“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.”
Learn more: New AI algorithm taught by humans learns beyond its training
The Latest on: AI algorithm heuristic training
[google_news title=”” keyword=”AI algorithm heuristic training” num_posts=”10″ blurb_length=”0″ show_thumb=”left”]
via Google News
The Latest on: AI algorithm heuristic training
- Ideal AI Dream Team: Technologists, Strategists, Users, And Ethicistson April 27, 2024 at 8:37 am
People outside the AI technical bubble need to have a solid understanding of how AI works, and what is required to make it responsible and productive.
- Does AI Know What an Apple Is? She Aims to Find Out.on April 24, 2024 at 5:00 pm
That’s a really exciting finding because it seems like [the model is] boiling down these little concepts and then applying general algorithms over them ... which are all over its training data. So we ...
- How Eko Health’s AI-powered stethoscope detects early signs of heart failureon April 17, 2024 at 4:16 pm
Eko Health taps tech old and new for its cardiac algorithms — and has sights set on the lungs next, co-founder and CEO Connor Landgraf says.
- DARPA Achieves Major Breakthrough with AI-Controlled Aircrafton April 17, 2024 at 1:48 pm
The agency conducted a dogfighting simulation pitting an AI-controlled F-16 against a real, human pilot in September.
- Q&A: Enhancing last-mile logistics with machine learningon April 17, 2024 at 12:44 pm
Across the country, hundreds of thousands of drivers deliver packages and parcels to customers and companies each day, with many click-to-door times averaging only a few days. Coordinating a supply ...
via Bing News