San Francisco-based entrepreneur Eugenia Kuyda created a software bot to resemble her friend who had died in an accident. Kuyda gathered up her friend's old text messages and fed them into a neural network built by developers at her artificial intelligence startup. The entrepreneur initially had doubts about the project, but followed on with it.

The term "artificial intelligence" sometimes conjures up images of the distant future, and a time when self-aware robots take over the planet and make human capabilities irrelevant, because the computer overlords are way better at everything humans do. While that vision may be workable for a hair-raising Halloween movie script, it's way off base when it comes to the realities of the current generation of artificial intelligence (AI). If you tackle this story from the highest level, AI isn't currently a force that is poised to overrun humans. Instead, AI is simply a group of technologies that will increasingly be used to augment human capabilities, and make us better at the things we do best. What's more, AI isn't a story set in the distant future.

Nicely done piece by Ronald van Loon, The 10 Algorithms Machine Learning Engineers Need to Know . I like these concise descriptions of useful techniques. Its good to know these for sharing with decision makers. Inclusion and descriptions are debatable, which is OK. These also include explanatory visuals of each each technique.

What are Recurrent Neural Networks and how can you use them? In this post I discuss the basics of Recurrent Neural Networks (RNNs) which are deep learning models that are becoming increasingly popular. I don't intend to get too heavily into the math and proofs behind why these work and am aiming for a more abstract understanding. Recurrent Neural Networks were created in the 1980's but have just been recently gaining popularity from advances to the networks designs and increased computational power from graphic processing units. They're especially useful with sequential data because each neuron or unit can use its internal memory to maintain information about the previous input.

Microsoft has announced that Microsoft Research has awarded The Alan Turing Institute with 5 million in Azure cloud computing credits. The move is all part of an effort to help the Alan Turing Institute achieve more and advance the potential of data science. Thanks to the Azure Credits, the more than 100 Alan Turing Institute research staff can now use Azure cloud services to provide their data scientists with an easy and accessible platform. "Azure cloud services will provide our data scientists with an easily accessible platform where they can prototype ideas with a fast turnaround of results, complementing local computing facilities available in the institute's five founding universities, and national resources such as the supercomputer ARCHER supported by EPSRC. We are delighted that Microsoft is enabling access to Azure cloud services and supporting this crucial element of our research infrastructure" Microsoft, meanwhile, is interested in how AI, and machine learning can be applied in exemplified ways.

Earlier this month Zanzi's Jayne Reddyhoff spoke at the world's first global Social Robotics and Artificial Intelligence (AI) conference at Oxford Brookes University, and it got us thinking… How can online businesses embrace AI and use it to their advantage? It's easy to feel threatened by the impending invasion of "robots" in the workplace, hijacking our jobs, and upending "traditional" human interactions expected at work. But guess what folks… robots are already here, and it's not all that bad. Embracing robotics in the workplace doesn't necessarily mean putting R2-D2 on the payroll. Instead social robotics, by which we mean incorporating "big data" or machine learning into business-as-usual, can begin to help us with the tasks mere mortals just aren't good at, thus freeing us up to do the more creative and challenging tasks.

Microsoft has announced it will take the next step in artificial intelligence (AI) by launching a group that tasked specifically with coming up with the machines of the future. The AI discovery group will be made up of 5,000 computer scientists and engineers in Microsoft's Research group, along with the company's Information Platform Group, Bing and Cortana product groups, and Ambient Computing and Robotics teams. "Today, AI is shifting the computer science research supply chain and blurring lines between research and product," Harry Shum, executive vice president of Microsoft's AI and Research Group, said. "End-to-end innovation in AI will not come from isolated research labs alone, but from the combination of at-scale production workloads together with deep technology advancements in algorithms, systems and experiences." He went on to explain how the group will help Microsoft become a frontrunner in the AI race, accelerating innovation and helping the company come up with new products to meet customer demand.

What are the major bottlenecks in making deep learning systems more effective? This question originally appeared on Quora. You can follow Quora on Twitter, Facebook, and Google . Get top stories and blog posts emailed to me each day. Newsletters may offer personalized content or advertisements.

So-called connected homes, which feature a network of devices and appliances that communicate with each other and can be remotely monitored and controlled, are becoming increasingly popular and affordable. By 2020, the connected home market could be worth nearly 150 billion globally, according to professional services firm PricewaterhouseCoopers. Most Americans surveyed by the company said that within the next decade, using a single remote to control everything in the home will be the norm. "Younger generations view these devices as adding to the livability of the home," said Mark Lesswing, chief technology officer for the National Assn. of Realtors trade group. "They're early adopters now, but in three years, I think this becomes the norm."

This is basically the same setting as semantic segmentation so any architecture that solves this problem can be adapted for your application. You can have a look at papers like U-Net, or Fully Convolutional Neural Networks, whose implementations are available for Caffe (I would also be surprised if there weren't re-implementations in other frameworks like TensorFlow or Theano). On a side note, I am personally using the very naive approach that you are describing (extracting patches around each pixel) and I have found that the performance loss is not that critical for my application. It might still be a good idea for you to try it anyway before switching to more "exotic" architectures.