We see that text data is ubiquitous in nature. There is a lot of text present in different forms such as posts, books, articles, and blogs. What is more interesting is the fact that there is a subset of Artificial Intelligence called Natural Language Processing (NLP) that would convert text into a form that could be used for machine learning. I know that sounds a lot but getting to know the details and the proper implementation of machine learning algorithms could ensure that one learns the important tools in the process. Since there are newer and better libraries being created to be used for machine learning purposes, it would make sense to learn some of the state-of-the-art tools that could be used for predictions. I've recently come across a challenge on Kaggle about predicting the difficulty of the text.

Imagine being able to apply the power of artificial intelligence (AI) to invent novel materials that can potentially revolutionize many industries such as pharmaceuticals, biotech, electronics, plastics, semiconductors, glass, energy, nanotech, metal alloys, composite materials, ceramics, optics, and many more. In 2018, pioneering physicists at Stanford University in Palo Alto, California, announced in PNAS (Proceedings of the National Academy of Sciences of the United States of America) the creation of a new AI program (Atom2Vec) that was able to recreate the periodic table of elements -- a milestone first step towards creating an AI that can discover new laws of nature, and invent novel materials and compounds [1]. Atom2Vec was able to achieve this within just a "few hours," versus the many centuries it took for humans [2]. The way this was achieved was a cross-disciplinary AI approach -- applying linguistic concepts to materials science. Stanford physicists applied Zellig S. Harris' hypothesis on the distributional structure of language to atoms instead of words.

You are free to share this article under the Attribution 4.0 International license. It took nearly a century of trial and error for human scientists to organize the periodic table of elements, arguably one of the greatest scientific achievements in chemistry, into its current form. "Instead of feeding in all of the words and sentences from a collection of texts, we fed Atom2Vec all the known chemical compounds…" Called Atom2Vec, the program successfully learned to distinguish between different atoms after analyzing a list of chemical compound names from an online database. The unsupervised AI then used concepts borrowed from the field of natural language processing--in particular, the idea that the properties of words can be understood by looking at other words surrounding them--to cluster the elements according to their chemical properties. "We wanted to know whether an AI can be smart enough to discover the periodic table on its own, and our team showed that it can," says study leader Shoucheng Zhang, a professor of physics at Stanford University.

It took nearly a century of trial and error for human scientists to organize the periodic table of elements, arguably one of the greatest scientific achievements in chemistry, into its current form. A Stanford team has developed an artificial intelligence program that recreated the period table of elements; they aim to harness that tool to discover and design new materials. A new artificial intelligence (AI) program developed by Stanford physicists accomplished the same feat in just a few hours. Called Atom2Vec, the program successfully learned to distinguish between different atoms after analyzing a list of chemical compound names from an online database. The unsupervised AI then used concepts borrowed from the field of natural language processing – in particular, the idea that the properties of words can be understood by looking at other words surrounding them – to cluster the elements according to their chemical properties. "We wanted to know whether an AI can be smart enough to discover the periodic table on its own, and our team showed that it can," said study leader Shoucheng Zhang, the J. G. Jackson and C. J. Wood Professor of Physics at Stanford's School of Humanities and Sciences.

"We wanted to know whether an AI can be smart enough to discover the periodic table on its own, and our team showed that it can," said study leader Shou-Cheng Zhang, the J. G. Jackson and C. J. Wood Professor of Physics at Stanford's School of Humanities and Sciences. Zhang says the research, published in the June 25 issue of Proceedings of the National Academy of Sciences, is an important first step toward a more ambitious goal of his, which is designing a replacement to the Turing test – the current gold standard for gauging machine intelligence. In order for an AI to pass the Turing test, it must be capable of responding to written questions in ways that are indistinguishable from a human. But Zhang thinks the test is flawed because it is subjective. "Humans are the product of evolution and our minds are cluttered with all sorts of irrationalities. For an AI to pass the Turing test, it would need to reproduce all of our human irrationalities," Zhang said.