Consider TPOT your Data Science Assistant. TPOT is a Python tool that automatically creates and optimizes machine learning pipelines using genetic programming. TPOT will automate the most tedious part of machine learning by intelligently exploring thousands of possible pipelines to find the best one for your data. Once TPOT is finished searching (or you get tired of waiting), it provides you with the Python code for the best pipeline it found so you can tinker with the pipeline from there. TPOT is built on top of scikit-learn, so all of the code it generates should look familiar... if you're familiar with scikit-learn, anyway.

At the Oregon Digital Government Summit held in Salem earlier this month, Michael Finch, CIO of Lane County, Ore., discussed some of the opportunities for cognitive learning and artificial intelligence at the local government level, citing the ability to better serve constituents with tailored services informed by usage data. Eyragon Eidam is the assistant news editor for Government Technology magazine, and covers legislation, social media and public safety. He can be reached at eeidam@erepublic.com.

Artificial intelligence is a major player at Cannes Lions this week. And while AI is expected to transform tech within five years, Alphabet Executive Chairman Eric Schmidt assured attendees that there's little chance it will become "evil" as Hollywood films have depicted. In fact, he said, there are imminent benefits of AI. "Computer vision is now better than human vision. It means self-driving cars probably see a lot better than you, especially if you are drunk." Meanwhile, British musician and producer Brian Eno spoke about creativity and AI, defending human intelligence. "AI is not as advanced as we expected it would be," he said.

Over at the Mercury News site, Ethan Baron has a fun story on tourists who travel (sometimes from other countries) to Silicon Valley to visit big companies such as Apple, Facebook, and Google. None of these places are set up to welcome civilians, and all are security conscious, so the visits usually don't amount to much more than posing in front of signage and maybe buying a tchotchke in a gift shop. As the article conveys, Google's campus is at least a tad more tourist-friendly than most: The giant sculptures of Android mascots are on a public thoroughfare and well worth a visit. The local color also spills out onto the streets of Mountain View, where every motorist gets to experience the novelty of dodging Googlers on colorful bikes. But really, if you've traveled to Silicon Valley to learn about Silicon Valley, my top recommendation would be to visit the wonderful Computer History Museum.

Machine Learning is a powerful tool, and if you attended our first session you learned the basics, now it's time to really dig in and try it for yourself. What do you do when the accuracy of your predictions doesn't meet your business needs? What can you do differently with the data or the algorithms to improve your results? Ideally you attended the first session to learn the basics, but if you missed it you can complete this tutorial to catch up on what you missed. In this session we will challenge you to train a model to get higher accuracy, get hands on and make changes to improve the results.

Machine Learning is a powerful tool, and if you attended our first session you learned the basics, now it's time to really dig in and try it for yourself. What do you do when the accuracy of your predictions doesn't meet your business needs? What can you do differently with the data or the algorithms to improve your results? Ideally you attended the first session to learn the basics, but if you missed it you can complete this tutorial to catch up on what you missed. In this session we will challenge you to train a model to get higher accuracy, get hands on and make changes to improve the results.

Robots probably won't kill people, but people could kill people with robots. That's the concern of an open letter signed by scientists and other interested parties -- including Elon Musk, Steve Wozniak and Stephen Hawking. If any major military power pushes ahead with AI weapon development, a global arms race is virtually inevitable, and the endpoint of this technological trajectory is obvious: autonomous weapons will become the Kalashnikovs of tomorrow. See also: Team KAIST won the 2015 DARPA Robotics Challenge, so now what? The Future of Life Institute, the volunteer-backed research organization which posted the letter, aims to "maximize the future benefits of AI while avoiding pitfalls," according to its website.

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.