In an industry where model size is often seen as a proxy for intelligence, IBM is charting a different course — one that values efficiency over enormity, and accessibility over abstraction. The 114-year-old tech giant’s four new Granite 4.0 Nano models, released today, range from just 350 million to 1.5 billion parameters, a fraction of …
Researchers at Tsinghua University developed the Optical Feature Extraction Engine (OFE2), an optical engine that processes data at 12.5 GHz using light rather than electricity. Its integrated diffraction and data preparation modules enable unprecedented speed and efficiency for AI tasks. Demonstrations in imaging and trading showed improved accuracy, lower latency, and reduced power demand. This …
Read more “Breakthrough optical processor lets AI compute at the speed of light”
When it comes to estimating how good we are at something, research consistently shows that we tend to rate ourselves as slightly better than average. This tendency is stronger in people who perform low on cognitive tests. It’s known as the Dunning-Kruger Effect (DKE): The worse people are at something, the more they tend to …
Read more “AI use makes us overestimate our cognitive performance, study reveals”
I altered the workflow a little bit from my previous post (using Hearmeman’s Animate v2 workflow). Added an int input and simple math to calculate the next sequence of frames and the skip frames in the VHS upload video node. I also extracted the last frame from every sequence generation and used a load image …
Time series data normally requires an in-depth understanding in order to build effective and insightful forecasting models.
Hallucinations pose a significant obstacle to the reliability and widespread adoption of language models, yet their accurate measurement remains a persistent challenge. While many task- and domain-specific metrics have been proposed to assess faithfulness and factuality concerns, the robustness and generalization of these metrics are still untested. In this paper, we conduct a large-scale empirical …
Read more “Evaluating Evaluation Metrics — The Mirage of Hallucination Detection”
Building and scaling generative AI models demands enormous resources, but this process can get tedious. Developers wrestle with managing job queues, provisioning clusters, and resolving dependencies just to ensure consistent results. This infrastructure overhead, along with the difficulty of discovering the optimal training recipe and navigating the endless maze of hyperparameter and model architecture choices, …
Read more “Announcing new capabilities in Vertex AI Training for large-scale training”
Watch out, DeepSeek and Qwen! There’s a new king of open source large language models (LLMs), especially when it comes to something enterprises are increasingly valuing: agentic tool use — that is, the ability to go off and use other software capabilities like web search or bespoke applications — without much human guidance. That model …
Read more “MiniMax-M2 is the new king of open source LLMs (especially for agentic tool calling)”
The new AI-powered Wikipedia competitor falsely claims that pornography worsened the AIDS epidemic and that social media may be fueling a rise in transgender people.
Many modern artificial intelligence (AI) applications, such as surgical robotics and real-time financial trading, depend on the ability to quickly extract key features from streams of raw data. This process is currently bottlenecked by traditional digital processors. The physical limits of conventional electronics prevent the reduction in latency and the gains in throughput required in …