Machine Learning and Science

 Good intro and positioning.   A bit of an overreach perhaps. 

How will machine learning change science?   by Tanya Petersen, Ecole Polytechnique Federale de Lausanne    in Techxplore

Machine learning has burst onto the scene in the past two decades and will be a defining technology of the future. It is transforming large sectors of society, including healthcare, education, transport, and food and industrial production, as well as having an enormous impact on science and research.

A subset of artificial intelligence, machine learning is a process that helps computers to learn without direct instruction, and from experience. It does this by using algorithms to identify patterns within data, which are then used to create models that can make predictions. And data is the key. Machine learning, and the spiraling availability of vast amounts of data, promises to revolutionize the production of knowledge. Indeed, today's exponential and virtuous cycle of growth in deep learning, among other technologies, has been compared to the Cambrian Explosion of half a billion years ago when life on Earth experienced a short period of very rapid diversification. ... ' 

Non Reproducible Machine Learning

 Always thought this was problematical.   How many experiments in papers are ultimately repeated, validated, with the same data?   Or ultimately are they just 'experientially' validated (or not) over time?.

Furious AI Researcher Creates a List of Non-reproducible Machine Learning Papers, By The Next Web,  March 10, 2021

On February 14, a researcher who was frustrated with reproducing the results of a machine learning research paper opened up a Reddit account under the username ContributionSecure14 and posted the r/MachineLearning subreddit: "I just spent a week implementing a paper as a baseline and failed to reproduce the results. I realized today after googling for a bit that a few others were also unable to reproduce the results. Is there a list of such papers? It will save people a lot of time and effort."

The post struck a nerve with other users on r/MachineLearning, which is the largest Reddit community for machine learning.

"Easier to compile a list of reproducible ones…," one user responded.

"Probably 50%-75% of all papers are unreproducible. It's sad, but it's true," another user wrote. "Think about it, most papers are 'optimized' to get into a conference. More often than not the authors know that a paper they're trying to get into a conference isn't very good! So they don't have to worry about reproducibility because nobody will try to reproduce them."

A few other users posted links to machine learning papers they had failed to implement and voiced their frustration with code implementation not being a requirement in ML conferences.

The next day, ContributionSecure14 created "Papers Without Code," a website that aims to create a centralized list of machine learning papers that are not implementable.

"I'm not sure if this is the best or worst idea ever but I figured it would be useful to collect a list of papers which people have tried to reproduce and failed," ContributionSecure14 wrote on r/MachineLearning. "This will give the authors a chance to either release their code, provide pointers or rescind the paper. My hope is that this incentivizes a healthier ML research culture around not publishing unreproducible work."

From The Next Web

Micrometeorites are Falling

Now for something completely different, from studies of mine long ago.   Did you know that there is a near continuous shower of space dust falling down on us?  Tiny cosmic meteorites.  And there are simple means of gathering some of these?  We used analytic methods to determine their nature and type.  Some of my earliest ML-style efforts.   Saw this repeated in a recent documentary:  Fireball by Werner Herzog.  Here supporting information.  The first more technical, the second a paywalled but more accessible NYT article.

https://pubs.geoscienceworld.org/gsa/geology/article/45/2/119/195213/An-urban-collection-of-modern-day-large

https://www.nytimes.com/2017/03/10/science/space-dust-on-earth.html 

AI Revolution in Science

Well done and non-technical explanation of this in Science Mag.   Not about the automation of these capabilities, but the techniques that can be used to work with the vast amounts of data being gathered. Most every segment of science and industry is examining these methods to simplify their data.

The AI revolution in science   By Tim Appenzeller 

Big data has met its match. In field after field, the ability to collect data has exploded—in biology, with its burgeoning databases of genomes and proteins; in astronomy, with the petabytes flowing from sky surveys; in social science, tapping millions of posts and tweets that ricochet around the internet. The flood of data can overwhelm human insight and analysis, but the computing advances that helped deliver it have also conjured powerful new tools for making sense of it all. 

AI is changing how we do science. 

In a revolution that extends across much of science, researchers are unleashing artificial intelligence (AI), often in the form of artificial neural networks, on the data torrents. Unlike earlier attempts at AI, such “deep learning” systems don’t need to be programmed with a human expert’s knowledge. Instead, they learn on their own, often from large training data sets, until they can see patterns and spot anomalies in data sets that are far larger and messier than human beings can cope with. .... "

Pluto and its Moons Influence World of Data

This blog isn't about astronomy, but it is my background, and just this week, new papers and remarkable images regarding Pluto and its Moons.  We can now see so far away and manage such huge amounts of data.  Reapplying these methods have led us to big data, analytics, and better understanding of all of our world.    To see further links between the two, see:  rocketdatascience.org/ 

Managing Very Big Data: Detailed Map of the Milky Way

The results of an experiment to display and navigate a combined observational image of the entire Milky Way Galaxy.   64 billion bits.   Description and links to a viewer.