"The development of a special military robot is one of the priorities of military construction in Russia," the Russian daily newspaper Komsomolskaya Pravda reported recently. The purpose of Iron Man, the newspaper continued, is to "replace the person in the battle or in emergency areas where there is a risk of explosion, fire, high background radiation, or other conditions that are harmful to humans." Experts have known that Russia has been trying in recent years to match the US and China in the development of robots, drones, and other war machines that are potentially autonomous. Today, those machines are remotely controlled. Iron Man and other recent developments illustrate how they're making progress.

The supplier for Apple and Samsung is leading a new push for automated manufacturing. According to reports, the world's largest electronics manufacturer Foxconn has replaced around 60,000 human factory workers with machines. Or, as a government publicist for the city of Kunshan told the South China Morning Post, the factory "reduced employee strength from 110,000 to 50,000 thanks to the introduction of robots. It has tasted success in reduction of labour costs." Although Foxconn confirmed to the BBC that it was working to automate much of its manufacturing operations, the company denied that the new robotic assembly line would mean fewer jobs for humans. Instead, the company says it is simply using the machines to "replace repetitive tasks previously done by employees" while allowing those employees to focus on more valuable parts of the manufacturing process like R&D and quality control.

AI & deep learning are powering interactive messaging services known as chatbots & virtual assistants, which use conversational interfaces to create deeper, more personalised one-to-one customer experiences. The Chatbot Track will explore the technical advancements in deep learning, NLP & predictive intelligence to create conversational self-learning bots for messaging platforms, healthcare, personalised services & more.

Would you share your home with a robot or work side by side with one? People are starting to do both, which has put the relationship we have with them under the spotlight and exposed both our love and fear of the machines that are increasingly becoming a crucial part of our lives. In Japan they grow so attached to their robot dogs that they hold funerals for them when they "die". Sony, the firm that began making the popular Aibo toys in 1999, decided to stop offering repairs in 2014, meaning once they broke down they were fit only for the scrapheap. But people weren't willing to throw them in the rubbish bin, wanting instead to say goodbye to them in the same way you would to a human or pet.

"I am six feet tall and my hazel eyes reflect my olive skin... I seek to connect with women who are romantics at heart… that are open to the possibility of true love". These are lines from the online dating profile of Robert Torres - a man who is serving four concurrent life sentences for aggravated sexual assault, including the rape of Texas nurse Lori Williams at knifepoint 20 years ago, while her two daughters slept in a room nearby. His other victims included a 63-year-old woman and her 16-year-old granddaughter. The advert contains no mention of any of these crimes.

State-of-the-art sequence labeling systems traditionally require large amounts of task-specific knowledge in the form of handcrafted features and data pre-processing. In this paper, we introduce a novel neutral network architecture that benefits from both word-and character-level representations automatically, by using combination of bidirectional LSTM, CNN and CRF. Our system is truly end-to-end, requiring no feature engineering or data pre-processing, thus making it applicable to a wide range of sequence labeling tasks. We evaluate our system on two data sets for two sequence labeling tasks -- Penn Treebank WSJ corpus for part-of-speech (POS) tagging and CoNLL 2003 corpus for named entity recognition (NER). We obtain state-of-the-art performance on both datasets -- 97.55% accuracy for POS tagging and 91.21% F1 for NER. 1 Introduction Linguistic sequence labeling, such as part-of- speech (POS) tagging and named entity recognition (NER), is one of the first stages in deep language understanding and its importance has been well recognized in the natural language processing community. Most traditional high performance sequence labeling models are linear statistical models, including Hidden Markov Models (HMM) and Conditional Random Fields (CRF) (Ratinov and Roth, 2009; Passos et al., 2014; Luo et al., 2015), which rely heavily on handcrafted features and task-specific resources. For example, English POS taggers benefit from carefully designed word spelling features; orthographic features and external resources such as gazetteers are widely used in NER. However, such task-specific knowledge is costly to develop (Ma and Xia, 2014), making sequence labeling models difficult to adapt to new tasks or new domains. In the past few years, nonlinear neural networks with as input distributed word representations, also known as word embeddings, have been broadly applied to NLP problems with great success.

The recovery of the rocket module proved once again that delivering payloads into deep orbit could be much cheaper in the near future. But this idea is still in its experimental stage and would require repeated successes to become part of normal operating procedures in 21st century space transport. SpaceX plans to use one of its four recovered first-stage rockets in a mission later this year. The rocket recovery was part of a successful mission to deliver an Asian communications satellite into so-called supersynchronous orbit, a position that puts a satellite more than 22,000 miles above the earth's surface in a way where it synchronizes with the planet's orbit in order to remain above the same area at all times. The satellite, Thaicom 8, will service communications and data transfer needs in Thailand, India and East Africa, according to nasaspaceflight.com.

Dream interpretation is the process of assigning meaning to dreams. In many ancient societies, such as those of Egypt and Greece, dreaming was considered a supernatural communication or a means of divine intervention, whose message could be unravelled by people with certain powers. In modern times, various schools of psychology and neurobiology have offered theories about the meaning and purpose of dreams. Most people currently appear to interpret dream content according to the Freudian theory of dreams in countries, as found by a study conducted in the United States, India, and South Korea.[1] People appear to believe dreams are particularly meaningful: they assign more meaning to dreams than to similar waking thoughts. For example, people report they would be more likely to cancel a trip they had planned that involved a plane flight if they dreamt of their plane crashing the night before than if they thought of their plane crashing the night before or the Department of Homeland Security issued a Federal warning.[1] However, people do not attribute equal importance to all dreams.

Every day, 730,000 comments and 420 billion statuses are posted on Facebook, 500 billion 140-character tweets are posted and 430,000 hours of new video is uploaded to YouTube. The Internet is a goldmine of data just waiting to be analyzed. Ever since social media crept deeper and deeper into our daily lives, governments and advertisers have been utilizing this data for myriad purposes. Now, a team of researchers at the University of Ottawa, University of Alberta and the Université de Montpellier in France is examining ways to use social media data to detect and monitor people who are potentially at risk of mental health issues. Using computer algorithms, the team will apply social web mining and "sentiment analysis methods" to troves of data generated through social media to detect at-risk individuals. Sentiment analysis is the process of identifying and categorizing opinions expressed in text through a computer program.

The marriage of technology and medicine has already afforded humanity with some pretty phenomenal achievements -- after all, we're closer to becoming bionic than ever before. But despite the numerous advances in the medical field, there's still one critical problem yet to be solved: misdiagnoses. A key component of patient care, reading and diagnosing medical images is becoming more important than ever with an aging global population, and Zebra Medical Vision believes it can help. By teaching computers to help radiologists, Zebra says, its products can help health care providers "analyze millions of imaging records to understand the risk profile of their patients, detect and predict disease, and assist in building and managing preventative care programs." On Tuesday, the Israeli company announced a new collaboration with Intermountain Healthcare, one of the largest health care providers in the U.S., with hopes of accelerating the creation of Zebra's imaging analytics engine and neural networks that will use the tech company's imaging dataset to assist radiologists with automated diagnostic algorithms.