Mumbai: Researchers are increasingly striving to help machines translate words from one language to another the way professional translators would. This implies that machines must understand the context of words and sentences, and make sense of idioms, phrases and jokes. However, despite the fact that billions of words are being translated daily by multilingual machine translation services like Google Translate, Microsoft Translator, Systran's Pure Neural Machine Translator, WordLingo, SDL FreeTranslation, China's Baidu, Russia's Yandex or Babel Fish, machines have a long way to go before they can function as fluently as humans do when speaking in, and translating, different tongues. Barak Turovsky, product lead at Google Translate--a free multilingual machine translation service from Google Inc.--understands this dilemma well. "Today, translation by machines can be likened to my five-year-old son speaking Russian. Since I speak fluent Russian, I know the mistakes he makes and how he forms words," he says.

The adoption and application of Artificial Intelligence (AI) continues to accelerate at an exponential rate in modern businesses. As referenced in the 2017 Tech Trend Report, AI is nearing completion of the next layer in technological advancement, integrated into everything individuals and organizations do. This trajectory is predicted to drive cumulative worldwide spending of $40.6 billion on AI projects by 2024 – according to Raconteur. This is expected to create mass opportunity for the pioneering businesses currently investing in AI development. Moving beyond the hype in existing media coverage, this post will uncover the reality behind what AI means for businesses today, in the near future, and beyond 2017.

Electronic assistants such as the Amazon Echo could be hacked by criminals to steal personal information, a security expert has warned. Millions have bought voice-activated speakers which play music, provide weather forecasts, order groceries and answer questions. The devices are brought to life by vocal commands such as'Alexa' or'Hello Google' but experts warn they pose a major security risk as they are always listening in and monitoring conversations. Criminals could hack into them to find out when families are away or steal credit card details when someone orders a takeaway over the phone. Cybersecurity expert Dr Jason Nurse warned: 'If hackers find a way to compromise these devices in our homes, they could have it recording all of the time and you wouldn't necessarily know.

Fears that machines programmed with artificial intelligence (AI) capabilities will first take human jobs and then take over the world may not be completely unfounded. A survey conducted by the University of Oxford and Yale University polled 352 AI experts and looked at what the leading minds in machine learning think the timing will be for a variety of AI capabilities, as well as superiority over humans in occupations, superiority in all tasks and potential social impact. The results might be somewhat concerning, depending on your feelings toward our future robot overlords. The study found that in the next 10 years, AI will outperform humans in activities such as language translation (by 2024) and truck driving (by 2027). Beyond that, robots will soon be writing best-selling books (by 2049) and performing surgery (by 2053).

We present a solution to scale spectral algorithms for learning sequence functions. We are interested in the case where these functions are sparse (that is, for most sequences they return 0). Spectral algorithms reduce the learning problem to the task of computing an SVD decomposition over a special type of matrix called the Hankel matrix. This matrix is designed to capture the relevant statistics of the training sequences. What is crucial is that to capture long range dependencies we must consider very large Hankel matrices. Thus the computation of the SVD becomes a critical bottleneck. Our solution finds a subset of rows and columns of the Hankel that realizes a compact and informative Hankel submatrix. The novelty lies in the way that this subset is selected: we exploit a maximal bipartite matching combinatorial algorithm to look for a sub-block with full structural rank, and show how computation of this sub-block can be further improved by exploiting the specific structure of Hankel matrices.

As automotive electronics continue to advance, cars are becoming more and more reliant on sensors to perform everyday driving operations. These sensors are omnipresent and help the car navigate, reduce accidents, and provide comfortable rides. However, they can also be used to learn about the drivers themselves. In this paper, we propose a method to predict, from sensor data collected at a single turn, the identity of a driver out of a given set of individuals. We cast the problem in terms of time series classification, where our dataset contains sensor readings at one turn, repeated several times by multiple drivers. We build a classifier to find unique patterns in each individual's driving style, which are visible in the data even on such a short road segment. To test our approach, we analyze a new dataset collected by AUDI AG and Audi Electronics Venture, where a fleet of test vehicles was equipped with automotive data loggers storing all sensor readings on real roads. We show that turns are particularly well-suited for detecting variations across drivers, especially when compared to straightaways. We then focus on the 12 most frequently made turns in the dataset, which include rural, urban, highway on-ramps, and more, obtaining accurate identification results and learning useful insights about driver behavior in a variety of settings.

In this tutorial I'll explain how to build a simple working Recurrent Neural Network in TensorFlow. This is the first in a series of seven parts where various aspects and techniques of building Recurrent Neural Networks in TensorFlow are covered. A short introduction to TensorFlow is available here. For now, let's get started with the RNN! It is short for "Recurrent Neural Network", and is basically a neural network that can be used when your data is treated as a sequence, where the particular order of the data-points matter.

Financial markets offer countless ways of making (or losing) money. A key distinction among them is the investment horizon, which can range from fractions of a second to years. Walnut Algorithms and Global Systematic Investors are new investment management firms representing the high-frequency and low-frequency sides, respectively. I sat down to talk with their founders about investing, data, and the challenges of starting up. Below is my talk with Guillaume Vidal, co-founder and CEO of Walnut Algorithms.

Alexa is about to be even more useful when it comes to your entertainment needs. Simple voice commands already let you navigate apps like Netflix, but tools to incorporate those types of commands are now available to any TV company, streaming service and other content providers. Amazon just introduced its Video Skill API, which will allow developers to expand how Alexa lets users find content in their apps. With this tool, entertainment apps could let their customers find specific shows or movies, search genres or even change the channel with voice commands. As long as you have an Amazon Echo or other Alexa device, video apps taking advantage of the new function will be navigable by voice.

To be able to drive safely and reliably, autonomous cars need to have a comprehensive understanding of what's going on around them. They need to recognize other cars, trucks, motorcycles, bikes, humans, traffic lights, street signs, and everything else that may end up on or near a road. They also have to do this in all kinds of weather and lighting conditions, which is why most (if not all) companies developing autonomous cars are spending a ludicrous (but necessary) amount of time and resources collecting data in an attempt to gain experience with every possible situation. In most cases, this technique depends on humans making annotations to enormous sets of data in order to train machine learning algorithms: hundreds or thousands of people looking at snapshots or videos taken by cars driving down streets, and drawing boxes around vehicles and road signs and labeling them, over and over. Researchers from the University of Michigan think there's a better way: Doing the whole thing in simulation instead, and they've shown that it can actually be more effective than using real data annotated by humans.