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🇺🇸 Machine learning job: Senior Machine Learning Engineer at Guru (work from anywhere in US!)

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AI/ML Job: Senior Machine Learning Engineer Senior Machine Learning Engineer at Guru Remote › 100% remote position (in the US) (Posted May 28 2022) Job description Guru is on a mission to reinvent the way people connect with information at work. Our knowledge management solution provides teams with expert-verified information where they work and when they need it most. Our goal is to give every team in the world trusted information so that they can do their best work. We're backed by an amazing group of investors and we're growing fast; in 2020 we raised a series C round which took our total funding to $70M. At Guru, we know that talent is everywhere.


Artificial intelligence learns 'song' of coral reefs

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England [UK], May 27 (ANI): According to new research, artificial intelligence (AI) can track the health of coral reefs by learning the "song of the reef." The research has been published in the journal, "Ecological Indicators". Coral reefs have a complex soundscape - and even experts have to conduct painstaking analyses to measure reef health based on sound recordings. In the study, University of Exeter scientists trained a computer algorithm using multiple recordings of healthy and degraded reefs, allowing the machine to learn the difference. The computer then analysed a host of new recordings, and successfully identified reef health 92 per cent of the time.


Top Use Cases of Computer Vision in Fintech

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Computer vision technology is steadily growing in popularity and use – the market is expected to to grow at a CAGR of 7.36 % over the 2021 – 2026 period. If we dig deeper, the predictions for 2028 state that the computer vision market will reach $13230 million, which is a crazy number to imagine. While computer vision is already used in healthcare, manufacturing, and other industries, the financial services industry has always been slightly hesitant about adopting new technologies. However, it slowly began embracing all the benefits that computer vision can bring – see the top use cases for computer vision in fintech below. Customer verification is critical in the financial services industry in order to prevent fraud.


Learning and Mastering CUDA 0x01 -- A quick dive into GPU programming

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CUDA has seen increased adoption in recent years, with many computationally intensive applications using it to accelerate their computations. Deep Learning has been one of the main targets for optimisation, given its recent advancements and prospects.


How to prioritize data strategy investments as a CDO - Journey to AI Blog

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My first task as a Chief Data Officer (CDO) is to implement a data strategy. Over the past 15 years, I've learned that an effective data strategy enables the enterprise's business strategy and is critical to elevate the role of a CDO from the backroom to the boardroom. A company's business strategy is its strategic vision to achieve its business goals. Data that can be managed, protected, and monetized effectively will provide insights into how to achieve those goals. A CDO works in collaboration with senior executives to steer a business to its strategic vision through a data strategy.


How To Visually Inspect The Quality Of Your Chatbot's NLU Model

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Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. It's free, we don't spam, and we never share your email address.


China and the EU regulate AI, US speculates

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While the European Union is playing the long game in drafting regulation for AI, China has been surprising many with quick yet profound regulations. A report compares the situation for both jurisdictions. On a different track, the U.S. government looks at ways to improve its AI research infrastructure. The European Union has a long-standing reputation for regulating many facets of life. Its GDPR has been something of a global hit. With its upcoming AI Act it attempts to safeguard human rights and society generally.


DeepMind: Why is AI so good at language? It's something in language itself

ZDNet

Can the frequency of language, and qualities such as polysemy, affect whether a neural network can suddenly solve tasks for which it was not specifically developed, known as "few-shot learning"? How is it that a program such as OpenAI's GPT-3 neural network can answer multiple choice questions, or write a poem in a particular style, despite never being programmed for those specific tasks? It may be because the human language has statistical properties that lead a neural network to expect the unexpected, according to new research by DeepMind, the AI unit of Google. Natural language, when viewed from the point of view of statistics, has qualities that are "non-uniform," such as words that can stand for multiple things, known as "polysemy," like the word "bank," meaning a place where you put money or a rising mound of earth. And words that sound the same can stand for different things, known as homonyms, like "here" and "hear." Those qualities of language are the focus of a paper posted on arXiv this month, "Data Distributional Properties Drive Emergent Few-Shot Learning in Transformers," by DeepMind scientists Stephanie C.Y. Chan, Adam Santoro, Andrew K. Lampinen, Jane X. Wang, Aaditya Singh, Pierre H. Richemond, Jay McClelland, and Felix Hill.


Global Big Data Conference

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Combinatorial optimization problems are complex problems with a discrete but large set of possible solutions. Some of the most renowned examples of these problems are the traveling salesman, the bin-packing, and the job-shop scheduling problems. Researchers at the Amazon Quantum Solutions Lab, part of the AWS Intelligent and Advanced Computer Technologies Labs, have recently developed a new tool to tackle combinatorial optimization problems, based on graph neural networks (GNNs). The approach developed by Schuetz, Brubaker and Katzgraber, published in Nature Machine Intelligence, could be used to optimize a variety of real-world problems. "Our work was very much inspired by customer needs," Martin Schuetz, one of the researchers who carried out the study, told TechXplore.


It's about time facial recognition tech firms took a look in the mirror John Naughton

The Guardian

Last week, the UK Information Commissioner's Office (ICO) slapped a £7.5m fine on a smallish tech company called Clearview AI for "using images of people in the UK, and elsewhere, that were collected from the web and social media to create a global online database that could be used for facial recognition". The ICO also issued an enforcement notice, ordering the company to stop obtaining and using the personal data of UK residents that is publicly available on the internet and to delete the data of UK residents from its systems. Since Clearview AI is not exactly a household name some background might be helpful. It's a US outfit that has "scraped" (ie digitally collected) more than 20bn images of people's faces from publicly available information on the internet and social media platforms all over the world to create an online database. The company uses this database to provide a service that allows customers to upload an image of a person to its app, which is then checked for a match against all the images in the database.