Over the last two years, we have highlighted deep learning use cases in enterprise areas including genomics, large-scale business analytics, and beyond, but there are still many market areas that are still building a profile for where such approaches fit into existing workflows. Even though model training and inference might be useful, for some areas that have complex simulation-driven workflows, there are great efficiencies that could come from deep neural nets, but integrating those elements is difficult. The oil and gas industry is one area where deep learning holds promise, at least in theory. For some steps in the resource discovery workflow, deep learning could lead to faster and more accurate results for potential discovery zones. Reservoir characterization is a critical step in this discovery process and is currently a hot area for explorations into how deep learning might be applied.

Ask any marketer what's on their to-do list in 2017, and they'll tell you they have a project underway to achieve a 360-degree view of the customer, Tealium's global CEO, Jeff Lunsford, says. "Any marketer is going to be looking to pull in data about that customer or a prospect from the myriad points where data is available in this new world," he says. "This could be IoT, mobile devices, or customer care. "Every marketer will nod yes, they want to leverage all the data they possibly can. So there's vision sync across the industry, the question is, how to do that." Tealium is one of a growing number of vendors looking to provide that answer with its Universal Data Hub, a software solution aimed at addressing data fragmentation for marketers across online and offline channels. The platform brings together the vendor's AudienceStream and DataAccess solutions with its iQ foundational technology. Since launching six years ago, Tealium has spent several years integrating its offering with more than 1000 applications across the marketing ecosystem, and recently raised another US$35m in capital, off the back of increased investment earlier in 2016, bringing total funding to $112.9m. Tealium now has 750 enterprise customers globally, from small digital-first companies to the largest, mature organisations. Australian clients include Cronulla Sharks, Nude by Nature, Greenstone Financial, and Melbourne University, while Asia-Pacific clients include Cathy Pacific. Speaking to CMO during a visit to Australia this week, Lunsford described Tealium as the "neutral layer down the stack of the marketing cloud", and the common management component organisations need in order to be able to exchange data across multiple best-of-breed systems in real time. Rather than competing with the large marketing cloud providers, he sees Tealium's role as being a complementary component. Not surprisingly, Lunsford sees technology as providing the foundational layer marketers need across customer touchpoints to pull that 360-degree vision off. "Companies use multiple software applications to create the customer experience, each has its own idea of the customer, and most don't talk to each other," he says. "The average Tealium customer has 26 software applications that contribute to the customer experience.

There have been numerous examples over the last two decades of how Natural Language Processing, or NLP, is being used by companies to provide an intelligent voice to gadgets and searches. Think, for instance, how the world of search engines--from Yahoo, Microsoft and Google--have changed the Internet with text-based search algorithms driving and augmenting the World Wide Web. NLP, though, does much more than just that and text analytics. NLP exploration on our current digital planet includes voice searches on automobiles and then, of course, the dictation mechanics of the software world. I have an 18-month-old who thrives on YouTube searches asking for'Peppa Pig' or'Mickey Mouse' series while my 5-year-old is exploring the world of content on YouTube (of course, with restricted parental control)--from watching the world of KungFu Panda to how to make dummy videos.

With the dust firmly settled on this year's CES we thought we'd take a look back at the show to see what stood out in terms of overall trends, with AI, security, connected cars, VR, AR and drones all making their mark. Did this year's show have the X-Factor? Well, to be honest it was probably more'The Voice'. The big shout, so to speak turned out to be Amazon's Alexa voice assistant, which seems to have broken out of its Echo cage and made its way into a wide variety of devices, from a number of third-party speakers, to'smart' fridge's and autonomous cleaning robots . However, while Alexa is the current poster child for smart AI, it has clearly has a long way to go to becoming truly smart.

Artificial intelligence is one of the hottest technology trends for 2017. And perhaps no company in the AI sector is hotter than NVIDIA, which has pushed from the desktop into the data center, evolving into a major player in high performance computing. NVIDIA's graphics processing (GPU) technology has been one of the biggest beneficiaries of the rise of specialized computing, gaining traction with workloads in supercomputing, artificial intelligence (AI) and connected cars. This trend is expected to accelerate in 2017, with more custom chips being introduced to target these workloads. After building a major beachhead in hyperscale data centers, NVIDIA's ambitions now extend to the enterprise data center. The company's new DGX-1 Deep Learning System is a "supercomputer in a box" – a hardware appliance designed to make AI data crunching more accessible.

A survey of business leaders has found Australian companies are the worst prepared for the arrival of artificial intelligence (AI) technologies among selected major economies, despite spending the second-largest amount of money on automation. Independent research agency Vanson Bourne was commissioned by IT company Infosys (which as a seller of an AI platform has a vested interest in promoting such technology) to poll 1,600 business leaders of companies with more than 1,000 staff and at least US$500m in annual revenue across Australia, China, the United States, Germany, France, India and the UK. According to the survey, released at the World Economic Forum last week, major Australian businesses invested an average of $7.9m last year in AI, behind only the US, but placed last in both the skills required for AI takeup and in plans to integrate AI. The Infosys Australia regional head, Andrew Groth, told the Guardian the survey demonstrates that Australia risks becoming uncompetitive. "The challenge is the skills situation," he said.

Part two of our series "A day in the life of a city" looks at the ways in which offices are changing and how cities are coping with the ever-growing problem of pollution. The morning rush hour is over and, if you live in a city in the developed world, you are likely to be settling down at your desk for the next eight or so hours. However, the office block and skyscraper, which have been part of our urban landscape since the end of the 19th Century, may also soon become surplus to requirements. Urban architect Anthony Townsend thinks cities need more creative approaches to how we work and is keen to reclaim the streets by creating pop-up workspaces in the parks and plazas of the financial district in New York. "Before the New York Stock Exchange, traders met under a tree on Wall Street to buy and sell shares. It is only in the last 50 years that we have taken that creative energy and sucked it up into office buildings and separated it from public space," he said.

Support Vector Machine has become an extremely popular algorithm. In this post I try to give a simple explanation for how it works and give a few examples using the the Python Scikits libraries. All code is available on Github. I'll have another post on the details of using Scikits and Sklearn. SVM is a supervised machine learning algorithm which can be used for classification or regression problems.

A survey of business leaders has found Australian companies are the worst prepared for the arrival of artificial intelligence (AI) technologies among selected major economies, despite spending the second-largest amount of money on automation. Independent research agency Vanson Bourne was commissioned by IT company Infosys (which as a seller of an AI platform has a vested interest in promoting such technology) to poll 1,600 business leaders of companies with more than 1,000 staff and at least US$500m in annual revenue across Australia, China, the United States, Germany, France, India and the UK. According to the survey, released at the World Economic Forum last week, major Australian businesses invested an average of $7.9m last year in AI, behind only the US, but placed last in both the skills required for AI takeup and in plans to integrate AI. The Infosys Australia regional head, Andrew Groth, told the Guardian the survey demonstrates that Australia risks becoming uncompetitive. "The challenge is the skills situation," he said.

Quoc Le, a software engineer at Google Brain, is one such human. Google Brain focuses on "deep learning," a part of artificial intelligence. Think of it as a sophisticated type of machine learning, which is the science of getting computers to learn from data. Deep learning uses multiple layers of algorithms, called neural networks, to process images, text and sentiments quickly and efficiently. The idea is for machines to eventually be able to make decisions as humans do.