"Dave Palmer, Director of Technology and ex-Mi5 and GCHQ at Darktrace, would like to offer his opinion on why the machine is ready to fight back on its own," reads the invite I'm sent by Darktrace's PR team. It is pitched as a defence of machine learning from the slightly off kilter perspective of security. There has been an awful lot written about machine learning and automation recently. Much of this takes the angle that the more work machines can do the less jobs will be available for human beings. The emphasis is scare mongering, an angle which often flies in the face of all the big possibilities afforded by machine learning which still, after all, require vast swathes of human assistance.

Artificial intelligence can solve routine tasks but still struggles to be creative or collaborate with us. Imagine if machines were as creative and as collaborative as humans. By working together with a creative computer intelligence, you might produce a beautiful piece of art in only a few hours. But even though artificial intelligence keeps improving, this dream is still far away. It turns out that teaching computer programs to play chess is much easier than teaching them to help us write symphonies.

A day spent at the inaugural AI Summit in London last week highlighted the inherent contradiction when it comes to artificial intelligence and the enterprise: AI has been on the industry's radar for decades, yet businesses aren't ready to deploy the technology just yet. Murray Shanahan, professor of cognitive robotics at Imperial College London said that artificial intelligence (AI) is the latest fashion in an industry that is known for them: "Larry Ellison famously said that there is nothing more fashion led than the IT industry. We can see AI as a new fashion, rebadging a load of old ideas, but essentially technology has always been about adding value to an organisation." David Schatsky, head of the trend-sensing program for the US innovation team at Deloitte is a little more positive, saying: "I have looked at literally hundreds of examples of organisations in every industry that have applied or are piloting cognitive technologies in some way. We've found that you can classify everything into one of these three buckets of applications: product, process and insight."

According to researchers, 2016 will be the year of AI. Believed to be replacing the screen age as one of the "hot consumer trends," the latest in AI allows for much more than robots, personal assistants, and self-driving cars. Facebook recently announced that the company is open-sourcing its Big Sur servers designed for deep learning (i.e. The move comes as no surprise. Google, IBM, and Microsoft already took decisive steps towards opening their AI technologies earlier this year.

'Colonel Sanders is raising a robot army to serve fried chicken at a restaurant near you. KFC's first automated restaurant, called Original, went live in Shanghai on April 25th, complete with an artificially intelligent robot manager named "Du Mi" who works at the front counter. According to Chinese news outlet Sohu, "'Du Mi' marks the first commercial use of artificial intelligence in the fast food industry. The artificial intelligence robot was launched by China's leading web services company Baidu during its World Conference in 2015."'

Companies or enterprises are half as likely to encounter malware as consumers and other users without professional IT management, according to the newest Microsoft Security Intelligence Report released Friday. The analysis, based on tens of billions of reports from computers using Microsoft security software between July and December 2015, found about 11 percent of domain-joined PCs encountered malware during the fourth quarter. In comparison, about 22 percent of non-domain-joined systems did so during that quarter. Domain-joined computers belong to an Active Directory Domain Services domain -- used almost exclusively in enterprise environments. Non-domain computers also encountered more software-based malware, such as Adware and browser modifiers or software bundlers, but domain-joined ones encountered slightly more ransomware.

There are a lot of machine learning algorithms and each algorithm is an island of research. You have to choose the level of detail that you study machine learning algorithms. There is a sweet spot if you are a developer interested in applied predictive modeling. This post describes that sweet spot and gives you a template that you can use to quickly understand any machine learning algorithm. Sweet Spot For Understanding Machine Learning Algorithms Photo by dmums, some rights reserved.

When Elon Musk, Stephen Hawking, Bill Gates and the UN start worrying about Artificial Intelligence in a serious manner it is time for us mortals to realize that it is going to be The Next Big Thing. Machine learning, robotics and VR/AR are already very much here. The applications available for the general public are still somewhat clumsy, but we are just waiting for the disruptive product to come out and change things forever. Even the big players are showing some very interesting signals: Sony just filed to patent new smart contact lenses. Societies and businesses have not yet been transformed in the way they will be.

The graphics processors, or GPUs, that make possible the eye-poppingly realistic graphics of games like "Quantum Break" are also really well-suited to powering artificial intelligence and other high-intensity tasks. It turns out that as video game graphics have gotten better, the hardware used to produce them is increasingly well-suited to powering the AI future envisioned by companies like Google and Facebook. "[After] 2007, all the big advances in FLOPS came from gaming video cards designed for high speed real time 3D rendering, and as an incredibly beneficial side effect, they also turn out to be crazily fast at machine learning tasks," wrote Stack Overflow founder Jeff Atwood in a March 2016 blog entry. In fact, when the Google DeepMind AI won its history-making Go series against Lee Sedol, it was sporting 1,202 CPUs, or traditional processors, and 176 Nvidia GPUs under the hood. Nvidia and Google are actually partners on artificial intelligence, dating back to the Google Brain image recognition system, as detailed in an Nvidia blog entry.

Chinese tech giant Lenovo is investing 500 million in startups after it announced a new fund. Unlike other corporates, Lenovo has a history of making deft investments. Its first fund, created in 2010 and 100 million in size, includes Israeli facial recognition startup Face, publicly listed Chinese firm iDreamsky and biometrics specialist Nok Nok Labs from the U.S. among its 40-plus company portfolio. With its second fund, Lenovo said it is looking to back companies with synergies to its businesses and, in particular, those in the cloud computing, big data, artificial intelligence, robots and other Internet services spaces. Beyond backing upcoming companies, Lenovo has also launched an incubator program for its own businesses.