Bots (short for software robots) have been around since the early days of computers. One compelling example of bots is chatbots, algorithms designed to hold a conversation with a human, as envisioned by Alan Turing in the 1950s.33 The dream of designing a computer algorithm that passes the Turing test has driven artificial intelligence research for decades, as witnessed by initiatives like the Loebner Prize, awarding progress in natural language processing.a Many things have changed since the early days of AI, when bots like Joseph Weizenbaum's ELIZA,39 mimicking a Rogerian psychotherapist, were developed as demonstrations or for delight. Today, social media ecosystems populated by hundreds of millions of individuals present real incentives--including economic and political ones--to design algorithms that exhibit human-like behavior. Such ecosystems also raise the bar of the challenge, as they introduce new dimensions to emulate in addition to content, including the social network, temporal activity, diffusion patterns, and sentiment expression. A social bot is a computer algorithm that automatically produces content and interacts with humans on social media, trying to emulate and possibly alter their behavior. Social bots have inhabited social media platforms for the past few years.7,24

Machine learning and artificial intelligence (AI) may sound intimidating, but Dean said enterprises don't need the technical resources of a company like Google to get started. There are now lots of options that let businesses bring their own data to machine learning platforms that contain pretrained models or algorithms that organizations can train themselves. Google offers such a service, and the Spark data processing engine contains a library of machine learning algorithms. Such offerings lower the bar to entry. Other speakers at the Spark conference agreed the time is ripe for machine learning applications across various vertical markets.

A team from the University of Oregon have developed a system that can read people's thoughts via brain scans, and rebuild the faces they were visualising in their heads. The study, led by Brice Kuhl and Hongmi Lee from the University of Oregon, used artificial intelligence (AI) that analysed brain activity in an attempt to reconstruct one of a series of faces that participants were seeing. It's not an exact science, but the AI did get close. "We can take someone's memory – which is typically something internal and private – and we can pull it out from their brains," Kuhl told Vox. "Some people use different definitions of mind reading, but certainly, that's getting close," Kuhl told Vox. Kuhl and his colleague Lee recently published a paper in The Journal of Neuroscience with a conclusion straight out of science fiction: Kuhl and Lee created images directly from memories using an MRI, some machine learning software, and a few hapless human guinea pigs.

"Apply Machine Learning like the great engineer you are, not like the great Machine Learning expert you aren't." This is the first sentence in a Google-internal document I read about how to apply ML. In my limited experience working as a server/analytics guy, data (and how to store/process it) has always been the source of most consideration and impact on the overall pipeline. Ask any Kaggle winner, and they will always say that the biggest gains usually come from being smart about representing data, rather than using some sort of complex algorithm. Even the CRISP data mining process has not one, but two stages dedicated solely to data understanding and preparation.

The benefits of machine learning continue to trickle down to smartphones and gadgets, and chipmaker Qualcomm wants to help speed up the process. The company is launching a new software development kit for its "machine intelligence platform" Zeroth. This SDK will make it easier for companies to run deep learning programs directly on devices like smartphones and drones -- if they're powered by one of Qualcomm's chips, of course. Right now, you're probably using all sorts of deep learning programs you don't know about. Companies like Google and Facebook use this sort of software for things like image and voice recognition, but usually, this process happens in the cloud, with the results beamed to your phone.

AI and machine learning have been a topic of science fiction for years, and they'll continue to be. But this talk isn't about science fiction--it's about all the amazing new things that machine learning is already doing for us today. It is already around us, in many many products. See where machine learning is used today and how you can use TensorFlow to make it a part of everything you do. See all the talks from Google I/O 2016 here: https://goo.gl/olw6kV

Amid a growing crisis in digital marketing, "personalization" has become a goal that many enterprises see as solution to consumer complaints of "irrelevance." However personalization is like a unicorn that many are chasing but few have captured. Customer data management provider Lytics this week introduced a new platform that promises to bring both greater simplicity and sophistication to personalization efforts across channels and devices. The pitch is "personalized marketing right out of the box." Lytics Personalization, as the product is called, uses an intuitive UI and machine learning to deliver more segmented experiences and messaging on customer websites.

Watson, developed by IBM, is a question-answering computer system capable of answering questions posed in natural language. Watson rose to national prominence in 2011 by competing on the television game show Jeopardy! Watson prevailed over the human competitors and received the first place prize of 1 million. Now, IBM is focusing its artificial intelligence prowess on the billion dollar advertising industry. Through its recent acquisition of the Weather Co. media properties, IBM is planning to roll out digital ads that people can communicate with this fall.

Rosetta navigation camera (NavCam) image taken on 17 June 2016 at 30.8 km from the centre of comet 67P/Churyumov-Gerasimenko. The image measures 2.7 km across and has a scale of about 2.6 m/pixel. The image has been cleaned to remove the more obvious bad pixels and cosmic ray artefacts, and intensities have been scaled. Another version of this image, which has been contrast enhanced, is available here. More images of comet 67P/Churyumov-Gerasimenko can be found in the '67P – by Rosetta' collection. This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 IGO License.

Self-driving cars may be the new direction for the automotive industry, even though realistically it could be decades before we start seeing them all around us. That isn't stopping some from pondering a moral dilemma these cars may pose. When you take the control away from the driver, you are giving the motor vehicle power to make judgment calls. In many ways, you give a machine the power to choose your fate in specific scenarios. This is the very thing physicist Stephen Hawking warned us about, as he believes artificial intelligence could lead to a very real Terminator scenario.