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) …
Amazon (NASDAQ:AMZN) got an early start in smart speakers with its Echo devices, but Alphabet (NASDAQ:GOOG) (NASDAQ:GOOGL) subsidiary Google is catching up quickly. Last year, Amazon took the lion's share of the market with Google a distant second in terms of sales. But during the first quarter this year, the sales between the two companies are much closer, with at least one research group indicating Google may have sold more devices than Amazon. This article originally appeared in The Motley Fool. Google sold 3.2 million Home devices in the first quarter, according to Canalys, more than the 2.5 million it estimates for Amazon.
For almost all machine learning projects, the main steps of the ideal solution remains same. For each step, I was doing some research on the web depending on my business object and jotting down the best resources I ran across. The resources include Online Courses, Kernels from Kaggle, Cheat Sheets and Blog Posts. Below I've listed them and categorised by each step (all of the resources are free except the ones that have'paid' in the end):
Never before have customers been more in control of the retail trade than today. Or has the retailer wrested control of the exchange? Let's revisit this in the light of new technologies and sensors deployed in this "game". In the sixties through the eighties, the Sears, Walmart and K-mart kind of super stores aggregated purchase information to decide what to buy and stock their shelves. Improving the scale of procurement they drove down the purchase price of things like Levi's Jeans to the detriment of the manufacturer.
Paco Nathan is a unicorn. It's a cliche, but gets the point across for someone who is equally versed in discussing AI with White House officials and Microsoft product managers, working on big data pipelines and organizing and part-taking in conferences such as Strata in his role as Director, Learning Group with O'Reilly Media. Nathan has a mix of diverse background, hands-on involvement and broad vision that enables him to engage in all of those, having been active in AI, Data Science and Software Engineering for decades. The trigger for our discussion was his Human in the Loop (HITL) framework for machine learning (ML), presented in Strata EU. Here's how it's related to artificial intelligence, how it works and why it matters.
In the Gong Labs series, we publish what we learn from analyzing sales conversations with AI. Subscribe here to read upcoming research. By the time you try that slick closing technique you read about online, it'll be too late. Your deal's fate will already have been sealed. The actions you take earlier in the sales process define your outcome.
NVIDIA (NASDAQ:NVDA) recently reported powerful fiscal first-quarter 2018 results. The graphics processing unit (GPU) specialist's revenue jumped 66%, GAAP earnings per share soared 151%, and adjusted EPS surged 141%. There was a wealth of information about the company's results and future prospects shared on the earnings call. Our focus here is on NVIDIA's data center, which is growing like gangbusters -- its revenue grew 71% year over year to $701 million in the quarter, accounting for 22% of the company's total revenue. We see the data center opportunity as very large, fueled by growing demand for accelerated computing and applications ranging from AI [artificial intelligence] to high-performance computing across multiple market segments and vertical industries.
NVIDIA (NASDAQ: NVDA) recently reported powerful fiscal first-quarter 2018 results. The graphics processing unit (GPU) specialist's revenue jumped 66%, GAAP earnings per share soared 151%, and adjusted EPS surged 141%. There was a wealth of information about the company's results and future prospects shared on the earnings call. Our focus here is on NVIDIA's data center, which is growing like gangbusters -- its revenue grew 71% year over year to $701 million in the quarter, accounting for 22% of the company's total revenue. We see the data center opportunity as very large, fueled by growing demand for accelerated computing and applications ranging from AI [artificial intelligence] to high-performance computing across multiple market segments and vertical industries.
There are two great times to make money in stock markets: the postcrash rebound and the end-of-cycle excess. Oil and technology fit the pattern perfectly in the past two years. Since the oil-price low of January 2016, the global oil and tech sectors have both made more than 80%, including dividends, beating the wider market's 53% return hands down. The oil sector was merely rebounding as oil prices tripled from their lows, but tech stocks were being led to heady heights by giddy enthusiasm for a bright future. More extreme proxies for the commodity and tech cycles have done even better.
It's time to revisit the discussion on recommendation engines. In this installment, I'm going to provide you a conceptual overview of the topic, and then, following that I'll show you how to build a recommendation engine in R. Ready? Before showing you how to build a recommendation engine in R, I need to get you up-to-speed on the concepts behind how recommendation engines work. In case you're totally new to marketing data science, let me illustrate the recommendation engine concept a little before proceeding. You know how, when you go buy something on Amazon, you see related products under the heading of'People who purchased this item also purchased…' (or something like that).
One year it might be an automaton programmed to launch Nerf balls. Another year it could be a cyborg tossing a Frisbee. Or, as exemplified just months ago, it could be a robot zipping about a competition floor, picking up crates and stacking them atop a large scale in a matter of minutes. Such is the world of the First Robotics Competition – a seasonal initiative that encourages high school students all across the country to build and operate robots designed to perform a specific task. Ultimately, these robotics teams duke it out in regional and national competitions held each academic year.