During the past week, I've said "Hey Cortana" more times than I have over the past couple of years combined. I've been testing the Harman Kardon Invoke speaker, which is powered by Cortana and includes a custom version of Linux inside. The Invoke speaker will go on sale in the US on October 22. While I've played a bit with a family member's Amazon Echo Dot, I never bought a voice-activated speaker for use at home. I was curious if, after using the Invoke for a week to do everything from set timers, to add items to my calendar, to play music would change my mind and make me want one.
In this special guest feature, Ran Sarig, Co-founder and CEO of Datorama, discusses the importance of applying machine learning to data integration or'cleansing' processes with speed and at a scale in order to keep up with the ever increasing number of data sources. And why Big Data needn't be a big mess anymore. Ran has 14 years of management, product, engineering and leadership experience. He co-founded Datorama in 2012 and is its chief executive officer. Prior to this, he worked for MediaMind as its VP of R&D where he managed a group of 130 engineers and product managers.
Microsoft is delivering the world's first fully neural on device translations in the Microsoft Translator app for Android, customized for the Huawei Mate 10 series. Microsoft achieved this breakthrough by partnering with Huawei to customize Microsoft's new neural technology for Huawei's new NPU (Neural Processing Unit) hardware. This results in dramatically better and faster offline translations as compared to existing offline packs. The Microsoft Translator app with these capabilities comes pre-installed on Huawei Mate 10 devices allowing every Mate 10 user to have native access to online quality level translations even when they are not connected to the Internet. Until now, due to the computational requirements of neural machine translation, it was not possible to do full Neural Machine Translation (NMT) on-device.
How can artificial intelligence transform businesses? We're already seeing a wave of innovation across industries. From real-world data, computers are learning to recognize patterns too complex, too massive, or too subtle for hand-crafted software or even humans. They and the Cleveland Cavaliers, the 2016 champs, use AI to put their teams at the top of their game. Retailers have been among the most active adopters of deep learning-powered intelligence.
In the field of machine learning, online learning refers to the collection of machine learning methods that learn from a sequence of data provided over time. In online learning, models update continuously as each data point arrives. You often hear online learning described as analyzing "data in motion," because it treats data as a running stream and it learns as the stream flows. Classical offline learning (batch learning) treats data as a static pool, assuming that all data is available at the time of training. Given a dataset, offline learning produces only one final model, with all the data considered simultaneously.
For Peter Cao, who has dedicated 16 years of his career to teaching chemistry in a high school in central China's Anhui province, in every teacher there lives a "doctor". He spends two to three hours a day grading assignments, a process the 38-year-old describes as "diagnosing". "By reviewing the homework of my pupils, I can have an overall picture about their understanding of the lessons I give," Cao said, adding that this "diagnosis" helps him draw up a teaching plan for the following day. But if the Chinese online education start-up Master Learner has its way, Cao and his 14 million fellow teachers in China will be able to hand this time-consuming review process to a "super teacher", a powerful "brain" capable of answering nearly 500 million of the most tested questions in China's middle schools as well as scoring high points in each Gaokao test, China's life-changing college entrance exam, for the past 30 years. If the super teacher sounds too smart to be human, that is because it is not.
When we talk about data science or artificial intelligence, the two very common terminologies that come into account are MACHINE LEARNING and DEEP LEARNING. But it is substantially seen that both the terms are faultily used interchangeably. So let us find out what is the difference between the two and how both the terms are interrelated with each other. The term machine learning refers to a technology which enables a device to perform a task without any human intervention. In other words, machine learning is that field of data science which consists of the algorithms that perform the learning procedure without human assistance.
Baidu chief executive Robin Li on Tuesday said the Chinese internet giant will have a self-driving bus on the road soon as it races for a lead in autonomous vehicles. Baidu is collaborating with an array of companies on autonomous cars, and is working with a large bus maker in China to have a self-driving bus running a route by next year, Li said in an on-stage interview late Tuesday at The Wall Street Journal D.Live conference in Laguna Beach, California. Most major automakers and technology titans including Google-parent Alphabet have been stepping up efforts on autonomous driving in recent years, convinced that these systems could eliminate most road accidents. Baidu's search engine dominates the Chinese internet, and online ads are a key revenue stream. But since a crackdown by authorities on Baidu's online advertising business after a much-publicized scandal over promoting a fake medical treatment, 'China's Google' is seeking to focus on artificial intelligence and is investing heavily in the sector.
Artificial intelligence has already begun influencing our online lives. Marketing trends are using AI to take the place of sales representatives with the installation of chatbots into the messaging world, but there are even more ways AI will affect internet behavior now and in the future. Take a look at some of the increasingly common AI marketing strategies. If you could predict how your customers would behave, you would have the ability to create more effective strategies to accommodate their behavior. With predictive customer service, that is now possible.