What does that mean for which sectors? What does it depend on? Answers to such questions are to be found in the study "Machine Learning in Companies", which we created together with Crisp Research and Hewlett Packard Enterprise (HPE). The study is based on a survey of 264 companies of various sectors, sizes and types in Germany, Austria and Switzerland, and provides decision makers and managers with an empirically substantiated overview of the status quo. It also offers findings and examples showing how to successfully plan and implement artificial intelligence and machine learning into a company.

The animated face of prototype robot GRACE, Graduate Robot Attending Conference, is tested by Carnegie Mellon University computer scientist Reid Simmons, right, in the lab at the school in Pittsburgh Tuesday, July 9, 2002. It may have been the first bit of fake news in the history of the Internet: in 1984, someone posted on Usenet that the Soviet Union was joining the network. It was a harmless April's Fools Day prank, a far cry from today's weaponized disinformation campaigns and unscrupulous fabrications designed to turn a quick profit. In 2017, misleading and maliciously false online content is so prolific that we humans have little hope of digging ourselves out of the mire. Instead, it looks increasingly likely that the machines will have to save us.

But one telehealth company – Now Healthcare Group – believes it can apply AI to its existing apps to relieve some of that strain. Former founder of an advertising company Lee Dentith founded AI healthcare startup Now Healthcare in 2014. He came up with the idea when he wanted to get one of his children to see a GP but struggled to get through, something that many people will be familiar with. Now Healthcare provides Europe's largest remote digital GP consultation platform, NowGP, and a subscription service with the NHS, Now Patient. Founder Dentith also had a background in technology, something that COO of the company Tim Ng regards as vital for its success.

Last month The Lawyer published the first-ever Offshore Litigation Survey, which revealed the cases on which the top firms have been active as well as firms' bench strengths. As part of the survey we also asked offshore firms about their technology-related innovations and how they project manage their litigation matters. Offshore firms have introduced a number of products for general efficiency purposes and have taken a variety of investment approaches. Bedell Cristin has brought in INTAP technology for time recording, while Appleby operates an internal portal that houses high-level information from a variety of data sources and presents them in an intelligent format in a single point of view. Advances in technology over the past year "have resulted in significant improvements around litigation processes and within the teams that support those functions", says the firm. Conyers Dill & Pearman and Harneys both use Relativity for document management and disclosure purposes, while Conyers is piloting litigation-specific file management software.

In July, we surveyed 1,600 Quartz readers--from 84 countries, though the majority of those who chose to take part came from the US--for their opinions about artificial intelligence, including about their perceptions of job loss to AI and robots. People were anxious; 90% of responders thought that up to half of jobs would be lost to automation within five years. That's a lot, more than most of the studies conclude, include studies conducted by Oxford University (pdf) and McKinsey Global Institute. But, paradoxically, we found that everyone thought it was going to happen to someone else. In our survey, 91% don't think there's any risk to their job and 94% don't think they'll be working for an AI boss--but 48% think they'll have an AI employee (all within five years).

I am a technology anthropologist who examines automation, algorithms and Artificial Intelligence (AI) in the context of preserving human agency. My dissertation focused on small independent fringe new technology makers in Silicon Valley, what they are making, and most critically, how the adoption of the outcomes of their efforts impact society and culture locally, and/or globally. I'm currently spending the summer in a corporate AI Research Group where I contribute to anthropological research on AI. I'm thrilled to blog for the renowned Savage Minds this month and hope many of you find value in my contributions. There is so much going on in the world that it is challenging to choose a single topic to write about--floods, fires, hurricanes, politics--as anthropologists in 2017, we are spoiled for choice.

The UK is first in our rankings, reflecting its world-leading centres for AI research and strong technology industry,

AWS DeepLens Looking for a new way to learn machine learning? Let a machine teach you with AWS DeepLens, the world's first deep learning enabled video camera for developers. Designed to connect securely to a variety of AWS offerings, including AWS IoT, Amazon SQS, Amazon SNS, and Amazon DynamoDB, AWS DeepLens uses Amazon Kinesis Video Streams to stream video back to AWS and Amazon Rekognition Video to apply advanced video analytics. Easy to customize and fully programmable with AWS Lambda, AWS DeepLens runs on any deep learning framework, including TensorFlow and Caffe.

Dynamic Boltzmann Machine (DyBM) has been shown highly efficient to predict time-series data. Gaussian DyBM is a DyBM that assumes the predicted data is generated by a Gaussian distribution whose first-order moment (mean) dynamically changes over time but its second-order moment (variance) is fixed. However, in many financial applications, the assumption is quite limiting in two aspects. First, even when the data follows a Gaussian distribution, its variance may change over time. Such variance is also related to important temporal economic indicators such as the market volatility. Second, financial time-series data often requires learning datasets generated by the generalized Gaussian distribution with an additional shape parameter that is important to approximate heavy-tailed distributions. Addressing those aspects, we show how to extend DyBM that results in significant performance improvement in predicting financial time-series data.

When performing regression on a dataset with $p$ variables, it is often of interest to go beyond using main linear effects and include interactions as products between individual variables. For small-scale problems, these interactions can be computed explicitly but this leads to a computational complexity of at least $\mathcal{O}(p^2)$ if done naively. This cost can be prohibitive if $p$ is very large. We introduce a new randomised algorithm that is able to discover interactions with high probability and under mild conditions has a runtime that is subquadratic in $p$. We show that strong interactions can be discovered in almost linear time, whilst finding weaker interactions requires $\mathcal{O}(p^\alpha)$ operations for $1 < \alpha < 2$ depending on their strength. The underlying idea is to transform interaction search into a closestpair problem which can be solved efficiently in subquadratic time. The algorithm is called $\mathit{xyz}$ and is implemented in the language R. We demonstrate its efficiency for application to genome-wide association studies, where more than $10^{11}$ interactions can be screened in under $280$ seconds with a single-core $1.2$ GHz CPU.