AI Makes New Scientific Discoveries By Analyzing 3.3 Million Scientific Abstracts

The emergence of AI scientific discoveries has been propelled by the remarkable capabilities of machine learning algorithms. By meticulously analyzing extensive volumes of scientific papers, these algorithms have unveiled unprecedented connections that were previously overlooked by human researchers. Through this groundbreaking approach, completely novel scientific discoveries have been made, revolutionizing our understanding of various fields of study. This extraordinary synergy between artificial intelligence and scientific research has propelled the boundaries of human knowledge, opening up new frontiers for exploration and advancement.

Revolutionizing Scientific Exploration: AI’s Unprecedented Discoveries

Researchers from Lawrence Berkeley National Laboratory created an algorithm which allowed their algorithm to create predictions for thermoelectric materials. Used with heating and cooling appliances, these thermoelectric materials convert heat to energy. However, the algorithm didn’t know the definition of thermoelectric. Nevertheless, even without the definition it was able to provide candidates for thermoelectric materials.

Researcher Anubhav Jain stated “it can read any paper on material science and can make connections that no scientists could.” Through assessing the language in 3.3 million abstracts related to material science, the algorithm learned about 500,000 new words. In addition, Jain explained that “the way that this Word2Vec algorithm works is that you train a neural network model to remove each word and predict what the words next to it will be. By training a neural network on a word, you get representations of words that can actually confer knowledge.”

In one experiment, the algorithm analyzed only papers published before 2009. After that it was able to discover one of the best thermoelectric materials four years before any scientists could. Lead author of the study Vahe Tshitoyan states that “this algorithm is unsupervised and it builds it won connections. You can use this for things like medical research or drug discovery. The information is out there. We just haven’t made these connections yet because you can’t read every article.” In conclusion, AI can make discoveries that a human may have overlooked because of the sheer amount of data it can sift through at a time. Read the original article by Nature here

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Andre Moncayo

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