If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Q: Where is AWS Innovate hosted? AWS Innovate is an online conference. After completing the online registration process, you will receive a confirmation email containing the login link that you will need in order to access the platform. Q: Can I watch the sessions after the event? All sessions will be available on demand from the day after the event until the end of November.
Imagine a scene, set in the future, where a child in Burning Man–style punk clothing is standing in front of a yurt powered by solar panels. There weren't many books with scenes like that in 2014, when Sarena Ulibarri, an editor, first grew interested in a genre of science fiction that imagines a renewable and sustainable future. Welcome to solarpunk, a new genre within science fiction that is a reaction against the perceived pessimism of present-day sci-fi and hopes to bring optimistic stories about the future with the aim of encouraging people to change the present. The first book that explicitly identified as solarpunk was Solarpunk: Histórias ecológicas e fantásticas em um mundo sustentável (Solarpunk: Ecological and Fantastic Stories in a Sustainable World), a Brazilian book published in 2012. In 2014, author Adam Flynn wrote Solarpunk: Notes Toward a Manifesto.
Machine Learning is an enormous field, and today we'll be working to analyze just a small subset of it. Supervised learning is one of Machine Learning's subfields. The idea behind Supervised Learning is that you first teach a system to understand your past data by providing many examples to a specific problem and desired output. Then, once the system is "trained", you can show it new inputs in order to predict the outputs. How would you build an email spam detector?
Stale industries with entrenched players who fail to innovate will continually be disrupted by forward-thinking industry entrants. We've seen it with cable, retail, transportation, you name it. Monopolies make great investments until the competitive moat is breached. Failing to pay attention to the customer and instead focusing solely on the bottom line is why companies like Amazon, Netflix, and Uber have risen to fame. But while these Goliath's continue to dominate their respective industries and dismiss incoming threats, the David's of the world quietly load up their slingshots.
The MatchX Development Kit is enabling you to test your LoRa prototype and build LoRa sensors quickly and cost-effectively. The Development kit has been used to build LoRa based water and parking sensors in a number of projects around the world that all require long-distance communication and battery longevity. Find the user guide here. MatchX's latest and most exciting technological breakthrough yet -- Edge AI Kit - will enable you to take your LoRa project one step further with an artificial intelligence algorithm designed for your specific data processing needs. Edge AI Kit is designed for running and testing artificial intelligence (AI) models on edge devices such as sensor nodes.
Interpretable generic multi-sensor anomaly detection: Despite having few failure examples, a historian will contain plenty of sensor data from regular operations. An approach can therefore be developed to monitor all sensors simultaneously from a compressor and alert an operator when any of those measurements are outside of the regular operational envelope based on historical observations. Preference should be given to machine learning algorithms which can relate such an alert back to the primary sensors which caused the alert. Thus, guiding the operator to where in the compression system a problem might be occurring. Failure heuristic-based anomaly detection: Compressors are physical systems and information on how a critical failure mode manifests itself in some subset of sensors is often known even if a given compressor hasn't observed that failure mode in its own history.
Bad actors are increasingly using more advanced methods to generate fake news and fool readers into thinking they are legitimate. AI-based text generators, including OpenAI's GPT-2 model, which try and imitate human writers play a big part in this. To mitigate this, researchers have developed tools to detect artificially generated text. However, new research from MIT suggests there might be a fundamental flaw in the way these detectors work. TNW's finance, blockchain, and business event is coming up soon Traditionally, these tools trace back a text's writing style to determine if it's written by humans or a bot.
The University of California, San Francisco is employing Nvidia technology to help develop artificial intelligence tools for clinical radiology. WHY IT MATTERS The two organizations will work together on several AI projects, including brain tumor segmentation, liver segmentation and clinical deployment, leveraging Nvidia's Clara healthcare toolkit and the tech giant's DGX-2 AI system. Clara Medical Imaging provides developers with the tools to build, manage and deploy intelligent imaging workflows and instruments, while Clara Genomics addresses the growing size and complexity of genomics sequencing and analysis with accelerated and intelligent computing. Powered by DGX software and the scalable architecture of Nvidia NVSwitch, the DGX-2 is a 2 petaFLOPS system combining 16 interconnected graphical processing units – the system could help UCSF researchers significantly cut the time to train AI models. The number of images acquired during common studies such as MRI and CT scans has swelled in recent years corresponding with the growing number of patients being imaged.
Researchers at TU Delft have developed a new supercompressible but strong material without conducting any experimental tests at all, using only artificial intelligence (AI). "AI gives you a treasure map, and the scientist needs to find the treasure," says Miguel Bessa, first author of a publication on this subject in Advanced Materials on 14 October. Miguel Bessa, assistant professor in materials science and engineering at TU Delft, got the inspiration for this research project during his time at the California Institute of Technology. At a corner of the Space Structures Lab, he noticed a satellite structure that could open long solar sails from a very small package. He wondered if it would be possible to design a highly compressible yet strong material that could be compressed into a small fraction of its volume.