Successful companies address customer needs better than the competition. However before you can address customer needs, you must first identify them. "What are my customers' needs? And how can I address them better than the competition?" A close attention to user experience (UX) will help answer the first question, and a clever use of artificial intelligence (AI) will help answer the second.

To the throngs of those who've put down their complicated digital SLR cameras in favor of easy-to-use smartphones, Ryan Stout has a message: Not so fast. Stout is the founder, CEO and lead developer of Arsenal, a six-person, Montana-based startup that's using computer vision to intelligently automate the abundant capabilities of DSLR cameras. Stout admits he himself had become a smartphone-dependent photographer. But several years ago, he decided to pull his camera out and start taking night photos. From setting shutter speeds, apertures and ISOs to choosing just the right filter, he was quickly reminded that photography is an intensely technical undertaking.

From the moment we humans first imagined having mechanical servants at our beck and call, we've assumed they would be constructed in our own image. Outfitted with arms and legs, heads and torsos, they would perform everyday tasks that we'd otherwise have to do ourselves. Like the indefatigable maid Rosie on "The Jetsons," the officious droid C-3PO in "Star Wars" and the tortured "host" Dolores Abernathy in "Westworld," the robotic helpmates of popular culture have been humanoid in form and function. It's time to rethink our assumptions. A robot invasion of our homes is underway, but the machines -- so-called smart speakers like Amazon Echo, Google Home and the forthcoming Apple HomePod -- look nothing like what we expected.

Its eyes, a complex configuration of cyan dots on a black, rounded screen of a face, sleepily open and it lets out a digitised approximation of a yawn. A compact device that looks like a blend of a forklift truck and PC monitor bred for maximum cuteness, the robot rolls blearily off its charging station on a pair of dinky treads before tilting its screen-face and noticing I'm there. Its eyes widen, then curve at the bottom as if making way for an unseen smile. "Daaaaan!" it announces with a happy jiggle, sounding not unlike Pixar Animation Studios' lovable robot creation, Wall-E. A message flashes up on my iPhone telling me that it, or rather he (being the gender that its manufacturer, Anki, has assigned Cozmo) wants to play a game. Cozmo's head droops, his eyes form into a pair of sadly reclining crescent moons and he sighs. But he quickly cheers up, giving a happy jiggle when I comply with his request for a fist bump and tap my knuckles against his eagerly raised arm.

Once in the realm of imagination, Artificial Intelligence (AI) is not just a reality now, but has taken markets by storm.

We had so much fun at our 2016 AMA that we're back again! We are a group of research scientists and engineers that work on the Google Brain team. You can learn more about us and our work at g.co/brain, including a list of our publications, our blog posts, our team's mission and culture, some of our particular areas of research, and can read about the experiences of our first cohort of Google Brain Residents who "graduated" in June of 2017. You can also learn more about the TensorFlow system that our group open-sourced at tensorflow.org in November, 2015. In less than two years since its open-source release, TensorFlow has attracted a vibrant community of developers, machine learning researchers and practitioners from all across the globe.

Artificial-intelligence research has been transformed by machine-learning systems called neural networks, which learn how to perform tasks by analyzing huge volumes of training data. During training, a neural net continually readjusts thousands of internal parameters until it can reliably perform some task, such as identifying objects in digital images or translating text from one language to another. But on their own, the final values of those parameters say very little about how the neural net does what it does. Understanding what neural networks are doing can help researchers improve their performance and transfer their insights to other applications, and computer scientists have recently developed some clever techniques for divining the computations of particular neural networks. But, at the 2017 Conference on Empirical Methods on Natural Language Processing starting this week, researchers from MIT's Computer Science and Artificial Intelligence Laboratory are presenting a new general-purpose technique for making sense of neural networks that are trained to perform natural-language-processing tasks, in which computers attempt to interpret freeform texts written in ordinary, or "natural," language (as opposed to a structured language, such as a database-query language).

A record number of visitors – 30,000 – turned out for the biannual science days hosted by the University of Zurich and the Swiss Federal Institute of Technology ETH Zurich. Over the course of three days, some 300 researchers welcomed visitors at 60 stands, 40 lectures, and 14 workshops covering topics like health, robotics and climate science as well as the linguistic and cultural sciences. People could watch and participate in live experiments and ask questions of the scientists on hand. The science cafés – where experts gave presentations and then discussed topics like artificial intelligence, financial investments, data journalism and gender in medicine – were filled to capacity. The weekend was also a chance to show how scientific findings can be transformed into hands-on business ideas.

The film scores tallied by Rotten Tomatoes are what many moviegoers use to decide how to spend their money. As you might expect, this doesn't make some folks in Hollywood too happy. In fact, they're pretty darn upset. The New York Times takes a look at the rift the movie ratings site has created with its Tomatometer. I'd argue studios should stop blaming a website and just start making better movies, but what do I know.

The first album composed and produced entirely by AI (artificial intelligence) was created using the artificial intelligence platform Amper. The album, 'I AM AI,' is a collaboration with singer Taryn Southern who provided parameters for Amper to work with. Amper Music CEO Drew Silverstein told the FOX Business Network's Maria Bartiromo on Mornings with Maria, "Amper is an artificial intelligence composer, performer and producer." Silverstein says that anyone can use the platform to create music even if they don't have a musical background. "Amper's designed to allow anyone around the world to be able to express themselves creatively through music regardless of their musical ability."