It appears not many people are into investment in Japan. As of last December, only about 16 percent of Japan's 1.7 quadrillion in personal financial assets was held in stocks, investment trusts or bonds. This is partly due to a general belief that investments are for the affluent or those with financial expertise. Others, meanwhile, just don't want to take the risk. But a new service known as robo-advisers might change this trend, allowing people to dive into investments more easily.

Any author would like to know if his/her article will be successful or not. Here is an attempt to deal with this task. We crawled 5000 URLs and for each URL we downloaded the title, body of the article and parameters: number of likes (not including Facebook likes), number of comments, number of views, article creation date and date of the last comment. First, we got rid of empty (or deleted), very short (less than 100 characters long) and "not found" articles, thus getting 2000 articles with associated parameters. Then we removed articles with missing parameters and ended up with only 1207 articles. Second, for every article we conducted tokenization of words.

There have been a lot of conversations about bots recently, ranging from where they should originate to what they should do. Bots have been called the dawn of a new form of commerce and BuddyScript 2.0 (look it up if you don't remember AOL). Bots will revolutionize the way we do business. Bots might even make your jam sandwich (see above for a demonstration). When considering the progress of automated helpers -- and specifically thinking about the kind of help we are likely to get while using messaging protocols -- maybe a jam sandwich maker is a stretch (for now). It is helpful, though, to think of the promise offered by a Bot in making some conversations and commerce much more efficient, effective, and enjoyable.

This article is an effort to make you into a "semi-expert" in artificial intelligence, cognitive computing, deep learning and neural networks from scratch. Here I will share a few cool learning resources for these topics. These resources include documentaries, TED talks, online lecture videos, and books. There are several videos and online books included in this post to help you learn these concepts. These resources vary from introductory to advanced learning.

American Academy of Arts and Sciences welcomes Carnegie Mellon's Tom Mitchell On Wednesday, April 20, the American Academy of Arts and Sciences (AAAS) announced its 2016 member class, which included Tom Mitchell, a member of Carnegie Mellon's Machine Learning Department. Mitchell has been with Carnegie Mellon since 1986 and was named Fredkin University Professor of Artificial Intelligence and Machine Learning. Mitchell also served for 10 years as the founding head of the Machine Learning Department. His research focuses on learning algorithms, with particular interest in analyzing MRIs to model brain cognition. In addition to his new membership in the AAAS, he has also been elected fellow of the American Association for the Advancement of Science, and served for two years as President of the Association for the Advancement of Artificial Intelligence.

At the IEEE International Conference on Big Data Security in New York City this month, researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and the machine learning start-up PatternEx, presented a paper about their new security system that combines machine learning approaches and input from human security experts. This system, called AI2 (named by merging "artificial intelligence" and "analyst intuition"), has an 85 percent success rate in identifying threats and a false positive rate of 4.4 percent over a raw data set of 3.6 billion log lines. According to the paper, the three major challenges faced by the security industry are a lack of labelled examples to model learning models on, constant evolution of attacker's methods, and limited reliance on security analysts to determine each threat's risk factor. In fact, stand-alone analyst-driven approaches are limited in their effectiveness because of the fact that attackers learn the behavior used by such systems to predict possible threats, and then work their way around that learned behavior in order to bypass security systems. Furthermore, only machine learning-based approaches can be inefficient based on the fact that they raise a need for human investigation every time they come across an anomaly.

Dreams are successions of images, ideas, emotions, and sensations that occur usually involuntarily in the mind during certain stages of sleep.[1] The content and purpose of dreams are not definitively understood, though they have been a topic of scientific speculation, as well as a subject of philosophical and religious interest, throughout recorded history. The scientific study of dreams is called oneirology.[2] Dreams mainly occur in the rapid-eye movement (REM) stage of sleep--when brain activity is high and resembles that of being awake. REM sleep is revealed by continuous movements of the eyes during sleep. At times, dreams may occur during other stages of sleep. However, these dreams tend to be much less vivid or memorable.[3] The length of a dream can vary; they may last for a few seconds, or approximately 20–30 minutes.[3] People are more likely to remember the dream if they are awakened during the REM phase. The average person has three to five dreams per night, and some may have up to seven;[4] however, most dreams are immediately or quickly forgotten.[5] Dreams tend to last longer as the night progresses. During a full eight-hour night sleep, most dreams occur in the typical two hours of REM.[6] In modern times, dreams have been seen as a connection to the unconscious mind. They range from normal and ordinary to overly surreal and bizarre. Dreams can have varying natures, such as being frightening, exciting, magical, melancholic, adventurous, or sexual. The events in dreams are generally outside the control of the dreamer, with the exception of lucid dreaming, where the dreamer is self-aware.[7]

When Zinedine Zidane, the then French captain, headbutted Italy's Marco Materazzi during the 2006 World Cup final, the clash quickly became one of the most infamous incidents in football history. What was not clear was what sparked the Frenchman's ire – Zidane said his mother had been insulted, a charge that Materazzi vigorously denied. The head-butt got Zidane sent off and Italy won the game. However, had there been technology there to identify what was said, the result could have been very different, Dr Helen Bear believes. "If a machine lip-reader was in existence, the other player [could] have got sent off too so it would have been 10 men against each other in a World Cup final," she argues.

Learning better together the background of various Machine Learning topics, their applications, the problems faced in diversified industry use-cases and the work-arounds. Usually, one topic is designated at a time, it may follow a book such as the classical Elements of Statistical Learning book written by the 2 famous Stanford professors Hastie & Tibshirani. Anyone can be up for presenting in the session as long as he/she is prepared and ready, maximum 2 speakers each session, at the end, Q&A as well as panel discussions and everybody votes for the best presenter and chips in 2 bucks to be an assembled prize for the winner. We will pick the majority vote on the interested topics for the following meet-up and pick the people from the 2nd question that can speak on it.

Singapore: Cognizant Technology Solutions Corp., a US-based information technology (IT) firm with most of its employees working out of India, expects its business growth in the Asia-Pacific region to outpace the company average this year, maintaining the trend seen in recent years, Jayajyoti Sengupta, vice-president and Asia-Pacific head, said in an interview. Automation, which includes robots, machine learning and artificial intelligence, will be among the new frontiers for Cognizant, as rote and repetitive processes become "digital, instrumented, analyzed and intelligent", he said. Cognizant has said it expects its revenue growth to slow to between 10% and 14.3% for the calendar year 2016. How do you see the situation in the Asia-Pacific? It would be pertinent to note that Cognizant's growth of 21% in calendar 2015 included revenues from the acquisition of TriZetto.