For decades, people in IT had taken delight in drafting Fortran’s obituary. Yet, this old language lives on. But in recent years, the Fortran user community has begun sounding alarms: Fortran shops are having difficulty finding young programmers to replace those who are leaving the workforce, because the young are not willing to devote their careers to this archaic language. At present, no language can rival, let alone surpass, Fortran when it comes to implementing long-lived, large-scale, massively-parallel scientific and engineering applications; not even C and C++. Yet, modern programmers know nothing about Fortran, nor have they any interest in it. Suffice it to say, Fortran has an image problem.

… It would seem that trying to shore up this mid-century language for the grind of the 21st Century verges on insanity. Not so. I contend that Fortran modernisation is worthwhile and necessary. I admit, though, that refreshing Fortran for the 2020s is but a forlorn hope, at present. — Read More

Google’s Open Knowledge Format makes AI-agent knowledge a free, vendor-neutral markdown standard. The same day it shipped, Google wired the format into the Knowledge Catalog it charges to run, and the spec leaves the paid serving layer out of scope. Openness, it turns out, is the strategy. — Read More

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As foundation models continue to improve, the lack of relevant context often limits what they can do, especially as they are used to build agentic systems. While these models can help you write code, summarize documents, or analyze a dataset, they still need the right information to produce accurate and actionable results. 

That’s why today, we’re introducing the Open Knowledge Format (OKF), an open specification that formalizes the LLM-wiki pattern into a portable, interoperable format. This is a vendor-neutral, agent- and human-friendly standard for representing the metadata, context, and curated knowledge that modern AI systems need. — Read More

Artificial intelligence is increasingly used to integrate and analyze multiple types of data formats, such as text, images, audio and video. One challenge slowing advances in multimodal AI, however, is the process of choosing the algorithmic method best aligned to the specific task an AI system needs to perform.

Scientists have developed a unified view of AI methods aimed at systemizing this process. The Journal of Machine Learning Research published the new framework for deriving algorithms, developed by physicists at Emory University. — Read More

The Linux Foundation, the nonprofit organization enabling mass innovation through open source, today announced the formation of the Agentic AI Foundation (AAIF), and founding contributions of three leading projects driving innovation in open source AI; Anthropic’s Model Context Protocol (MCP), Block’s goose, and OpenAI’s AGENTS.md. 

The advent of agentic AI represents a new era of autonomous decision making and coordination across AI systems that will transform and revolutionize entire industries. The AAIF provides a neutral, open foundation to ensure this critical capability evolves transparently, collaboratively, and in ways that advance the adoption of leading open source AI projects. — Read More

There are several emerging AI technologies that aim to enhance surgical care pathways over the coming decade. In particular, these are related to (1) diagnostics, (2) pre-operative planning, (3) intra-operative guidance and (4) surgical robotics.¹ This trend has been mirrored bn the sharp increase in the number of surgical studies evaluating the use of AI.

Despite this fervour, very few AI devices have reached the point of clinical implementation within surgical environments.² This disconnect between ‘in silico bench’ and ‘bedside’ is a multifaceted issue related to technological, regulatory, and economic factors. However, this divide is also exacerbated by the variable quality of study reporting in this field; an issue perpetuated by the absence of AI-specific reporting guidelines for both pre-clinical and clinical AI studies. Read More

More than 20 AI startups have banded together to create the AI Infrastructure Alliance in order to build a software and hardware stack for machine learning and adopt common standards. The alliance brings together companies like Algorithmia; Determined AI, which works with deep learning; data monitoring startup WhyLabs; and Pachyderm, a data science company that raised $16 million last year in a round led by M12, formerly Microsoft Ventures. A spokesperson for the alliance said partner organizations have raised about $200 million in funding from investors.

Dan Jeffries, chief tech evangelist at Pachyderm, will serve as director of the alliance. He said the group began to form from conversations that started over a year ago. Participants include a number of companies whose founders have experience running systems at scale within Big Tech companies. For example, WhyLabs CEO and cofounder Alessya Visnjic worked on fixing machine learning issues at Amazon, and Jeffries previously worked with machine learning at Red Hat. Read More

An artificial intelligence (AI) software program is one that can sense, reason, act and adapt. It does so by first ‘learning’ from a large and diverse data set, which it uses to train models about the data. Once trained, the model is then deployed to infer results from similar, new or unseen data, for example turning verbal speech into text, identifying anomalies in a series of images, or calculating when a piece of machinery is about to fail. We show this sequence in Figure 1.

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