IIT Madras is all set to develop AISoft, an artificial intelligence solution for solving engineering problems and FairKM, a software that can remove caste, race and sex bias from AI. Both developed software can work with sparse data sets and this makes them stand out when compared to other commercially available software.
Crux of the Matter
Dream Team A team of researchers led by Vishal Nandigana, assistant professor, Department of Mechanical Engineering, IIT-Madras developed the AI and Deep Learning algorithms for AISoft. The formed flow models can develop solutions to engineering problems in fields such as thermal management, semiconductors, aerospace, and electronic cooling applications.
Need for Removing Bias AI algorithms learn from human behavior and if the human behavior is biased the AI is automatically biased. There were ‘fair clustering’ techniques that were already prevalent but they could only incorporate one parameter. Dr. Deepak Padmanabhan’s FairKM can take into consideration multiple parameters including various kinds of biases.
Future Impact The AISoft idea being new is looked upon by various research groups around the world, who can further use Convolutional Neural Networks (CNN) or Conditional Generative Adversarial network (C-GAN) for required output generation. The FairKM can be put to immediate use in the third world and developing countries that are often criticized for bias in terms of ‘sensitive attributes’.
Curiopedia
Deep learning is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised. Deep learning architectures such as deep neural networks, deep belief networks, recurrent neural networks and convolutional neural networks have been applied to fields including computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics, drug design, medical image analysis, material inspection and board game programs, where they have produced results comparable to and in some cases surpassing human expert performance. Artificial neural networks (ANNs) were inspired by information processing and distributed communication nodes in biological systems. ANNs have various differences from biological brains. Specifically, neural networks tend to be static and symbolic, while the biological brain of most living organisms is dynamic (plastic) and analog. More Info
Curated Coverage
Comments