Andrew Ng 50,482 views Location Based Services (video 5/8) - Duration: 3:58.

Jan Tian stood in nervous silence in the departure hall of Beijing Capital International Airport. Beside him, his sister held an envelope containing a thousand yuan, close to her entire year's wages. It was May 1993 and China's capital was humid, its parks ablaze with tulips, crab apples and red azaleas. But Tian, who had graduated from Beijing University a decade earlier and now worked in Vancouver for the video game company Electronic Arts, had not come to sightsee. The previous week, he had received a phone call to say that his father had suffered a stroke and Tian's bosses had booked him an emergency flight to China. After a week, the doctors had given their prognosis: Tian's father would be paralysed down his left side, but would recover. As concern yielded to relief, Tian's thoughts returned to the work he had left behind in Canada. The release date for EA Soccer, his current project, had recently been brought forward, after an executive walked past an office and heard staff, who were playing an early version of the game, whooping with excitement. For the game to be on shelves by Christmas, it would need to be finished by October. They had less than five months. While Tian and his dozen-or-so colleagues believed fervently in the project, EA's other executives were less enamoured.

A wave of startups is emerging in famously risk-averse Japan as cash-rich corporations increasingly delegate the task of keeping pace with technologies such as artificial intelligence and robotics to smaller, nimbler businesses. Japan has been dry ground for startups, given the shame that entrepreneurs and investors associate with failure, but it is on track for a record funding year for unlisted startups, exceeding the dot-com bubble of 2000, according to a private research firm. "The fundraising environment has improved a lot compared with a few years ago," said Ken Tamagawa, 40-year-old CEO of Soracom Inc, which helps companies set up platforms allowing devices to communicate with each other via the "internet of things." It raised ¥3 billion ($25.6 million) from Mitsui & Co. and an investment fund in which Toyota Motor Corp. has a stake. Dozens of companies, including electronics maker Omron Corp. and real estate developer Mitsui Fudosan Co., have set up venture capital funds to seek returns or to team up with smaller companies.

Smile Vector is a Twitter bot that can make any celebrity smile. Its results aren't perfect, but they're created completely automatically, and it's just a small hint of what's to come as artificial intelligence opens a new world of image, audio, and video fakery. Imagine a version of Photoshop that can edit an image as easily as you can edit a Word document -- will we ever trust our own eyes again? "I definitely think that this will be a quantum step forward," Tom White, the creator of Smile Vector, tells The Verge. "Not only in our ability to manipulate images but really their prevalence in our society."

We present a novel method for hierarchical topic detection where topics are obtained by clustering documents in multiple ways. Specifically, we model document collections using a class of graphical models called hierarchical latent tree models (HLTMs). The variables at the bottom level of an HLTM are observed binary variables that represent the presence/absence of words in a document. The variables at other levels are binary latent variables, with those at the lowest latent level representing word co-occurrence patterns and those at higher levels representing co-occurrence of patterns at the level below. Each latent variable gives a soft partition of the documents, and document clusters in the partitions are interpreted as topics. Latent variables at high levels of the hierarchy capture long-range word co-occurrence patterns and hence give thematically more general topics, while those at low levels of the hierarchy capture short-range word co-occurrence patterns and give thematically more specific topics. Unlike LDA-based topic models, HLTMs do not refer to a document generation process and use word variables instead of token variables. They use a tree structure to model the relationships between topics and words, which is conducive to the discovery of meaningful topics and topic hierarchies.

Online and stochastic learning has emerged as powerful tool in large scale optimization. In this work, we generalize the Douglas-Rachford splitting (DRs) method for minimizing composite functions to online and stochastic settings (to our best knowledge this is the first time DRs been generalized to sequential version). We first establish an $O(1/\sqrt{T})$ regret bound for batch DRs method. Then we proved that the online DRs splitting method enjoy an $O(1)$ regret bound and stochastic DRs splitting has a convergence rate of $O(1/\sqrt{T})$. The proof is simple and intuitive, and the results and technique can be served as a initiate for the research on the large scale machine learning employ the DRs method. Numerical experiments of the proposed method demonstrate the effectiveness of the online and stochastic update rule, and further confirm our regret and convergence analysis.

Correntropy is a second order statistical measure in kernel space, which has been successfully applied in robust learning and signal processing. In this paper, we define a nonsecond order statistical measure in kernel space, called the kernel mean-p power error (KMPE), including the correntropic loss (CLoss) as a special case. Some basic properties of KMPE are presented. In particular, we apply the KMPE to extreme learning machine (ELM) and principal component analysis (PCA), and develop two robust learning algorithms, namely ELM-KMPE and PCA-KMPE. Experimental results on synthetic and benchmark data show that the developed algorithms can achieve consistently better performance when compared with some existing methods.

In this paper, a Wide Learning architecture is proposed that attempts to automate the feature engineering portion of the machine learning (ML) pipeline. Feature engineering is widely considered as the most time consuming and expert knowledge demanding portion of any ML task. The proposed feature recommendation approach is tested on 3 healthcare datasets: a) PhysioNet Challenge 2016 dataset of phonocardiogram (PCG) signals, b) MIMIC II blood pressure classification dataset of photoplethysmogram (PPG) signals and c) an emotion classification dataset of PPG signals. While the proposed method beats the state of the art techniques for 2nd and 3rd dataset, it reaches 94.38% of the accuracy level of the winner of PhysioNet Challenge 2016. In all cases, the effort to reach a satisfactory performance was drastically less (a few days) than manual feature engineering.

Stunning composite image was created from ten frames Shows ISS transiting the sun at five miles per second on Saturday, Dec. 17, 2016 Shows ISS transiting the sun at five miles per second on Saturday, Dec. 17, 2016 Taken from California, it shows the International Space Station, with a crew of six onboard, in silhouette as it transits the sun at roughly five miles per second on Saturday, Dec. 17, 2016. Struggle to find the right emoji already? Facebook adds... Now robots can have KIDS: Researchers create machines that... Want to make yourself appear more masculine? Wear deodorant,... Giant space factories and orbiting solar panels could... Struggle to find the right emoji already? Facebook adds... Now robots can have KIDS: Researchers create machines that... Want to make yourself appear more masculine?

Sparks of electricity emanating from a Tesla coil at the Mendeleyevskaya metro station in Moscow, Russia, January 24, 2016. Editor's Note: This is an excerpt from John Wasik's new book, "Lightning Strikes: Timeless Lessons in Creativity from the Life and Work of Nikola Tesla" (Sterling, 2016), slightly edited for this column. World-changing inventions made Nikola Tesla a celebrity in his own time, but something otherworldly makes him transcend his era and remain a perpetual beacon for our civilization 70 years after his death. He's now an immortal rock star, an icon for billionaires, cyberpunks, artists and "maker" inventors who are still fiddling with everyday machines in their basements and garages. Search engine designers, energy czars, musicians, artists and creators everywhere feel his influence.