If you are a recruiter, then you'd agree that the one thing most recruiters would love to have is a recruiting source of top candidates that helps fill all their positions quickly. Imagine you had a gold mine, that one source that is extremely simple to use. That one source where you can just pick out the best hires, present them to your hiring managers and close positions in a jiffy? You'd say "Stop making things up. Nothing like that exists, really.
Oft cited as the blow that is knocking the wind out of the services business, AI has flexed its disruptive muscle. While Uber's advanced algorithms have produced efficiencies in ride sharing and differential pricing that have proved difficult for traditional taxi fleets to compete against, AirBnB is using advanced AI to find the perfect match between host and guest, creating an experience that will shine in comparison to high cost hotel rooms. But AI is more than a disruptive force that will displace businesses or replace workers – the technology is moving mainstream to provide decision support in an increasingly broad range of traditional sectors. A good example of this mainstreet extension of AI can be found in the experience of Vancouver-based Goldcorp Inc., which is using IBM's Watson to optimize exploration. Goldcorp is one of the largest gold mining operations in the world; however, mining in general is characterized as a'high risk, high reward' activity in which ore discovery can have a significant impact on profitability.
Recently we attended the Unearthed Data Science event in Melbourne. A gold mining company -- Newcrest Mining -- provided operating data for a number of its plants, with the aim that some of the teams attending could provide useful solutions grounded in Data Science. One particular system caught our eye -- the autoclaves. This ore is rich is sulphide minerals (sulfide if you're American) such as iron pyrite (FeS2) (aka "Fool's Gold"). Sulphides inhibit the processing techniques used to extract gold from ores, so it's ideal if you can get rid of them.
The landscape of data is ever-changing, meaning analysts need to evolve both their thinking and data collection methods to stay ahead of the curve. In many cases, data that might have been considered unique, uncommon or unattainably expensive just a few years ago is now widely used and often very affordable. It is the analysts who take advantage of these untapped data sources, while they remain untapped, who can reap the rewards by gaining a competitive advantage before the rest of their industry or peers catch on. This type of data is often referred to as alternative data, and with the ever-increasing levels of data available in the modern world comes the opportunity to gain unique insights, competitive industry advantage, and boosted profits. It is perhaps no surprise then to hear that the scramble to get hold of such data has been dubbed the new gold rush.
Chris Nicholson co-founded Skymind and Deeplearning4j, the most popular deep-learning framework for Java. Quitting Twitter is easy -- I've done it a hundred times. Someone called it "a clown car that drove into a gold mine," and like all clown cars, Twitter makes the passengers get out once in awhile. If I go back, it's because I'm addicted. For an information junkie, that little bubble is hard to resist.
Nvidia, a publicly traded company that makes graphics processing units (GPUs), has been focusing its business more and more completely on artificial intelligence (A.I.) after having managed to sell considerable quantities of GPUs for that type of computing work to big companies like Facebook and Google. Those GPUs sit in servers, rather than desktops, laptops, or mobile devices, where Nvidia sticks GPUs for gaming, image processing, and other workloads. But the use of Nvidia's GPUs for A.I., and specifically deep learning -- an approach that involves training artificial neural networks on bunches of data, such as images, and then getting the neural networks to make inferences about new data -- has gained particular traction in the technology industry. Now Nvidia wants to see government agencies adopt and expand their use of deep learning -- which today typically relies on GPUs -- particularly during the training phase. "One of the reasons why I'm going to Washington is I want to talk to a lot of government customers and find out what they're most interested in and what they want to find out about," Nvidia chief scientist Bill Dally told VentureBeat in an interview.
Machine learning is kind of magic right? But is it the kind of magic that can make us rich? And I don't mean lucrative consulting gig rich, I mean digging valuable metals out of the ground rich. Also I'd been meaning to try out some transfer learning and looking around for a good topic to try it on. Transfer learning is where you take a pre-trained convolution (or other) network and use it for your task.