Open model developers have emerged as key actors in the political economy of artificial intelligence (AI), but we still have a limited understanding of collaborative practices in the open AI ecosystem. This paper responds to this gap with a three-part quantitative analysis of development activity on the Hugging Face (HF) Hub, a popular platform for building, sharing, and demonstrating models. First, various types of activity across 348,181 model, 65,761 dataset, and 156,642 space repositories exhibit right-skewed distributions. Activity is extremely imbalanced between repositories; for example, over 70% of models have 0 downloads, while 1% account for 99% of downloads. Furthermore, licenses matter: there are statistically significant differences in collaboration patterns in model repositories with permissive, restrictive, and no licenses. Second, we analyse a snapshot of the social network structure of collaboration in model repositories, finding that the community has a core-periphery structure, with a core of prolific developers and a majority of isolate developers (89%). Upon removing the isolate developers from the network, collaboration is characterised by high reciprocity regardless of developers' network positions. Third, we examine model adoption through the lens of model usage in spaces, finding that a minority of models, developed by a handful of companies, are widely used on the HF Hub. Overall, activity on the HF Hub is characterised by Pareto distributions, congruent with OSS development patterns on platforms like GitHub. We conclude with recommendations for researchers, companies, and policymakers to advance our understanding of open AI development.

What I like the most about robotic simulations is their sheer ability to make software development and testing process time-efficient. Working with robots (to a large extent on prototypes, and often remotely) over the last decade has helped me come up with a simple rule -- do as much as you can with the simulation, use the actual robot hardware when you absolutely have to. Software for robots HAS TO run on robots, there is no way around it. However, there is plenty of simulation-based testing that can expedite your route to software deployment on the robot, and robot deployment on-site. I've spent the bulk of my time working with wheeled mobile robots and my choice of simulators for application development and testing is centered around that.

With several programming languages outshining in the market, choosing the right one is always a daunting task when you are in the early stages of leading your startup. Whether you want to build a minimum viable product (MVP) to gain attention for your concept or want to release your finished product as soon as possible in the market, the choice of a programming language should be wise and based on sound reasons. Not all programming languages will suit your business requirements. Startups must carefully consider the popularity of the language, budget, speed of development, libraries, integrations, scalability, stability, software security, and cost of developers before choosing a programming language. It is for this reason Python is often considered one of the best startup programming languages, as it satisfies all these requirements.

Artificial Intelligence (AI) is moving from the realm of science fiction to widespread enterprise scalability. Even ten years ago, AI workloads were almost exclusively utilized by a small number of very profitable companies that had the resources to experiment and hire an extensive team of data scientists. Today, AI is used in a number of everyday tools, from language recognition to health care prediction and nearly every industry in between. AI is also now deployed at the edge, not just inside massive data processing facilities. That trend will continue in the coming years.

Since the HAL9000 and Star Trek's M-5 Multitronic, the power and capabilities of AI have always been oversold by both Hollywood and Silicon Valley. Although we're still waiting on machines that can carry on an intelligent conversation, AI has been creeping into many objects in our everyday lives behind the scenes, making them more useful and proactive. People are most familiar with the intelligent assistants built into devices like the Amazon Echo, Google Nest Hub and Apple HomePod, but as I wrote more than three years ago, these rely on cloud backend services for most of their smarts, using local hardware primarily to recognize their wake word and listen for follow-up questions. The combination allows surprisingly sophisticated deep and machine learning models to run on embedded systems. Until recently, shoehorning AI software into a battery-powered device has required data scientists skilled in working with the constraints of an embedded SoC, but recent advances in AI development and automation frameworks, categorically termed TinyML, greatly expands the realm of smart devices.

In recent years, the applications of Artificial Intelligence have improved considerably around the world. With the growth of corporate activities at work, cloud computing has become a central part of AI evolution. In addition, as customers increasingly use their devices, businesses are becoming more aware of the need to bring the technology to those devices to be closer to customers and better serve their needs. This is why the Edge Computing market will continue to grow in the coming years. Edge AI is a system that uses Machine Learning algorithms to process data generated by a hardware device at the local level.

Facebook today announced that Microsoft has expanded its participation in PyTorch, the social network's machine learning framework, to take ownership of the development and maintenance of the PyTorch build for Windows. The intent is to bring the experience on Windows in line with other platforms, like Linux; historically, PyTorch on Windows has lagged behind due to a lack of test coverage, a convoluted installation experience, and missing functionality. PyTorch, which Facebook publicly released in January 2017, is an open source machine learning library based on Torch, a scientific computing framework and script language that in turn is based on the Lua programming language. While TensorFlow has been around slightly longer (since November 2015), PyTorch continues to see rapid uptake in the data science and developer community. It claimed one of the top spots for fastest-growing open source projects last year, according to GitHub's 2018 Octoverse report, and Facebook recently revealed that in 2019 the number of contributors to the platform grew more than 50% year-over-year to nearly 1,200.

AutoML is still a novice concept, albeit an exciting one with rapid advances in Machine Learning and Deep Learning.

On April 16, 2020, Intel and Udacity jointly announced their new Intel Edge AI for IoT Developers Nanodegree program to train the developer community in deep learning and computer vision. If you are wondering where AI is headed, now you know, it's headed to the edge. Edge computing is the concept of storing data and computing data directly at the location where it is needed. The global edge computing market is forecasted to reach 1.12 trillion dollars by 2023. Intel and Udacity aim to train 1 million developers.

Oracle announced the general availability of Java 14 (Oracle JDK 14). Java 14 continues Oracle's commitment to accelerate innovation by delivering new enhancements to enterprises and the developer community with a new feature release every six months. The latest Java Development Kit (JDK) delivers new features, including two new highly anticipated preview features – Pattern Matching for instanceof (JEP 305) and Records (JEP 359), as well as a second preview of Text Blocks (JEP 368). Additionally, the latest Java release adds Java language support for switch expressions, exposes new APIs for continuous monitoring of JDK Flight Recorder data, extends the availability of the low-latency Z Garbage Collector to macOS and Windows, and adds, in incubator modules, the packaging of self-contained Java applications and a new Foreign memory access API for safe, efficient access to memory outside of the Java heap. Recommended AI News: Spherix Incorporated Changing Name to AIkido Pharma Inc. to Reflect Increased Focus on AI and ML in Drug Development "Java 14 is further validation of the benefits of the six-month release cadence, giving developers access to features that they would otherwise be waiting years to get their hands on," said Georges Saab, vice president of development, Java Platform, Oracle.