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Category: robotics/AI – Page 2090

  • There has been a 14X increase in the number of active AI startups since 2000. Crunchbase, VentureSource, and Sand Hill Econometrics were also used for completing this analysis with AI startups in Crunchbase cross-referenced to venture-backed companies in the VentureSource database. Any venture-backed companies from the Crunchbase list that were identified in the VentureSource database were included.

  • The share of jobs requiring AI skills has grown 4.5X since 2013., The growth of the share of US jobs requiring AI skills on the Indeed.com platform was calculated by first identifying AI-related jobs using titles and keywords in descriptions. Job growth is a calculated as a multiple of the share of jobs on the Indeed platform that required AI skills in the U.S. starting in January 2013. The study also calculated the growth of the share of jobs requiring AI skills on the Indeed.com platform, by country. Despite the rapid growth of the Canada and UK. AI job markets, Indeed.com reports they are respectively still 5% and 27% of the absolute size of the US AI job market.

  • Machine Learning, Deep Learning and Natural Language Processing (NLP) are the three most in-demand skills on Monster.com. Just two years ago NLP had been predicted to be the most in-demand skill for application developers creating new AI apps. In addition to skills creating AI apps, machine learning techniques, Python, Java, C++, experience with open source development environments, Spark, MATLAB, and Hadoop are the most in-demand skills. Based on an analysis of Monster.com entries as of today, the median salary is $127,000 in the U.S. for Data Scientists, Senior Data Scientists, Artificial Intelligence Consultants and Machine Learning Managers.

  • Error rates for image labeling have fallen from 28.5% to below 2.5% since 2010. AI’s inflection point for Object Detection task of the Large Scale Visual Recognition Challenge (LSVRC) Competition occurred in 2014. On this specific test, AI is now more accurate than human These findings are from the competition data from the leaderboards for each LSVRC competition hosted on the ImageNet website.

  • Global revenues from AI for enterprise applications is projected to grow from $1.62B in 2018 to $31.2B in 2025 attaining a 52.59% CAGR in the forecast period. Image recognition and tagging, patient data processing, localization and mapping, predictive maintenance, use of algorithms and machine learning to predict and thwart security threats, intelligent recruitment, and HR systems are a few of the many enterprise application use cases predicted to fuel the projected rapid growth of AI in the enterprise. Source: Statista.

  • 84% of enterprises believe investing in AI will lead to greater competitive advantages. 75% believe that AI will open up new businesses while also providing competitors new ways to gain access to their markets. 63% believe the pressure to reduce costs will require the use of AI. Source: Statista.

  • 87% of current AI adopters said they were using or considering using AI for sales forecasting and for improving e-mail marketing. 61% of all respondents said that they currently used or were planning to use AI for sales forecasting. The following graphic compares adoption rates of current AI adopters versus all respondents. Source: Statista.

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The £13m RemoveDebris spacecraft was taken to the ISS in April and stored onboard ahead of Wednesday’s release.

The spacecraft was pushed out of an airlock where a robotic arm then picked it up gave it a gentle nudge down and away from the 400km-high lab.

In the process, RemoveDebris became the largest satellite to ever be deployed from the International Space Station. The time was about 12:35 BST.

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Driverless vehicles could eliminate millions of jobs in the future, from cabbies to truckers to food delivery workers. But the companies that are hoping to hasten the adoption of this disruptive technology don’t want to seem callous to this brewing labor crisis, so they are joining forces to study the “human impact” of robot cars.

The Partnership for Transportation Innovation and Opportunity (PTIO) is a newly formed group comprised of most of the major companies that are building and testing on self-driving cars. This includes legacy automakers like Ford, Toyota, and Daimler; tech giants like Waymo (née Google), Uber, and Lyft; and logistics providers like FedEx and the American Trucking Association. The new organization is being formed as a 501©(6), which allows it to accept donations like a nonprofit and lobby government like a chamber of commerce.

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Nature article presents an AI developed by Google’s Medical Brain team which outperforms hospitals’ own warning system in predicting the death risk among hospit…al patients.

Google’s Medical Brain team is now training its AI to predict the death risk among hospital patients — and its early results show it has slightly higher accuracy than a hospital’s own warning system.

Bloomberg describes the healthcare potential of the Medical Brain’s findings, including its ability to use previously unusable information in order to reach its predictions. The AI, once fed this data, made predictions about the likelihood of death, discharge, and readmission.

In a paper published in Nature in May, from Google’s team, it says of its predictive algorithm:

Continue reading “Google says its AI is better at predicting death than hospitals” | >

Neural networks running on GPUs have achieved some amazing advances in artificial intelligence, but the two are accidental bedfellows. IBM researchers hope a new chip design tailored specifically to run neural nets could provide a faster and more efficient alternative.

It wasn’t until the turn of this decade that researchers realized GPUs (graphics processing units) designed for video games could be used as hardware accelerators to run much bigger neural networks than previously possible.

That was thanks to these chips’ ability to carry out lots of computations in parallel rather than having to work through them sequentially like a traditional CPU. That’s particularly useful for simultaneously calculating the weights of the hundreds of neurons that make up today’s deep learning networks.

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A space launch every 3 hours may soon be possible using rockets carried on a fully autonomous unmanned airplane, a new startup company suggests.

Alabama-based startup Aevum aims to per mission, using an air-launch system called Ravn.

“Ravn is designed to launch every 180 minutes,” Jay Skylus, Aevum’s CEO and chief launch architect, told Space.com. “Other launch vehicles fly only a handful of times a year with an average of 18 months of lead time.” [Rocket Launches: The Latest Liftoffs, Photos & Videos].

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