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
Tag Archives: Standards
Developing Specific Reporting Standards in Artificial Intelligence Centred Research
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
Band of AI startups launch ‘rebel alliance’ for interoperability
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
Protocols, Not Platforms: A Technological Approach to Free Speech
After a decade or so of the general sentiment being in favor of the internet and social media as a way to enable more speech and improve the marketplace of ideas, in the last few years the view has shifted dramatically—now it seems that almost no one is happy. Some feel that these platforms have become cesspools of trolling, bigotry, and hatred. 1. Zachary Laub, Hate Speech on Social Media: Global Comparisons, Council on Foreign Rel. (Jun. 7, 2019), https://www.cfr.org/backgrounder/hate-speech-social-media-global-comparisons. Meanwhile, others feel that these platforms have become too aggressive in policing language and are systematically silencing or censoring certain viewpoints. 2. Tony Romm, Republicans Accused Facebook, Google and Twitter of Bias. Democrats Called the Hearing ‘Dumb.’, Wash. Post (Jul. 17, 2018), https://www.washingtonpost.com/technology/2018/07/17/republicans-accused-facebook-google-twitter-bias-democrats-called-hearing-dumb/?utm_term=.895b34499816. And that’s not even touching on the question of privacy and what these platforms are doing (or not doing) with all of the data they collect.
… This article proposes an entirely different approach—one that might seem counterintuitive but might actually provide for a workable plan that enables more free speech, while minimizing the impact of trolling, hateful speech, and large-scale disinformation efforts.
… That approach: build protocols, not platforms.
To be clear, this is an approach that would bring us back to the way the internet used to be. The early internet involved many different protocols—instructions and standards that anyone could then use to build a compatible interface. Email used SMTP (Simple Mail Transfer Protocol). Read More
The Seven Patterns Of AI
Intel's 5 Steps to an AI Proof of Concept
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.
Intel’s AI Readiness Model
To aid organizations wherever they are on their AI journeys, Intel has created a Readiness Model to help decision makers understand where to prioritize efforts. We have developed this based on our experience working with customers across a range of scenarios and industry verticals. Examples include manufacturing companies wanting to improve quality control, and financial services organizations looking to use AI in algorithmic trading. This paper provides guidance on how to judge an organization’s ability and readiness to use AI to generate business value, and includes a list of questions which you can use to guide your own self-assessment activities. Read More
Assessing Technology Readiness for Artificial Intelligence and Machine Learning based Innovations
Every innovation begins with an idea. To make this idea a valuable novelty worth investing in requires identification, assessment and management of innovation projects under two primary aspects: The Market Readiness Level (MRL) measures if there is actually a market willing to buy the envisioned product. The Technology Readiness Level (TRL) measures the capability to produce the product. The READINESS navigator is a state of the art software tool that supports innovators and investors in managing these aspects of innovation projects. The existing technology readiness levels neatly model the production of physical goods but fall short in assessing data based products such as those based on Artificial Intelligence (AI) and Machine Learning (ML). In this paper we describe our extension of the READINESS navigator with AI and ML relevant readiness levels and evaluate its usefulness in the context of 25 different AI projects. Read More
The call for a Data Science Readiness Level
In the 1970s, NASA developed the Technical Readiness Level (TRL) scale to measure research and development of cutting edge technology. Their purpose is to estimate the maturity of a technology during the acquisition process and are scaled from 1 to 9 with 9 being the most mature. TRLs enable consistent, uniform discussions of technical maturity across different types of technologies.
This concept is well known to researchers seeking grants from many government agencies, but seems to have lost favor in other engineering applications. With the growing cutting edge discoveries in Artificial Intelligence, Machine Learning, and Data Science this blog will explore use of this scale to measure progress and guide success of data science projects by linking them to a value on the TRL scale. Read More
Rick's Cafe AI 