Research into word embeddings is one of the most interesting in the deep learning world at the moment, even though they were introduced as early as 2003 by Bengio, et al. Most prominently among these new techniques has been a group of related algorithm commonly referred to as Word2Vec which came out of google research.[ In particular we are going to examine some desired properties of word embeddings and the shortcomings of other popular approaches centered around the concept of a Bag of Words (henceforth referred to simply as Bow) such as Latent Semantic Analysis. This shall motivate a detailed exposition of how and why Word2Vec works and whether the word embeddings derived from this method can remedy some of the shortcomings of BoW based approaches. Word2Vec and the concept of word embeddings originate in the domain of NLP, however as we shall see the idea of words in the context of a sentence or a surrounding word window can be generalized to any problem domain dealing with sequences or sets of related data points.

In this podcast, Rob Thomas, general manager, IBM Analytics, discusses how investments in machine learning within private cloud deployments can contribute to customer business success. Thomas will speak on this topic, 15 February 2017, at the IBM Machine Learning Launch Event. Register for the event's livestream to see the presentation and learn how IBM is helping organizations accelerate the use of machine learning solutions to deliver better, faster business results.

The marketing industry has experienced a surge of new technologies in the past few years. While this creates the potential for complete transformation, the marketing landscape over the past year has remained fairly steady. As 2017 begins, the question remains whether this will be the year that technology is fully embraced by the marketing community. Will it be a transitional year with common practices being adapted to reflect new technology trends or will it witness major developments that threaten to take marketers by surprise? In a world where customers are constantly demanding more, customer experience (CX) has become difficult and challenging to perfect, putting extra pressure on marketers.

Organizations have a brief window to experiment and become familiar with the strategies and technologies needed to get ready for an AI-first world, according to a new report from Avanade, the leading digital and cloud services provider. The Avanade Technology Vision 2017, looking at emerging trends for the next three years, finds that we are on the cusp of a new decade of digital disruption powered by artificial intelligence and automation. It states that the emerging AI-first era will bring powerful opportunities and capabilities to organizations – similar to the PC revolution of the 1990s – but they must begin transforming now. The Avanade report highlights that the emerging AI-first era is already creating new ways for organizations to interact with, serve, and empower customers and employees. For example, by augmenting employees' capabilities using AI – including intelligent automation, Robotic Process Automation (RPA) and physical automation – organizations will enable workers to achieve far more, faster, with more intelligence-driven actions that deliver better results.

The machine-learned smarts that enable Microsoft's Skype Translator, Bing and Cortana to accomplish tasks such as translating conversations, compiling knowledge and understanding the intent of spoken words are increasingly finding their way into third-party applications that people use every day. These advances in the democratization of artificial intelligence are coming in part from Microsoft Cognitive Services, a collection of 25 tools that allow developers to add features such as emotion and sentiment detection, vision and speech recognition, and language understanding to their applications with zero expertise in machine learning. "Cognitive Services is about taking all of the machine learning and AI smarts that we have in this company and exposing them to developers through easy-to-use APIs, so that they don't have to invent the technology themselves," said Mike Seltzer, a principal researcher in the Speech and Dialog Research Group at Microsoft's research lab in Redmond, Washington. "In most cases, it takes a ton of time, a ton of data, a ton of expertise, and a ton of compute to build a state-of-the-art machine-learned model," he explained. Take one of the tools that deals with speech recognition, for example.

McKinsey's new report on the future of automation notes that humans are better than robots at: spotting new patterns, logical reasoning, creativity, coordination between multiple agents, natural language understanding, identifying social and emotional states, responding to social and emotional states, displaying social and emotional states, and moving around diverse environments.

Bots have been built into devices and operating in the background for many years (think Google search engine), but only recently have software companies started to exploit their capabilities beyond simple querying of information followed by a programmed response. Just like Apple opened the app market to allow developers to write their own apps back in 2008, this revolution has now begun for AI and bots. In 2016, tech companies finally released platforms that allow third parties to start building and deploying solutions for the technology. From Facebook, Amazon, Apple, Google and even enterprise tools such as Slack, the SDKs and tools have been rolled out. Today, developers don't need their own platform to create an Alexa skill – it is made available to them by Amazon.

Researchers at Google and several universities recently made key progress in developing artificial intelligence (AI) that is itself able to write AI software, reports MIT Technology Review . In an experiment, AI researchers from Google Brain allowed software to develop the design for a machine learning system to recognize human speech. This software produced better results than software designs for machine learning that had previously been created by people, the researchers write in a scientific paper submitted to a conference. This paper has not yet been subject to peer review – so testing of these results by other researchers still remains to be done. Other research groups have also reported similar progress in recent months in the field of machine learning using artificial neural networks – the most promising technology in artificial intelligence in recent years.

Scality CEO Jérôme Lecat takes a look at how deep learning can transform businesses. If you work in information technology the chances are you have noticed regular articles in the media about artificial intelligence (AI), machine learning and deep learning. Some commentators make no distinction between these terms and they often use them interchangeably. But to attribute the same meaning to these names is an oversimplification which is unhelpful to those looking for new ways to add value to their businesses. While AI, machine learning and deep learning are often intertwined, they hinge upon different technologies and have their own unique attributes.

But look five years down the road to 2022, and "branding" the way we understand it today may hardly exist at all. According to experts, the future is rife with personalized logos, brands built by artificial intelligence, and even brands literally living inside our bodies. Jez Frampton, global CEO of the branding agency Interbrand, describes it as the "Age of You." To eliminate friction between brands and consumers. Of course, this could be construed as a tremendous invasion of privacy--a dystopian vision in which the separation between who we are and what we buy dissolves into the ether.