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The competition to develop the most advanced Large Language Models (LLMs) has seen major advancements, with the four AI giants, OpenAI, Meta, Anthropic, and Google DeepMind, at the forefront. These LLMs are reshaping industries and significantly impacting the AI-powered applications we use daily, such as virtual assistants, customer support chatbots, and translation services.
Last week I ran a one-day class on NLG evaluation for IBM in Dublin. It covered many topics at a fairly high level. The overall goal was to give people more insights about different types of evaluation and what goes wrong in evaluations; hopefully this will both help people do better evaluations themselves, and also be more critical of weak evaluations in published papers.
Large Language Models (LLMs), initially limited to text-based processing, faced significant challenges in comprehending visual data. This limitation led to the development of Visual Language Models (VLMs), which integrate visual understanding with language processing. Early models like VisualGLM, built on architectures such as BLIP-2 and ChatGLM-6B, represented initial efforts in multi-modal integration.
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Author(s): Souradip Pal Originally published on Towards AI. This member-only story is on us. Upgrade to access all of Medium. Imagine sifting through rows of data in a spreadsheet packed with numbers that look impressive at first glance. But when you try to analyze them, the digits feel like a maze, hard to interpret and even harder to draw conclusions from.
Machine learning has made significant advancements, particularly through deep learning techniques. These advancements rely heavily on optimization algorithms to train large-scale models for various tasks, including language processing and image classification. At the core of this process lies the challenge of minimizing complex, non-convex loss functions.
Machine learning has made significant advancements, particularly through deep learning techniques. These advancements rely heavily on optimization algorithms to train large-scale models for various tasks, including language processing and image classification. At the core of this process lies the challenge of minimizing complex, non-convex loss functions.
Last Updated on September 8, 2024 by Editorial Team Author(s): Souradip Pal Originally published on Towards AI. This member-only story is on us. Upgrade to access all of Medium. A girl looking at a screen containing mixed variables. Source: Image generated by Dall-E Imagine you’re working on a brand-new data project, the kind that makes your hands twitch with excitement.
Neural Architecture Search (NAS) has emerged as a powerful tool for automating the design of neural network architectures, providing a clear advantage over manual design methods. It significantly reduces the time and expert effort required in architecture development. However, traditional NAS faces significant challenges as it depends on extensive computational resources, particularly GPUs, to navigate large search spaces and identify optimal architectures.
ChatGPT-Maker Mulls New $2,000/Month Rate Is the party over for everyday users of ChatGPT? Tech pub The Information reports that the maker of ChatGPT — OpenAI — is mulling plans to jack-up the price of future versions of the wonder-bot to as much as $2,000/month. Currently, a basic subscription to ChatGPT costs $20/month. Observes a story by Thomson Reuters: “The reported pricing discussions come after media reports said Apple and chip giant Nvidia were in talks to invest in Op
Large language models (LLMs) have revolutionized natural language processing (NLP), particularly for English and other data-rich languages. However, this rapid advancement has created a significant development gap for underrepresented languages, with Cantonese being a prime example. Despite being spoken by over 85 million people and holding economic importance in regions like the Guangdong-Hong Kong-Macau Greater Bay Area, Singapore, and North America, Cantonese remains severely underrepresented
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Generating user intent from a sequence of user interface (UI) actions is a core challenge in comprehensive UI understanding. Recent advancements in multimodal large language models (MLLMs) have led to substantial progress in this area, but their demands for extensive model parameters, computing power, and high latency makes them impractical for scenarios requiring lightweight, on-device solutions with low latency or heightened privacy.
Adapting 2D-based segmentation models to effectively process and segment 3D data presents a significant challenge in the field of computer vision. Traditional approaches often struggle to preserve the inherent spatial relationships in 3D data, leading to inaccuracies in segmentation. This challenge is critical for advancing applications like autonomous driving, robotics, and virtual reality, where a precise understanding of complex 3D environments is essential.
Articles LLama3 paper from Meta is a long paper(only 92 pages), but covers a variety of different topics when it comes to train large models. There are a number of learnings for reliability and scalability challenges of these models as outlined in the table 5 of the above paper: Reliability and Scalability Challenges in Training Llama 3 The development and training of Llama 3, particularly the 405B parameter model, presented significant reliability and scalability challenges.
GNNs have excelled in analyzing structured data but face challenges with dynamic, temporal graphs. Traditional forecasting, often used in fields like economics and biology, relied on statistical models for time-series data. Deep learning, particularly GNNs, shifted focus to non-Euclidean data like social and biological networks. However, applying GNNs to dynamic graphs, where relationships constantly evolve, still needs to be improved.
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Created Using Ideogram Next Week in The Sequence: Edge 429: Our series about state space models(SSMs) continues with an exploration of MambaByte including its original paper. We also discuss the MindsDB platform for building AI systems. Edge 430: We dive into The AI Scientist, an agent for scientific experimentation. You can subscribe to The Sequence below: TheSequence is a reader-supported publication.
Microsoft addresses the complex challenges of integrating geospatial data into machine learning workflows. Working with such data is difficult due to its heterogeneity, coming in multiple formats and varying resolutions, and its complexity, involving features like occlusions, scale variations, and atmospheric interference. Additionally, geospatial datasets are large and computationally expensive to process, while a lack of standardized tools has historically hindered research and development in
LLMs like GPT-4, MedPaLM-2, and Med-Gemini perform well on medical benchmarks but need help to replicate physicians’ diagnostic abilities. Unlike doctors who gather patient information through structured questioning and examinations, LLMs often need more logical consistency and specialized knowledge, leading to inadequate diagnostic reasoning.
Together AI has introduced a groundbreaking technique known as TEAL ( T raining-Fre e A ctivation Sparsity in L LMs) that has the potential to advance the field of efficient machine learning model inference significantly. The company, a leader in open-source AI models, has been exploring innovative ways to optimize model performance, especially in environments with limited memory resources.
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Introduction to EXAONE 3.0: The Vision and Objectives EXAONE 3.0 represents a significant milestone in the evolution of language models developed by LG AI Research , particularly within Expert AI. The name “ EXAONE ” derives from “ EX pert A I for Every ONE ,” encapsulating LG AI Research ‘s commitment to democratizing access to expert-level artificial intelligence capabilities.
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