Chris Baker is managing director of UK Enterprise at Concur. What reactions did people have to the movie trailer for Morgan (which was created entirely -- and for the first time -- by an AI bot, and a pretty famous one at that)? Which is a fair reaction. Computers can now write, read, learn and speak. And for some, this is pretty scary -- people are terrified that bots will snatch their jobs and eventually take over the world and render humans useless (films like I, Robot haven't helped this).

Analysis Research in AI is expanding quickly, and the UK and US governments have begun to notice. Official reports about the new technology and future strategies were dropped by both governments this month. Blighty's Science and Technology Committee released Robotics and Artificial Intelligence, while the White House delivered Preparing for the Future of Artificial Intelligence and National Artificial Intelligence Research and Development Strategic Plan. The titles of the British and American reports provide a clue as to how both governments are responding. There is no "preparing" or "strategic plan" in the UK's reports.

We are looking for smart, energetic individuals to help us develop and launch groundbreaking career-centered products. We are currently seeking a Machine Learning Engineer to join our team in San Jose, CA. At RiseSmart, the engineering culture is talent-centric. We believe in bringing together a team of talented engineers that are passionate about programming, technologies and developing solutions for complex human capital issues. About the Job Samples of products and the kind solutions we are developing: • Job and Profile Matching Engine: Build distributed search engine of jobs and candidates.

Topic identification is a top-of-the-list need for organizations working with large volumes of online, social, and enterprise text. Along with entity resolution, relation extraction, summarization, and sentiment analysis, topic modeling is a key natural language processing (NLP) function. Premise number 2: Applied NLP -- text analytics -- remains as much art as science, requiring a combination of domain and technical expertise. How better to explore topic modeling and NLP advances than via an interview with a leading practitioner? This article features an interview with Lev Konstantinovskiy, a data scientist who is community manager for gensim, which offers open-source topic modeling for Python programmers.

There's been a lot of buzz over the last two years around the "Internet of Things," or IoT. However, more recently a subcategory of IoT, the Industrial Internet of Things (IIoT), has been getting a lot of well-deserved attention. For those who've not yet heard of this trend, the IIoT is basically the use of Internet of Things (IoT) technologies in manufacturing. It brings together many key technologies--including machine learning, big data, sensors, machine-to-machine (M2M) computing, and more--in an orchestrated fashion within manufacturing operations. The IIoT promises to drive massive economic transformations in the coming years across multiple industries, including manufacturing, health care, and mining.

This post is authored by Xibin Gao, Data Scientist, Debraj GuhaThakurta, Senior Data Scientist, Gopi Kumar, Principal Program Manager, and Hang Zhang, Senior Data Science Manager, at Microsoft. Data scientists typically spend a significant amount of time writing code seeking answers to the above questions. Although datasets differ between projects, much of the code can be generalized into data science utilities that can be reused across projects, thus helping with productivity. Additionally, such utilities can help data scientists work on specific tasks in a project in a guided mode, ensuring consistency and completeness of the underlying tasks. These two utilities, which run in CRAN-R, can be accessed from this GitHub site.

After you make predictions, you need to know if they are any good. There are standard measures that we can use to summarize how good a set of predictions actually are. In this tutorial, you will discover how to implement four standard prediction evaluation metrics from scratch in Python. How To Implement Machine Learning Algorithm Performance Metrics From Scratch With Python Photo by Hernán Piñera, some rights reserved. You must estimate the quality of a set of predictions when training a machine learning model.

We tend to think of R and other such ML tools only in the context of the workplace, to do "weighty" things aimed at saving millions. A little judicious use of R may help us hugely in our personal lives too. The ideas of regression, classification trees etc. can be powerful tools in valuation, as I found out. Recently, I was in a five-car accident on the infamous 101 in the San Francisco bay area. Luckily, none of us required an ambulance and all of us walked away.

To recap what we have learned from the WikiLeaks emails so far: How to make creamy risotto. That CNN's Donna Brazile might have slipped the Hillary Clinton campaign a question before a town hall debate. Oh, and how long it takes Clinton's team to figure out how to reply to a single Marco Rubio tweet (eight and a half hours, approximately). The emails apparently showed that at the end of July, the Clinton campaign put their heads down when Rubio tweeted "After Clinton's failed'reset' with Putin, now she wants to do a'reset' with Castro. She is making another mistake" around 7:30 a.m.

NVIDIA, which is Tesla Motors' supplier for the Visual Computing Modules (VCM), is also working on autonomous driving technology. NVIDIA's approach is different from conventional methods and relies someewhat on artificial intelligence. As Tesla abandons MobileEye hardware, NVIDIA is hinted as a possible new supplier for new generation Autopilot. "In contrast to the usual approach to operating self-driving cars, we did not program any explicit object detection, mapping, path planning or control components into this car. Instead, the car learns on its own to create all necessary internal representations necessary to steer, simply by observing human drivers. Similarly, the car can drive on the road that is overgrown with grass and bushes without the need to create a vegetation detection system. All it takes is about twenty example runs driven by humans at different times of the day. Learning to drive in these complex environments demonstrates new capabilities of deep neural networks. The car also learns to generalize its driving behavior. This video includes a clip that shows a car that was trained only on California roads successfully driving itself in New Jersey."