Paradigm Breakthrough, it Needs a Risk Taker

Some organizations and companies at some stage need a paradigm breakthrough.

Apple is(was) a computer company, now cell phones?  Can you imagine if the culture at Apple 6 years ago was against a paradigm shift to move to cell phones.  The next normal path in bringing a user friendly computer to people is: computer, then music then the personal beautiful cellphone. Apple is a great example of a company that knows when to make a positive successful paradigm shift. Financially, consider what Apple would be today if the iPhone was not released.

Google a search company, now cell phones?  (Financially, Google is an advertising company.)

My guess is 15 years ago some marketing person or engineer suggested adding a camera to a cell phone, and maybe lost his job over the crazy idea.  Today it’s hard to find a phone without a camera.

Drew a software engineer, entrepreneur, blog writer, on camera person, now songwriting?

Some of us have stopped dreaming, we like predictable, stable and it has always worked, so why change it.

It is hard for a company culture to accept a new possibility, when the existing seems to be going well.  Change is difficult and must be done slowly and carefully. People do like change, but want it slowly.

But one has to consider:  when is the proper time for a paradigm shift!

Palouse Falls, Washington

Palouse Falls, Washington

December 10, 2014

Convolutional Neural Networks

Convolutional Neural Networks got inspired from the way our human visual cortex works (human eye) and classifies images like dog, cat, man, women, car, jet, or computer.
Convolutional Neural Networks are used for the classification of images.They do a great job because the training of the neural network breaks up the image into component parts and does classification based on various parts of the image. So a dog or cat or numerical number is learned by the various properties of the image. Here are some great links:

best tutorial: Convolutional Neural Networks (CNNs / ConvNets)

Google’s TensorFlow neural networks

Visualizing and Understanding Deep Neural Networks by Matt Zeiler

Visualizing and Understanding Convolutional Networks

Deep Learning Reading List

Google’s TensorFlow: Convolutional Neural Networks

Music — Artificial Intelligence

wow, look at the ability for Machine Learning and Artificial Intelligence to learn your music rhythm and melody and play a duet on it.

Try it: Here!    How does it work: Here!

It is here based on Google’s TensorFlow Machine Learning open source software.  Check out this link about “Make Music and Art Using Machine Learning” at   Then look at Learning from A.I. Duet .  Some real possibilities for expanding music and allowing software to help accompany the musician.  Yes this will not make one a better musician, but it sure helps to make more music.  The software is part of the work of the Google Brain team who work on Magenta project in collaboration with the Google Creative Lab team.

Now to take this to the next level, consider this note on
Style-conditioned music generation

Piano in the sunlight

Piano in the sunlight

Artificial Intelligence – notes

With machine learning, Neural Networks are looking promising for deep learning. But there is the rise of Bayesians, which is researchers doing Artificial Intelligence (AI) through the scientific method starting with an hypothesis. There is lots of trial and error designing a Neural Network, lots of parameters to tweak as one tries to get the data to correlate. Can machine learning and neural networks help us design better neural networks?

Great video on Deep Learning  and Multilayer Neural Networks,  Deep Learning Summer School, Montreal 2015

Visual features: From Fourier to Gabor,  Deep Learning Summer School, Montreal 2015

What is a MOOC? —  a study course made available over the Internet for free to a large number of people

The Columbia River in the snow

The Columbia River in the snow

Creativity and more…

To be creative, one must start somewhere. Creativity is bringing things together. But just brain-storm and consider things even outside the box, things that are impossible to achieve (so we think).  Try a different mathematical solution, a different marketing or engineering solution.  Success sometimes comes by just tweaking things just a little.

Consider the configuration of artificial intelligence and deep learning neural networks. There are so many ways to configure and initialize them.  So many different types of neural nodes and ways to mathematically connect the nodes also called Perceptrons.

A person who never made a mistake never tried anything new — possibly said by Albert Einstein

Here is a great set of project results to consider for stimulating creativity in Machine Learning…
Higgs Boson Machine Learning Challenge

near the Columbia River in the snow

near the Columbia River in the snow

Machine Learning (AI) – some great courses

Maybe the top classes in Machine learning and Artificial Intelligence.
Machine Learning – Stanford University | Coursera Andrew Ng, uses Octave/Matlab
Intro to Machine Learning Course | Udacity taught by Sebastian Thrun and Katie Malone, uses sk-learn, a Python platform
Deep Learning by Google | Udacity, uses sk-learn, a Python platform

Intro to Artificial Intelligence Class Online – Udacity

Intro to Artificial Intelligence – Udacity

Artificial Intelligence (AI) – Columbia University

Machine Learning and algorithms – Columbia University

Machine Learning Engineer Nanodegree | Udacity
Unsupervised Feature Learning and Deep Learning — Andrew Ng, Stanford
Machine Learning: Google’s Vision – Google I/O 2016
For introductory material on machine learning and neural networks:
‘Deep Learning’ at Berkeley,

Great AI Platforms – Artificial Intelligence

Great Google AI Platform: – Machine Intelligence
Also look at: Google Cloud Machine Intelligence and Google Cloud Machine Learning

Some AI platforms:

A Tour of Machine Learning Algorithms

Most platforms seem to use python programming language (or c++) for the interface.

Some Terms

  • Data Analysis, Data Mining, Machine Learning and Mathematical Modeling are tools.
  • Artificial Intelligence — automatic ways of reasoning.
  • Machine Learning — turning data in to information and making decisions.
  • Data Mining — extracting information from lots of data
  • Artificial neural network (ANN)
  • Pattern recognition



Creativity Thoughts from my Tweets

Creativity thoughts from my tweets

Creativity starts with having fun trying to meet a real need.

Thinking of others improves creativity since usually it benefits others or society.

Creativity peaks in the morning when fresh, in the shower, out for a run or a walk in a new place. Complaining keeps us from moving forward, but Use adversity to stimulate creativity.

Creative people guard their time, sometimes say no to new projects.

Seeing patterns and making connections lead to insights and ideas.

Negative thoughts impair creativity, but sometimes the negative tension can urge us on to a new creative solution.

Creativity is thinking up new things.


Interface Technology for the Brain

Today people are developing technology to interface directly with the brain. One of the more safe ones (besides just a keyboard and mouse) is EEG (ElectroEncephaloGram). With EEG electrical probes are attached to the head on the surface. These connections are wired to a pre-amplifier or instrument amplifier, digitized and feed into a PC or Mac computer for analysis.

Several very low frequency brain waves have been discovered. The waves are actually radio waves (electro-magnetic) but the magnetic component is so small that just the electrical component is picked up.

Here is a few great articles:
Brain Computer Interfaces
Interface Tech for the Brain