Free catered lunch and a dog-friendly office are two of the perks offered by an educational technology company in Palo Alto, Calif., that's looking to hire a machine learning engineer. The position, posted on Dice, will pay between 140,000 and 160,000 to the right candidate who's skilled in machine learning platforms as well as data mining, statistical modeling, and natural language processing. Job-seekers who possess those skills typically could expect multiple job offers, says Matt Leighton, director of recruitment at Mondo, which specializes in digital marketing and technology staffing. The job titles vary from company to company; some might post positions in search of a data scientist or machine learning engineer, others might be after a natural language processing (NLP) programmer or cognitive computing engineer. But hiring companies are seeking the same talent: "They're people who create algorithms through code that allow computers to self-learn," Leighton says.

Earlier this year, Andreessen Horowitz investor Chris Dixon noted that how challenging it's become for investors to help groom promising AI startups, given how quickly Facebook, Google, and Amazon are bringing aboard related talent. Dixon noted, for example, that Wit.ai, a Y Combinator startup that built voice-activated interfaces that Facebook bought and which now powers its Messenger platform, was only in Andreessen Horowitz's portfolio for a few months when Facebook bought it. But firm co-founder Marc Andreessen said on stage at Disrupt today that the firm is beginning to see things swing in the opposite direction. "Two years ago, it seemed like four or five companies were hoovering up all the talent . . . I think something like 1,500 people over four years [were involved in] building Alexa," the technology that powers Amazon's voice-controlled home computer Echo.

Yi Wang was hearing the same refrain over and over: Why are English classes in China so expensive? The former Google product manager decided to do something about it and started an app called LiuLiShuo, which basically means "speaking fluently" in Mandarin. The app, which claims more than 30 million users, is one of scores of English-learning startups looking to disrupt China's hidebound language schools. To differentiate itself from products started by Internet giants like Baidu and Tencent, LiuLiShuo brings gaming and social media features to the genre. Users win points when they move to the next level and text each other encouragement and tips.

Reuters is the latest major news agency to embrace content automation. But the news organization has struck partnerships with Graphiq Inc. and Wibbitz Ltd., to automate the creation of simple graphics and video clips, respectively, to run alongside relevant, human-reported Reuters news content on the third party sites that pay for and run it. Graphiq Inc. based in Santa Barbara, Calif., has integrated its free-to-use visualizations platform with Reuters News Agency to make the simple graphs and visualizations it creates in thousands of Reuters articles wherever they run on 3rd party sites. According to Graphiq CEO Kevin O'Connor, Graphiq works with hundreds of publishers, including TechCrunch and now Reuters, to put dynamically generated infographics into articles. The company's systems ingest data from public and private sources to create instant infographics.

Tech columnist Jennifer Jolly takes a spin in a self-driving Ford Fusion and gets the scoop on how the technology works. Mercedes-Benz, whose engineers have been working on self-driving car technology, is eager to increase the size of its engineering team both in Silicon Valley and in Germany. SAN FRANCISCO -- So you say you want join the automotive revolution? Over the past few years, only elite roboticists have been positioned to heed the self-driving car's call to action. Armed with degrees from places such as Carnegie Mellon University and experience at institutions such as NASA, these tech whizzes have been highly sought after by technology and automotive companies looking to build the future.

Sure, the autonomous era will wipe out a lot of jobs. Automakers, tech titans, and startups are racing to essentially put four million truckers, cabbies and other drivers out of work. But like all radical technological shifts, self-driving cars will provide opportunities, too--for those with the right skills. Working in the most compelling part of this field requires an understanding of deep learning, the branch of artificial intelligence that trains computers to do things like discern pedestrians from lamp posts. Universities can't crank out graduates fast enough.

After learning so much from Kaggle's collaborative community over the past eight months since I first joined, I wanted to share some of my favorite data science resources including suggestions from my fellow Kagglers. Like many others who have a seemingly endless queue of languages and techniques we hope to learn, I had tried MOOCs like Udacity and coding platforms like HackerRank. Right before joining Kaggle earlier this year, I was working through Andrew Ng's famed machine learning Coursera. Following the blogs, newsletters, and podcasts I'm sharing here is another way I try to stay (or become) savvy about topics in machine learning, data visualization, and industry trends. This list is far from exhaustive, so if you have any favs that are tragically missing, please add them to the comments!

Want to understand how Amazon recommends items we might be interested to buy? Want to predict the selling price of your house? It is all possible because of Machine Learning technology – it is about the science of getting computers to act without being explicitly programmed. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level artificial intelligence. Come join us for a session on'Introduction to Machine Learning,' complete with snacks, drinks, and networking.

Mercedes-Benz, whose engineers have been working on self-driving car technology, is eager to increase the size of its engineering team both in Silicon Valley and in Germany. SAN FRANCISCO - So you say you want join the automotive revolution? Over the past few years, only elite roboticists have been positioned to heed the self-driving car's call to action. Armed with degrees from places such as Carnegie Mellon University and experience at institutions such as NASA, these tech titans have been highly sought after by technology and automotive companies looking to build the future. But now massive open online course pioneer Udacity has a proposition: Give the Web-based education outfit 36 weeks and 2,400, and they'll turn graduates onto jobs at autonomous-car partner companies Mercedes-Benz, Didi Chuxing, Nvidia and Otto.

Timandra Harkness is a regular on BBC Radio 4, writing and presenting BBC Radio 4's FutureProofing series and documentaries such as Data, Data Everywhere, and Personality Politics. Her book Big Data: does size matter? A regular public speaker and chair on scientific and technological topics, she works with the Cheltenham Science Festival, the British Council, the Institute of Ideas, the Wellcome Collection and a Robotics conference in Moscow, among many others. She is a member of the Royal Statistical Society and has 86% of a Mathematics and Statistics degree with the Open University. She hopes to reach 100% in 2017.