Big data and predictive models are currently used and are going to be pervasive affecting our lives. There are many positive news coming from these new algorithms and techniques. At the same time, it is important to be aware of their impact, biases and how to deal with the imperfections and abuses that inevitable will come with them.

Weapons of Math Destruction by Cathy O’Neil is an extremely good and important book on this subject and I highly recommend it to all the people that want to understand how society is and will be affected by all these new algorithms and predictive models.

The Dentist Office Software Story is an excellent blog post by Fred Wilson at avc.com.

There were a couple of sentences what caught my attention and I want to emphasize here. Below the excerpt where I have highlighted the sentences in bold.

This story is designed to illustrate the fact that software alone is a commodity. There is nothing stopping anyone from copying the feature set, making it better, cheaper, and faster. And they will do that. This is the reality that Brad and I stared at in 2003 as we were developing our initial investment thesis for USV. We saw the cloud coming but did not want to invest in commodity software delivered in the cloud. So we asked ourselves, “what will provide defensibility” and the answer we came to was networks of users, transactions, or data inside the software. We felt that if an entrepreneur could include something other than features and functions in their software, something that was not a commodity, then their software would be more defensible. That led us to social media, to Delicious, Tumblr, and Twitter. And marketplaces like Etsy, Lending Club, and Kickstarter. And enterprise oriented networks like Workmarket, C2FO, and SiftScience. We have not perfectly executed our investment strategy by any means. We’ve missed a lot of amazing networks. And we’ve invested in things that weren’t even close to networks. But all of that said, our thesis has delivered for us and we stick to it as much as we can.

Homo Deus: A Brief History of Tomorrow by Yuval Noah Harari is out. A must read book. His previous book is awesome Sapiens: A Brief History of Humankind

In the concluding chapter he writes:

Yet if we take the really grand view of life, all other problems and developments are overshadowed by three interlinked processes:

1.  Science is converging on an all-encompassing dogma, which says that organisms are algorithms, and life is data processing.
2.  Intelligence is decoupling from consciousness.
3.  Non-conscious but highly intelligent algorithms may soon know us better than we know ourselves.
These three processes raise three key questions, which I hope will stick in your mind long after you have finished this book:

1.  Are organisms really just algorithms, and is life really just data processing?
2.  What’s more valuable – intelligence or consciousness?
3.  What will happen to society, politics and daily life when non-conscious but highly intelligent algorithms know us better than we know ourselves?

I have been reading The Inevitable by Kevin Kelly. A great book that I highly recommend.

Here I report a few excerpts from the “Flowing” chapter to describe how flows of data are inevitable and what we can do about it.

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The Internet is the world’s largest copy machine. At its most fundamental level this machine copies every action, every character, every thought we make while we ride upon it.

If something can be copied – a song,a movie, a book – and it touches the internet, it will copied.

The information age is driven by digital copies – exact and free.

In this new supersaturated digital universe of infinite free digital duplication, copies are so ubiquitous, so cheap – free, in fact – that the only things truly valuable are those that cannot be copied.

When copies are superabundant, they become worthless. Instead, stuff that can’t be copied become scarce and valuable.

Here are eight generatives that are “better than free”:

Immediacy: Getting something the moment it is released, or even better, produced by its creators.

Personalization: providing something personalized according to your taste, your reading taste, your living room, etc.

Interpretation: interpretation of genomic info, healthcare, travels, consulting Linux, etc.

Authenticity: make sure you have the real thing, the real software app

Accessibility: example are cloud services to access info wherever and whenever you want with full backup, security.

Embodiment: live concerts, live lessons, printed book, Ted talks

Patronage: fans want to pay creators

Discoverability: providing guides or ways to help discover new things, reading lists, videos, movies

Some very good books and debates on growth, innovation and what it means for us.
Robert Gordon, The End of Innovation, the End of Growth

Erik Brynjolfsson, The Key to Growth, Race with the Machines

The future of work and innovation: Robert Gordon and Erik Brynjolfsson debate at TED2013

BOOKS
The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies by Erik Brynjolfsson, Andrew McAfee

The Rise and Fall of American Growth: The U.S. Standard of Living since the Civil War by Robert Gordon

World after capital, Albert Wenger

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

Robots, drones and more, the future of agriculture.

Good article on the topic, The Future of Agriculture

I enjoyed reading the Ancestor’s tale, by Dawkins, excellent book on our ancestors and how we got here where we are now.

Another good book, Life’s Greatest Secret: The Race to Crack the Genetic Code” by Matthew Cobb

And looking forward to reading, The Gene, An Intimate History by Siddhartha Mukherjee.

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

If you are interested where technology is going, watch the Google I/O Keynote. It is very long, if you want to have just a short quick summary, go to 1:46:50