Pesquisadores da Universidade de Toronto e da LG AI Research desenvolveram um algoritmo de inteligaªncia artificial "explica¡vel" (XAI) que pode ajudar a identificar e eliminar defeitos nas telas de exibia§a£o.
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)
Imagens de mapas de calor são usadas para avaliar a precisão de um novo algoritmo de inteligaªncia artificial explica¡vel que os pesquisadores da U of T e da LG desenvolveram para detectar defeitos nas telas de exibição da LG. Crédito: Mahesh Sudhakar
Pesquisadores da Universidade de Toronto e da LG AI Research desenvolveram um algoritmo de inteligaªncia artificial "explica¡vel" (XAI) que pode ajudar a identificar e eliminar defeitos nas telas de exibição.
O novo algoritmo , que superou abordagens compara¡veis ​​em benchmarks da indaºstria, foi desenvolvido por meio de uma colaboração de pesquisa de IA contanua entre a LG e a U of T que foi expandida em 2019 com foco em aplicativos de IA para empresas.
Os pesquisadores dizem que o algoritmo XAI pode ser potencialmente aplicado em outros campos que exigem uma janela de como o aprendizado de ma¡quina toma suas decisaµes, incluindo a interpretação de dados de exames médicos.
"A explicabilidade e a interpretabilidade referem-se ao cumprimento dos padraµes de qualidade que estabelecemos para nosmesmos como engenheiros e são exigidos pelo usua¡rio final", disse Kostas Plataniotis, professor do departamento de engenharia elanãtrica e de computação de Edward S. Rogers Saªnior na Faculdade de Aplicação Ciência e Engenharia. "Com o XAI, não existe um tamanho aºnico para todos." Vocaª tem que perguntar para quem estãodesenvolvendo. a‰ para outro desenvolvedor de aprendizado de ma¡quina? Ou épara um médico ou advogado? "
A equipe de pesquisa também incluiu o recente graduado em Engenharia da U of T, Mahesh Sudhakar, e o candidato a mestrado Sam Sattarzadeh, bem como pesquisadores liderados por Jongseong Jang da LG AI Research Canada - parte do braa§o global de pesquisa e desenvolvimento da empresa.
O XAI éum campo emergente que aborda problemas com a abordagem de 'caixa preta' das estratanãgias de aprendizado de ma¡quina.
Em um modelo de caixa preta, um computador pode receber um conjunto de dados de treinamento na forma de milhões de imagens rotuladas. Ao analisar os dados, o algoritmo aprende a associar certos recursos da entrada (imagens) a certas saadas (ra³tulos). Eventualmente, ele pode anexar ra³tulos corretamente a imagens que nunca viu antes.
A ma¡quina decide por si mesma a quais aspectos da imagem prestar atenção e quais ignorar, o que significa que seus designers nunca sabera£o exatamente como ela chega a um resultado.
Mas esse modelo de "caixa preta" apresenta desafios quando éaplicado a áreas como saúde, direito e seguros.
"Por exemplo, um modelo [de aprendizado de ma¡quina] pode determinar que um paciente tem 90 por cento de chance de ter um tumor", diz Sudhakar. "As consequaªncias de agir com base em informações imprecisas ou tendenciosas são literalmente vida ou morte. Para entender e interpretar totalmente a previsão do modelo, o médico precisa saber como o algoritmo chegou a ela."
Mapas de calor de imagens de benchmark da indústria mostram uma comparação
qualitativa do algoritmo XAI da equipe (SISE, extrema direita) com outros manãtodos XAI de
última geração. Crédito: Mahesh Sudhakar
Em contraste com o aprendizado de ma¡quina tradicional, o XAI foi projetado para ser uma abordagem de "caixa de vidro" que torna a tomada de decisão transparente. Os algoritmos XAI são executados simultaneamente com os algoritmos tradicionais para auditar a validade e onívelde seu desempenho de aprendizagem. A abordagem também oferece oportunidades para realizar a depuração e encontrar eficiências de treinamento.
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Sudhakar diz que, falando de maneira geral, existem duas metodologias para desenvolver um algoritmo XAI - cada uma com vantagens e desvantagens.
O primeiro, conhecido como retropropagação, depende da arquitetura de IA subjacente para calcular rapidamente como a previsão da rede corresponde a sua entrada. A segunda, conhecida como perturbação, sacrifica alguma velocidade pela precisão e envolve a alteração das entradas de dados e o rastreamento das saadas correspondentes para determinar a compensação necessa¡ria.
“Nossos parceiros da LG desejavam uma nova tecnologia que combinasse as vantagens de ambasâ€, diz Sudhakar. "Eles tinham um modelo existente [de aprendizado de ma¡quina] que identificava pea§as defeituosas em produtos LG com monitores, e nossa tarefa era melhorar a precisão dos mapas de calor de alta resolução de possaveis defeitos, mantendo um tempo de execução aceita¡vel."
O algoritmo XAI resultante da equipe, Semantic Input Sampling for Explanation (SISE), édescrito em um artigo recente para a 35ª Conferência AAAI sobre Inteligaªncia Artificial.
“Vemos potencial no SISE para aplicação generalizadaâ€, diz Plataniotis. "O problema e a intenção do cena¡rio especafico sempre exigira£o ajustes no algoritmo - mas esses mapas de calor ou 'mapas de explicação' poderiam ser interpretados mais facilmente, por exemplo, por um profissional médico."
“O objetivo da LG em fazer parceria com a University of Toronto ése tornar um lider mundial em inovação de IAâ€, disse Jang. "Esta primeira conquista no XAI fala dos esforços contanuos de nossa empresa para fazer contribuições em várias áreas, como funcionalidade dos produtos LG, inovação na fabricação, gerenciamento da cadeia de suprimentos, eficiência na descoberta de materiais e outros, usando IA para aumentar a satisfação do cliente."
A professora Deepa Kundur, chefe do departamento de engenharia elanãtrica e de computação, diz que sucessos como esse são um bom exemplo do valor da colaboração com parceiros da indaºstria.
“Quando ambos os grupos de pesquisadores vão a mesa com seus respectivos pontos de vista, muitas vezes pode acelerar a resolução de problemasâ€, diz Kundur. "a‰ inestima¡vel para os alunos de pós-graduação serem expostos a esse processo."
Embora tenha sido um desafio para a equipe atingir a precisão agressiva e as metas de tempo de execução dentro do projeto de um ano - ao mesmo tempo em que fazia malabarismos com os fusos hora¡rios de Toronto / Seul e trabalhava sob as restrições do COVID-19 - Sudhakar diz que a oportunidade de gerar uma solução prática para um fabricante de renome mundial, valeu a pena o esfora§o.
“Foi bom para nosentender como funciona exatamente a indaºstriaâ€, diz Sudhakar. "Os objetivos da LG eram ambiciosos, mas tivemos um apoio muito encorajador deles, com feedback sobre ideias ou analogias para explorar. Foi muito empolgante."