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Machine-learning and AI software are trained with initial sets of data such as images of cats and it learns to identify feline images as more such data are fed. A common example is Google throwing up better results as more people search for the same information.
Use of AI applications is becoming mainstream in areas such as healthcare, payments processing, deploying drones to monitor crowds, and for facial recognition in offices and airports.
“If your data input is not clear and vetted, the AI machine could throw up surprising results and that could end up being hazardous. In autonomous driving, the AI engine should be trained properly on all road signs.
If the input sign is different, then it could change the course of the vehicle, leading to a catastrophe,” R Venkatesha Babu, Associate Professor at IISc’s Department of Computational Sciences, told ET.
“The system also needs to have enough cyber security measures to prevent hackers from intruding and altering inputs,” he said.
Babu and his students Konda Reddy Mopuri and Aditya Ganesan, in a paper published in the prestigious Trans. Pattern Analysis and Machine Intelligence of IEEE, have demonstrated how errors introduced in machine-learning algorithms could throw up varied results—like African chameleon for a missile, and a custard apple for a banana...
More Work on AI Needed
Analysts say the research throws light on the “hype of AI” and that more work is needed to improve its efficiency and security. “If the technologies can be confused so easily, we are in trouble. It is like having computers that can easily be hacked; the first generations had practically no security,” said Vivek Wadhwa, a Distinguished Fellow at Carnegie Mellon University’s College of Engineering.
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Source: Gadgets Now
