Verusen is a leading technology company that uses artificial intelligence to provide visibility, digitization and prediction of materials data and inventory for complex supply chains. Intelligent controls enforce inventory procedures to help prevent future inventory spikes, while predictive capabilities optimize allocation and procurement needs. The result is a data foundation you can trust to move quickly to innovate and support related Industry 4.0 initiatives. Verusen is venture-backed by leading investors from San Francisco to Boston, and is a Signature Company at Georgia Tech's Advanced Technology Development Center (ATDC). Verusen is a portfolio company of SAP.iO.

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About us Hello, we are GetGround. We are a well-funded series A Tech scaleup in Europe and Asia with a mission to make the world fairer and more productive. We are doing this by making assets more transparent, trustworthy and accessible - starting with real estate. Incomes have stagnated, so more access to wealth generating things like real estate less wealth inequality. We are disruptors building a global network for trading illiquid assets - and we have first mover advantage!

Nextdoor is where you connect to the neighborhoods that matter to you so you can belong. Our purpose is to cultivate a kinder world where everyone has a neighborhood they can rely on. Neighbors around the world turn to Nextdoor daily to receive trusted information, give and get help, get things done, and build real-world connections with those nearby -- neighbors, businesses, and public services. Today, neighbors rely on Nextdoor in more than 295,000 neighborhoods across 11 countries. At Nextdoor, we empower our employees to build stronger local communities.

Galileo, Inc. is hiring for Full Time Senior Staff Software Engineer, Data Engineering - New York City or Remote - a Senior-level AI/ML/Data Science role offering benefits such as Flex hours, Insurance, Startup environment

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As public relations evolve, so do the tools we use. Why it matters: Artificial intelligence (AI) is changing the public relations game -- and saving time -- by examining crisis statements, pitches and press releases for specific keywords to better predict how they will land. How it works: Axios got a first look at PRophet 2.0, an AI-driven platform that scans press materials for keywords that match the recent coverage of active reporters -- those who publish at least once a week -- and ranks the match based on sentiment and outlet reach. State of play: This technology could save publicists a lot of time -- and it's an investment 70% of chief communication officers are willing to make, Edelman found. Zoom out: AI tools like this have worked for other industries, according to Kwittken.

Welcome to part two of the Food AI series of papers! Last week, I wrote an article explaining the seminal im2recipe paper that introduced cross-modality into dealing with machine learning related to food applications such as searching for the correct recipe using a photo, automatically determining the number of calories in a dish or improving the performance of various recipe recommendation and ranking systems. I refer the reader to that article for an introduction and motivation to the problem and for details regarding the Recipe1M dataset and evaluation metrics. As mentioned in the previous article, these explanations aim at charting the progress of research in a particular domain of machine learning. So, today, we will be looking at the paper titled "Dividing and Conquering Cross-Modal Recipe Retrieval: from Nearest Neighbours Baselines to SoTA" published in 2019.

Since its debut earlier this year, tens of thousands of developers around the world have downloaded the source code and used it to render spectacular scenes, sharing eye-catching results on social media. The research behind Instant NeRF is being honored as a best paper at SIGGRAPH -- which runs Aug. 8-11 in Vancouver and online -- for its contribution to the future of computer graphics research. One of just five papers selected for this award, it's among 17 papers and workshops with NVIDIA authors that are being presented at the conference, covering topics spanning neural rendering, 3D simulation, holography and more. NVIDIA recently held an Instant NeRF sweepstakes, asking developers to share 3D scenes created with the software for a chance to win a high-end NVIDIA GPU. Hundreds participated, posting 3D scenes of landmarks like Stonehenge, their backyards and even their pets.

Almost a year ago to the day, the Center for Research on Foundation Models (CRFM) – which was then a new initiative of the Stanford Institute for Human-Centered Artificial Intelligence (HAI) – held a virtual workshop on Foundation Models. They chose "Foundation" as the name for these models as they entail training one model on a huge amount of data, then adapting it to many applications. The data can be text, images, speech, and more. The tasks that such models can perform include – but are not limited to – answering questions, analyzing sentiments, extracting information form text, labeling images, and recognizing objects. These Foundation Models use self-supervised learning, and they work because they can effectively apply knowledge learned in one task to another task.