That's just one of "five practical research problems" proposed by scientists at Google, OpenAI, Stanford and Berkeley in a paper called "Concrete Problems in AI Safety" (pdf). Others included "safe exploration" issues, or how to stop a curious cleaning robot from sticking a wet mop in an electrical socket, and "avoiding negative side effects" such as a robot breaking granny's vase when cleaning in a rush. The problems may seem a bit silly, when compared to an AI-induced doomsday, but Google researcher Chris Olah wrote, "These are all forward thinking, long-term research questions – minor issues today, but important to address for future systems." A particularly interesting portion of the paper was devoted to avoiding reward hacking, or how to stop AI from gaming its reward function. "Imagine that an agent discovers a buffer overflow in its reward function: it may then use this to get extremely high reward in an unintended way."

AI is coming for your paintbrush too… A new iOS app, called Prisma, is using deep learning algorithms to turn smartphone photos into stylized artworks based on different artwork/graphical styles. Snap or choose your photo, select an'art filter' to be applied and then wait as the app works its algorithmic magic -- returning your stylized image in a matter of seconds, along with options to share it to your social networks. So if you've ever wanted your bedroom to resemble a rotoscope animation, or your selfie to have shades of manga, or your hopeless sketching skills not to hold back your yearning to create a web comic then Prisma is definitely the app for you… Prisma was launched only last week but has already garnered some 1.6 million downloads, CEO and co-founder Alexey Moiseenkov tells TechCrunch, on the phone from Moscow where the team is currently based. The key to this early growth is clearly the app's prominently placed social share function, which prompts users to post to Instagram as soon as they receive their processed shot. And just this week the Facebook-owned photo-sharing behemoth revealed it had more than doubled its monthly active users over the past two years -- reaching a whopping 500 million MAUs.

Machine learning, and principally deep learning, is an area of intense interest in computer science today. Tech giants including (but certainly not limited to) Google, Facebook, Baidu, IBM, Microsoft are spending an enormous amount of money and effort to hire the best machine learning researchers. Deep learning has outperformed traditional computer vision (CV) technology in recent years. In the 2010 ImageNet Challenge, the best traditional CV algorithm had an error rate of 28.2% which meant that it got about 72 out of 100 images correct. In 2011, the best algorithm clocked in at 25.8% error rate.

Facebook today is publishing an academic paper and a blog post detailing Torchnet, a new piece of open-source software that's designed to streamline deep learning, a type of artificial intelligence. Deep learning is a trendy approach that involves training artificial neural networks on lots of data, like photos, and then getting the neural networks to make predictions about new data. Rather than build a completely new deep learning framework, of which there are many, Facebook chose to build on top of Torch, an open-source library to which Facebook has previously contributed. "It makes it really easy to, for instance, completely hide the costs for I/O [input/output], which is something that a lot of people need if you want to train a practical large-scale deep learning system," Laurens van der Maaten, a research scientist in Facebook's Artificial Intelligence Research (FAIR) lab, told VentureBeat in an interview. Torchnet, which is written in Lua and can run on standard x86 chips or graphics processing units (GPUs), also lets programmers reuse certain code, which means doing less work and lowering the chances of introducing bugs, said van der Maaten.

Chat bots seem to be extremely popular these days, every other tech company is announcing some form of intelligent language interface. The truth is that language is everywhere, it's the way we communicate and the way we manage our thoughts. Most, if not all, of our culture & knowledge is encoded and stored in some language. One can think that if we manage to tap to that source of information efficiently then we are a step closer to create ground breaking knowledge systems. Of course, chat-bots are not even close to "solving" the language problem, after all language is as broad as our thoughts.

Generalized linear models with nonlinear feature transformations are widely used for large-scale regression and classification problems with sparse inputs. Memorization of feature interactions through a wide set of cross-product feature transformations are effective and interpretable, while generalization requires more feature engineering effort. With less feature engineering, deep neural networks can generalize better to unseen feature combinations through low-dimensional dense embeddings learned for the sparse features. However, deep neural networks with embeddings can over-generalize and recommend less relevant items when the user-item interactions are sparse and high-rank. In this paper, we present Wide & Deep learning---jointly trained wide linear models and deep neural networks---to combine the benefits of memorization and generalization for recommender systems. We productionized and evaluated the system on Google Play, a commercial mobile app store with over one billion active users and over one million apps. Online experiment results show that Wide & Deep significantly increased app acquisitions compared with wide-only and deep-only models. We have also open-sourced our implementation in TensorFlow.

Google is ringing in 2014 with a spending spree, first dropping 3.2 billion to acquire Nest Technologies and now spending a reported 400 million (or more) on the UK-based artificial intelligence outfit DeepMind. It's no secret that Google has an interest in artificial intelligence; after all, technologies derived from AI research help fuel Google's core search and advertising businesses. AI also plays a key role in Google's mobile services, its autonomous cars, and its growing stable of robotics technologies. And with the addition of futurist Ray Kurzweil to its ranks in 2012, Google also has the grandfather of "strong AI" on board, a man who forecasts that intelligent machines may exist by midcentury. If all this sounds troubling, don't worry: Google's acquisition of DeepMind isn't about fusing a mechanical brain with faster-than-human robots and giving birth to the misanthropic Skynet computer network from the Terminator franchise.

There's been much talk about how artificial intelligence will benefit society, but what about the potential impacts that AI has when the system is poorly designed and creates problems? This is a question several researchers and OpenAI, a non-profit artificial intelligence research company, tackled in a recent paper. The paper was written by researchers from Google Brain, Stanford University and the University of California, Berkeley, as well as John Schulman, research scientist at OpenAI. It's titled Concrete Problems in AI Safety, and it looks at research problems around ensuring that modern machine learning systems operate as intended. Researchers have started to focus on safety research in the machine learning community, including a recent paper from DeepMind and the Future of Humanity Institute that looked at how to make sure that human interventions during the learning process would not induce a bias toward undesirable behaviors in machine learning robots. But, according to a blog post by OpenAI, many machine learning researchers are wondering just how much safety research can be done today.

DeepMind, the artificial intelligence research lab acquired by Google for a reported 400 million in 2014, has called on the UK government to consider funding degree courses that focus on machine learning, which is a subfield of AI. The company -- cofounded by Demis Hassabis, Shane Legg and Mustafa Suleyman in 2011 -- said the government needs to support the next generation of machine learning experts if it wants the UK to cement its position as a world leader in AI. Writing in evidence submitted to a parliamentary inquiry into robotics and AI last month, DeepMind said: "The government should consider funding for machine learning masters and PhD programmes at British universities, to encourage more research in the field and nurture the next generation of scientists who will help preserve the UK's preeminent position." The company added: "This funding could also include direct support for modules within programmes that train machine learning researchers in the ethics of data science and increasingly autonomous decision-making, to ensure that the pursuit of beneficial outcomes is embedded in the science of machine learning at every level." Machine learning masters degrees and PhDs can cost individuals upwards of 10,000 at the top universities.

Google Research cooperated with OpenAI, Stanford University, and the University of California to publish a research paper highlighting the five main problems with machine learning systems that can lead to accidents. The problems, although minor now, can escalate to concerning levels during artificial intelligence development and operation.