Must See AI News for Tech Functions This Week - 16 December 2024
Explore new AI tools and platforms! Gemini 2.0 Flash, Replit Agents, Ruliad's DeepThought-8B, Meta's AI Data Center, Google's Willow Quantum Chip, NotebookLM, Auto-RAG, and more.
AI Development Tools and Platforms
1️⃣ Gemini 2.0 Flash
Google's latest model offers double the speed of Gemini 1.5 Pro, with enhanced capabilities across multimodal understanding, text, code, video, and spatial reasoning. It introduces a Multimodal Live API for real-time applications with streaming audio and video, natural conversational patterns, and tool integration.
Why it matters: This model pushes the boundaries of real-time AI applications, enabling more interactive and responsive AI experiences.
Tech behind it: Integrates with Google AI Studio, featuring new starter apps for spatial understanding, video analysis, and Google Maps exploration. It supports native tool use, including Google Search, code execution, and custom function calling.
2️⃣ Replit Agents
Replit's new Assistant and Agent tools streamline development with features like code generation, environment setup, dependency management, and application deployment based on natural language instructions. It also introduces React support for enhanced visual outputs and a checkpoint billing system for usage-based pricing.
Why it matters: Democratizes application development, potentially leading to a surge in new applications from both technical and non-technical users.
Tech behind it: Integrates with Replit’s infrastructure, allowing access to databases and deployment tools without third-party services.
3️⃣ Ruliad AI Releases DeepThought-8B
A new small language model built on LLaMA-3.1, DeepThought-8B features test-time compute scaling and delivers transparent reasoning. It documents each step of its thinking in JSON format and can take as many reasoning steps as needed to solve complex problems.
Why it matters: This model makes the inference process more transparent and controllable, enhancing the trustworthiness and interpretability of AI decisions.
Tech behind it: Built on LLaMA-3.1 8B, available through Ruliad’s chat application, with plans to open a developer API and release open model weights.
An open-source fine-tuned Llama 3.2 model optimized for phones and laptops. It supports function calling, JSON mode, and structured outputs.
Why it matters: This model brings advanced AI capabilities to resource-constrained devices, making powerful AI tools more accessible.
Tech behind it: Available on Hugging Face with GGUF quantized versions, designed to run efficiently on consumer hardware.
AI Infrastructure and Deployment Solutions
Meta is investing $10 billion to build its largest-ever data center in Louisiana, designed for AI processing and scheduled for completion by 2030.
Why it matters: This investment underscores the growing need for specialized infrastructure to support the increasing demands of AI processing.
Tech behind it: The data center will be equipped to handle AI workloads, reflecting Meta's commitment to advancing AI capabilities.
2️⃣ Google's Willow Quantum Chip
Google's new quantum chip achieves exponential error reduction as it scales and completes computations in under 5 minutes that would take supercomputers 10^25 years.
Why it matters: This breakthrough demonstrates significant progress towards practical quantum computing, which could revolutionize AI and other computationally intensive fields.
Tech behind it: Utilizes 105 qubits and maintains quantum states for extended periods, enabling more reliable complex calculations. Manufactured at Google's new quantum fabrication facility.
Data Processing and Analysis Tools
1️⃣ NotebookLM Expands to Audio and Video
Google's NotebookLM now supports YouTube and audio files, capable of transcribing, summarizing, and analyzing multimedia content. It features interactive audio discussions with AI hosts.
Why it matters: Makes NotebookLM more versatile for content creators and researchers working with rich media formats.
Tech behind it: Powered by Gemini 1.5 for transcription and summarization, enhancing NotebookLM’s multimodal capabilities.
2️⃣ Auto-RAG
A new iterative retrieval model that gives LLMs autonomous decision-making capabilities for retrieving and using external knowledge. LLMs independently determine when and what information to retrieve through multi-turn dialogues with the retriever.
Why it matters: This framework significantly outperforms existing iterative retrieval methods while requiring fewer retrievals per query, making RAG more efficient and reliable.
Tech behind it: Fine-tunes open-source LLMs on synthesized reasoning-based instructions, teaching them to systematically plan retrievals and queries to gather sufficient knowledge to answer questions.
AI Integration Frameworks and SDKs
1️⃣ Gemini Multimodal Live API
Develop real-time applications with streaming audio and video, natural conversational patterns, and tool integration via the new Multimodal Live API.
Why it matters: Enables the creation of interactive, responsive AI applications that can process and generate outputs from different media types in real-time.
Tech behind it: Integrated into Google AI Studio, supporting real-time processing and generation from streaming audio and video inputs.
2️⃣ Eleven Labs' Conversational AI Platform
Build and deploy voice AI agents that can speak realistically across web, mobile, and phone systems in real-time. The platform allows creating agents by selecting or creating custom voices, integrating preferred LLMs, and defining the agent's persona and knowledge base.
Why it matters: Facilitates the creation of realistic voice AI agents for various applications, enhancing user interaction across different platforms.
Tech behind it: Integration is straightforward using ElevenLabs' WebSocket API, React, JavaScript, Python, and Swift/iOS SDKs.