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Recap of Google I/O 2024: Gemini 1.5, Project Astra, AI-powered Search Engine
Google recently hosted its annual developer conference, Google I/O 2024, where numerous announcements were made regarding Google’s apps and services. As anticipated, AI was a focal point of the event, being incorporated into almost all Google products. Here is a summary of the major announcements from the event.
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Google Brings Gemini Nano to Chrome to Enable On-Device Generative AI
At its Google I/O 2024 developer conference, Google announced it is working to make support for on-device large language models a reality by bringing the smallest of its Gemini models, Gemini Nano, to Chrome.
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OpenAI Announces New Flagship Model GPT-4o
OpenAI recently announced the latest version of their GPT AI foundation model, GPT-4o. GPT-4o is faster than the previous version of GPT-4 and has improved capabilities in handling speech, vision, and multilingual tasks, outperforming all models except Google's Gemini on several benchmarks.
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AI Lab Extension Allows Podman Desktop Users to Experiment with LLMs Locally
One year after its 1.0 release, Podman Desktop announced the Podman AI Lab plugin promising to help developers start working with Large Language Models on their machines. Podman AI Lab streamlines LLM workflows featuring generative AI exploration, built-in recipe catalogue, curated models, local model serving, OpenAI-compatible API, code snippets, and playground environments.
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Apple Open-Sources One Billion Parameter Language Model OpenELM
Apple released OpenELM, a Transformer-based language model. OpenELM uses a scaled-attention mechanism for more efficient parameter allocation and outperforms similarly-sized models while requiring fewer tokens for training.
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Meta Releases Llama 3 Open-Source LLM
Meta AI released Llama 3, the latest generation of their open-source large language model (LLM) family. The model is available in 8B and 70B parameter sizes, each with a base and instruction-tuned variant. Llama3 outperforms other LLMs of the same parameter size on standard LLM benchmarks.
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The Devoxx Genie IntelliJ Plugin Provides Access to Local or Cloud Based LLM Models
Devoxx Genie, a 100% Java based JetBrains IntelliJ IDEA Plugin, uses local- or cloud-based Large Language Models (LLMs) for generating unit tests and explaining, reviewing and improving source code.
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Ines Montani at QCon London: Economies of Scale Can’t Monopolise the AI Revolution
During her presentation at QCon London, Ines Montani, co-founder and CEO of explosion.ai (the maker of spaCy), stated that economies of scale are not enough to create monopolies in the AI space and that open-source techniques and models will allow everybody to keep up with the “Gen AI revolution”.
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Enhancing Developer Experience for Creating Artificial Intelligence Applications
For one company, large language models created a breakthrough in artificial intelligence (AI) by shifting to crafting prompts and utilizing APIs without a need for AI science expertise. To enhance developer experience and craft applications and tools, they defined and established principles around simplicity, immediate accessibility, security and quality, and cost efficiency.
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Google Text Embedding Model Gecko Distills Large Language Models for Improved Performance
Gecko is a text embedding model that Google created by distilling knowledge from large language models into a general-purpose model. Gecko is trained using a novel approach on a variety of tasks including document retrieval, semantic similarity, and classification, and aims to be as general-purpose as it goes as well as highly performant.
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Large Language Models for Code by Loubna Ben Allal at QCon London
At QCon London, Loubna Ben Allal discussed Large Language Models (LLMs) for code. She discussed the lifecycle of code completion models, which consists of pre-training on vast codebases and finetuning and continuous adaptation. She specifically discussed open-source models, which are powered by platforms like Hugging Face.
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Navigating LLM Deployment: Tips, Tricks and Techniques by Meryem Arik at QCon London
At QCon London, Meryem Arik discussed deploying Large Language Models (LLMs). While initial proofs of concept benefit from hosted solutions, scaling demands self-hosting to cut costs, enhance performance with tailored models, and meet privacy and security requirements. She emphasized understanding deployment limits, quantization for efficiency, and optimizing inference to fully use GPU resources.
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Databrix Announces DBRX, an Open Source General Purpose LLM
Databricks launched DBRX, a new open-source large language model (LLM) that aims to redefine the standards of open models and outperform well-known competitors on industry benchmarks.
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Apple Researchers Detail Method to Combine Different LLMs to Achieve State-of-the-Art Performance
Many large language models (LLMs) have become available recently, both closed and open source further leading to the creation of combined models known as Multimodal LLMs (MLLMs). Yet, few or none of them unveil what design choices were made to create them, say Apple researchers who distilled principles and lessons to design state-of-the-art (SOTA) Multimodal LLMs.
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Researchers Open-Source LLM Jailbreak Defense Algorithm SafeDecoding
Researchers from the University of Washington, the Pennsylvania State University, and Allen Institute for AI have open-sourced SafeDecoding, a technique for protecting large language models (LLMs) against jailbreak attacks. SafeDecoding outperforms baseline jailbreak defenses without incurring significant computational overhead.