Introduction to Artificial Intelligence and Deep LearningMichael L. George, Sr. CEO AI Technologies, Inc.

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We are all familiar with task explicit computer programs such as those used in accounting, payroll, ERP, etc. But our brain does not appear to use task explicit programs, rather it uses a learning algorithm. You may have read about a famous experiment using a ferret. The nerve leading to the animal’s auditory cortex was cut and replaced with its optic nerve. The result was the ferret staggered around until it learned to see by experience, using the auditory cortex rather than the visual cortex! In other words, the brain has a learning algorithm, whether it is to see, hear, etc. The accompanying graphic (see below) depicts a comparison between the human brain and Neural Network Deep Learning. Neural Networks can be used to minimize the cost and cycle time of Job Shop Manufacturing, Product Development, etc. Neural Networks accomplish this using “Deep Learning” … the process giving computers the ability to learn without being explicitly programmed.

So, how then does a machine learn? It “gains knowledge through being taught by experience” just like the brain. For example, when we try to optimize the sequence of manufacturing products resulting in the lowest cost with on-time delivery, we find this problem is mathematically unsolvable. A near-perfect solution can be found by a laborious iterative method known as “Branch and Bound”. For real factories this process takes too long on even the fastest PC. Instead, we solve several thousand sample problems in manufacturing using the “Cloud” for Branch and Bound computing. We pose one of these training problems to the Neural Network which then provides its’ estimate of which sequence of part number production yields the lowest cost. This is shown in the brain graphic as “output”. This output is compared to the “Cloud” training example output. The error made by the Neural Network drives a “loss function” which is reduced by changing the weights (W) of each node via the arrows leading from Output back to each W, until its’ output matches the training example. This is known as “Back Propagation” and is the Neural Network’s learning algorithm. The Neural Network thus learns from this training example and thousands of others calculated in the “Cloud”. Now if the Neural Network encounters a situation which is different from any of these training examples, it provides an accurate 75% lower setup time than a random setup output sequence in less than a second!

Neural Networks have been studied for many decades but have only become a proven practical tool since 2012. What has happened? We are interested in extracting features of the data, characteristics which can be used to label the data. This was previously a manual process that took months of effort. Deep Learning made it possible to eliminate this manual process. Note on the graphic the notation “ith Hidden Layer”. This layer is “hidden” because it is neither an input nor an output. Now by increasing the “depth” to “i = 1,2 or 3”, three or more hidden layers or more, we’re able to automatically extract features of the data which is known as Deep Learning. This was an eminently more practical solution than manual Machine Learning methods with one layer. These weights are retained and constitute the “learning” of the machine. Given the many competing methods, it took Professor Geoff Hinton et al of the University of Toronto about 5 years to win over the long-term critics of Neural Networks by demonstrating its superior capability in a pattern recognition contest in 2012.

Deep Learning Neural Networks are thus a practical tool which continues to display its’ superior power by way of examples. At the bottom right-hand side of the figure, we provide several examples of the application of Deep Learning. Neural Networks which are now superior to humans in voice and face recognition as well as in classifying malignant or benign tumors and determining which customers should be given loans. The optimization of Job Shop manufacturing discussed in my upcoming book* is far beyond human capability… Deep Learning Neural Networks are thus Super-Human! What then is the role of humans? Human’s are not limited to thinking about just one domain at a time, as is the case with Neural Networks. At the bottom left-hand side of the figure, we note that Human reason can perceive opportunities across several domains, make an informed response and discover utterly new solutions.

*George, M. et al “Lean Six Sigma in the Age of Artificial Intelligence” McGraw-Hill Jan 2019.

The Brain: Inspiration for the Neural Network and Deep Learning

OUTPUT

OUTPUT

OUTPUT

OUTPUT

Sensory Input Human BrainReasoning

Deep Learning Neural NetworkSuper-Human Performance

SIGHT

SOUND

SMELL

TASTE

TOUCH

ERP/MRP DATA

Accounting DATA

“Cloud” Training

Customer Delivery Schedule

Image Data

HUMAN INTELLIGENCE ARTIFICIAL INTELLIGENCE

Valuable OutputPerception

Informed ResponseNew Discovery

Voice Recognition

Face Recognition

Super-Human Performance In:

ith Hidden Layer

ai

bi

ci

Cancer Diagnosis

Bank Loan Defaults

Job Shop Manufacturing: • Lowest Cost • On-time Delivery • 10-20% More Capacity

Data Input

w

w

w

Human beings excel in Perception and New Discoveries. Neural Networks (NN) learn from experience, and are not explicitly programmed. NN receive “training solutions” that are computed offline. The NN “learns” when its’ solution is not as low in cost as the training solution, and modifies the Weights w of its output to achieve the lowest cost. When a new problem arises, this learning allows a fast solution to achieve minimum cost by the NN.

Michael George, Sr.CEOMichael L. George, Sr. is the founder of AI Technologies, Inc. and the George Group Consultants. He received a B.A. in Physics from the University of California, and an M.S. in Physics from the University of Illinois. His newest book “Lean Six Sigma in the Age of Artificial Intelligence” will be published in January 2019.

Michael George, Jr.PresidentMichael George, Jr. is President of AI Technologies, Inc. Prior to that he was President of Blackland Group, LLC, and Vice President of George Group Federal Services. Mr. George earned a B.A. in Physics and a Master in Business Administration from Southern Methodist University.

Dan BlackwellVice PresidentDaniel K. Blackwell received a BS IE. He has built an international reputation as a leading expert in AI Pull systems design and AI Generic Setup Reduction*. A Lean Six Sigma Master Blackbelt, he has worked with Mike George for over 20 years at George Group Consultants.

* “Lean Six Sigma in the Age of Artificial Intelligence” Chapters 2 and 9.

Mitch JohnsonVice PresidentMitch Johnson is Vice President of AI Technologies, Inc. and a former Vice President with George Group Consulting. He was the Managing Partner leading the implementation of Lean Six Sigma in NAVAIR. Mitch holds an MBA and BSIE degree and is a deeply experienced LSS practitioner committed to the practical application of AI solutions for driving business and operations performance improvement.

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