American AI giants are backing a new effort to establish open standards for building agentic software and tools. OpenAI, Anthropic, and Block have cofounded a new open source organization--the Agentic AI Foundation--to promote standards for artificial intelligence agents. The three companies are also transferring ownership of some widely used agentic technologies over to the foundation. This includes Anthropic's Model Context Protocol (MCP), which allows agents to connect and interact; OpenAI's Agents.md These technologies were already free to use, but through the new foundation it will be possible for others to contribute to their development.

We integrate with lots of other tools including Pandas, AWS Sagemaker, MLflow, Flask, Ray, RAPIDS, Apache Kafka, and more. If you have any questions, comments, or just want to hang out with us, please join our Slack Community. In addition to joining the Slack Community, you can also help this project by giving us a in the upper right corner of this page. They capture key statistical properties of data, such as the distribution (far beyond simple mean, median, and standard deviation measures), the number of missing values, and a wide range of configurable custom metrics. By capturing these summary statistics, we are able to accurately represent the data and enable all of the use cases described in the introduction.

About 5 years ago we proposed that all robots should be fitted with the robot equivalent of an aircraft Flight Data Recorder to continuously record sensor and relevant internal status data. We call this an ethical black box (EBB). We argued that an ethical black box will play a key role in the processes of discovering why and how a robot caused an accident, and thus an essential part of establishing accountability and responsibility. Since then, within the RoboTIPS project, we have developed and tested several model EBBs, including one for an e-puck robot that I wrote about in this blog, and another for the MIRO robot. With some experience under our belts, we have now drafted an Open Standard for the EBB for social robots – initially as a paper submitted to the International Conference on Robots Ethics and Standards.

Enterprise cloud data firm Cloudera has issued a call for industry participation to help define universal open standards for machine learning operations (MLOps) and machine learning model governance. MLOps revolves around implementing machine-learning in production, notably around the infrastructure and tooling needed to deploy machine-learning algorithms and data pipelines reliably and at scale. By leveraging the community, Cloudera hopes to help companies make the most of their machine learning platforms and pave the way forward for the future of MLOps. The challenge of deploying and governing machine learning models at scale needs to be addressed at the industry level, says Doug Cutting, the chief architect at Cloudera. He pointed to Apache Atlas as being the best-positioned framework to integrate data management for explainable, interoperable and reproducible MLOps workflows.

As an Associate Member, Kalray was proud to represent the Khronos Group during the keynote sessions at Autosens 2019, held in Brussels, Belgium, from Sept.17 to 19, 2019. Stephane Strahm, Senior Product Manager at Kalray, talked about "Open minds to Open Standards for the deep learning automotive solutions". In order to efficiently orchestrate the adoption of the automotive industry's rapidly advancing connected technology, it is always mandatory in evaluating standards or creating standards, to improve interoperability of components as systems' complexity grows. This is happening nowhere more so than in the area of intelligent data compute for tactical driving systems – autonomous vehicles. Khronos' open standards are a key solution to providing versatility in the supply chain and embracing more of the collective AI development community to solve tomorrow's goals.

Machine learning (ML) has become one of the most critical capabilities for modern businesses to grow and stay competitive today. From automating internal processes to optimizing the design, creation and marketing processes behind virtually every product consumed, ML models have permeated almost every aspect of our work and personal lives -- and for businesses, the stakes have never been higher. Failing to adopt ML as a core competency will result in major competitive disadvantages that will define the next market leaders. Because of this, business and technology leaders need to implement ML models across their entire organization, spanning a large spectrum of use cases. However, this sense of urgency, combined with growing regulatory scrutiny, creates new and unique governance challenges that are currently difficult to manage.

Three years ago, we had maybe six or less AI accelerators, today there's over two dozen, and more are coming. One of the first commercially available AI training accelerators was the GPU, and the undisputed leader of that segment was Nvidia. Nvidia was already preeminent in machine learning (ML) and deep-learning (DL) applications and adding neural net acceleration was a logical and rather straight-forward step for the company. Nvidia also brought a treasure-trove of applications with their GPUs based on the company's proprietary development language CUDA. The company developed CUDA in 2006 and empowered hundreds of Universities to give courses on it. As a result, the thousands of computer science graduates every year came out of school knowing how and wanting to use CUDA.

Three years ago, we had maybe six or less AI accelerators, today there's over two dozen, and more are coming. One of the first commercially available AI training accelerators was the GPU, and the undisputed leader of that segment was Nvidia. Nvidia was already preeminent in machine learning (ML) and deep-learning (DL) applications and adding neural net acceleration was a logical and rather straight-forward step for the company. Nvidia also brought a treasure-trove of applications with their GPUs based on the company's proprietary development language CUDA. The company developed CUDA in 2006 and empowered hundreds of Universities to give courses on it. As a result, the thousands of computer science graduates every year came out of school knowing how and wanting to use CUDA.

IoT or Internet of things involves embedding sensors in traditionally dumb devices and enabling internet connectivity in them to collect a range of ambient data. IoT devices can sense their surroundings and communicate this data over the Internet, and they can in turn also be remotely controlled. This expands the range of internet-enabled devices far beyond desktops, laptops, and mobile phones, and the applications of IoT are endless. Today, IoT is disrupting many industries: some examples of IoT systems are smart fridges, heart monitors, small weather stations. A Gartner study on IoT devices indicates that there will be more than 20 billion IoT devices by 2020, and as of today, there are about a billion PCs and two and a half billion smartphones on the internet.

In 2017, the Chicago Police Department created six high-tech police hubs located throughout the city's more crime-ridden neighborhoods. Dubbed "Strategic Decision Support Centers," the hubs are a blend of human expertise and high-end technology, including surveillance cameras, gunshot detection platforms, predictive mapping and data analytics. Since the centers went live, crime in adjacent neighborhoods has gone down. In two of the districts that have had some of the city's highest crime rates, the decline in crime rates has been so significant, the numbers are helping to drive down the city's overall crime numbers. "Before this project started, I would have said there's no way technology can have this kind of impact," said Jonathan Lewin, chief of CPD's Bureau of Technical Services.