This article was originally published on CMSWire by Sean Zinsmeister, Vice President of Product Marketing at Infer. The buzz surrounding machine learning and artificial intelligence (AI) in the consumer world has rapidly bled over into the enterprise. Much of this hype stems from the new consumer trends that hint at the possibilities of AI, such as self-driving cars and intelligent voice-first products like Amazon's Alexa and Apple's Siri. At the same time, mainstream cloud adoption and ever-increasing computing power in the form of new solutions like Google Spanner are accelerating the development, accuracy and speed of AI's underlying foundations, from data availability and spam detection, to machine learning, predictive analytics and natural language processing. So it should come as no surprise that sales and marketing leaders are questioning what all this means for their departments and companies.

Machine learning is a field of artificial intelligence that uses statistical techniques to give computer systems the ability to "learn" (e.g., progressively improve performance on a specific task) from data, without being explicitly programmed. Think of how efficiently (or not) Gmail detects spam emails, or how good text-to-speech has become with the rise of Siri, Alexa, and Google Home. 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.

Amazon rolled out more than a dozen new devices today in its annual hardware event atop a biodome at company headquarters in Seattle, including eyeglasses and a ring that speak with Alexa, several new speakers, and lots of new devices for the home. Sprinkled in between hardware announcements were a bunch of new ways Alexa is getting smarter. In all, like last year, nearly a dozen new features were introduced today. Here are some of the standouts. One unique offering introduced today is the Education Skills API, a way for skills developers and school districts to connect with popular edtech services like Canvas, Kickboard, and Coursera.

If you make a purchase by clicking one of our links, we may earn a small share of the revenue. However, our picks and opinions are independent from USA TODAY's newsroom and any business incentives. The spookiest holiday of all is just around the corner--and you're going to need more than a few pumpkins scattered around your porch to properly decorate. Halloween is on Thursday, Oct. 31, which means you've only got a few weeks to add an eerie flair to your home before hoards of trick-or-treaters arrive. From smart plugs to spider webs, we've rounded up 10 essentials you'll need to decorate for this Halloween.

Artificial intelligence is the ability of computer or computer enabled robotic system to process information and produce outcomes in a manner similar to the thought process of humans in learning, decision making and solving problems. By extension, the goal of AI systems is to develop systems capable of tackling complex problems in ways similar to human logic and reasoning. It is the science and engineering of making intelligent machines, especially intelligent computer programmes. Artificial intelligence system understands language and its nuances, solves complex problems, recognises images and learns from experience. Machines thus replaces human driven processes to deliver work faster and more efficiently. AI is a major source and reason for the business productivity. Project management AI has proven its capability on enhancing the team performance and project outcomes. Leveraging AI to automate and improve data sets utilised in project execution will allow organization to realise optimal investment value in the project and potentially identify savings that could be leveraged for further investments in product development leading to organizational growth.

Supply chains are hugely important in our world of physical goods. Companies invest a lot of money and effort to continually optimize and improve them, and that's not likely to ever stop. Save 5% on transportation costs? As artificial intelligence begins to reshape our digital world via AI-infused goods, services, and processes, it will require its own supply chain: mind-boggling quantities of data. Mountains of information must be continually gathered, processed, refined, and reassembled to produce the desired intelligence and decision-making.

What happens to your body, when you start drinking coffee? There has been quite a speculation about artificial intelligence and machine learning, but Artificial Intelligence has an overkill of data, opening new doors to technology. Though the two terms Artificial Intelligence and Machine learning are always together, several people are unaware of the difference between the two. Therefore, let us kick off with the example of a popular term, virtual professional assistance. Some of the operating system, such as Siri (the famous component of Apple Inc. IOS, Mac operating system, TV operating system, and Apple watch operating system), Cortana (Microsoft has Cortana, a virtual assistant for Windows 10), and Google now (Google search presents a predictive cards with data with regular updates for Android and iOS Google app) are meant for your personal virtual assistance on Windows, Android, and IOS.

Tile doesn't want people to forget about it, especially if Apple debuts its own item tracker. Today, Tile announced a minor update to its Google Assistant integration that'll allow Assistant to directly ring a Tile. The functionality isn't launching until later this year, but clearly, Tile wants to get its news out ahead of Apple. Before, Tile just had a Google Assistant skill, which required users to say, "Hey Google, ask Tile to find my phone." Later this year, they can just say, "Hey Google, find my phone," and the Tile attached to their phone will ring.

Blog: medium @newfrontcreative Biography Scott Haines is a Principal Software Engineer on the Voice Insights team at Twilio. His focus has been on the architecture and development of a real-time (sub 250ms), highly available, trustworthy analytics system. His team is providing near real-time analytics that processes / aggregates and analyzes multiple terabytes of global sensor data daily. Scott helped drive Apache Spark adoption at Twilio and actively teaches and consulting teams internally. Scott's past experience was at Yahoo! where he built a real-time recommendation engine and targeted ranking / ratings analytics which helped serve personalized page content for millions of customers of Yahoo Games.

ADDITIONAL SHARED DECODER ON SIAMESE MUL TI-VIEW ENCODERS FOR LEARNING ACOUSTIC WORD EMBEDDINGS Myunghun Jung, Hyungjun Lim, Jahyun Goo, Y oungmoon Jung, and Hoirin Kim School of Electrical Engineering, KAIST, Daejeon, Republic of Korea ABSTRACT Acoustic word embeddings -- fixed-dimensional vector representations of arbitrary-length words -- have attracted increasing interest in query-by-example spoken term detection. Recently, on the fact that the orthography of text labels partly reflects the phonetic similarity between the words' pronunciation, a multi-view approach has been introduced that jointly learns acoustic and text embeddings. It showed that it is possible to learn discriminative embeddings by designing the objective which takes text labels as well as word segments. In this paper, we propose a network architecture that expands the multi-view approach by combining the Siamese multi-view encoders with a shared decoder network to maximize the effect of the relationship between acoustic and text em-beddings in embedding space. Discriminatively trained with multi-view triplet loss and decoding loss, our proposed approach achieves better performance on acoustic word discrimination task with the WSJ dataset, resulting in 11.1% relative improvement in average precision. Index T erms -- acoustic word embedding, query-by- example spoken term detection, multi-view learning, Siamese network, encoder-decoder 1. INTRODUCTION Query-by-example spoken term detection (QbE-STD) is the task of retrieving a spoken query from a set of speech utterances. Amazon Echo, Google Home, Apple Siri), the QbE-STD has drawn interest as a technique that can be applied to wake-up or command word detection, search engine, etc.