Workshop Goals This workshop aimed to identify initial challenges and opportunities for national resources for AI research (e.g., compute, data, models, etc.) and to facilitate planning for the envisioned National AI Research Resource (NAIRR). Participants included AI and cyberinfrastructure (CI) experts. Significant Findings 1. AI researchers confront unprecedented scale that goes well beyond generative AI 2. National investments in AI research resources have been insufficient 3. The suboptimal usability of current resources is compromising AI investigation topics 4. The cadence and intensity of AI conference publications is unlike other research areas 5. Better practices for managing local resources are needed 6. Access to AI research resources is very uneven for different institutions 7. There is an opportunity for greater alignment between CI and AI efforts 8. AI research needs warrant unique approaches to CI and to national shared resources Critical Needs Participants identified ten prototypical AI workflows in two major areas with an immediate need for large-scale resources.

With the goal of uncovering the challenges faced by European AI students during their research endeavors, we surveyed 28 AI doctoral candidates from 13 European countries. The outcomes underscore challenges in three key areas: (1) the findability and quality of AI resources such as datasets, models, and experiments; (2) the difficulties in replicating the experiments in AI papers; (3) and the lack of trustworthiness and interdisciplinarity. From our findings, it appears that although early stage AI researchers generally tend to share their AI resources, they lack motivation or knowledge to engage more in dataset and code preparation and curation, and ethical assessments, and are not used to cooperate with well-versed experts in application domains. Furthermore, we examine existing practices in data governance and reproducibility both in computer science and in artificial intelligence. For instance, only a minority of venues actively promote reproducibility initiatives such as reproducibility evaluations. Critically, there is need for immediate adoption of responsible and reproducible AI research practices, crucial for society at large, and essential for the AI research community in particular. This paper proposes a combination of social and technical recommendations to overcome the identified challenges. Socially, we propose the general adoption of reproducibility initiatives in AI conferences and journals, as well as improved interdisciplinary collaboration, especially in data governance practices. On the technical front, we call for enhanced tools to better support versioning control of datasets and code, and a computing infrastructure that facilitates the sharing and discovery of AI resources, as well as the sharing, execution, and verification of experiments.

It's no secret that artificial intelligence (AI) is one of the hottest topics in the tech world today. Every day, it seems like there's a new story about how AI is being used to improve some aspect of our lives, from personal assistants to driverless cars. Given all the hype, it's no wonder that educators are eager to introduce AI concepts to their students. Now, thanks to resources inside Intel's 5-module teaching kit for AI inference teaching the Intel Distribution of OpenVINO toolkit, it is easier than ever to introduce the concepts of deep learning AI to students. Get your students hands-on coding experience with this teacher kit, which comes with a lesson plan, 5-modules of workbooks, videos, quizzes, and Jupyter* Notebook coding lab tutorials.

Pledging to focus the resources of the federal government to develop artificial intelligence that will enhance national and economic security and prosperity, President Donald J. Trump has signed an executive order to launch the American AI Initiative. The order is the second action the Trump administration has taken in relation to AI technologies. It follows an AI summit hosted by the White House in May 2018. Today's executive order marks another step towards advancing a technology that is being used to create self-driving cars, find cures for cancer, fight human trafficking, design better products, and offer consumers the thing they want to buy before they even know they want it. The executive order comes at a time when China is considered a competitive threat in AI advances.

Pledging to focus the resources of the federal government to develop artificial intelligence that will enhance national and economic security and prosperity, President Donald J. Trump has signed an executive order to launch the American AI Initiative. The order is the second action the Trump administration has taken in relation to AI technologies. It follows an AI summit hosted by the White House in May 2018. Today's executive order marks another step towards advancing a technology that is being used to create self-driving cars, find cures for cancer, fight human trafficking, design better products, and offer consumers the thing they want to buy before they even know they want it. The executive order comes at a time when China is considered a competitive threat in AI advances.

Crypto is decentralizing, AI is centralizing, according to Peter Thiel. Although the venture capitalist has followed this remark with a somewhat strange ideological classification for these technologies, the premise rings true. Artificial intelligence advancement is now in the hands of huge companies such as Google, IBM, Microsoft and their Chinese counterparts. Machine learning relies on data – the more the better – and platforms such as these have proven skilled in collecting it. They have used their competitive edge to make AI products better which draws in more users and more data – a great example of leveraging network effects.

If you're involved in technology, you have almost certainly been hearing a lot about artificial intelligence (AI) and machine learning (ML) recently. Digital Reality partners Google, Microsoft, Facebook, Apple and NVIDIA have all begun working on projects in this space. Google has announced the development of new purpose-built chips, Microsoft is offering online classes about how to use and develop AI solutions, Facebook and Apple are developing their own AI technology and companies like Intel and NVIDIA are releasing new hardware to support AI. These technologies seem to be pervasive across all industries, but two questions many organizations are asking are "What does it mean to me?" and "How do I use it?" The combinations of advanced algorithms and massive quantities of data promise to transform every industry by pushing businesses to new levels of efficiency and enabling distinct competitive advantages.

At First4Lawyers, we are very proud of the fact that the business celebrates its tenth anniversary this year, and it is tempting to spend much time reflecting on the last ten years, how the industry has changed or how in some cases it hasn't. However, with a few weeks of 2018 now under our belts and insurers already irking the industry, such as with the news that policies cost drivers on average an extra £70, or 9% on 2016, I prefer to look ahead and see where the industry is heading....

Artificial Intelligence (AI) is proving to be a massively disruptive force, one that is leading to the digital transformation of businesses at a faster pace than most of us would have imagined. This curriculum is primarily oriented towards these two personas which meet the demands of Undergraduate and Postgraduate students. The target profile here is developer who is yet to use Microsoft AI tools and APIs to infuse intelligence into their applications. This profile relates to developers and data scientists who currently build AI and machine learning solutions and want to know how to do this with Microsoft's tools, framework and processes, such as the Azure Machine Learning Workbench and the Team Data Science Process. Services covered include, Cognitive Services and Azure Bot Services.

Pentagon official: AI and machine learning to revolutionize the US intelligence community 2. How AI could spot lung cancer sooner – and save lives 3. AI researchers can now access optimized deep learning framework containers in the cloud 4. AI4ALL improves student access to AI resources through NVIDIA partnership 5. READ BLOG 6. HOW AI COULD SPOT LUNG CANCER SOONER – AND SAVE LIVES Lung cancer is the most common cancer worldwide. More than 80 percent of people with lung cancer die within five years of being diagnosed, and half die within a year. H. Michael Park, co- founder of startup Innovation DX, is working to improve those odds. In December, his St. Louis-based medical analytics company plans to release its first product -- a GPU-accelerated AI system that detects lung cancer in its early stages from a simple chest X-ray. "Lung cancer is so deadly today because it's diagnosed so late. READ BLOG 7. AI RESEARCHERS CAN NOW INNOVATE IN MINUTES, NOT WEEKS, WITH NVIDIA GPU CLOUD NVIDIA announced the NVIDIA GPU Cloud (NGC), a cloud-based platform that gives developers convenient access – deskside or the cloud -- to a comprehensive software suite for harnessing the transformative powers of AI. Jim McHugh, vice president and general manager of DGX Systems at NVIDIA, shared: "We're designing a cloud platform that will unleash AI developers, so they can build a smarter world.