It's no secret that technology is revolutionising consumer and brand interaction. In most cases, consumers are changing their behaviour faster than most retailers can adapt their marketing strategies. Marketers must engage savvy shoppers across a plethora of channels, the competition is intense, and customer satisfaction and retention have become top priorities for most brands. In our modern era, it is imperative to know and UNDERSTAND who our customers are, what they like/dislike, what will motivate them to buy or buy again, and why they leave. It is vital to have a forward-looking approach and to predict answers to questions such as: "What will my customer be interested in next week? In which city is my customer likely to shop? What is the most effective channel to connect with customers when they are ready to buy? Which products are my prospects waiting for?"

Thus, given a sequence of frames corresponding to an utterance, the model is required to produce, for each frame, a probability distribution over the alphabet. During the training phase, the softmax outputs are fed into a CTC cost function (more on this shortly) which uses the actual transcripts to (i) score the model's predictions, and (ii) generate an error signal quantifying the accuracy of the model's predictions. The overall goal is to train the model to increase the overall score of its predictions relative to the actual transcripts. Empirically, we have found that using stochastic gradient descent with momentum paired with gradient clipping leads to the best performing models. Deeper networks (seven layers or more) also tend to perform better in general.

Public sector agencies must adopt emerging technologies – including machine learning, artificial intelligence, and biometrics – to attract and retain more technically adept employees, a new report from Accenture recommends. It said this is critical to addressing a widening skills gap and strong competition from a better financed private sector. According to the report, "Emerging Technologies in Public Service," the need to attract technically proficient employees is becoming even more urgent as the existing workforce continues to age, creating an irrevocable loss of institutional knowledge unless action is taken now. The report emphasized that hiring and developing people with the necessary skills, including the need for emerging technology specialists, is one of the top three challenges across all industries and countries today," the report noted. "The very concept of work is being redefined as different generations enter and exit the workforce in a rapidly changing technological landscape," said Terry Hemken, who leads Accenture's Health & Public Service Analytics Insights for Government business. "Government leaders must make every effort to reskill their people to be relevant in the future and ready to adapt to change." Survey respondents said emerging technologies will augment existing roles rather than replace them. Automating tasks, whether through artificial intelligence, machine learning or other technologies, frees up employees to focus on activities that are more critical and more closely aligned with citizen needs, according to the research. In fact, eight in 10 respondents said that implementing emerging technologies will improve job satisfaction and can aid staff retention, partly by automating certain repetitive tasks and making others more aligned with citizens' direct needs. Nearly 60 percent of respondents also said that being able to implement projects using emerging technologies would require significant investment in reskilling existing staff. "Responsive and responsible leaders must ensure that their people are relevant and adaptable to keep pace with technology," Hemken said. "Creating the future workforce now is the responsibility of the very highest levels of an organization.

Seiya Tokui is a researcher at Preferred Networks, Inc. and a Ph.D. student at the University of Tokyo, from which he received the master's degree in mathematical informatics in 2012. He is the lead developer of Chainer, a deep learning framework. His research interests include deep learning and generative models.

Pundits are quick to hype AI and machine learning as the future of everything. But, anyone who has been caught screaming at Siri for its lack of understanding of the most basic of queries knows that we have a long, ponderous way to go before "we have arrived." That's why I find Gil Press' summary of the recent O'Reilly AI Conference so helpful and important. Some of the observations are banal ("AI is not going to exterminate us, AI is going to empower us"), but others capture the essence of what makes AI so promising...and beguiling. The first observation ("AI is difficult") seems obvious, yet for all the wrong reasons.

An Introduction to'Machine Learning' -- I came across this article and thought it was worth a share, the original article was surrounded in adverts and difficult to read, so I make no apologies for plagiarizing it! I have kept the original link at the bottom of the article, enjoy . . . The concept that a computer program can learn and adapt to new data without human interference. Machine learning is a field of artificial intelligence that keeps a computer's built-in algorithms current regardless of changes in the worldwide economy. Various sectors of the economy are dealing with huge amounts of data available in different formats from disparate sources.

For example, let's say you want to teach a computer to distinguish between a cat and a dog. You might say cats have four legs, a tail, and pointed ears. Dogs have four legs, a tail, and floppy ears. But if you show that machine a chihuahua or a corgi, will it say it's a cat? Between tail length, fur texture, and color, there's a lot of labels a developer would have to program manually to help a traditional computer spot the difference. But with machine learning, you feed the computer thousands of photos of cats and dogs so it can learn to spot the difference by experience -- or in the world of machine learning -- lots of data.

What can we actually expect in 2017? Here's what happens in an "Internet Minute": We leave data footprints in many other ways. Which means more of us are taming data for better insights that guide our decisions. Data helps us focus and make better choices. Yet some truths hide among a high number of variables. Others stay hidden because questions are deemed too hard to answer.

Machine learning is a buzzword in the technology world right now, and for good reason: It represents a major step forward in how computers can learn. Very basically, a machine learning algorithm is given a "teaching set" of data, then asked to use that data to answer a question. For example, you might provide a computer a teaching set of photographs, some of which say, "this is a cat" and some of which say, "this is not a cat." Then you could show the computer a series of new photos and it would begin to identify which photos were of cats. Machine learning then continues to add to its teaching set.

At first I liked tinkering with computers and learn computer programming languages, after graduating high school I started to develop the concept of work on data processing and I've completed it. More recently the IT world the term Deep Learning (DL) number of campuses or institutions have been developing this concept, and many experts of computer data or data processing experts began to talk about it. I do not know that it is actually a concept I have done resemblance to Deep Learning or part of Deep Learning but once I learned it was different, DL they mean is to show something of what they are looking for based on the data input as much as possible so that what they the purpose is to learn to structure the deepest and provide advisory or decision, but it relates to the search engine or internet network application using algorithms, meaning that when it is applied in the world of the stock market as Wall Street, the working concept Deep Learning will detect fraud there is. Deep Learning systems work similar to the concept of the brain where the objects are visible to the eye to be delivered to specific parts to be stored and studied by contrasting the existing data and the use of certain alogritma method to render a decision as well as a warning signal. Deep Learning tend to use super computers or computer large capacity for looking at the use of data (big data), big data here can mean pictures, numbers, files, chat, text, web pages, maps of the world, the code algoritmatik, core decision made deep learning is seen in a comparison of all the data held (such as scanned photos) means more data entry means more comparisons, and if more and more comparisons, the decision is getting better, so that a deficiency also that deep learning must wear a large-capacity computers.