Five days from now Google will publish open-source tools that will focus its machine-learning engine on music and art. But one London startup, named Jukedeck, has been working on getting machines to automatically generate original music for years. You can even generate your own ditty, composed by artificial intelligence, right now on Jukedeck's website. Jukedeck lets anyone use its machine-learning engine to generate tunes hosted on its website. The engine then produces an original piece of music.

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 danger-sensing abilities of the newly developed robot system far exceed those of the Robot in the classic TV series Lost in Space. The danger-sensing abilities of the newly developed robot system far exceed those of the Robot in the classic TV series Lost in Space. Researchers are developing a system to teach robots how to feel pain. That might seem counterintuitive, as IEEE Spectrum points out. After all, "One of the most useful things about robots is that they don't feel pain."

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.

Who needs Art critics when a computer can do the job? Visitors to the Tate will be invited to access an Artificial Intelligence (AI) programme which uses algorithms to explain the relevance of works in the collection. "We can't wait to begin working with Tate, Microsoft and a talented team of AI specialists to create this living, seeing, algorithm." Tate Britain has awarded the 15,000 IK prize and a 90,000 production budget to the Italian team behind Recognition, a research project which will merge AI and art, to "uncover the hidden links between current events and art from the Tate collection." Supported by Microsoft, the Fabrica team, based in Treviso, will use powerful algorithms and "machine learning" to search through Tate's vast digital collection and archive and news images of current events, unearthing "hidden relationships between how the world has been represented in image form, in the past and present."

A Tesla Model S driver has crashed into the back of a van while the car's autopilot, active cruise control and automatic emergency brake were activated. The driver, from Zurich, has blamed the crash on Tesla and claims the entire front of his car must be replaced. Many have been quick to point out that the autopilot system is in a testing phase, and Tesla warns all drivers that they should not surrender full control to the car. Since the video has gained popularity online, the driver - who wishes not the be named - has said the problem was not with autopilot, but with the automatic emergency brake and cruise control. A Tesla Model S driver has crashed into the back of a van while the car's autopilot and automatic emergency brake were activated.

DeepMind, an artificial intelligence company owned by Google, has attempted to justify why it needs access to millions of NHS patient records for a kidney monitoring app, after a new investigation from New Scientist questioned whether an ethical approval process should have been obtained first. The AI research lab, acquired by Google in 2014 for around 400 million, signed a data-sharing agreement with the Royal Free London NHS Foundation Trust on 29 September 2015. The agreement gives Google DeepMind access to the names, addresses, and medical conditions of the 1.6 million patients that are treated at Barnet, Chase Farm, and the Royal Free hospitals each year, as well as data on all patients treated by the Trust in the past five years. This week, New Scientist questioned why Google DeepMind needs access to so much data on so many people, including those who have never experienced kidney problems, for the app, which is called Streams. Streams -- used by Royal Free clinicians in three separate trials since December 2015 -- is designed to detect acute kidney injury (AKI), a condition that kills more than 1,000 people a month.

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.

Apple now has the tech in place to give its digital assistant a big boost thanks to a UK-based company called VocalIQ it bought last year. In fact, it was so impressive that Apple bought VocalIQ before the company could finish and release its smartphone app. After the acquisition, Apple kept most of the VocalIQ team and let them work out of their Cambridge office and integrate the product into Siri. Before Apple bought the company, VocalIQ tested its product against Siri, Google Now, and Cortana, and the results were impressive. Users asked each AI questions using normal language, not the robotic commands you're used to using with digital assistants.

The rise of artificial intelligence makes for sensational headlines. Over the last few months the hype around'bots' has gone into overdrive following Facebook and Microsoft's forays into the area. And while intelligent software agents are not new, we are clearly entering a new wave of innovation around bots and related software that has the potential to impact our personal lives, our business lives and business operations in general. The current blossoming of machine-learning is driven by both technical and environmental factors, and influenced by business and consumer-oriented perspectives. Businesses struggle with the corporate brain drain caused by the continuous churn of employees, and increasingly turn to technology to capture and share information, to somehow retain and capture the knowledge that powers organizational processes.