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) …
Google just made a major move to expand its revenue beyond the realm of digital advertising. At the keynote address of this year's I/O developer conference, Google's CEO announced that the company will be selling AI computer chips, called Cloud Tensor Processing Units (TPUs), via Google Cloud service. Urs Hölzle, Google's veteran technical chief, told Bloomberg that the chip is "basically a supercomputer for machine learning. The field is rapidly evolving. For us, it's very important to advance machine learning for our own purposes and to be the best cloud."
This is the Part II of my blog series that simply distils the key terminology of Big Data (see Part I here). These are the remaining 16 key concepts that you should understand if you want to learn more about Big Data. Structured data is data that can be arranged neatly into charts and tables consisting of rows, columns or multi-dimensioned matrixes. This is traditionally the way that computers have stored data, and information in this format can be easily and simply processed and mined for insights. Data gathered from machines is often a good example of structured data, where various data points--speed, temperature, rate of failure, RPM, etc.--can be neatly recorded and tabulated for analysis.
For businesses to survive today, they have to embrace new trends quickly. We're no longer living in the world where new developments, like airplane travel or television, take decades to reach market saturation. Today's technology adoption cycle is blink-and-you'll-miss-it fast, even compared to the advent of computers, mobile devices and the internet, which transformed their respective markets in just a few decades. We are now living in the age of exponential connectivity. It's hard to keep up with all the new devices out there that can provide connected data and, therefore, a real-time look at how buyers are making their decisions.
In a previous post, we described the details of NSynth (Neural Audio Synthesis), a new approach to audio synthesis using neural networks. We hinted at further releases to enable you to make your own music with these technologies. Today, we're excited to follow through on that promise by releasing a playable set of neural synthesizer instruments: The goal of Magenta is not just to develop new generative algorithms, but to "close the creative loop". We want to empower creators with tools built with machine learning that also inspire future research directions. Instead of using AI in the place of human creativity, we strive to infuse our tools with deeper understanding so that they are more intuitive and inspiring.
Artificial intelligence, and specifically machine learning, has hit the mainstream. With innovative and accessible machine learning services and APIs, from image recognition to chatbots, machine learning is growing increasingly important for both the core functionality of apps, and the features that make it stand out from the crowd. Before we talk about the stages of developing machine learning, let's first look at what machine learning is powering today. Through the application of machine learning, apps are making their mark on the world. Analyzing and identifying what something is based on an image file or video feed.
Artificial Intelligence Natural Stupidity patch 1. Garment should be clean & freshly laundered (including new items). If edge of patch can be lifted, repeat step 6. Permanence of application guaranteed by sewing. Do NOT use on unironable fabrics (low melting point) such as nylons, vinyls, or leathers.
Sonya, an entrepreneur, is setting out to establish her own business. After researching and analyzing market opportunities, she decides to create an ecommerce business selling vintage coffee makers to the aging millennial market -- now a nostalgic item because of automatic kitchens. Here's how Sonya puts a variety of bots to work to make it happen. Sonya has recently finished her Masters in Automated Business Administration (MABA), which equips business professionals to coordinate bots for unique business purposes and manage mixed workforces. She starts by identifying the type of business bots she will need for this new venture.
The biggest issue facing artificial intelligence right now is the question of'Why did the AI make a decision?' The problem we have now in research and academia is the lack of collaborative research concerning AI from multiple fields--science, engineering, medical, arts. We have a hard enough time telling people why the AI made a certain decision. Actually, what drives reverse engineering of the brain and the personalization of AI is not research in academia, it's more the lawyers coming in and asking'Why is the AI making these decisions?'
Artificial intelligence doesn't have to be super-sophisticated to make a difference in people's lives, according to a new Yale University study. Even "dumb AI" can help human groups. In a series of experiments using teams of human players and robotic AI players, the inclusion of "bots" boosted the performance of human groups and the individual players, researchers found. The study appears in the May 18 edition of the journal Nature. "Much of the current conversation about artificial intelligence has to do with whether AI is a substitute for human beings.
Spotify has made its fourth acquisition of the year after it announced that AI startup Niland has joined its ranks. Paris-based Niland offered an API-based product focused on providing more accurate search and recommendation options for music. Spotify said the French company will join its R&D team which is based in New York to help hone its personalization and recommendation features for users. "Niland has changed the game for how AI technology can optimize music search and recommendation capabilities and shares Spotify's passion for surfacing the right content to the right user at the right time," Spotify said in a statement. "We will keep working on new ways to better understand music to craft better innovative listening and discovery experiences," Niland's founding team wrote on its website.