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Math Acceleration

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 I recently posted a video playlist simplifying statistics for non-math majors. You can find it here: https://www.youtube.com/playlist?list=PLXCDMNgGQVJUHxZghu6QMxbGzHKvUJJaZ More importantly, however, is the exciting trend happening in engineering today; that of math acceleration . This trend involves computer chips specifically designed to speed up simple math calculations, such as the ones described in the Statistics videos. What is particularly exciting is the fact that these same chips can also accelerate math calculations for other uses. Applications such as virtual reality (VR) and artificial intelligence (AI) are also moving forwards quickly thanks to these devices. Video 6 from the aforementioned statistics playlist has a short discussion of these applications, as well as specifics regarding a nifty product from NVIDIA (a chip manufacturer) called the Jetson Nano. This is a small computer that fits in the palm of your hand, but besides a conventional computer central proce...
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Auto-Encoder-CODEC This is a continuation of Auto-Encoder or Auto-Associative How To Examples This repository includes "how to" examples of auto-associative neural networks. These are sometimes called Auto-Encoders. These networks have the ability to encode and decode (CODEC) sample data in the most efficient and low-loss way. The minimization of the loss function is accomplished through the use of gradient descent training algorithms. Example 3 - MNIST Digit ConvAutoencoder This "how-to" example comes from a competition held on the kaggle.com website. WHAT IS THE OVERARCHING GOAL? Recognize handwritten digits from the MNIST dataset. Do it with CNN generated "features" or "encoded" version of the input - the center of an autoencoder. The subject matter expert (SME) can recognize digits. They qualitatively rate the amount of data lost due to encoded compression. If the SME can no longer make a classification from a decoded input, they qualitativel...
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Reverse Inference Example Software:  This points to example software on GitHub where you can see some of these concepts in action. Here is the link: https://github.com/prof-nussbaum/Reverse_Inference  Reverse_Inference Working backwards through a deep convolutional network, to recreate the input image - and identify areas for improvement. Please see this article for more details on this technique, that I call "Reading the Robot Mind." https://readingtherobotmind.blogspot.com/2021/03/reading-robot-mind-deep-convolution.html What if we could read the robot's mind? It sounds like a silly statement, but if you could read a person's mind, you could "see" what they were thinking of when they mention a classification. If they say "I am seeing a dog" - reading their mind would give you additional details about the dog, or even perhaps, see what they are seeing. This is the same premise for Artificial Intelligence and Machine Learning. I know that deep conv...
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Reading the Financial Robot's Mind Personification aside... I've used the catch phrase "Reading the Robot's Mind" to point out the need to manage societal impact of automated pattern recognition systems and artificial intelligence (AI). One of the larger societal impacts of such systems is when a AI is the decision maker for financial outcomes that affect people's lives. I was glad to be able to attend several of the informative sessions at the recent Washington DC Financial Technology Week (#dcfintechweek2020) event, and I wanted to share some of the takeaways that I gathered. As you likely already know, machine learning (ML) must be validated before it is put into live practice, and being able to assess the robustness of the ML model is of tantamount importance. Most developers use a standard "in-sample/out-of-sample" testing model for such an assessment, and also often similarly use "data quality validation" and "outcome monitoring ...
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Reading the Robot Mind: Deep Convolution Networks Decoded Even as "Explainable AI" is all the rage, coding/transformation fidelity is a critical success factor. Whether you are using frequency bands from Fourier transforms, statistical features of Wavelet decomposition, or various filters in Convolution Networks - researchers must be able to perform the reverse coding/transformation to see if they have retained sufficient information for classification. Without this, they are only guessing via network architecture trial and error. These tools are sorely lacking in Keras on Tensorflow in Python, so I wrote my own. I would like to see these more generalized and made into public libraries. Question: Who can point me to current work in this area, or can give advice on next steps in my effort? EXAMPLE: In a deep convolution network trained to recognize Bengali handwriting, this diagram examines the 64 filters after six layers of 2 dimensional convolution. It performs the reverse t...
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Robot Bias - Do We Need New Laws? Bias in NLP based chat moderation Natural Language Processing, or NLP, seeks to have machines act as chat-robots in ways similar to that telephone call to a robot we're all so familiar with. NLP seeks to eliminate the "press or say one" and replace it with a text chat session that is, well, more natural. NLP functionality has also expanded its role into that of a robot chat moderator. The moderator's function is to weed out "toxic" statements. As defined by The Conversation AI team, a research initiative founded by Jigsaw and Google (both part of Alphabet), a "toxic" statement is one that is "rude, disrespectful or otherwise likely to make someone leave a discussion." The Conversation AI team [ goes on to explain ] the problem of Hidden Bias. "When [our team] first built toxicity models, they found that the models incorrectly learned to associate the names of frequently attacked identities with toxic...
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"Update Robot Doctor Now?"​ Gulp! In April 2019 the US Food and Drug Administration proposed a new regulatory framework that would cover when a robot doctor gets a software upgrade. Yikes! Naturally, they didn't use the term "Robot Doctor." The FDA calls a robot doctor an "Artificial Intelligence/Machine Learning (AI/ML) Based Software as a Medical Device (SaMD)." Whether you call it a robot doctor or a AI/ML SaMD, they show up in your daily life in the form of devices and machines that use software to inform or even drive medical decisions, and in certain cases, even treat or diagnose without the need for human intervention. These are definitely very different classes of devices! Most of the devices we encounter deal with non-serious healthcare situations, like standing on a bathroom scale that also informs you of your BMI. Sometimes though, the healthcare situations are serious or even critical. It makes sense to treat each of these situations, and a...
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