When the AI bubble bursts, the nerds will do their best work. Suddenly, and not long ago, our dearest tech industry leaders began to suggest caution. Sam Altman said that AI is in a bubble "for sure," albeit one formed around "a kernel of truth." Mark Zuckerberg said an AI bubble "is quite possible," though "if the models keep on growing in capability year over year and demand keeps growing, then maybe there is no collapse, or something." Even Eric Schmidt is saying to calm down about artificial general intelligence and focus on competing with China .

Fairlearn is an open source project to help practitioners assess and improve fairness of artificial intelligence (AI) systems. The associated Python library, also named fairlearn, supports evaluation of a model's output across affected populations and includes several algorithms for mitigating fairness issues. Grounded in the understanding that fairness is a sociotechnical challenge, the project integrates learning resources that aid practitioners in considering a system's broader societal context.

It has been less than 18 months since we published our last MAD landscape, and it has been full of drama. When we left, the data world was booming in the wake of the gigantic Snowflake IPO, with a whole ecosystem of startups organizing around it. Since then, of course, public markets crashed, a recessionary economy appeared and VC funding dried up. A whole generation of data/AI startups has had to adapt to a new reality. Meanwhile, the last few months saw the unmistakable, exponential acceleration of Generative AI, with arguably the formation of a new mini-bubble.

As good as artificial intelligence (AI) has become in answering queries and writing code, there will still be a need for developers, says GitHub CEO Thomas Dohmke. That's because human intelligence still reigns when it comes to solving complex problems, and people can do it more productively with the help of AI to offload menial tasks. Dohmke should know, as GitHub is used by millions of open source developers around the world not only to host their code, but increasingly to automate their software builds, testing and deployment through continuous integration and continuous deployment (CI/CD). On a recent trip to key markets in Asia, Dohmke spoke to Computer Weekly about GitHub's work in the region, its synergies with Microsoft, which acquired GitHub in 2018, and how GitHub's Copilot AI assistant and Codespaces cloud-based development environment can improve the lives of developers. Can you tell me more about your time in the Asia-Pacific (APAC) region and what you're hoping to accomplish while you're here?

In open source project governance, there has been a lot of concern about how to measure developers' contributions. However, extremely sparse work has focused on enabling developers to improve their contributions, while it is significant and valuable. In this paper, we introduce a deep reinforcement learning framework named Open Source Software(OSS) Mentor, which can be trained from empirical knowledge and then adaptively help developers improve their contributions. Extensive experiments demonstrate that OSS Mentor significantly outperforms excellent experimental results. Moreover, it is the first time that the presented framework explores deep reinforcement learning techniques to manage open source software, which enables us to design a more robust framework to improve developers' contributions.

Open source is fertile ground for transformative software, especially in cutting-edge domains like artificial intelligence (AI) and machine learning. The open source ethos and collaboration tools make it easier for teams to share code and data and build on the success of others. This article looks at 13 open source projects that are remaking the world of AI and machine learning. Some are elaborate software packages that support new algorithms. Others are more subtly transformative.

As enterprises increasingly try to put their data to work using artificial intelligence and machine learning, the landscape of vendors and open source projects can be daunting. As FirstMark partner Matt Turck has written, in 2021 the industry saw a "rapid emergence of a whole new generation of data and ML startups," and in 2022, this trend looks set to continue. AI/ML is so hot, in fact, that even with a recession looming CIOs remain loath to cut spending on AI/ML projects. So where will enterprises spend that money? To help you navigate the sometimes bewildering array of AI/ML options out there, I talked with data science professionals to get their picks on the most innovative companies in AI/ML.

The purpose behind this article is to give data scientists / analysts (or any non engineering focused individual) the rundown on how to use GitHub and what best practices to adhere to. The tutorial will consist of a combination guidelines using the UI and command line (terminal). The naming convention for Git commands are consistent across the platforms provided by GitHub so the skills should be exchangeable if you prefer to use Github desktop or GitLab instead of the web UI or command line. The following is the outline for the article. GitHub or any version control software is important for any software development projects, including those which are data driven. GitHub is a software which allows version control of your projects through a tool known as Git.

Today at Build 2022, Microsoft unveiled Project Volterra, a device powered by Qualcomm's Snapdragon platform that's designed to let developers explore "AI scenarios" via Qualcomm's new Snapdragon Neural Processing Engine (SNPE) for Windows toolkit. The hardware arrives alongside support in Windows for neural processing units (NPUs), or dedicated chips tailored for AI- and machine learning-specific workloads. Dedicated AI chips, which speed up AI processing while reducing the impact on battery, have become common in mobile devices like smartphones. But as apps like AI-powered image upscalers come into wider use, manufacturers have been adding such chips to their laptop lineups. M1 Macs feature Apple's Neural Engine, for instance, and Microsoft's Surface Pro X has the SQ1 (which was co-developed with Qualcomm).

Open Source Software (OSS) is a proven model that delivers tangible benefits to businesses, including improved time-to-market, reduced costs, and increased flexibility. OSS is pervasive in the technology landscape and beyond it, with adoption across multiple industries. In a 2022 survey by Red Hat, 95 percent of IT leaders said they are using open source in their IT infrastructure, which will only increase. Artificial intelligence (AI) is no different from any other technology domain where OSS dominates. In a recent paper published by Linux Foundation Research, written by Dr. Ibrahim Haddad, General Manager of the LF AI & Data Foundation, over 300 critical open source projects have been identified offering over 500 million lines of code, contributed by more than 35,000 developers who work side by side to advance the state of technology in an open, collaborative, and transparent way. As with other industries, OSS adoption in the AI field has increased the use of open source in products and services, contributions to existing projects, the creation of projects fostering collaboration, and the development of new technologies due to this amazing success story.