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How is AI Saving the Future

how_is_AI_Saving_the_Future_cerelabs_12_05_2016
Meanwhile the talk of AI being the number one risk of human extinction is going on, there are lot many ways it is helping humanity. Recent developments in Machine Learning are helping scientists to solve difficult problems ranging from climate change to finding the cure for cancer.

It will be a daunting task for humans to understand enormous amount of data that is generated all over the world. Machine Learning is helping scientists to use algorithms that learn from data and find patterns.

Below is a list of few of the problems AI is working on to help find solutions which otherwise would not have been possible:

  • Cancer Diagnostics: Recently, scientists at University of California (UCLA) applied Deep Learning to extract features for achieving high accuracy in label-free cell classification. This technique will help in faster cancer diagnostics, and thus will save a lot of lives.

  • Low Cost Renewable Energy: Artificial-intelligence is helping wind power forecasts of unprecedented accuracy that are making it possible for Colorado to use far more renewable energy, at lower cost.

  • Global Conservation: National Science Foundation (NSF) funded researchers are using Artificial Intelligence to solve poaching and illegal logging. They have created an AI-driven application called Protection Assistant for Wildlife Security (PAWS) which has led to significant improvements when tested in Uganda and Malaysia in 2014, thus protecting forests and wildlife.

  • Precision Based Medicine: AI is turning out to be a powerful tool for precision-based medicine, where treatments are tailored made. Custom diagnostics and treatments seems possible because of the recent advancements in AI.

We at Cere Labs are continuously thinking how we can develop applications based on AI that can help humanity, especially in healthcare. We will keep you updated of the progress we make in this area. AI is not just about robots winning a game of chess or  a game of GO.

References:

  1. Chen, C. L. et al. Deep Learning in Label-free Cell Classification. Sci. Rep. 6, 21471; doi: 10.1038/srep21471 (2016).
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