One goal of AI work in natural language is to enable communication between people and computers without resorting to memorization of complex commands and procedures. Automatic translation – enabling scientists, business people and just plain folks to interact easily with people around the world – is another goal. Both are just part of the broad field of AI and natural language, along with the cognitive science aspect of using computers to study how humans understand language.
Is the FHE teaching capability and capacity improving as fast as it should? Some of our knowledge about teaching and learning go back to Greek times and still hold true. But that is not to say that more recent research and technology should be ignored. It is generally accepted that Moore's Law is ...
What is a Voice Interface? Ranging from Apple's Siri, Amazon's Alexa and Microsoft's Cortana the use of voice-based communication through devices like a Mobile App is in demand. You might be doubtful with written instructions on the computer but the use of voice interface communication has exceeded...
As we sweep into the 4th Industrial Revolution driven by artificial intelligence, organisations are scrambling to implement Chatbots to be the face of their new machine-driven operations. Here are a few things you should know about them: Chatbots are often just seen as an automated text chat channe...
Artificial intelligence will continue to be buzzing in wealth management in 2018. But there's a short list of professionals who actually understand AI and can clearly explain how advisors and wealth management firms will benefit from it now and in the future. To help break it down, WealthMangement.com We asked Fritz to unpack AI in a way anyone in the industry can understand and even act on it. Prior to founding F2 Strategy, Fritz was the CTO for First Republic Private Wealth Management.
Today's artificial intelligence market is not easy to quantify. Besides the lack of consensus on a coherent definition for "artificial intelligence" as a term, the field's nascent stage of development makes it difficult to carve out silos or hard barriers of where one industry or application ends, and another begins. In one of our more popular recent articles, we aimed to derive a valuation of the artificial intelligence market, based on current market research and our own insights. In this week's article, I've set out to determine more of a "lay of the land" of the AI industry, including it's various segments and application areas. If you're interested in how developments in machine learning and AI might impact your own company or business, then keeping an eye on trends of industry and application growth is pertinent; we hope that this article will be a good jumping off point to some of the most thought-out assessments of AI and it's "segments" as we could collate from the web.
The impact of AI on business and the role it may play in improving efficiency of operations and driving revenue is a main focus of the research conducted at TechEmergence. However, there are also a growing number of altruistic applications of AI that are being leveraged today. The ability to identify effective and sustainable solutions for some of the world's greatest challenges such as health, education and the environment present opportunities for profit but also for positive impact on humanity. We'll conclude with some of the future implications of altruistic AI applications discussed in these three sectors. Our aim was to cover AI use cases not commonly covered in our industry verticals, use cases commonly neglected because of a small market size or a more public "good", rather than a result that could provide a tangible "ROI" for companies.)
For most businesses, machine learning seems close to rocket science, appearing expensive and talent demanding. And, if you're aiming at building another Netflix recommendation system, it really is. But the trend of making everything-as-a-service has affected this sophisticated sphere, too. You can jump-start an ML initiative without much investment, which would be the right move if you are new to data science and just want to grab the low hanging fruit. One of ML's most inspiring stories is the one about a Japanese farmer who decided to sort cucumbers automatically to help his parents with this painstaking operation.
This post is a collection of best practices for using neural networks in Natural Language Processing. There has been a running joke in the NLP community that an LSTM with attention will yield state-of-the-art performance on any task. While this has been true over the course of the last two years, the NLP community is slowly moving away from this now standard baseline and towards more interesting models. However, we as a community do not want to spend the next two years independently (re-)discovering the next LSTM with attention. We do not want to reinvent tricks or methods that have already been shown to work.
Getting a review unit late, as is the case with the Google Home Max, gives me the benefit of reading a lot of people's opinions of a product before I formulate my own. And reviewing a lot of similar speakers before I evaluate the one at hand gives me a broad base of experience upon which to formulate mine. Based on those two fronts, the Google Home Max has been praised just a wee bit overenthusiastically. That said, the Google Home Max is the best smart speaker I've heard. Amazon certainly has nothing close to it in terms of audio performance, and neither do any of the manufacturers building Echo clones.
What do we do when we need any information? Simple: "We Ask, and Google Tells". But if the answer depends on multiple variables, then the existing Ask-Tell model tends to sputter. State of the art search engines usually cannot handle such requests. We would have to search for information available in bits and pieces and then try to filter and assemble relevant parts together.