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) …
TLDR: Neural Networks are powerful but complex and opaque tools. Using Topological Data Analysis, we can describe the functioning and learning of a convolutional neural network in a compact and understandable way. The implications of the finding are profound and can accelerate the development of a wide range of applications from self-driving everything to GDPR. Neural networks have demonstrated a great deal of success in the study of various kinds of data, including images, text, time series, and many others. One issue that restricts their applicability, however, is the fact that it is not understood in any kind of detail how they work.
Michael Nielsen provides a visual demonstration in his web book Neural Networks and Deep Learning that a 1-layer deep neural network can match any function . It is just a matter of the number of neurons to get a prediction that is arbitrarily close – the more the neurons the better the approximation. There is the Universal Approximation Theorem as well that supplies a rigorous proof of the same.But the known issues with overfitting remain and the obtained network model is only good for the range of the training data. That is, if the training data consisted only of inputs with there would be no reason to expect the obtained network model to work outside of that range. This series of posts are about obtaining network models that are unique, generic and exact.
It's no secret that identity fraud is a growing problem: A record 16.7 million US adults experienced identity fraud in 2017, marking an 8% increase from the year before, according to Javelin's 2018 Identity Fraud study. The amount of fraudulent transactions, massive data breaches, and instances of identity theft continues to rise as hackers and fraudsters become more sophisticated. ID scanning solutions have various strengths; some simply scan an ID's barcode, whereas more robust software performs forensic and biometric tests to ensure that an ID is not forged. Artificial intelligence and its subsets of machine learning and deep learning make it possible to accurately process, verify, and authenticate identities at scale. Identity documents, such as driver's licenses and passports, are scanned to test various elements of an ID, either on premises or remotely with mobile devices.
When pianists play a musical piece on a piano, their body reacts to the music. Their fingers strike piano keys to create music. They move their arms to play on different octaves. Violin players draw the bow with one hand across the strings and touch lightly or pluck the strings with the other hand's fingers. Faster bowing produces a faster music pace.
AI-as-a-service (AIaaS) is becoming increasingly popular, with the likes of Amazon AI (which includes Rekognition), Clarifai, Google Cloud Vision, IBM Watson, and Microsoft Cognitive Services gaining traction. One of the main economic drivers within AIaaS is the prevalence of microtransactions. Visual cognition startup CloudSight has announced that it will now support Bitcoin Lightning payments, accepting microtransactions to gather and share visual knowledge to allow AI to learn from AI. CloudSight utilizes data to train deep learning neural networks to automatically caption images. With a database of over half a billion images and all the associated metadata, CloudSight says its image recognition API is one of over 30 patents pending worldwide. The incorporation of Bitcoin Lightning means that microtransactions between devices can happen at great speed, unlocking an exchange of information that was previously difficult.
The racing industry is on the fast track to driverless racecars, thanks to AI. At the center of this evolution is Roborace, the world's first autonomous racing competition. Conceived by renowned car designer Daniel Simon -- a former Bugatti designer who's gone on to create various cars for Hollywood -- the "Robocar" is designed, developed, and built by the Roborace organization. Teams compete by writing the software and developing deep neural networks that consume the sensor data to see, think, and act. The cars -- which are 4.8-meters-long -- can reach speeds of over 300 kilometers per hour.
To inspire you in 2018, we wanted to share our 2nd Annual Applied AI Digest Review – a recap of all the major AI news and trends of 2017. We are clearly not in a steady-state situation. Is this a bubble or a revolution? The answer surely includes a bit of revolution--the fields of vision and speech recognition have been turned over by great empirical successes created by deep neural architectures and more generally machine learning has found plentiful real-world uses. Artificial intelligence offers limitless potential for the financial industry, but at Fortune's MPW International Summit on Tuesday Mastercard vice chairman Ann Cairns also called attention to some of its existential risks.
You don't have to write even a single line of code to win this competition. So for all those who knows the concept part but are not from a coding background can finally not only participate but also win it and for others they finally don't have to worry whether the code will compile or not? Today soda bottle companies send a person to stores to find out if they need to restock some soda bottles that have run out on weekly basis. There are 100s of thousands of these coolers at different stores all across the world. This takes large amount of human labor in travel and counting bottles.
"We believe that current computing solutions don't stack up for running neural networks (i.e., deep learning) at scale in resource-constrained environments," said Orr Danon, CEO, Hailo Technologies. "Our observation is that the key deficiency is in the architecture of the computer, which was designed for running classical rule-based software. With our technology, it will be possible to bring state-of-the-art deep learning into devices outside the data center at reasonable power and cost. We believe this will enable many interesting use cases, automotive being a leading one."
Good results come from a brain-inspired system that uses the IBM TrueNorth neuromorphic chips together with a pair of vision sensors that act like eyes. Together the system can respond to changes in the environment to provide imagery in stereo with a sense of depth. Simply put, they're able to hone in on the action, and ignore extraneous visual noise. In the research paper "A Low Power, High Throughput, Fully Event-Based Stereo System" the team reported results of 200x less power per pixel than a comparable DVS system while achieving competitive accuracies.