Google's latest advance in machine learning could make the world a little smaller. The company is reëngineering its translation service after Google researchers invented a system that is significantly more accurate. In a competition that pitted the new software against human translators, it came close to matching the fluency of humans for some languages, such as when translating from English to Spanish. Google has already begun rolling out the new system for translations from Chinese to English (see examples showing the improvement). The company expects to replace its current translation system altogether.

On this episode, I am so happy to have Pedro Domingos who is a professor at the University of Washington. He's the leading researcher in machine learning and recently wrote an amazing book called The Master Algorithm. I was fortunate enough to have a long and fascinating conversation with him over dinner one night which I hoped would never end but that ended up leading to this episode which I think you will love. In this conversation we explore the sources of knowledge, the five major schools of machine learning, why white collar jobs are easier to replace than blue collar jobs, machine wars, self-driving cars and so much more. Transcript: A complete transcript is available for members.

Apple has refused to join the likes of Google and Facebook on a new consortium that will aim to ensure artificial intelligence (AI) technology is developed in a safe, ethical, and transparent manner. The consortium -- announced on Wednesday and called Partnership on AI -- also counts Amazon, Microsoft, and IBM among its founding members. But Apple's playing hard to get. Eric Horvitz, technical fellow and managing director at Microsoft Research, said: "We've been in discussions with Apple and they're enthusiastic about the effort. I personally hope to see them join."

These days, a lot of workers are worried about robots taking their jobs, but now robots are taking jobs that literally no one else was doing--like poring through every inch of people's dense expense reports. AppZen, a startup that provides automated auditing services, has just expanded its software, which can now not only read basic items on attached receipts, but also scan entire documents to look for clues about invalid charges. Anant Kale, the company's CEO, tells Fast Company that the new offering, called ReceiptIQ, can audit 100% of the expense reports that employees submit, spotting them for "accidental fraud . . . The upgrade required moving beyond optical character recognition to computer vision that can understand the entire receipt, such as recognizing company logos. Many hotels put their logos on receipts rather than spelling out their name, says Kale. ReceiptIQ analysis goes deeper by looking for context, such as whether car rental bills include a fuel service charge--a penalty for not returning the car with a full gas tank.

The world's largest technology companies hold the keys to some of the largest databases on our planet. Much like goods and coins before it, data is becoming an important currency for the modern world. The data's value is rooted in its applications to artificial intelligence. Whichever company owns the data, effectively owns AI. Right now that means companies like Facebook, Amazon, Alphabet, IBM and Microsoft have a ton of power.

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Any conversation about the current state of tech would be incomplete without mention of Machine Learning--tech companies like Uber, Microsoft, Google, and more are doubling down on the latest wave of programming that's making once futuristic inventions like artificial intelligence, self-driving cars, and smart homes a reality. Believe it or not, you can learn the fundamentals of Machine Learning even those with little to no programming background. Right now, you can save hundreds on the Complete Machine Learning Bundle, featuring 10 in-depth courses and over 60 hours of hands-on training to get you up to speed on the latest machine learning skills. Normally over 700, Engadget readers can get the full bundle for just 39.99 at GDGT Deals. Throughout this comprehensive training bundle, you'll learn fundamental machine learning concepts like decision trees, deep learning, and NLP (neuro-linguistic programming), get hands on training in Python, Java, and other essential coding languages, and apply your skills building recommendation systems and solving big data problems.

Artificial intelligence is one of those tech terms that seems to inevitably conjure up images (and jokes) of computer overlords running sci-fi dystopias -- or, more recently, robots taking over human jobs. But AI is already here: It's powering your voice-activated digital personal assistants and Web searches, guiding automated features on your car and translating foreign texts, detecting your friends in photos you post on social media and filtering your spam. But as practical uses of AI have exploded in recent years, one critical element remains missing: an industrywide set of ethics standards or best practices to guide the growing field. Now, the industry heavyweights are partnering to fill that gap. Called the Partnership on Artificial Intelligence to Benefit People and Society, the group consists of Amazon, Facebook, Google, Microsoft and IBM. Apple is also in talks to join.

Just a few weeks ago, we posted about some incredible research from Vijay Kumar's lab at the University of Pennsylvania getting quadrotors to zip through narrow gaps using only onboard localization. This is a big deal, because it means that drones are getting closer to being able to aggressively avoid obstacles without depending on external localization systems. The one little asterisk to this research was that the quadrotors were provided the location and orientation of the gap in advance, rather than having to figure it out for themselves. Yesterday, Davide Falanga, Elias Mueggler, Matthias Faessler, and Professor Davide Scaramuzza, who leads the Robotics and Perception Group at the University of Zurich, shared some research that they've just submitted to ICRA 2017. It's the same kind of aggressive quadrotor maneuvering, except absolutely everything is done on board, including obstacle perception.

Every day, we talk to artificial intelligence practitioners who are either labeling data internally for training AI models, or they're using crowdsourcing (or outsourcing) for the labeling/annotating. Both are bummers; that's why we exist. Recently, we covered the issues with an in-house approach. If you need only simplistic training data--say, categorized images or ranked articles--then maybe a crowdsourcing solution will cut it. But if you need more sophisticated data, you need good tooling--stuff to do bounding boxes, polygons, segmentation masks, pixel-level annotation, semantic segmentation, and so on.