EmTech AI 2025: How AI is revolutionizing science

In October, 20 companies joined The Crunchbase Unicorn Board, contributing a total of $44.5 billion in value. This marks the highest valuation added for a new cohort in the past three years, with artificial intelligence (AI) leading the growth.

AI is revolutionizing various industries, but effective infrastructure is crucial for optimal performance. This ebook outlines strategies to enhance AI workloads, including optimizing infrastructure for applications like chatbots, utilizing dynamic batching and KV caching to reduce costs, and leveraging technologies like NVIDIA GPUs and Kubernetes for scalability.

A team of quantum physicists has developed a new version of the AI model DeepSeek R1, named DeepSeek R1 Slim, which is 55% smaller and removes the censorship imposed by its original Chinese creators. This advancement was made by Multiverse Computing, a Spanish company specializing in quantum-inspired AI techniques.

Artificial intelligence (AI) is expected to become a significant topic in the upcoming 2026 midterms and the 2028 election, following its limited presence in the 2024 election. Concerns are rising regarding the financial investments in AI, potential federal bailouts, energy consumption, labor displacement, and the reliability of AI outputs. Politicians are increasingly voicing opposition to AI, indicating a growing political landscape surrounding this technology.

The CEO of Algebris Investments has advised investors to decrease their investments in leading technology firms, citing concerns about a potential significant correction in the AI sector. This bearish outlook reflects a growing skepticism regarding the sustainability of AI valuations amidst increasing market volatility.

The article discusses SparseST, a new approach in spatiotemporal data mining (STDM) that addresses the challenges of computational expense associated with Convolutional Long Short-Term Memory (ConvLSTM) models. These models, while effective in various applications such as transportation and healthcare, are often too resource-intensive for edge devices. The need for efficient AI solutions is emphasized, particularly in the context of edge computing, to maintain performance while reducing computational costs.

The article introduces Gen-ViRe, a new benchmark for generative visual reasoning that addresses the limitations of current models in simulating real-world dynamics. While Chain-of-Thought (CoT) prompting has advanced symbolic reasoning in large language models (LLMs), it is limited to discrete text. Gen-ViRe aims to evaluate Chain-of-Frames (CoF) reasoning, which translates thought into visual sequences, thereby assessing cognitive abilities in multi-step planning and abstract reasoning. This benchmark seeks to fill a gap in understanding model capabilities.

Bladder cancer is a prevalent malignancy with a high recurrence rate of up to 78%, necessitating precise post-operative monitoring. Multi-sequence contrast-enhanced MRI is commonly utilized for recurrence detection, but interpreting these scans is challenging due to post-surgical changes. This study introduces a curated multi-sequence, multi-modal MRI dataset designed for bladder cancer recurrence prediction and proposes H-CNN-ViT, a new model aimed at enhancing prediction accuracy in this critical area.