Machine learning and its evolution

Machine learning algorithms are becoming more “intelligent” and replacing people in many tasks. What’s next?

When (not if) Machine learning will create a superintelligence, then how we will deal with it? How do we control it? How do we avoid to become extinct because of that? Read Nick Bostrom’s book, Superintelligence: Paths, Dangers, Strategies

Is there a unifying algorithm in Machine Learning? Read Pedro Domingos’ book, The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World

And while algorithms are increasingly used, they have bias and problems that must be taken into account, see Artificial Intelligence’s White Guy Problem

Deep learning

Deep learning what is it?
Check the Quora answer What are the practical applications of deep learning? by Chris Nicholson, co-founder of skymind.io , co-creator of deeplearning4j.org

Deep learning is basically machine perception. What is perception? It is the power to interpret sensory data. Two main ways we interpret things are by naming what we sense; e.g. we hear a sound as we say ourselves “That’s my daughter’s voice.” Or we see a haze of photons and we say “That’s my mother’s face.” If we don’t have names for things, we can still recognize similarities and dissimilarities. You might see two faces and know that they were mother and daughter, without knowing their names; or you might hear to voices and know that they came from the same town or state by their accent. Algorithms train to name things through supervised learning, and to cluster things through unsupervised learning. The difference between supervised and unsupervised learning is whether you have a labeled training set to work with or not. The labels you apply to data are simply the outcomes you care about. Maybe you care about identifying people in images. Maybe you care about identifying angry or spammy emails, which are all just unstructured blobs of text. Maybe you’re looking at time series data — a stream of numbers — and you care about whether the next instances in the time series will be higher or lower.

So deep learning, working with other algorithms, can help you classify, cluster and predict. It does so by learning to read the signals, or structure, in data automatically. When deep learning algorithms train, they make guesses about the data, measure the error of their guesses against the training set, and then correct the way they make guesses in order to become more accurate. This is optimization.

Now imagine that, with deep learning, you can classify, cluster or predict anything you have data about: images, video, sound, text and DNA, time series (touch, stock markets, economic tables, the weather).  That is, anything that humans can sense and that our technology can digitize. You have multiplied your ability to analyze what’s happening in the world by many times. With deep learning, we are basically giving society the ability to behave much more intelligently, by accurately interpreting what’s happening in the world around us with software.

Prediction alone is a huge power, and the applications are fairly obvious. Classification sounds banal, but by naming something, you can decide how to respond. If an email is spam, you send it to the spam folder and save the reader time. If the face captured by your front door camera is your mother, maybe you tell the smart lock to open the door. If a X-ray shows a tumorous pattern, you flag it for deeper examination by medical experts.

Use your imagination. We’ve prepared a short list of use cases here:

Deep learning use cases

Epigenetics, what is it?

A recent debate has started after Siddhartha Mukherjee  published an article about epigenetics. The piece, called “Same but different” (subtitle: “How epigenetics can blur the line between nature and nurture”). Mukherjee, a physician, is well known for writing the Pulitzer-Prize-winning book (2011) The Emperor of All Maladies: A Biography of Cancer.

Jerry Coyne published in his blog Why Evolution is True some of the critics scientists had with the Mukherjee piece:

  1. The New Yorker screws up big time with science: researchers criticize the Mukherjee piece on epigenetics
  2. Researchers criticize the Mukherjee piece on epigenetics: Part 2

Then in An Epigenetics Controversy, Siddhartha Muhkerjee responds to the critique of his recent New Yorker piece

Coyne writes again, L’Affaire Mukherjee: the last word , excerpt from the post:

” So, for the record, let me say this: all of us, including Mukherjee, agree on the gist of what follows (though I don’t know if he’d sign off on this wording):

There is absolutely no evidence for any Lamarckian form of evolution based on “epigenetic” markers on the DNA produced by the environment. Further speculations about this—and claims that it shows that the modern theory of evolution is wrong—are misguided and should be ignored pending some real evidence. “

The Vox writes an article on this discussion: Why scientists are infuriated with a New Yorker article on epigenetics

Coyne again Dreadful science journalism at Vox: all interpretations of science are equal, but some are cuter than others

“The problem with Mukherjee’s piece, of course, is that he presented a story—that epigenetic markers and histone-protein modifications are THE mediators of differential gene expression in differentiated cells, working as a kind of “epigenetic code”—for which there is virtually no evidence. This was the cute and intriguing tale that he told readers of the New Yorker, who, of course, loved the good writing and assumed what Mukherjee said was accurate.

But what he left out—to the readers’ detriment—was the true story of gene regulation as we know it: a story identifying protein “transcription factors” and short bits of RNA as the factors that regulate gene expression. As Mark Ptashne and John Greally noted, neither Drosophila nor Caenorhabditis worms have DNA “markers,” yet both organisms—paradigms for the study of genetics and development—develop just fine, thank you. That alone should give pause to people like Mukherjee or the Epigenesis Mavens, and it comes on top of the lack of evidence for epigenetic or histone-regulated control of genes.
“The problem with Mukherjee’s piece, of course, is that he presented a story—that epigenetic markers and histone-protein modifications are THE mediators of differential gene expression in differentiated cells, working as a kind of “epigenetic code”—for which there is virtually no evidence.”

And from the comments in the blog, Coyne himself:

“The problem with Mukherjee’s piece, of course, is that he presented a story—that epigenetic markers and histone-protein modifications are THE mediators of differential gene expression in differentiated cells, working as a kind of “epigenetic code”—for which there is virtually no evidence.”

The New York Time has reviewed Siddhartha Mukherjee latest book, The Genes: an intimate history

Fortune writes Siddhartha Mukherjee, Author Of Bestselling Cancer Book, Starts Biotech Company And Answers Criticism

And one good summary from Nature: Researcher under fire for New Yorker epigenetics article

Michael Eisen a biologist at UC Berkeley writes in his blog The Imprinter of All Maladies

On WhyEvolutionIsTrue blog PLOS Biology weighs in on Mukherjee affair: “Writing for Story distorts and cripples explanatory prose”

Scientific American in Gene Regulation, Illustrated

Senior Editor, Current Biology writes to the New Yorker, Mukherjee replies The Regulators

Looking forward to reading Siddhartha Mukherjee latest book, The Genes: an intimate history

The Rise of Robots

Robots are coming. It is a certainly a great thing. They will be able to do many things for us and help us in many jobs. Probably they will be better than us. Traditionally Robots replaced low skilled workers performing repetitive tasks. This is not the case any more. Robots are better than humans in many cognitive tasks. The questions then becomes, what to do to when Robots will replace us in the workforce. Will only a small set of people benefit leaving many others behind? We need to rethink our society and with it the way we want to move forward with this inevitable revolution.
Some books and articles on the subject:

The History of Tomorrow’s By: Yuval Noah Harari
The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies, by Erik Brynjolfsson, Andrew McAfee
Rise of the Robots: Technology and the Threat of a Jobless Future, by Martin Ford
The Fourth Industrial Revolution: A Davos Reader by Gideon Rose
World After Capital, by Albert Wengel

Deep Learning Is Going to Teach Us All the Lesson of Our Lives: Jobs Are for Machines by Scott Santens

Genetics, the future and implications

A few links and a book to get an understanding of the power of genetics and its implications.

From Scientific American, Scientists Synthesize Bacteria with Smallest Genome Yet

Excerpt:

Genomics entrepreneur Craig Venter has created a synthetic cell that contains the smallest genome of any known, independent organism. Functioning with 473 genes, the cell is a milestone in his team’s 20-year quest to reduce life to its bare essentials and, by extension, to design life from scratch.

Nature on the same topic:  ‘Minimal’ cell raises stakes in race to harness synthetic life

Nature: Governance: Learn from DIY biologists

Very good book on human GMO, GMO Sapiens: The Life-Changing Science of Designer Babies by Paul Knoepfler, his twitter handle @pknoepfler and blog http://www.ipscell.com/paul/

 
Inside the garage labs of DIY gene hackers, whose hobby may terrify you