Yes, something very basic you can do is to apply to your time-series (data consumption over time) a decomposition (seasonal, trend, etc). With that you will see if in the previous years your data consumption trend was stable and if a change in the trend appears once you introduced your picocells. You can do that in "R" with just one function decompose() applying that to your data. This function will give you back a set of values that when plotting will show you several charts where you will see if that upward trend is present.
Any time series classification or regression forecasting involves the Y prediction at't n' given the X and Y information available till time T. Obviously no data scientist or statistician can deploy the system without back testing and validating the performance of model in history. Using the future actual information in training data which could be termed as "Look Ahead Bias" is probably the gravest mistake a data scientist can make. Even the sentence "we cannot make use future data in training" sounds too obvious and simple in theory, anyone unknowingly can add look ahead bias in complex forecasting problems. The discussion becomes important when you put in so much efforts in researching and building the model only to realize later that the back testing framework was using future data. It will also cost the data scientist a lot when the model is approved by Top Management and at the time of deploying the model realizing that we don't have the future data.
Global consulting firm Accenture notes that stand-alone, AI-powered digital voice assistant devices are being used "for a range of consumer services such as playing music, turning the heat and lights on and off, and providing news, weather and sports scores." That's to be expected, of course - that's what they're for - but in an online survey of 21,000 consumers in 19 countries, Accenture discovered that digital virtual assistants are becoming "the central hub for home activities in Australia." Again, that's the whole point of digital assistants - they're meant to be the hub of your digital home, not just answering questions but helping you control your other connected devices, but naturally, it's always good to see this being confirmed by actual users. In addition, "three quarters (75%) of these owners said they use their smartphones less for entertainment, more than two thirds use them less for online purchasing and more than half for general information searches (71% and 55%, respectively)." David Sovie, global MD of Accenture's High Tech business said: "Digital voice assistant devices are challenging smartphones as the central hub for all activities in the home.
Although chatbots have been around since the release of ELIZA in 1966, texting and chatbots have only recently begun to revolutionize customer service. We're seeing industries like retail, travel and events adopt chatbots with great success. Now it's time for health clubs to do the same or get left behind to new ways of working out. We're building LegDay to lead the charge. LegDay makes a customer service chatbot for gyms.
Predictions for Artificial Intelligence in 2018 Dueling neural networks At the banner AI academic gathering held recently in Barcelona, the Neural Information Processing Systems conference, much of the buzz was about a new machine-learning technique known as generative adversarial networks. Invented by Ian Goodfellow, now a research scientist at OpenAI, generative adversarial networks, or GANs, are systems consisting of one network that generates new data after learning from a training set, and another that tries to discriminate between real and fake data. By working together, these networks can produce very realistic synthetic data. The approach could be used to generate video-game scenery, de-blur pixelated video footage, or apply stylistic changes to computer-generated designs. Yoshua Bengio, one of the world's leading experts on machine learning (and Goodfellow's PhD advisor at the University of Montreal), said at NIPS that the approach is especially exciting because it offers a powerful way for computers to learn from unlabeled data--something many believe may hold the key to making computers a lot more intelligent in years to come.
CES showcases the tech trends that will shape the year ahead. See the most important products that will impact businesses and professionals. According to Penn, Telstra has only physical implementation and chipset aspects remaining in its preparation work for the extensive 5G trial on the Gold Coast that will take place during the Commonwealth Games in April. "We've orchestrated the spectrum availability; we have effectively signed up the arrangements with the equipment manufacturer, which is Ericsson that we're trialling it with, and so it's pretty well advanced," he told ZDNet during CES. "It's just some actual physical implementation of infrastructure we have to put in place [and] we've got to work with chipset manufacturers such as Qualcomm and Intel in terms of making sure the 5G chipsets are available, and also some of the equipment manufacturers of handsets to make sure we've got those ready as well."
In the world of sports sponsorship, companies like Block Six Analytics are helping sponsors develop their campaigns in real-time. One example featured on the company's website is of a partnership between the Dallas Cowboys and Pepsico. The drinks company had secured signage rights for an LED tunnel cover at the AT&T Stadium, for which it wanted real-time analytics rather than an end-of-season report to make creative yet data-driven decisions as the season progressed. Block Six's promise was to, a few days after any given game, provide Pepsico with a value report and suggest ways in which they could improve that value. An example of how this affected Pepsico's strategy is when the company's senior leadership suggested the sign included an actual image of the bottle, at the expense of logo size.
Artificial intelligence is starting to permeate the technology that we use everyday. That's especially true in smartphones, where apps that rely on machine learning and AI are increasingly common. Statista mapped out the survey results of Deloitte's Global Mobile Consumer Survey which determined just how aware smartphone users are of technologies like machine learning and AI in their phones. Turns out, people are becoming progressively aware of the advances in machine learning, especially when it comes to day-to-day features like predictive text and route suggestions. People are talking more and more about AI and machine learning, and for good reason: Both technologies received a lot of buzz in 2017, good and bad.
Consumers who own in-home digital voice assistant devices are using their smartphones less often for entertainment and online purchasing, according to an online survey of 21,000 consumers in 19 countries by Accenture. The survey revealed that two-thirds of consumers who own digital voice assistants – powered by early stage artificial intelligence – said they use their smartphones for fewer applications in the home since acquiring the devices. Nearly two-thirds of these owners said they use their smartphones less for entertainment, and more than half use them less for online purchasing and general information searches. "Digital voice assistant devices are challenging smartphones as the central hub for all activities in the home," said David Sovie, global managing director of Accenture's High Tech business. "These low-cost devices deliver valuable and practical benefits and are relatively easy to use, and their rapidly growing popularity is one of the most striking trends in the high-tech industry."
Qualcomm executives called the new Asus Lyra Voice a model for routers going forward: partly a speaker, partly a router, and powered by Amazon's Alexa assistant. The company said it's trying to convince ISPs like Cox and Comcast to move in that direction. Unlike AMD and Intel, you can't buy Qualcomm chips to build your own smartphones and routers. But when company executives take the stage at CES to describe how future products should look, it's worth paying attention. Qualcomm announced three versions of its Smart Audio Platform--complete with a quad-core ARM A53 processor, audio DSP, 802.11ac