Researchers have created a Raspberry Pi-powered robotic lab that detects and profiles the behaviour of thousands of fruit-flies in real-time. The researchers, from Imperial College London, built the mini Pi-powered robotics lab to help scale up analyses of fruit flies, which have become popular proxy for scientists to study human genes and the wiring of the brain. The researchers call the lab an ethoscope, an open-source hardware and software platform for "ethomics", which uses machine vision to study animal behaviour. And while computer-assisted analysis promises to revolutionize research techniques for Drosophila (fruit fly) neuroscientists, the researchers argue its potential is constrained by custom hardware, which adds cost and often aren't scalable. The Raspberry Pi-based ethnoscope offers scientists a modular design that can be built with 3D-printed components or even LEGO bricks at a cost of €100 per ethoscope.
If you've seen Maximum Overdrive, then you know exactly where I'm coming from. If you haven't, then I highly suggest you watch it – it could very well be foreshadowing of what's to come. Seriously, though, artificial intelligence (AI) is starting to teach itself algorithms and it's actually quite amazing. Over the past few decades, companies and groups have used machine learning to get a number of tasks completed. As technology as a whole grows bigger and becomes more developed, many of these groups are essentially holding a mirror up to technology's face and showing it how to teach itself how to get work done.
When Charles Reilly and Donald Ingber set out to make their short film--In the Beginning, an homage of sorts to Star Wars that (spoilers) tells the tale of a single sperm's triumph in a literal life or death race to fertilize an egg--they had just one goal. Ingber, the founding director of the Wyss Institute for Biologically Inspired Engineering at Harvard University, and Reilly, a biochemistry researcher also at the Wyss Institute, wanted the animated film to be scientifically accurate. To achieve this, the two reached for techniques more common to the silver screen than the lab. They sought digital imaging software ordinarily used by video game designers and film animators. Not only did they achieve cinematic glory, but by reaching far outside the scientific silo they were able to happen upon a new discovery: an understanding of the molecular-level mechanics that let a sperm whip its tail back and forth to fuel its need for speed.
French scientists say they may have found a potential cause of dyslexia which could be treatable, hidden in tiny cells in the human eye. In a small study they found that most dyslexics had dominant round spots in both eyes - rather than in just one - leading to blurring and confusion. UK experts said the research was "very exciting" and highlighted the link between vision and dyslexia. But they said not all dyslexics were likely to have the same problem. People with dyslexia have difficulties learning to read, spell or write despite normal intelligence.
It has been estimated that AI could add $837 billion to the UK economy by 2035. Scientists are therefore looking into automating the process of machine learning and have AI teach AI. (MIT Technology Review) 3. Google's AI is better at creating AI than its developers Creating algorithms is difficult, but has been one of the last areas of superiority over the machines. Google's AutoML system just created a series of machine-learning codes with higher rates of efficiency than those made by the researchers.
Back in May, Google revealed its AutoML project; artificial intelligence (AI) designed to help them create other AIs. Now, Google has announced that AutoML has beaten the human AI engineers at their own game by building machine-learning software that's more efficient and powerful than the best human-designed systems. An AutoML system recently broke a record for categorising images by their content, scoring 82 percent. While that's a relatively simple task, AutoML also beat the human-built system at a more complex task integral to autonomous robots and augmented reality: marking the location of multiple objects in an image. For that task, AutoML scored 43 percent versus the human-built system's 39 percent.
Istanbul, a city of 14 million people and a crossroads of cultural exchange dating back millennia, may also be where Turkey's next major earthquake strikes. Cities along the North Anatolian Fault, which stretches from eastern Turkey to the Aegean Sea, have experienced an advancing series of strong quakes during the past 80 years, beginning in 1939 when a devastating 7.8-magnitude rupture leveled the city of Erzincan and killed 33,000 people. Most recently, in 1999, 7.4-magnitude quake near the city of İzmit left 17,000 dead and half a million homeless. A few months later, another shock hit Düzce, 60 miles away. Brendan Meade, an applied computational scientist and associate professor of earth and planetary sciences, recently built a computer model of conditions in the North Anatolian Fault.
Back in May, Google revealed its AutoML project; artificial intelligence (AI) designed to help them create other AIs. Now, Google has announced that AutoML has beaten the human AI engineers at their own game by building machine-learning software that's more efficient and powerful than the best human-designed systems. An AutoML system recently broke a record for categorizing images by their content, scoring 82 percent. While that's a relatively simple task, AutoML also beat the human-built system at a more complex task integral to autonomous robots and augmented reality: marking the location of multiple objects in an image. For that task, AutoML scored 43 percent versus the human-built system's 39 percent.
US scientists are using artificial intelligence to predict whether breast lesions identified from a biopsy will turn out to cancerous. The machine learning system has been tested on 335 high-risk lesions, and correctly diagnosed 97% as malignant. It reduced the number of unnecessary surgeries by more than 30%, the scientists said. One breast cancer specialist said that the research was "useful". The machine learning system was trained on information about such lesions, the system looks for patterns among a range of data points, such as demographics, family history, biopsies and pathology reports.
IBM scientists Thomas Brunschwiler and Rahel Straessle are developing machine learning algorithms to interpret the IoT data. COPD, is a progressive lung disease which causes breathlessness and is often caused by cigarette smoke and air pollution. By 2030, it is expected to be the third leading cause of death worldwide, with 90% occurring in low and middle-income countries, according to the World Health Organization. The Centers for Disease Control and Prevention reports that by 2020 the expected cost of medical care for adults in the US with COPD will be more than $90 billion, mainly due to complications and multiple hospitalizations, many of which are preventable with better healthcare management and more personalized and frequent patient support. Management and prevention of COPD is the focus of a new research project presented today at the 19th annual IEEE Healthcom Conference, in Dalian, China.