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) …
The CBD industry is growing at an astounding rate. So fast, in fact, that some companies are struggling to keep up with demand. And new companies are cropping up daily to get in on this brand new billion-dollar industry. But as the market becomes flooded with online outlets, it is essential for new companies to make proper use of the highly valuable resources that big data and artificial intelligence can offer. Comprehensive data analytics help companies in the CBD gummy industry in every area of business, from reducing costs while maintaining quality of the product, to managing inventory, to reaching customers in new and impactful ways.
The use of artificial intelligence is set to revolutionize how people create music but still, robots will not replace humans in the art of making melodies, attendees of a conference about art and music were told on Sunday. "Artificial intelligence will not replace good artists and composers," François Pachet, a scientist, composer and the director of the Spotify Creator Technology Research Lab, told participants of the TechnoArt 2019 conference in Tel Aviv. "AI will change the way people make art, but it won't replace them." Pachet is considered a pioneer of computer music, and specifically its interaction with AI. At Spotify he leads development of AI-based tools for musicians.
Machine learning (ML) is an amazing field that enables a huge number of powerful and interesting techniques. ML is a broad field that has applications in many areas. From image processing to conservation, ML provides unique solutions to problems old and new. Here are some interesting and cool applications of machine learning. Neural networks (NNs) and deep neural networks (DNNs) are very popular machine learning techniques. This type of modeling is used in many of the best-known applications of ML.
As a general rule of thumb for learning, you will only understand something in detail once you are able to build it on your own. This is particularly true for technical subjects like machine learning and neural networks. During your first encounter with deep learning, you can take a few large steps by learning and mastering a high level framework such as TensorFlow or PyTorch. However, if you want to go really deep, you need to go back to square one: you need to build your own neural network and optimizer from scratch! This is a common rite of passage for deep learning engineers and researchers.
Regression methods have provided a foundation for modeling structure-property relationships in materials science1,2,3,4,5,6. These methods take as input a data set of known material compositions along with some property (e.g. Each material, in turn, is described in terms of one or more features that represent aspects of structure, chemistry, bonding and/or microstructure in an abstract, high-dimensional space. The success of regression is based on its ability to capture the relative variation in a property as a function of the features, eventually culminating in the prediction of new materials with desired properties. Materials design is an optimization problem with the goal of maximizing (or minimizing) some desired property of a material, denoted by y, by varying certain features, denoted by x.
Cambridge, UK-based biotech startup Mogrify, which is working on systematizing the development of novel cell therapies in areas such as regenerative medicine, has closed an initial $16 million Series A. The raise follows a $4M seed in February -- taking its total raised to date to $20M. Put simply, Mogrify's approach entails analysis of vast amounts of genomic data in order to identify the specific energetic changes needed to flip an adult cell from one type to another without having to reset it to a stem cell state -- with huge potential utility for a wide variety of therapeutic use-cases. "What we're trying to do with Mogrify is systematize that process where you can say here's my source cell, here's my target cell, here are the differences between the networks… and here are the most likely points of intervention that we're going to have to make to drive the fate of an adult cell to another adult cell without going through a stem cell stage," says CEO and investor Dr Darrin Disley. So far he says it's successfully converted 15 cells out of 15 tries. "We're now rapidly moving those on through our own programs and partnership programs," he adds.
Formula E Racing, like its Formula 1 counterpart, relies on speed and strategy to win. But how do you crunch through the reams of data that you can get from an electric race car and analyze it in a way that would help your driver and your racing team beat the competition? And that's why he has partnered with Sanjay Srivastava, Chief Digital Officer of Genpact, to leverage data analytics and artificial intelligence (AI) to build a multi-layer platform that turns a mountain of data into actionable analysis. Formula E racing produces different types of data across many fronts. There's a set of telemetry data from the cars, a stream of large data sets that cars produce while they are on the road, and data from competing drivers and their vehicles. Then there's data gleaned from weather, satellite, traffic, and road patterns. All that needs a data analytics system that can interpolate the information as it comes in from all these sources and analyze it in real-time in a way that the driver and the racing team can absorb and act upon instantaneously. But, as Sylvain points out, that's easier said than done, especially since a Formula E race happens in just one day, and every second counts. As Sylvain and Sanjay explain, it starts with knowing how to structure the incoming information so that the driver and engineers can act upon it quickly. That means setting up the correct algorithms, developing an analytical infrastructure that--with the help of AI--integrates all of the different types of data, and synchronizing it to give the driver and engineers the whole picture and predict the likeliest outcomes in any given scenario in order to make the right decisions during the race. That also means creating a user interface for the data that's both comprehensive and instantly comprehensible to the driver. The work that Sylvain and Sanjay are doing has notable implications for business that goes beyond racing. The technologies they are developing will trickle down to make electric cars and sustainable energy better. The analytics tools they are creating can potentially be utilized by other companies to make better sense of data coming from multiple sources in order to make well-informed business and digital transformation decisions and do so quickly, and to manage their resources more efficiently. This transcript has been edited for length and clarity. Michael Krigsman: Formula E Racing involves cars, speed, data, and advanced technologies such as AI and machine learning.
Radio 3 is launching a new weekly programme dedicated to video game soundtracks. Running from Saturday 26 October, the hour-long show will be presented by composer Jessica Curry, who won a Bafta for her work with UK studio The Chinese Room and created and presented Classic FM's video game music programme, High Score. "[BBC presenter and journalist] Tom Service and his producer Brian Jackson came to interview me for Radio 3 at Chinese Room a couple of years ago, and we all really hit it off," said Curry. "Tom's an avid gamer and there was a definite feeling of excitement about the gaming scene and the music that's being composed for games. "Lots of people think that it's all battle music and aggression.
The world's top technology and business leaders gather this week in Yerevan, Armenia for the 23rd World Congress on Information Technology. WCIT is organized by the World Information Technology & Services Alliance, the Consortium of ICT associations of 83 countries representing 90 percent of the industry. Running for 40 years, the WCIT is one of the oldest and most prestigious ICT events in the world that brings together CEOs, investors, policy makers, government officials, academics, and technologists to discuss the current state of the industry as well as where it is going. This year, WCIT has focused its attention on the Power of Decentralization: Promise and Peril. During the congress, held from 6 to 9 October at the Karen Demirchyan Sports and Concert Complex, ICT leaders will explore how information and communications technology (ICT) is transforming the world and our lives, both for better and for worse.