is based more on pattern matching than true reasoning. The study, conducted by six AI researchers at Apple, found no evidence of formal reasoning in language models, indicating that their behavior is better explained by sophisticated pattern matching. Changing names in problems could alter results by approximately 10%, highlighting the fragility of LLMs' reasoning capabilities. The researchers developed a new task called GSM-NoOp, which demonstrated LLMs' vulnerability to distracting information when...
read Oct 11, 2024How to maximize ROI on your LLM inference costs
Continuous LLM performance improvements drive ROI: NVIDIA consistently optimizes large language models (LLMs) to enhance throughput and reduce latency, maximizing return on infrastructure investments for real-time applications. NVIDIA regularly optimizes state-of-the-art community models, including Meta's Llama, Google's Gemma, Microsoft's Phi, and their own NVLM-D-72B. These optimizations allow customers to serve more complex models and reduce the infrastructure needed to host them. Performance improvements occur at every layer of the technology stack, including the TensorRT-LLM library. Significant performance gains: Recent advancements in NVIDIA's platforms have resulted in substantial improvements in LLM performance. Minimum latency performance for the open-source Llama 70B model...
read Oct 11, 2024AI startup Writer launches new model to rival OpenAI
Emerging AI powerhouse: Writer, a San Francisco-based AI startup, is making waves in the generative AI market with its cost-effective approach to model training and ambitious fundraising plans. Writer has launched a new large AI model aimed at competing with enterprise offerings from established players like OpenAI and Anthropic. The company claims to have spent only about $700,000 to train its latest model, significantly less than the millions typically required by competitors. Writer is currently in the process of raising up to $200 million from investors at a $1.9 billion valuation, nearly quadrupling its valuation from September 2023. Innovative cost-cutting...
read Oct 11, 2024DeepMind test exposes limits of long-context AI models
Long-context LLMs face reasoning challenges: DeepMind's Michelangelo benchmark reveals that while large language models (LLMs) with extended context windows have improved in information retrieval, they struggle with complex reasoning tasks over large datasets. Google DeepMind researchers developed Michelangelo to evaluate the long-context reasoning capabilities of LLMs, addressing limitations in existing benchmarks. The benchmark aims to assess models' ability to understand relationships and structures within vast amounts of information, rather than just retrieving isolated facts. Michelangelo consists of three core tasks: Latent List, Multi-round Co-reference Resolution (MRCR), and "I Don't Know" (IDK), each designed to test different aspects of long-context reasoning....
read Oct 11, 2024Researchers unveil Aria, a new multimodal open-source model
Introducing Aria: A groundbreaking open-source multimodal AI model: Researchers have unveiled Aria, an innovative open-source multimodal native mixture-of-experts model that demonstrates top-tier performance across a wide range of multimodal, language, and coding tasks. Key features and capabilities: Aria represents a significant advancement in multimodal AI, offering a powerful and versatile solution for integrating diverse types of information. The model boasts 3.9 billion activated parameters per visual token and 3.5 billion activated parameters per text token, enabling it to process and understand complex multimodal inputs effectively. Aria outperforms existing models like Pixtral-12B and Llama3.2-11B, and competes with the best proprietary models...
read Oct 10, 2024Cornell researchers develop technique that enhances RAG system performance
Revolutionizing retrieval-augmented generation: Researchers at Cornell University have introduced a groundbreaking technique called "contextual document embeddings" that significantly enhances the performance of large language models (LLMs) in retrieval-augmented generation (RAG) systems. The challenge with traditional methods: Standard retrieval approaches often struggle to account for context-specific details in specialized datasets, limiting their effectiveness in certain applications. Bi-encoders, commonly used in RAG systems, create fixed representations of documents and store them in vector databases for efficient retrieval. However, these models, trained on generic data, often fall short when dealing with nuanced, application-specific datasets. In some cases, classic statistical methods like BM25 outperform...
read Oct 10, 2024Australian AI identifies an astounding 160,000 new viruses
Groundbreaking AI discovery in virology: A machine learning model at the University of Sydney has identified an unprecedented 161,979 new RNA viruses, significantly expanding our understanding of viral biodiversity on Earth. The AI algorithm, named LucaProt, analyzed vast amounts of genetic data to identify previously unrecognized viruses by cross-referencing their genetic information with known viral protein structures used for replication. This discovery process, which would have taken much longer using traditional methods, demonstrates the potential of AI in accelerating scientific research and discovery in the field of virology. Expanding the viral landscape: The study's findings extend beyond the realm of...
read Oct 9, 2024Walmart unveils Wallaby LLM, diversifying its AI strategy
Walmart's AI Innovation: Introducing Wallaby LLM: Walmart, the retail giant, is expanding its artificial intelligence capabilities with the development of Wallaby, a suite of retail-focused large language models (LLMs) designed to enhance customer service and operational efficiency. Wallaby's unique features and potential applications: Trained on decades of Walmart data, Wallaby is designed to understand the specific language patterns of Walmart employees and customers, aiming to provide more natural and aligned responses with the company's core values. Desirée Gosby, vice president of Emerging Technology at Walmart Global Tech, revealed that Wallaby is currently undergoing extensive internal testing, particularly with Walmart associates....
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