A dream is a succession of images, ideas, emotions, and sensations that usually occurs involuntarily in the mind during certain stages of sleep.[1] The content and purpose of dreams are not fully 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]

Machine learning, AI, Conv Nets, Deep Learning, and Neural Nets … all rapidly maturing Artificial Intelligence technologies that have simultaneously become household jargon in the Valley. Tesla's self driving car, Amazon Alexa, Google Search, Facebook tag recommendations, Microsoft Cortana, and Apple Siri … all novel products leveraging the above mentioned AI technologies, developed by large tech mainstays, and increasingly popular nation wide. Technocrati cocktail banter is developed and largely kept in-house by large technology incumbents to develop new products and disrupt adjacent industries. That said, historically a rapid rise and maturation of a new technology germinates within the the confines of a select few labs, institutions, social classes, and corporations before hitting a critical juncture when, via technological or economic means, it rapidly democratizes and is made available to everyone. Clarifai's growing product suite around developer centric AI tools are leading exactly that charge: democratizing the Artificial Intelligence revolution.

A team of computer and legal scientists from the UK worked alongside Daniel Preoțiuc-Pietro – a postdoctoral researcher in natural language processing and machine learning from the University of Pennsylvania – to extract case information published by the ECtHR. They identified English language data sets for 584 cases relating to Articles 3, 6 and 8 of the Convention. Article 3 forbids torture and inhuman and degrading treatment (250 cases); Article 6 protects the right to a fair trial (80 cases) and Article 8 provides a right to respect for one's "private and family life, his home and his correspondence" (254 cases). They then applied an AI algorithm to find patterns in the text. To prevent bias and mislearning, they selected an equal number of violation and non-violation cases.

According to Gartner, there will be more than 20 billion connected devices worldwide by 2020. Today's enterprises are already benefitting greatly from a strong, connected workforce, but as Internet of Things (IoT) enabled devices move forward, saturating the market, is it possible for them to outpace their own benefits? After all, while the continuing surge of IoT devices is creating an onslaught of data requiring storage and retention, advancements in the IoT world are still bound by how quickly and efficiently data can be computed, and value extracted. Interestingly, the current resurgence of artificial intelligence (AI) technology may provide an antidote to the flood of data today's digital world is facing. With such rapid innovations in both spaces taking place, what can we expect from their converging paths?

We propose an approximate strategy to efficiently train neural network based language models over very large vocabularies. Our approach, called adaptive softmax, circumvents the linear dependency on the vocabulary size by exploiting the unbalanced word distribution to form clusters that explicitly minimize the expectation of computational complexity. Our approach further reduces the computational cost by exploiting the specificities of modern architectures and matrix-matrix vector operations, making it particularly suited for graphical processing units. Our experiments carried out on standard benchmarks, such as EuroParl and One Billion Word, show that our approach brings a large gain in efficiency over standard approximations while achieving an accuracy close to that of the full softmax.

Experts Jeff Prosise and Christopher Harrison joined us for a live Q&A session about Azure Machine Learning and Azure Stream Analytics. Check out the recording to learn more about how to perform sophisticated predictive analytics from data sets large or small, using an assortment of included algorithms or using code algorithms of your own in R and Python. Plus, learn how to set up real-time analytic computations on data streaming from devices, sensors, websites, social media, apps, and more.

I'd pick a universally accessible binary classification problem: learning which foods are yummy and which are yucky. We want to teach a computer to recognize which foods are yummy and which foods are yucky. But the computer doesn't have a mouth or any way of tasting the food. Instead, we need to teach it by showing it examples of foods ("labeled training data"), some of which are yummy foods ("positive examples") and some of which are yucky foods ("negative examples"). For each labeled example, we also provide the computer with ways to describe the food ("features").

An unnamed regionally-managed health care provider partnered with ThingWorx's machine learning platform to detect patterns in data that would lead to better patient care and reduce costly readmissions for patients with ischemic heart disease, according to a case study provided by Thingworx. The solution predicts high-risk patients and provides caregivers insight into why flagged patients should receive extra care across their network using health care IoT. The unspecified health care network includes two major hospitals and a network of outpatient and preventative care providers. It has more than 1,000 patient beds, a home health care service, preventive medicine, rehabilitation services, a network of primary care physicians and a range of outpatient services. According to Thingworx, its client is one of the largest health providers in the country.

UPDATE: Mar 20, 2016 - Added my new follow-up course on Deep Learning, which covers ways to speed up and improve vanilla backpropagation: momentum and Nesterov momentum, adaptive learning rate algorithms like AdaGrad and RMSProp, utilizing the GPU on AWS EC2, and stochastic batch gradient descent. We look at TensorFlow and Theano starting from the basics - variables, functions, expressions, and simple optimizations - from there, building a neural network seems simple! Deep learning is all the rage these days. What exactly is deep learning? Well, it all boils down to neural networks.

They finally did it: They made "artificial intelligence" a buzzword. Typically, buzzwords don't come from decades-old evolving disciplines of computer science. Making "machine learning," "AI" and "neural nets" into buzzwords means that millions of developers are likely having their first experience with this stuff now. In that vein, we bring you a nice long list of machine learning, deep learning, neural network and artificial intelligence how-to's. Buzzword or not, it's fairly obvious this stuff will be a big part of enterprise software for the next few decades.