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Neuroscience has long been a field of divide and conquer. Researchers typically map specific cognitive functions to isolated brain regions—like motion to area V5 or faces to the fusiform gyrus—using models tailored to narrow experimental paradigms. While this has provided deep insights, the resulting landscape is fragmented, lacking a unified framework to explain how the human brain integrates multisensory information. Meta’s FAIR team has introduced TRIBE v2 , a tri-modal foundation model designed to bridge this gap. By aligning the latent representations of state-of-the-art AI architectures with human brain activity, TRIBE v2 predicts high-resolution fMRI responses across diverse naturalistic and experimental conditions. https://ift.tt/9QBlTtI The Architecture: Multi-modal Integration TRIBE v2 does not learn to ‘see’ or ‘hear’ from scratch. Instead, it leverages the representational alignment between deep neural networks and the primate brain. The architecture consists of thr...

Google has released Gemini 3.1 Flash Live in preview for developers through the Gemini Live API in Google AI Studio. This model targets low-latency, more natural, and more reliable real-time voice interactions, serving as Google’s ‘highest-quality audio and speech model to date.’ By natively processing multimodal streams, the release provides a technical foundation for building voice-first agents that move beyond the latency constraints of traditional turn-based LLM architectures. https://ift.tt/LPBl5c9 Is it the end of ‘Wait-Time Stack ‘? The core problem with previous voice-AI implementations was the ‘wait-time stack’: Voice Activity Detection (VAD) would wait for silence, then Transcribe (STT), then Generate (LLM), then Synthesize (TTS). By the time the AI spoke, the human had already moved on. Gemini 3.1 Flash Live collapses this stack through native audio processing. The model doesn’t just ‘read’ a transcript; it processes acoustic nuances directly. According to Google’s i...

In this tutorial, we work directly with Qwen3.5 models distilled with Claude-style reasoning and set up a Colab pipeline that lets us switch between a 27B GGUF variant and a lightweight 2B 4-bit version with a single flag. We start by validating GPU availability, then conditionally install either llama.cpp or transformers with bitsandbytes, depending on the selected path. Both branches are unified through shared generate_fn and stream_fn interfaces, ensuring consistent inference across backends. We also implement a ChatSession class for multi-turn interaction and build utilities to parse <think> traces, allowing us to explicitly separate reasoning from final outputs during execution. Copy Code Copied Use a different Browser MODEL_PATH = "2B_HF" import torch if not torch.cuda.is_available(): raise RuntimeError( " No GPU! Go to Runtime → Change runtime type → T4 GPU." ) gpu_name = torch.cuda.get_device_name(0) vram_gb = torch.cuda....

In the landscape of enterprise AI, the bridge between unstructured audio and actionable text has often been a bottleneck of proprietary APIs and complex cascaded pipelines. Today, Cohere—a company traditionally known for its text-generation and embedding models—has officially stepped into the Automatic Speech Recognition (ASR) market with the release of their latest model ‘ Cohere Transcribe ‘. The Architecture: Why Conformer Matters To understand the Cohere Transcribe model, one must look past the ‘Transformer’ label. While the model is an encoder-decoder architecture , it specifically utilizes a large Conformer encoder paired with a lightweight Transformer decoder . A Conformer is a hybrid architecture that combines the strengths of Convolutional Neural Networks (CNNs) and Transformers. In ASR, local features (like specific phonemes or rapid transitions in sound) are often handled better by CNNs, while global context (the meaning of the sentence) is the domain of Transformers. ...

Tencent AI Lab has released Covo-Audio , a 7B-parameter end-to-end Large Audio Language Model (LALM). The model is designed to unify speech processing and language intelligence by directly processing continuous audio inputs and generating audio outputs within a single architecture. System Architecture The Covo-Audio framework consists of four primary components designed for seamless cross-modal interaction: Audio Encoder : The model utilizes Whisper-large-v3 as its primary encoder due to its robustness against background noise and varied accents. This component operates at a frame rate of 50 Hz . Audio Adapter : To bridge the encoder and the LLM, a specialized adapter employs three downsampling modules, integrating linear and convolution layers to reduce the frame rate from 50 Hz to 6.25 Hz . LLM Backbone : The system is built upon Qwen2.5-7B-Base , which has been adapted to process interleaved sequences of continuous acoustic features and textual tokens. Speech Tokenizer and D...