Technology is evolving at warp speed, with an estimated 5 billion connected devices creating a gargantuan network of data. AI (Artificial Intelligence) is certainly the next big thing. Further, the ability to store huge volumes of data on cloud along with easier access to intelligent algorithms is set to make AI the next big wave in business innovation. AI for CRM is crafted to help businesses become smarter and more predictive about their customers. Everyone and almost everything is connected, a never before phenomenon in the history of mankind!

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.

It may sound like YouTube has been possessed, but the demonic sounds coming from the clip below are voice commands to access a smartphone's virtual assistant. Researchers have found an attack that uses'hidden voice commands' embedded within clips that lets hackers prompt the assistant to perform a number of tasks. This attack lets hackers make phone calls, use Venmo to transfer money or worse, download malware giving cyberthieves complete control of the handset. Researchers have found an attack that uses'hidden voice commands' embedded within YouTube videos that lets hackers prompt the assistant to perform a number of tasks. They placed an Android 10ft away from the speaker and the'demonic sounds' said'OK Google' Researchers found that hackers are able to embed'hidden voice commands' in YouTube clips to control a smartphones virtual assistant.

Irrespective of your ideological viewpoint, the collective Women's marches around the world this past weekend were inspiring and illustrative of the collective power of women. Simultaneously, regions of the United States were being ravaged by deadly tornadic storms. Modern computer models were critical in identifying the evolution of atmospheric conditions to support severe weather. There is a curious connection between these two trains of thought. Trust me, I am about to make the connection.

Every post I read that says it is going to explain the relationship between artificial intelligence, machine learning, and deep learning goes way off the deep end into extremely technical terms and huge amounts of detail. So I decided to take a crack at a simple 2-minute explainer. Machine learning: Enabling computers to learn, usually "on their own," often with big datasets. Deep learning: Building multiple layers of abstractions on datasets to construct higher-level meaning. Sometimes, of course, it's easier to understand by walking through an example.

Citi Ventures, an arm of the bank that combines investments in startups with innovation developed in its six Citi Global Innovation Labs around the world, has announced a strategic investment in Feedzai, a machine learning company with a focus on real-time fraud prevention in ecommerce and banking. Feedzai said the investment will support its continued expansion of offerings to the financial services ecosystem. "Citi Ventures is actively exploring is the application of machine learning, which we are looking across multiple sectors including security, customer service, compliance, and data and analytics," said Ramneek Gupta, managing director and co-head of investing. In machine learning, it has invested in Feedzai, Cylance and Ayasdi "current portfolio companies deploying machine learning in innovative ways," Gupta said. "We have used Ayasdi's technology in several use cases and also provided valuable feedback and suggestions that helped Ayasdi evolve its product architecture for deployment into our own complex data environments. Our partnerships have also enabled us to learn a great deal from Cylance and Feedzai."

Let's start with definitions as they relate to big data: Artificial Intelligence: AI's goal is to understand neural function and imitate that ability of the brain to learn from a given set of circumstances. The brain is basically, in many cases, a Rube Goldberg machine. We have this limited set of evolutionary building blocks that we are able to use to get to a complex end state. Back in the 1980s, AI was a popular field among scientists: how experts do what they do, and how to reduce those tasks to a set of rules and program computers with those rules, effectively replacing the experts. Researchers wanted to teach computers to diagnose disease, translate languages, to even figure out what we wanted but didn't know ourselves.