AI isn’t the problem — rushing it into the wrong tasks without the right data, expertise or guardrails is what makes projects fall apart.
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AI model cracks yeast DNA code to turbocharge protein drug output
MIT researchers have built an AI language model that learns the internal coding patterns of a yeast species widely used to manufacture protein-based drugs, then rewrites gene sequences to push protein ...
Abstract: Reliable and timely data collection poses a significant challenge for underwater wireless sensor networks (UWSNs), primarily due to the extremely low data rate of underwater communication ...
Chinese AI startup Zhipu AI aka Z.ai has released its GLM-4.6V series, a new generation of open-source vision-language models (VLMs) optimized for multimodal reasoning, frontend automation, and ...
Recurrent neural networks (RNNs) have been a very popular predictive modelling choice for sequence based applications. Here we consider RNNs for time series forecasting. The proposed distinct ...
Most learning-based speech enhancement pipelines depend on paired clean–noisy recordings, which are expensive or impossible to collect at scale in real-world conditions. Unsupervised routes like ...
ABSTRACT: This work presents an innovative Intrusion Detection System (IDS) for Edge-IoT environments, based on an unsupervised architecture combining LSTM networks and Autoencoders. Deployed on ...
If you are a tech fanatic, you may have heard of the Mu Language Model from Microsoft. It is an SLM, or a Small Language Model, that runs on your device locally. Unlike cloud-dependent AIs, MU ...
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