“Hey Siri, how’s my hair?”
Your smartphone may soon be able to give you an honest answer, thanks to a new machine learning algorithm designed by U of T Engineering researchers Parham Aarabi (ECE) and Wenzhi Guo (ECE MASc 1T5).
The team designed an algorithm that learns directly from human instructions, rather than an existing set of examples, and outperformed conventional methods of training neural networks by 160 per cent. But more surprisingly, their algorithm also outperformed its own training by nine per cent — it learned to recognize hair in pictures with greater reliability than that enabled by the training, marking a significant leap forward for artificial intelligence.
Aarabi and Guo trained their algorithm to identify people’s hair in photographs — a much more challenging task for computers than it is for humans.
“Our algorithm learned to correctly classify difficult, borderline cases — distinguishing the texture of hair versus the texture of the background,” says Aarabi. “What we saw was like a teacher instructing a child, and the child learning beyond what the teacher taught her initially.”
Humans “teach” neural networks — computer networks that learn dynamically — by providing a set of labeled data and asking the neural network to make decisions based on the samples it’s seen. For example, you could train a neural network to identify sky in a photograph by showing it hundreds of pictures with the sky labeled.
This algorithm is different: it learns directly from human trainers. With this model, called heuristic training, humans provide direct instructions that are used to pre-classify training samples rather than a set of fixed examples. Trainers program the algorithm with guidelines such as “Sky is likely to be varying shades of blue,” and “Pixels near the top of the image are more likely to be sky than pixels at the bottom.”
Their work is published in the journal IEEE Transactions on Neural Networks and Learning Systems.
This heuristic training approach holds considerable promise for addressing one of the biggest challenges for neural networks: making correct classifications of previously unknown or unlabeled data. This is crucial for applying machine learning to new situations, such as correctly identifying cancerous tissues for medical diagnostics, or classifying all the objects surrounding and approaching a self-driving car.
“Applying heuristic training to hair segmentation is just a start,” says Guo. “We’re keen to apply our method to other fields and a range of applications, from medicine to transportation.”
The Latest on: AI algorithm heuristic training
via Google News
The Latest on: AI algorithm heuristic training
- Researchers Easily Trick Cylance's AI-Based Antivirus Into Thinking Malware Is 'Goodware'on July 18, 2019 at 8:30 am
But during training, the system also examines the ... He also said that not using backup signatures or heuristics to doublecheck the algorithm’s conclusion, and relying on the AI instead, caused the ...
- Salesforce’s AI grasps commonsense reasoningon June 27, 2019 at 6:01 am
Sophisticated AI models are ... a machine learning algorithm to predict what will happen when you push a ball off a table or when a person trips down the stairs. Unless it has been explicitly “taught” ...
- Adobe AI learns painting styles to reproduce artwork in under a minuteon June 18, 2019 at 8:57 am
If the winners of last year’s international RobotArt competition are any indication, algorithms aren’t half bad at painting ... which they supplied to the AI model such that it learned to paint ...
- From AI Algorithm To Implementationon April 10, 2019 at 12:04 am
But the training problem is almost hopeless to verify as a combined ... Now we are talking about taking AI algorithms and mapping them onto hardware. That is algorithm creation itself. Why does it ...
- When AI algorithms fail, who you gonna call?on January 13, 2017 at 10:02 am
So if there’s nothing wrong with this approach to training AI, and I am in that camp ... with error based on biases driven by these subtle, unconscious heuristics. So relying on our algorithms might ...
- AI Learns Things That Humans Didn’t Teach Iton November 22, 2016 at 8:36 am
Machine learning technology in neural networks has been pushing artificial intelligence ... of training neural networks (exposure to existing sets of examples), their algorithm learned directly from ...
- Siri to become more 'honest' after new AI algorithmon November 17, 2016 at 7:58 pm
It learned to recognise hair in pictures with greater reliability than that enabled by the ... leap forward for artificial intelligence. Researchers Parham Aarabi and Wenzhi Guo from University of ...
- New AI algorithm taught by humans learns beyond its trainingon November 16, 2016 at 7:58 am
their algorithm also outperformed its own training by nine per cent—it learned to recognize hair in pictures with greater reliability than that enabled by the training, marking a significant leap ...
- New AI algorithm taught by humans learns beyond its trainingon November 15, 2016 at 4:00 pm
their algorithm also outperformed its own training by nine per cent -- it learned to recognize hair in pictures with greater reliability than that enabled by the training, marking a significant leap ...
via Bing News