Each week SwissCognitive selects 9 articles from the Artificial Intelligence Universe to share with our community.
This week you can read about how AI beats doctors in diagnosing brain tumours, why AI is a job generator rather than a job killer, how AI could prevent natural disasters, how robots may fix plane engines in the future and more.
The SwissCognitive Team
Developed by the Artificial Intelligence Research Centre for Neurological Disorders and a research team from the Capital Medical University, BioMind made correct diagnoses in 87% of 225 cases in about 15 minutes, while a team of 15 senior doctors achieved only an accuracy of 66%. The AI also provided correct predictions in 83% of brain hematoma expansion cases, whereas the group of physicians managed only 63%.
Billionaires including Bill Gates and Elon Musk have argued that robots will basically replace humans at work. People are now worried about vast swathes of unemployment where a few wealthy people own the robots and the rest survive on government handouts. But a new PwC forecast suggests artificial intelligence will create as many jobs as it destroys.
“AI is already helping to free up a lot of our time without us having to think about it. Customers are able to make quick decisions because of the data and software available, meaning that rather than having to trawl through thousands of options for a suitable item, the choices are partly navigated and curated for them.” Chief executive of re:infer, Edward Challis
The combination of high-resolution imaging and sensor technologies, AI, and cloud computing is giving conservationists deeper insight into the health of the planet. The result is a near-real-time readout of Earth’s vital signs, firing off alerts and alarms whenever the ailing patient takes a turn for the worse.
Rolls-Royce, a company perhaps best known for airplane engines and fine automobiles, is now creating tiny cockroaches that can crawl inside tight spaces to spot potential problems and perform routine maintenance. These roaches aren’t of the household variety, however; they’re robots.
Most municipal and private-sector infrastructure leaders seldom if ever think about how technologies like artificial intelligence (AI) and machine learning can help improve physical systems like roads, mass transit networks or water utilities. Yet, the opportunities presented to infrastructure decision-makers by the advent of data analytics, AI and machine learning are fast becoming too compelling to ignore.
For many years, companies tried to use biometric authentication such as voice, fingerprint, retina and face scans as alternatives to passwords. But for the most part, these techniques required expensive hardware and could easily be circumvented. Meanwhile, these technologies quickly break under poor lighting condition or in the case that the user changes their facial hair style or wear a hat. AI can add a level of enhancement to biometric authentication that makes it (almost) hack proof and smart enough to avoid irritating the user.
The current world speed record for riding a bike down a straight, flat road was set in 2012 by a Dutch team, but the Swiss have a plan to topple their rivals — with a little help from machine learning. An algorithm trained on aerodynamics could streamline their bike, perhaps cutting air resistance by enough to set a new record.
In the past, machine learning was only capable of interpolating data—making predictions about situations that are "between" other, known situations. It was incapable of extrapolating—making predictions about situations outside of the known—because it learns to fit the known data as closely as possible locally, regardless of how it performs outside of these situations. But now, a new machine learning method has been developed that addresses these problems.