The BMW Motorrad VISION NEXT 100 concept motorcycle is unveiled in Santa Monica, Calif., on the last of four international stops of BMW's tour into its next 100 years. The motorcycle is self balancing. But looking deeper, saying that it wants to imagine the company a century from now, BMW's leaders are focused on robotic cars, electric power, new and recycled materials replacing steel and radically new manufacturing techniques, including those that would enhance its manufacturing footprint in the U.S., where the factory in Spartanburg, S.C., is on its way to becoming the company's largest. The German automaker brought its Vision Next 100 presentation to an airport hangar here this week after several stops around the world to talk about what it sees in the future and to and show off four prototype vehicles, including a new self-balancing motorcycle in which riders never need to put their feet down. No matter how people choose to transport themselves, BMW says it wants to be a leader at the high end of the market.

DeepMind's latest AI has a "working memory" so that it can learn how to solve tasks for itself – such as how best to get from A to B on the London tube network. "The thing can learn to compute what it has to, rather than being programmed," says Murray Shanahan at Imperial College, London, who wasn't involved with the work. Called a Differentiable Neural Computer (DNC), the system succeeds because it combines neural networks, which are good at learning but not so good at storing data, with an external memory. It can retrieve items from its memory in the order they were recorded – a key innovation that ensures they don't get overwritten too quickly and helps the system tackle complicated data it hasn't seen before. The DNC works out how to interpret a data set on its own, following some basic training on random graphs.

British lawmakers want more transparency and less bias in decision-making -- not their own, of course, but in decisions made by AI systems. As more and more software systems and connected devices employ artificial intelligence technologies to make decisions for their owners, the lawmakers want to know what's behind their thinking. The U.K. Parliament's Science and Technology Committee has been studying the need for more regulation in the fields of robotics and artificial intelligence. Recent advances in AI technology raise a host of social, ethical and legal questions, the committee's members said in a report published Wednesday. We need, they said, to think about whether transparency in decisions made by AI systems is important; whether it's possible to minimise bias being accidentally built into them; and how we might verify that such technology is operating as intended and will not lead to unwanted or unpredictable behaviors.

Before assessing R and Python, I will start with Wolfram Mathematica. You can handle lists and matrices easily, you have all the best mathematical functions, backup of Wolfram Alpha and extremely sophisticated graphics visualizations, that allow you, for instance, to make and visualize an animated gradient descent, animate different weights for a given neural network, choose a specific Machine Learning algorithm and automatically classify your dataset in classes, plot stunning 3D visualizations, make animations and manipulate variables values dynamically at the same time you see the output of your calculation. It has 4.65 Gb size and comes with all libraries integrated. It's a great program when you know the formulae for Machine Learning algorithms, so you can build them from scratch, in a completely customized way. You can also do face recognition, geolocation of objects with 3D plots of map surface, handle cellular automata like any other and develop social networks models with artificial intelligence completely customized.

TensorFlow is an open-source library from Google developed for creating deep learning models. Please fill out the following form so that the printable cheat sheet can be sent to you via e-mail.

If you're like many people, you have thousands of photos on your phone, long forgotten after you've posted a few on Instagram or Facebook. They don't have to stay forgotten. Apple and Google are both applying a form of artificial intelligence called "machine learning" to organize your pictures and video -- and along the way, help you rediscover last year's vacation, dinner with close friends and a casual summer outing to the park. Apple's tools are part of last month's iOS 10 system update for iPhones and iPads. The Google Photos app for Apple and Android devices has a digital assistant to automatically organize these memories -- and Google signaled last week that it will only get smarter.

We're going to be blunt: Chatbots in their current form aren't great. We were promised bots that would change the way we interact with businesses and services, but instead we have interactive bots that perform worse than apps. They are primarily focused on taps or interactive graphical interfaces, and conversing with them using natural language is nearly impossible. Take an example of Poncho Weather on Facebook Messenger. Let's say I'm going to a conference next Monday in San Diego and want to know what the forecast is.

There are many clichés about lawyers, but one that even lawyers buy into is that we are slow to adopt new technology. It may be heresy for me to say this, but I do not believe it is true. Yes, the behemoth that is the profession as a whole is slow to adopt change of any kind – tech, business, and otherwise. But the behemoth is a creature of the lowest common denominator. It does not define us all.

You know you reversed over me in the Yaris a year ago today. I assume it was an accident. That's what the coroner decided. So, er Roger, how are we managing to have this conversation if I've been dead for a year? I was feeling a little guilty about the way things had worked out, Deirdre, and discovered this new technology: griefbots.

A startup from California is using GPUs and big data to predict what homes are likely to buy solar panels. PowerScout is using GPUs on the Amazon cloud and cuDNN to train their deep learning models on a mix of data from commercial databases and LIDAR to detect solar panels from satellite images, and to also detect the presence of trees near homes that could cast shade onto roofs. The tools the startup developed can also help estimate how much energy could be harvested from a home's rooftop without needing to take measurements in person with a decent degree of accuracy. From the information, they can target direct mail and online marketing to the most promising customers and quickly give them online estimates. Then, those who are interested in rooftop solar can choose a financing plan and get connected to a local installation partner to have it installed.