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Introduction The newest model collection from Microsoft’s Small Language Models (SLMs) family is called Phi-3. They surpass models of comparable and greater sizes on a variety of benchmarks in language, reasoning, coding, and math. They are made to be extremely powerful and economical. With Phi-3 models available, Azure clients have access to a wider range […] The post What Makes Microsoft Phi 3.5 SLMs a Game-Changer for Generative AI?
A critical challenge in training large language models (LLMs) for reasoning tasks is identifying the most compute-efficient method for generating synthetic data that enhances model performance. Traditionally, stronger and more expensive language models (SE models) have been relied upon to produce high-quality synthetic data for fine-tuning. However, this approach is resource-intensive and restricts the amount of data that can be generated within a fixed computing budget.
Terms like “data governance,” “Generative AI” and “large language models” are becoming commonplace in the workplace. But for business leaders, it takes more.
The digital age has led to a massive increase in the amount of text-based content available online, from research papers and articles to social media posts and corporate documents. Traditional search engines often fall short, providing only a list of relevant documents without delivering comprehensive and contextually accurate answers to specific queries.
AI is reshaping marketing and sales, empowering professionals to work smarter, faster, and more effectively. This webinar will provide a practical introduction to AI, focusing on its current applications, transformative potential, and strategies for successful implementation in your organization. Using real-world examples and actionable insights, we’ll examine how businesses are leveraging AI to increase efficiency, enhance personalization, and drive measurable results.
Via Shutterstock Believe it or not, the moment you installed Python on your computer, you also installed other wonderful tools. One of them is SQLite. SQLite is an embedded, file-based relational database management system (RDBMS) that can be used in our Python applications without having to install any additional software. Instead, we only need to import the built-in Python library sqlite3 to use this database.
Time series modeling is vital across many fields, including demand planning, anomaly detection, and weather forecasting, but it faces challenges like high dimensionality, non-linearity, and distribution shifts. While traditional methods rely on task-specific neural network designs, there is potential for adapting foundational small-scale pretrained language models (SLMs) for universal time series applications.
Time series modeling is vital across many fields, including demand planning, anomaly detection, and weather forecasting, but it faces challenges like high dimensionality, non-linearity, and distribution shifts. While traditional methods rely on task-specific neural network designs, there is potential for adapting foundational small-scale pretrained language models (SLMs) for universal time series applications.
Within the Databricks Community, there is a technical blog where community members share best practices, tutorials and insights on data analytics, data engineering.
Researchers at Alibaba have announced the release of Qwen2-VL, the latest iteration of vision language models based on Qwen2 within the Qwen model family. This new version represents a significant leap forward in multimodal AI capabilities, building upon the foundation established by its predecessor, Qwen-VL. The advancements in Qwen2-VL open up exciting possibilities for a wide range of applications in visual understanding and interaction, following a year of intensive development efforts.
Computer vision (CV) infrastructure can fundamentally change how firms perform tasks, automating manual work, closing safety gaps, and enabling real-time decision-making. However, not every team, project, or firm is a prime candidate for full-service computer vision infrastructure. Before making the leap to implementation, you must assess whether you are ready for such a transformation.
Multimodal large language models (MLLMs) represent a significant leap in artificial intelligence by combining visual and linguistic information to understand better and interpret complex real-world scenarios. These models are designed to see, comprehend, and reason about visual inputs, making them invaluable in optical character recognition (OCR) and document analysis tasks.
Speaker: Joe Stephens, J.D., Attorney and Law Professor
Ready to cut through the AI hype and learn exactly how to use these tools in your legal work? Join this webinar to get practical guidance from attorney and AI legal expert, Joe Stephens, who understands what really matters for legal professionals! What You'll Learn: Evaluate AI Tools Like a Pro 🔍 Learn which tools are worth your time and how to spot potential security risks before they become problems.
The days of police reports typed with one-finger by exasperated peacekeepers may soon go the way of brass knuckles. Cops in Oklahoma City are now using an AI chatbot — linked to their body camera — to write pursuits and arrests in real-time. Observes Oklahoma City Police Sergeant Matt Gilmore regarding the AI’s report on a recent incident: “It was a better report than I could have ever written — and it was 100% accurate.” Other city police departments giving A
A team of researchers from the Institute of Automation, Chinese Academy of Sciences, and the University of California, Berkeley Propose K-Sort Arena: a novel benchmarking platform designed to evaluate visual generative models efficiently and reliably. As the field of visual generation advances rapidly, with new models emerging frequently, there is an urgent need for effective evaluation methods that can keep pace.
Articles Following last week’s newsletter, I wanted to learn more about the feature analysis and especially interpretability of the model more and found an excellent post form AlignmentForum about the mechanistic interpretability of the GPT-2 model, focusing on how it represents and processes calendar-related information. The post goes into details on the geometry of the residual stream in layer 8 of GPT-2, aiming to understand how the model encodes and manipulates date-related features.
Large Language Models (LLMs) have revolutionized natural language processing but face significant challenges in handling very long sequences. The primary issue stems from the Transformer architecture’s quadratic complexity relative to sequence length and its substantial key-value (KV) cache requirements. These limitations severely impact the models’ efficiency, particularly during inference, making them prohibitively slow for generating extended sequences.
Forget predictions, let’s focus on priorities for the year and explore how to supercharge your employee experience. Join Miriam Connaughton and Carolyn Clark as they discuss key HR trends for 2025—and how to turn them into actionable strategies for your organization. In this dynamic webinar, our esteemed speakers will share expert insights and practical tips to help your employee experience adapt and thrive.
Created Using Ideogram Next Week in The Sequence: Edge 427: Our series about state space models(SSM) continues with a review of AI21’s Jamba, a model that combines transformers and SSMs. We discuss Jamba’s original research paper and the DeepEval framework. Edge 428: We dive into PromptPoet, Character.ai’s framework for prompt optimization.
The field of information retrieval (IR) has rapidly evolved, especially with the integration of neural networks, which have transformed how data is retrieved and processed. Neural retrieval systems have become increasingly important, particularly those using dense and multi-vector models. These models encode queries and documents as high-dimensional vectors and capture relevance signals beyond keyword matching, allowing for more nuanced retrieval processes.
The implementation of Neural Networks (NNs) is significantly increasing as a means of improving the precision of Molecular Dynamics (MD) simulations. This could lead to new applications in a wide range of scientific fields. Understanding the behavior of molecular systems requires MD simulations, but conventional approaches frequently suffer from issues with accuracy or computational efficiency.
Cohere For AI unveiled two significant advancements in AI models with the release of the C4AI Command R+ 08-2024 and C4AI Command R 08-2024 models. These state-of-the-art language models are designed to push what’s achievable with AI, especially in terms of text generation, reasoning, and tool use. They offer profound implications for both research and practical applications across various domains.
Speaker: Joe Stephens, J.D., Attorney and Law Professor
Get ready to uncover what attorneys really need from you when it comes to trial prep in this new webinar! Attorney and law professor, Joe Stephens, J.D., will share proven techniques for anticipating attorney needs, organizing critical documents, and transforming complex information into compelling case presentations. Key Learning Objectives: Organization That Makes Sense 🎯 Learn how to structure and organize case materials in ways that align with how attorneys actually work and think.
If you regularly follow AI updates, the AI Safety Bill in California should have caught your attention and is causing a lot of debate in Silicon Valley. SB 1047, the Safe and Secure Innovation for Frontier Artificial Intelligence Models Act, was passed by the State Assembly and Senate. This is a big step forward in California’s efforts to control artificial intelligence (AI).
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