The likes of China -- who among other things is building cruise missiles with a certain degree of autonomy -- are nipping away at America's heels. The Pentagon has put artificial intelligence at the centre of its strategy to maintain the United States' position as the world's dominant military power, earmarking $US18 billion ($23.5 billion) over the next three years for developing the technology. Speaking from San Francisco ahead of a major AI industry conference, Prof Walsh said unlike previous arms races, much of the progress in AI development was being made by private corporations. "It's the same sort of technology that is going to go into autonomous cars which is going to be a good thing ... but giving it the right to make life or death decisions (in the battlefield) is probably a bad idea," Prof Walsh said.

Google's artificial intelligence DeepMind, became famous by beating the South Korean, professional GO player Lee Sedol. Lee Sedol played a historic five game match against Google DeepMind's AlphaGo computer program in March 2016. AlphaGo won the match and became the world's very first computer which had defeated a world class human player on GO. After achieving the impossible, DeepMind now has a very different challenge to focus on; Social Dilemmas. The Artificial Intelligence (AI) department of Google developed and used new theoretic game scenarios to see if AI can learn to work together for a mutual benefit or not.

One of the oldest automakers in the United States is making a billion-dollar bet that one day, owning a car may not be a necessity of American life. Ford Motor announced on Friday its plans to invest $1 billion over the next five years in Argo AI, an artificial intelligence start-up formed in December that is focused on developing autonomous vehicle technology. The move is Ford's biggest effort to move into self-driving car research. Argo AI will develop the technology exclusively for Ford at first, and then plans to license its technology to others. The investment is also a way for Ford, which is more than century old, to tap into Silicon Valley talent and make headway in a competitive space.

Autonomous weapons are being increasingly sought my militaries around the world, but experts fear the worst. AUTONOMOUS robots with the ability to make life or death decisions and snuff out the enemy could very soon be a common feature of warfare, as a new-age arms race between world powers heats up. Harnessing artificial intelligence -- and weaponising it for the battlefield and to gain advantage in cyber warfare -- has the US, Chinese, Russian and other governments furiously working away to gain the edge over their global counterparts. But researchers warn of the incredible dangers involved and the "terrifying future" we risk courting. "The arms race is already starting," said Professor Toby Walsh from UNSW's School of Computer Science and Engineering.

Are you familiar with self-driving cars? These things would not be possible without the help of Machine Learning--the study of pattern recognition and prediction within the field of computer science. This course is taught by Stanford-educated, Silicon Valley experts that have decades of direct experience under their belts. They will teach you, in the simplest way possible (and with major visual techniques), to put Machine Learning and Python into action. With these skills under your belt, your programming skills will take a whole new level of power.

Machine learning is at its best when there's way too much information for any human to comb through manually, like making high-volume stock trades or surfacing the best posts from hundreds of friends on Facebook. Now one Estonia-based startup, Teleport, is using this idea, coupled with images from Google Street View, to automatically look around cities and see if people will like them based on their lifestyle preferences. In a Medium post, Teleport co-founder Silver Keskkula walks through an example of the process. First, he plots 10,000 randomized points throughout a city, and grabs images taken by Google Street View. Then those images are run through computer-vision algorithms that identify objects, people, and buildings, and describes them in a short sentence.

Babak Hodjat believes humans are too emotional for the stock market. So he's started one of the first hedge funds run completely by artificial intelligence. "Humans have bias and sensitivities, conscious and unconscious," says Hodjat, a computer scientist who helped lay the groundwork for Apple's Siri. "It's well documented we humans make mistakes. For me, it's scarier to be relying on those human-based intuitions and justifications than relying on purely what the data and statistics are telling you."

AI needs three ingredients: (A) lots of data (B) lots of computing power and (C) modern computing expertise. Internet platforms have all three. Startups rarely either have or can afford (A) and (B) but incumbents can and sometimes don't have (C). So there are natural strategic partnerships to be made. Almost all revenue generating AI is supervised learning.

Sales teams use many systems and applications to run their operations. There are CRM, SFA, order management and billing applications to help capture customer and account information and manage various customer processes. These applications help sales to manage their day-to-day tasks, but are these tools helping them sell more? Why is it that even today, sales teams feel they don't have enough actionable, timely and contextual information to offer the right solution to a prospect and close the deal? Why is it that selling remains more of an art form than a repeatable scientific method? Of course, people play a large role there.

It has never been easier to build AI or machine learning-based systems than it is today. The ubiquity of cutting edge open-source tools such as TensorFlow, Torch, and Spark, coupled with the availability of massive amounts of computation power through AWS, Google Cloud, or other cloud providers, means that you can train cutting-edge models from your laptop over an afternoon coffee. Though not at the forefront of the AI hype train, the unsung hero of the AI revolution is data -- lots and lots of labeled and annotated data, curated with the elbow grease of great research groups and companies who recognize that the democratization of data is a necessary step towards accelerating AI. However, most products involving machine learning or AI rely heavily on proprietary datasets that are often not released, as this provides implicit defensibility. With that said, it can be hard to piece through what public datasets are useful to look at, which are viable for a proof of concept, and what datasets can be useful as a potential product or feature validation step before you collect your own proprietary data. It's important to remember that good performance on data set doesn't guarantee a machine learning system will perform well in real product scenarios.