Commonwealth Bank of Australia is developing artificial intelligence technology to help with cyber security, fraud detection and regulatory compliance, in a sign banks are attempting to put the power of big data to use in reducing risk. CBA's chief information officer David Whiteing told The Australian Financial Review that so-called machine learning technology will be used to help the bank make sense of large sets of "noisy" data and alert management to areas requiring their attention. The work will come alongside a broader program of innovative projects investigating the possibilities of technologies such as the internet of things, quantum computing and the blockchain. "When you have a large data set and people can't see the signal for the noise, machine learning capability will sift out and provide you with the top five things that are worthy of further investigation," Mr Whiteing said.

Amazon today announced three new artificial intelligence-related toolkits for developers building apps on Amazon Web Services. At the company's AWS re:invent conference in Las Vegas, Amazon showed how developers can use three new services -- Amazon Lex, Amazon Polly, Amazon Rekognition -- to build artificial intelligence features into apps for platforms like Slack, Facebook Messenger, ZenDesk, and others. The idea is to let developers utilize the machine learning algorithms and technology that Amazon has already created for its own processes and services like Alexa. Instead of developing their own AI software, AWS customers can simply use an API call or the AWS Management Console to incorporate AI features into their own apps. AWS CEO Andy Jassy noted that Amazon has been building AI and machine learning technology for 20 years and said that there are now thousands of people "dedicated to AI in our business."

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Andrew Greenstein, an app developer from San Francisco, started journaling a few months ago. He tries to write for five minutes every day, but it's challenging to set aside the time. Still, he's read that journaling reduces stress and can help with goal-setting, so he's trying to make it a habit. At the Disrupt London Hackathon, Greenstein and his team built The Emotion Journal, a voice journaling app that performs real-time emotional analysis to detect the user's feelings and chart their emotional state over time. By day, Greenstein is the CEO of SF AppWorks, a digital agency.

Uber's ride-hailing service is set to get smarter. The company announced on Monday that it bought New York-based AI start-up Geometric Intelligence and will soon launch its own AI research lab, creatively named Uber AI Labs. Geometric Intelligence's 15 specialists in machine learning will help drive Uber's research in self-driving cars (confirmed by Geometric Intelligence CEO Gary Marcus to the The Wall Street Journal). Autonomous vehicles are a hot research area for the automotive industry right now, and Uber is keen to make its mark on the cutting-edge technology, along with carmakers like BMW and technology titans like Google. While Uber's own fleet of self-driving cars tool around San Francisco (I've seen them), pick up passengers in Pittsburgh and go on the occasional beer run, it needs to do much more to not be left behind in the dust. In addition to machine learning, Uber can also use AI to help calculate the most efficient route, better match carpool riders and more accurately tell customers when their car will arrive, chief product officer Jeff Holden wrote in Uber's blog post announcement.

Uber is coming closer to its self-driving cars goal. The ride-sharing company announced Monday it launched Uber AI Labs, a new department based in San Francisco which will focus on research in artificial intelligence and machine learning. Uber also revealed it has acquired the AI research startup Geometric Intelligence. The 15 members of the startup will form the primary core of the AI Labs team. With the company's new moves, Uber is betting that artificial intelligence could improve its services.

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Most of today's commercially available drones have four rotors and more or less look the same (other than that one that's foldable). So if you wanted to own a drone that had, say, five copters or a completely different design altogether, you'd probably have to make your own, which can be a really tedious process. If you don't know where to start customizing your flying masterpiece, MIT's new system could be your first stop. The institute's Computer Science and Artificial Intelligence Laboratory (CSAIL)'s latest project is what it says is the first platform that lets you design, simulate and build your own drone. Through the project interface, you can pick your own propellers, rotors and rods from MIT's database, as well as determine the size, shape and structure of your device.

Some say over 60-70% time is spent in data cleaning, munging and bringing data to a suitable format such that machine learning models can be applied on that data. This post focuses on the second part, i.e., applying machine learning models, including the preprocessing steps. The pipelines discussed in this post come as a result of over a hundred machine learning competitions that I've taken part in. It must be noted that the discussion here is very general but very useful and there can also be very complicated methods which exist and are practised by professionals. Before applying the machine learning models, the data must be converted to a tabular form.

Investors poured a record high $1.05B into artificial intelligence startups in Q2'16, and AI is already affecting more areas of our lives than many people realize. Even retail and e-commerce companies are increasingly integrating the technology. Recently there's been a rush of AI announcements and acquisitions by major retailers: Just this week, Etsy acquired Blackbird to enhance its search functionality through AI, followed the very next day by Amazon acquiring Angel.ai And earlier this month, e-commerce unicorn Houzz (see our full unicorn tracker here) announced a deep learning initiative to help users find and buy products by clicking on images. Using CB Insights data, we dove into the wide array of AI startups focused on retailers and e-commerce businesses, including AI-powered personal shopping apps, natural language processing and image recognition tools for shopping websites, predictive inventory allocation tools, and more.