Artificial Intelligence

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Learning words from pictures

Learning words from pictures
Speech recognition systems, such as those that convert speech to text on cellphones, are generally the result of machine learning. A computer pores through thousands or even millions of audio files and their transcriptions, and learns which acoustic features correspond to which typed words. But transcribing recordings is costly, time-consuming work, which has limited speech recognition to a small subset of languages spoken in wealthy nations.
8th December 2016

£100,000 for research into automation and AI

The Defence Science & Technology Laboratory (Dstl) has up to £100,000 available to fund research into how automation and machine intelligence can analyse data to enhance decision making in the defence and security sectors. Dstl is continuing to invest in novel procurement routes and is collaborating with the Digital Catapult Centre to run a one and a half day workshop. The aim is to stimulate debate, generate ideas and to forge links with potential suppliers, before interested parties submit detailed proposals.
7th December 2016

The week the IoT grew up

The week the IoT grew up
  Steve Rogerson found that this year’s electronica saw IoT hype give way to reality.
5th December 2016


Computer learns to recognise sounds by watching video

Computer learns to recognise sounds by watching video
  In recent years, computers have gotten remarkably good at recognising speech and images: Think of the dictation software on most cellphones, or the algorithms that automatically identify people in photos posted to Facebook. But recognition of natural sounds — such as crowds cheering or waves crashing — has lagged behind.
5th December 2016

Predictions for next year's electronics industry

Predictions for next year's electronics industry
  As 2016 draws to a close, embedded systems consultancy ByteSnap Design share their predictions for what may dominate the electronics industry in 2017.
24th November 2016

Internet robot investigates human creativity

Internet robot investigates human creativity
Tom White, senior lecturer in Victoria's School of Design, has created Smilevector—a bot that examines images of people, then adds or removes smiles to their faces. "It has examined hundreds of thousands of faces to learn the difference between images, by finding relations and reapplying them," says Mr White. "When the computer finds an image it looks to identify if the person is smiling or not. If there isn't a smile, it adds one, but if there is a smile then it takes it away.
18th November 2016

AI system surfs web to improve its performance

AI system surfs web to improve its performance
Of the vast wealth of information unlocked by the Internet, most is plain text. The data necessary to answer myriad questions — about, say, the correlations between the industrial use of certain chemicals and incidents of disease, or between patterns of news coverage and voter-poll results — may all be online. But extracting it from plain text and organising it for quantitative analysis may be prohibitively time consuming.
14th November 2016

Sensors to monitor bridges enable them to tweet

Sensors to monitor bridges enable them to tweet
While bridge collapses are rare, there have been enough of them to raise concerns in some parts of the world that their condition is not sufficiently monitored. Sweden is taking a hi-tech approach to its aging infrastructure. Researchers from KTH Royal Institute of Technology in Stockholm are rigging up the country’s bridges with multiple sensors that allow early detection of wear and tear. The bridges can even tweet throughout the course of a day.
31st October 2016

Making computers explain themselves

Making computers explain themselves
In recent years, the best-performing systems in artificial-intelligence research have come courtesy of neural networks, which look for patterns in training data that yield useful predictions or classifications. A neural net might, for instance, be trained to recognise certain objects in digital images or to infer the topics of texts. But neural nets are black boxes. After training, a network may be very good at classifying data, but even its creators will have no idea why.
31st October 2016

Automating big-data analysis

Automating big-data analysis
Last year, MIT researchers presented a system that automated a crucial step in big-data analysis: the selection of a “feature set,” or aspects of the data that are useful for making predictions. The researchers entered the system in several data science contests, where it outperformed most of the human competitors and took only hours instead of months to perform its analyses.
21st October 2016


Artificial Intelligence documents


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