AI Holistic Adoption for Manufacturing and Operations: Program

(“AI Holistic Adoption for Manufacturing and Operations” is a four-part series which focuses on the executive leadership perspective including key execution topics required for the enterprise digital transformation journey and holistic adoption of AI for manufacturing and operations organizations. Planned topics include: Value, Program, Data and Ethics. Here we address the Program.) 

The Executive Leadership Perspective  

The first article of this series described the fundamental responsibility of executive leaders to focus the enterprise Digital Transformation and AI Adoption on Value. AI Holistic Adoption drives the value focus and is the combination of addressing the needs of the people, processes, and tools associated with the AI solution. This is a critical perspective shift from the comfort of data science to the applicability of true customer engagement.   

Once the executive leader has truly anchored their vision of the enterprise’s digital transformation and AI adoption in this perspective, they must enable their teams by addressing the formation of their analytics program.   

Analytics Program Deliverable  

A key deliverable of the analytics program is the analytics library. It is important for the executive leader to recognize however that enterprise-wide AI Holistic Adoption requires more strategic elements from the analytics program beyond the basic “library of AI algorithms”. 

 To achieve AI Holistic Adoption, the analytics program must address the analytics library development, library portal, analytics design package, security elements and implementation governance. 

Analytics Library Development   

In AI Holistic Adoption, the analytics library is defined as a library of value analytics (VA).  Each VA is associated with user stories and brings specific value. Each VA also has a design package which enables holistic adoption and enterprise level scaling. For most enterprises, the initial step in analytics library development is to review the existing analytics inventory for value, determine alignment with the value objectives of the enterprise, and identify value gaps.    

The executive leader looking to rapidly scale their analytics program will drive their analytics library development to leverage internal teams, cross enterprise development teams and third party analytics developers. Evolving your analytics program to support multi-sourcing is a complex enterprise program endeavor. 

Typically, the starting point for most companies is a highly customized set of dispersed analytics development efforts with pockets of individuals creating analytics. These analytics are known primarily by the originating developing team, “hard coded” in the offer, and are very difficult if not impossible to maintain, upgrade, or reuse. If third party analytics are used or analytics-as-a-service (AaaS) purchased, then the “black box” effect can occur where the company stakeholders do not have visibility into aspects of the analytics performance that are necessary to confirm value and ensure integration, quality, and reliability.  

To scale analytics library development, the analytics program must deliver a suite of mechanisms to give visibility and control over the analytics. This is accomplished with the portal, design package, security elements and implementation governance.  

Library Portal 

The library portal is used to hold the value analytics library and is critical for successful global visibility, access and management of the analytics library. These are key capabilities for the scalable growth of an enterprise analytics program which may include multi-sourcing of analytics.  

The library portal provides visibility and utilization of library content across the enterprise. For the digital transformation and connectivity goals of the enterprise, this is invaluable as it provides teams with the insight into existing cross enterprise analytics capabilities and corresponding access as required.  

The Library Portal must also provide accessibility to the key design elements of the value analytics (VA). The set of design elements is referred to as the analytics design package.  

Analytics Design Package  

The analytics design package defines the use of the value analytics and the combination of design elements necessary to execute, reproduce, transport, and manage the VA. This package addresses the transportability, reproducibility and quality of the VA.  

The analytics design package is a supercharged analytics delivery mechanism which provides development communities with both a standardized analytics design methodology and modularity required to scale, supporting an enterprise level analytics program. Although specifics vary by business, the table below provides an example summary of potential design package components, associated value and stakeholders.  

Table: High level example of component, value and stakeholder 

Analytics Design Package Component Value delivered Stakeholder 
User Story & results Benchmark Mockup demo & results example Data Scientists Marketing/Sales Integration development 
Algorithm Code Date assessment results Data Scientist 
Baseline Data Set Reference input data defines assumptions and criteria associated with input date Data Scientists Marketing/Sales Development SME 
Training Model Analytics Quality and Security Data Scientists/Development  
Simulation Engine Simulation recreation Development 
User Value Configuration Configuration specific to target customer or business segment Development  Marketing/Sales 
Test Suite Analytics results against predefined quality criteria. Data Scientists/Development 

When transporting a VA within the enterprise or bringing in as an AaaS from a third party, the components of the analytics design package set the design standards.  These standards reduce development time, shorten time to market, provide modularity and improve operational efficiencies associated with integration and reuse across the enterprise. 

Security Elements  

The security of the analytics program has multiple facets.  It needs to address basic system-level security, analytics design package security and stakeholder control access security.    

System-level security should follow the necessary enterprise level network and application security requirements. In most cases, the analytics program can leverage established enterprise system security policies.    

Depending on the use cases addressed, the security considerations for the analytics design package are critical for certain elements of the package. The analytics design package must carefully consider each element and typically have a minimum of high security for the training model and the results benchmark.   

Finally, stakeholder control access security enables specific stakeholder roles to have different visibility and control points in the overall analytics. This is key for initial and evolutionary solution execution. 

Implementation Governance 

The implementation governance of the analytics program addresses multi-source implementation, data access and integration.  For each of these the corresponding topics of consistency, quality, deployment and run mode maintenance must be considered.  

The implementation governance specifies how third party or other enterprise organizations’ analytics will go through three phases of development for incorporation into the analytics solution of choice.  The three phases are 1) VA identification & review 2) VA integration into development 3) VA deployment and maintenance.   

Identification and review include use of the analytics design package elements for analysis of fit-for-purpose and a deeper review in a proof-of-concept, if needed.  VA integration into the offer development requires either code incorporation into the offer or creation of transferred data integration points in the system (as with AaaS).  The VA deployment process incorporates key needs of marketing, deployment planning and launch execution topics.  Maintenance is the run mode plan for maintenance of the VA long term. 

Through the VA integration phase of implementation governance and the data suite component of the analytics design package, the analytics program addresses key aspects of big data for overall AI Holistic Adoption.  Data will be the next topic of the AI Holistic Adoption series.  


Netflix App review Topic Modeling

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Hi, all! In the rise of interest in NLP and the advent of evermore accurate algorithms, it is exciting to start entering the world of NLP. Here, I chose Amazon Mobile App reviews which is publicly available in S3 bucket in AWS US East Region. First, let me briefly introduce the background.

Amazon Appstore for Android opened on 3/22/2011 and was made available in nearly 200 countries. Developers are paid 70% of the list price of the app or in-app purchase. The potential client of this project is developers who find the needs of consumers and maintain the quality assurance by debug/manage functionalities in a prompt manner.

Netflix app has one of the most reviews in this dataset. There are 12,566 reviews used for topic modeling and 6,283 hold-out reviews.


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  1. Preprocessing: Remove punctuations/stop words, creating bigrams, part-of-speech tagging with nouns, lemmatizing, and creating a dictionary.
  2. Tune hyperparameters: Choose the number of topics and alpha which determines the degree of mixture of topics in each document that has the highest coherence score.
  3. Select the model based on three criteria: 1) Interpretability, 2) Distinguishability from other topics, and 3) Coherence scores
  4. Label the topics myself based on the relevance of terms which is probability of word appearance given each topic, reading representative documents of each topic, and visualizing word cloud.
  5. Train BERT model with the labelled topics and Compare the results with the topic model

After hyperparameter tuning, I chose LDA-Mallet(which uses Gibbs sampling instead of variational inference) which met the three criteria in the best way. Most of all, the intertopic distance map by pyLDAvis convinced me to go with this model.

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The topics are scattered across the plot and fairly distant from each other. The size of the circle which represents the amount of tokens/bag of words is evenly large. These are ideal conditions for a topic model.

Word clouds are not the most helpful for labelling itself, but it is helpful in detecting the dominance of keywords in each topic. For example, the reviews under ‘User Experience’ has ‘kid’ as the most frequent word. This is because the reviewers writing their user experience, many of them expressed concerns about unrestricted access to contents which can be seen by their kids.

Using these labels predicted by LDA model, I was curious how BERT would classify them with these labels.

Bidirectional Encoder Representations from Transformers (BERT) is a state-of-the-art NLP pre-training technique developed by Google and published in 2018. The model uses transformers which considers different aspects of words such as semantic, syntax, vocabulary, etc while also considering its positions in each sentence. With these attention layers running in parallel with GPU’s, it’s much faster than RNN/LSTM which learns sequentially. There’s an extremely helpful youtube video that explains the strengths of BERT in a nutshell by Leo Dirac at this link.

After running 5 epochs of stochastic gradient descent, I noticed that the model is getting more and more overfit. I couldn’t optimize this time because I didn’t set up cloud or GPU to speed up the training.

LDA-Mallet and BERT have only 43% class agreement with 6,283 unseen reviews. Let’s compare their most representative review in 2D plot. Each color represents a review.

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UMAP 2d-projection of most representative documents’ word embeddings. LDA-Mallet(left) and BERT(right)

On the left, a review tends to contain words that are close to each other except the trouble-shooting-related review and platform/device-related review which are quite polarized. This is a bag-of-words scheme of topic modeling where simply words themselves determine the topic.

In contrast, words don’t simply determine the topic in the right panel. For example, the words ‘past’ and ‘present’ are semantically close to each other, but they end up in different topics depending on what words appear in the review, in other words, its context.

To compare their classifications in a more friendly way, I made some user-interface that takes a review and spews out the topic distribution.

The first review is clearly about trouble-shooting. Both models predict it as trouble-shooting, but BERT predicts with higher probability.

The second review has mixed feelings about the app. LDA predicts as ‘Value’ (which is labeled so with the reviews about the value of subscribing Netflix over the other vendors) with 32.1% and ‘Shows’ with 29%. On the other hand, BERT thinks it’s 53% likely ‘Trouble-shooting’.

The third review is a sarcastic positive review which starts with a rather negative word ‘Warning’. LDA predicts as ‘Shows’ while Netflix predicts 35% likely ‘Trouble-shooting’.

The last review is a sarcastic negative review. LDA predicts it strongly as ‘Service’ while BERT predicts it as ‘Trouble-shooting’.

What conclusions can we make from these differences?

LDA model or other topic models would work well with reviews that have words that are coherent with the context as well. Sarcasm is one type that topic models will not catch often.

The BERT model could not detect the first sarcastic review, but it picked up the negative sentiment from the second sarcastic review. I was surprised that BERT was not confused with strong positive word such as ‘amazing’.

In the upcoming project, I will probably revisit BERT embeddings and use them for detecting sentiment. Better yet, with more sarcastic reviews.


AI Machine Learning Efforts Encounter A Carbon Footprint Blemish

By Lance Eliot, the AI Trends Insider

Green AI is arising.

Recent news about the benefits of Machine Learning (ML) and Deep Learning (DL) has taken a slightly downbeat turn toward pointing out that there is a potential ecological cost associated with these systems. In particular, AI developers and AI researchers need to be mindful of the adverse and damaging carbon footprint that they are generating while crafting ML/DL capabilities.

It is a so-called “green” or environmental wake-up call for AI that is worth hearing.

Let’s first review the nature of carbon footprints (CFPs) that are already quite familiar to all of us, such as the carbon belching transportation industry.

A carbon footprint is usually expressed as the amount of carbon dioxide emissions spewed forth, including for example when you fly in a commercial plane from Los Angeles to New York, or when you drive your gasoline-powered car from Silicon Valley to Silicon Beach.

Carbon accounting is used to figure out how much a machine or system produces in terms of its carbon footprint when being utilized and can be calculated for planes, cars, washing machines, refrigerators, and just about anything that emits carbon fumes.

We all seem to now know that our cars are emitting various greenhouse gasses including the dreaded carbon dioxide vapors that have numerous adverse environmental impacts. Some are quick to point out that hybrid cars that use both gasoline and electrical power tend to have a lower carbon footprint than conventional cars, while Electrical Vehicles (EV’s) are essentially zero carbon emissions at the tailpipe.

Calculating Carbon Footprints For A Car

When ascertaining the carbon footprint of a machine or device, it is easy to fall into the mental trap of only considering the emissions that occur when the apparatus is in use. A gasoline car might emit 200 grams of carbon dioxide per kilometer traveled, while a hybrid-electric might produce about half at 92 grams, and an EV presumably at 0 grams, per EPA and Department of Energy.

See this U.S. government website for detailed estimates about carbon emissions of cars:

Though the direct carbon footprint aspect does indeed involve what happens during the utilization effort of a machine or device, there is also the indirect carbon footprint that requires our equal attention, involving both upstream and downstream elements that contribute to a fuller picture of the true carbon footprint involved. For example, a conventional gasoline-powered car might generate perhaps 28 percent of its total life-time carbon dioxide emissions when the car was originally manufactured and shipped to being sold.

You might at first be normally thinking like this:

  • Total CFP of a car = CFP while burning gasoline

But it should be more like this:

  • Total CFP of a car = CFP when the car is made + CFP while burning gasoline

Let’s define “CFP Made” as a factor about the carbon footprint when a car is manufactured and shipped, and another factor we’ll call “CFP FuelUse” that represents the carbon footprint while the car is operating.

For the full lifecycle of a car, we need to add more factors into the equation.

There is a carbon footprint when the gasoline itself is being generated, I’ll call it “CFP FuelGen,” and thus we should include not just the CFP when the fuel is consumed but also when the fuel was originally processed or generated. Furthermore, once a car has seen its day and will be put aside and no longer used, there is a carbon footprint associated with disposing or scrapping of the car (“CFP Disposal”).

This also brings up a facet about EV’s. The attention of EV’s as having zero CFP at the tailpipe is somewhat misleading when considering the total lifecycle CFP since you should also be including the carbon footprint required to generate the electrical power that gets charged into the EV and then is consumed while the EV is driving around. We’ll assign that amount to the CFP FuelGen factor.

The expanded formula is:

  • Total CFP of a car = CFP Made + CFP FuelUse + CFP FuelGen + CFP Disposal

Let’s rearrange the factors to group together the one-time carbon footprint amounts, which would be the CFP Made and CFP Disposal, and group together the ongoing usage carbon footprint amounts, which would be the CFP FuelUse and CFP FuelGen. This makes sense since the fuel used and the fuel generated factors are going to vary depending upon how much a particular car is being driven. Presumably, a low mileage driven car that mainly sits in your garage would have a smaller grand-total over its lifetime of the CFP consumption amount than would a car that’s being driven all the time and racking up tons of miles.

The rearranged overall formula is:

  • Total CFP of a car = (CFP Made + CFP Disposal) + (CFP FuelUse + CFP FuelGen)

Next, I’d like to add a twist that very few are considering when it comes to the emergence of self-driving autonomous cars, namely the carbon footprint associated with the AI Machine Learning for driverless cars.

Let’s call that amount as “CFP ML” and add it to the equation.

  • Total CFP of a car = (CFP Made + CFP Disposal) + (CFP FuelUse + CFP FuelGen) + CFP ML

You might be puzzled as to what this new factor consists of and why it is being included. Allow me to elaborate.

AI Machine Learning As A Carbon Footprint

In a recent study done at the University of Massachusetts, researchers examined several AI Machine Learning or Deep Learning systems that are being used for Natural Language Processing (NLP) and tried to estimate how much of a carbon footprint was expended in developing those NLP systems (see the study at this link here:

You likely already know something about NLP if you’ve ever had a dialogue with Alexa or Siri. Those popular voice interactive systems are trained via a large-scale or deep Artificial Neural Network (ANN), a kind of computer-based model that simplistically mimics brain-like neurons and neural networks, and are a vital area of AI for having systems that can “learn” based on datasets provided to them.

Those of you versed in computers might be perplexed that the development of an AI Machine Learning system would somehow produce CFP since it is merely software running on computer hardware, and it is not a plane or a car.

Well, if you consider that there is electrical energy used to power the computer hardware, which is used to be able to run the software that then produces the ML model, you could then assert that the crafting of the AI Machine Learning system has caused some amount of CFP via however the electricity itself was generated to power the ML training operation.

According to the calculations done by the researchers, a somewhat minor or modest NLP ML model consumed an estimated 78,468 pounds of carbon dioxide emissions for its training, while a larger NLP ML consumed an estimated 626,155 pounds during training. As a basis for comparison, they report that an average car over its lifetime might consume 126,000 pounds of carbon dioxide emissions.

A key means of calculating the carbon dioxide produced was based on the EPA’s formula of total electrical power consumed is multiplied by a factor of 0.954 to arrive at the average CFP in pounds per kilowatt-hour and as based on assumptions of power generation plants in the United States.

Significance Of The CFP For Machine Learning

Why should you care about the CFP of the AI Machine Learning for an autonomous car?

Presumably, conventional cars don’t have to include the CFP ML factor since a conventional car does not encompass such a capability, therefore the factor would have a value of zero in the case of a conventional car. Meanwhile, for a driverless car, the CFP ML would have some determinable value and would need to be added into the total CFP calculation for driverless cars.

Essentially, it burdens the carbon footprint of a driverless car and tends to heighten the CFP in comparison to a conventional car.

For those of you that might react instantly to this aspect, I don’t think though that this means that the sky is falling and that we should somehow put the brakes on developing autonomous cars, you ought to consider these salient topics:

  • If the AI ML is being deployed across a fleet of driverless cars, perhaps in the hundreds, thousands, or eventually millions of autonomous cars, and if the AI ML is the same instance for each of those driverless cars, the amount of CFP for the AI ML production is divided across all of those driverless cars and therefore likely a relatively small fractional addition of CFP on a per driverless car basis.
  • Autonomous cars are more than likely to be EVs, partially due to the handy aspect that an EV is adept at storing electrical power, of which the driverless car sensors and computer processors slurp up and need profusely. Thus, the platform for the autonomous car is already going to be significantly cutting down on CFP due to using an EV.
  • Ongoing algorithmic improvements in being able to produce AI ML is bound to make it more efficient to create such models and therefore either decrease the amount of time required to produce the models (accordingly likely reducing the electrical power consumed) or can better use the electrical power in terms of faster processing by the hardware or software.
  • For semi-autonomous cars, you can expect that we’ll see AI ML being used there too, in addition to the fully autonomous cars, and therefore the reality will be that the CFP of the AI ML will apply to eventually all cars since conventional cars will gradually be usurped by semi-autonomous and fully autonomous cars.
  • Some might argue that the CFP of the AI ML ought to be tossed into the CFP Made bucket, meaning that it is just another CFP component within the effort to manufacture the autonomous car. And, if so, based on preliminary analyses, it would seem like the CFP AI ML is rather inconsequential in comparison to the rest of the CFP for making and shipping a car.

For those of you interested in trying out an experimental impact tracker in your AI ML developments, there are various tools coming available, including for example this one posted at GitHub that was developed jointly by Stanford University, Facebook AI Research, and McGill University:

As they say, your mileage may vary in terms of using any of these emerging tracking tools and you should proceed mindfully and with appropriate due diligence for applicability and soundness.

For my framework about AI autonomous cars, see the link here:

Why this is a moonshot effort, see my explanation here:

For more about the levels as a type of Richter scale, see my discussion here:

For the argument about bifurcating the levels, see my explanation here:


There’s an additional consideration for the CFP of AI ML.

You could claim that there is a CFP AI ML for the originating of the Machine Learning model that will be driving the autonomous car, and then there is the ongoing updating and upgrading involved too.

Therefore, the CFP AI ML is more than just a one-time CFP, it is also part of the ongoing grouping too.

Let’s split it across the two groupings:

  • Total CFP of a car = (CFP Made + CFP Disposal + CFP ML1) + (CFP FuelUse + CFP FuelGen + CFP ML2)

You can go even deeper and point out that some of the AI ML will be taking place in-the-cloud of the automaker or tech firm and then be pushed down into the driverless car (via Over-The-Air or OTA electronic communications), while some of the AI ML might be also occurring in the on-board systems of the autonomous car. In that case, there’s the CFP to be calculated for the cloud-based AI ML and then a different calculation to determine the CFP of the onboard AI ML.

There are some that point out that you can burden a lot of things in our society if you are going to be considering the amount of electrical power that they use, and perhaps it is unfair to suddenly bring up the CFP of AI ML, doing so in isolation of the myriad of other ways in which CFP arises due to any kind of computer-based system.

In the case of autonomous cars, it is also pertinent to consider not just the “costs” side of things, which includes the carbon footprint factor, but also the benefits side of things.

Even if there is some attributable amount of CFP for driverless cars, it would be prudent to consider what kinds of benefits we’ll derive as a society and weigh that against the CFP aspects. Without taking into account the hoped-for benefits, including the potential of human lives saved, the potential for mobility access to all and including the mobility marginalized, and other societal transformations, you get a much more robust picture.

In that sense, we need to figure out this equation:

  • Societal ROI of autonomous cars = Societal benefits – Societal costs

We don’t yet know how it is going to pan out, but most are hoping that the societal benefits will readily outweigh the societal costs, and therefore the ROI for self-driving driverless autonomous cars will be hefty and leave us all nearly breathless as such.


How to Use Feature Extraction on Tabular Data for Machine Learning

Machine learning predictive modeling performance is only as good as your data, and your data is only as good as the way you prepare it for modeling.

The most common approach to data preparation is to study a dataset and review the expectations of a machine learning algorithm, then carefully choose the most appropriate data preparation techniques to transform the raw data to best meet the expectations of the algorithm. This is slow, expensive, and requires a vast amount of expertise.

An alternative approach to data preparation is to apply a suite of common and commonly useful data preparation techniques to the raw data in parallel and combine the results of all of the transforms together into a single large dataset from which a model can be fit and evaluated.

This is an alternative philosophy for data preparation that treats data transforms as an approach to extract salient features from raw data to expose the structure of the problem to the learning algorithms. It requires learning algorithms that are scalable of weight input features and using those input features that are most relevant to the target that is being predicted.

This approach requires less expertise, is computationally effective compared to a full grid search of data preparation methods, and can aid in the discovery of unintuitive data preparation solutions that achieve good or best performance for a given predictive modeling problem.

In this tutorial, you will discover how to use feature extraction for data preparation with tabular data.

After completing this tutorial, you will know:

Feature extraction provides an alternate approach to data preparation for tabular data, where all data transforms are applied in parallel to raw input data and combined together to create one large dataset.
How to use the feature extraction method for data preparation to improve model performance over a baseline for a standard classification dataset.
How to add feature selection to the feature extraction modeling pipeline to give a further lift in modeling performance on a standard dataset.
Discover data cleaning, feature selection, data transforms, dimensionality reduction and much more in my new book, with 30 step-by-step tutorials and full Python source code.

Let’s get started.

How to Use Feature Extraction on Tabular Data for Data Preparation
How to Use Feature Extraction on Tabular Data for Data Preparation
Photo by Nicolas Valdes, some rights reserved.

Tutorial Overview
This tutorial is divided into three parts; they are:

Feature Extraction Technique for Data Preparation
Dataset and Performance Baseline
Wine Classification Dataset
Baseline Model Performance
Feature Extraction Approach to Data Preparation
Feature Extraction Technique for Data Preparation
Data preparation can be challenging.

The approach that is most often prescribed and followed is to analyze the dataset, review the requirements of the algorithms, and transform the raw data to best meet the expectations of the algorithms.

This can be effective, but is also slow and can require deep expertise both with data analysis and machine learning algorithms.

An alternative approach is to treat the preparation of input variables as a hyperparameter of the modeling pipeline and to tune it along with the choice of algorithm and algorithm configuration.

This too can be an effective approach exposing unintuitive solutions and requiring very little expertise, although it can be computationally expensive.

An approach that seeks a middle ground between these two approaches to data preparation is to treat the transformation of input data as a feature engineering or feature extraction procedure. This involves applying a suite of common or commonly useful data preparation techniques to the raw data, then aggregating all features together to create one large dataset, then fit and evaluate a model on this data.

The philosophy of the approach treats each data preparation technique as a transform that extracts salient features from raw data to be presented to the learning algorithm. Ideally, such transforms untangle complex relationships and compound input variables, in turn allowing the use of simpler modeling algorithms, such as linear machine learning techniques.

For lack of a better name, we will refer to this as the “Feature Engineering Method” or the “Feature Extraction Method” for configuring data preparation for a predictive modeling project.

It allows data analysis and algorithm expertise to be used in the selection of data preparation methods and allows unintuitive solutions to be found but at a much lower computational cost.

The exclusion in the number of input features can also be explicitly addressed through the use of feature selection techniques that attempt to rank order the importance or value of the vast number of extracted features and only select a small subset of the most relevant to predicting the target variable.

We can explore this approach to data preparation with a worked example.

Before we dive into a worked example, let’s first select a standard dataset and develop a baseline in performance.


Machine Learning and Artificial Intelligence in Radar Technology

Technology is always changing. A large percentage of it will make our lives easier by enhancing how we learn or go about our daily jobs in ways that were never thought before. Artificial intelligence and machine learning stand at the forefront of technology’s future, including their use in radar technology. The purpose of this article is to define what AI and machine learning are, how they relate to each other and what their role may be in radar technology.

Simply put, artificial intelligence is technology that incorporates human intelligence to machines. This is accomplished by the machine following a set of problem-solving algorithms to complete tasks.

The roots of AI are rooted in different research disciplines, including computer science, futures and philosophy. AI research is separated into streams that relate to the AI application’s objective of “thinking vs. acting” or “human-like decision vs. ideal, rational decision.” This utilizes four research currents:

1)Cognitive Modeling – thinking like a human
2) Turing Test – acting like a human when interacting with humans
3) Laws of Thought – a weak AI pretends to think while a strong AI is mind that has mental states
4) Rational Agent – the intelligence is produced through the act of agents that are characterized by five traits that include:
Operating autonomously
Perception of their environment
Persisting over an extended time period
Adapting to change
Creating and pursuing goals
Artificial intelligence agents can be categorized into four different types:

1) Simple reflex agent that reacts to sensor data
2) Model-based reflex agent that considers the agent’s internal state
3) Goal-based agent that determines the best decision to achieve its goals based on binary logic
4) Utility-based agent whose function is to maximize its utility
5) Any of the four agents can become a learning agent through the extension of its programming.

The term machine learning is used to describe techniques that can be used to solve a variety of real-world problems by using computer systems that are able to solve problems through learning instead of being programmed to solve problems.

Some machine learning systems are able to work without constant supervision. Others use supervised learning techniques that apply an algorithm on a set of known data points to gain insight on an unknown set of data to construct a model.

A third type, reinforcement learning continually learns from its observations that are obtained through interacting with its environment through iteration.

Creating a machine learning model typically employs three main phases:

Model initiation where the user defines the problem, prepares and processes the chosen data set and chooses the applicable machine learning algorithm
Performance estimation where various parameter combinations that describe the algorithm are validated and the best performing one is chosen
Deployment of model to begin solving the task on the unseen data
Machine learning adapts and mimics the cognitive abilities of human beings, but in an isolated manner.

Despite their differences, there is some confusion regarding what each technology does. This confusion is often exacerbated by the fact that both terms are often mistakenly used interchangeably. In reality, AI depends on machine learning to accomplish its goals.


Research reflects how AI sees through the looking glass

Text is backward. Clocks run counterclockwise. Cars drive on the wrong side of the road. Right hands become left hands.

Intrigued by how reflection changes images in subtle and not-so-subtle ways, a team of Cornell University researchers used artificial intelligence to investigate what sets originals apart from their reflections. Their algorithms learned to pick up on unexpected clues such as hair parts, gaze direction and, surprisingly, beards — findings with implications for training machine learning models and detecting faked images.

“The universe is not symmetrical. If you flip an image, there are differences,” said Noah Snavely, associate professor of computer science at Cornell Tech and senior author of the study, “Visual Chirality,” presented at the 2020 Conference on Computer Vision and Pattern Recognition, held virtually June 14-19. “I’m intrigued by the discoveries you can make with new ways of gleaning information.”

Zhiqui Lin is the paper’s first author; co-authors are Abe Davis, assistant professor of computer science, and Cornell Tech postdoctoral researcher Jin Sun.

Differentiating between original images and reflections is a surprisingly easy task for AI, Snavely said — a basic deep learning algorithm can quickly learn how to classify if an image has been flipped with 60% to 90% accuracy, depending on the kinds of images used to train the algorithm. Many of the clues it picks up on are difficult for humans to notice.
For this study, the team developed technology to create a heat map that indicates the parts of the image that are of interest to the algorithm, to gain insight into how it makes these decisions.

They discovered, not surprisingly, that the most commonly used clue was text, which looks different backward in every written language. To learn more, they removed images with text from their data set, and found that the next set of characteristics the model focused on included wrist watches, shirt collars (buttons tend to be on the left side), faces and phones — which most people tend to carry in their right hands — as well as other factors revealing right-handedness.

The researchers were intrigued by the algorithm’s tendency to focus on faces, which don’t seem obviously asymmetrical. “In some ways, it left more questions than answers,” Snavely said.

They then conducted another study focusing on faces and found that the heat map lit up on areas including hair part, eye gaze — most people, for reasons the researchers don’t know, gaze to the left in portrait photos — and beards.

Snavely said he and his team members have no idea what information the algorithm is finding in beards, but they hypothesized that the way people comb or shave their faces could reveal handedness.
“It’s a form of visual discovery,” Snavely said. “If you can run machine learning at scale on millions and millions of images, maybe you can start to discover new facts about the world.”

Each of these clues individually may be unreliable, but the algorithm can build greater confidence by combining multiple clues, the findings showed. The researchers also found that the algorithm uses low-level signals, stemming from the way cameras process images, to make its decisions.

Though more study is needed, the findings could impact the way machine learning models are trained. These models need vast numbers of images in order to learn how to classify and identify pictures, so computer scientists often use reflections of existing images to effectively double their datasets.

Examining how these reflected images differ from the originals could reveal information about possible biases in machine learning that might lead to inaccurate results, Snavely said.

“This leads to an open question for the computer vision community, which is, when is it OK to do this flipping to augment your dataset, and when is it not OK?” he said. “I’m hoping this will get people to think more about these questions and start to develop tools to understand how it’s biasing the algorithm.”

Understanding how reflection changes an image could also help use AI to identify images that have been faked or doctored — an issue of growing concern on the internet.

“This is perhaps a new tool or insight that can be used in the universe of image forensics, if you want to tell if something is real or not,” Snavely said.


AI Being Applied to Improve Health, Better Predict Life of Batteries

AI techniques are being applied by researchers aiming to extend the life and monitor the health of batteries, with the aim of powering the next generation of electric vehicles and consumer electronics.

Researchers at Cambridge and Newcastle Universities have designed a machine learning method that can predict battery health with ten times the accuracy of the current industry standard, according to an account in ScienceDaily. The promise is to develop safer and more reliable batteries.

In a new way to monitor batteries, the researchers sent electrical pulses into them and monitored the response. The measurements were then processed by a machine learning algorithm to enable a prediction of the battery’s health and useful life. The method is non-invasive and can be added on to any battery system.

The inability to predict the remaining useful charge in lithium-ion batteries is a limitation to the adoption of electric vehicles, and annoyance to mobile phone users. Current methods for predicting battery health are based on tracking the current and voltage during battery charging and discharging. The new methods capture more about what is happening inside the battery and can better detect subtle changes.

“Safety and reliability are the most important design criteria as we develop batteries that can pack a lot of energy in a small space,” stated Dr. Alpha Lee from Cambridge’s Cavendish Laboratory, who co-led the research. “By improving the software that monitors charging and discharging, and using data-driven software to control the charging process, I believe we can power a big improvement in battery performance.”

Dr. Alpha Lee, Cavendish Laboratory, Cambridge University

The researchers performed over 20,000 experimental measurements to train the model in how to spot signs of battery aging. The model learns how to distinguish important signals from irrelevant noise. The model learns which electrical signals are most correlated with aging, which then allows the researchers to design specific experiments to probe more deeply why batteries degrade.

“Machine learning complements and augments physical understanding,” stated co-author Dr Yunwei Zhang, also from the Cavendish Laboratory, in .”The interpretable signals identified by our machine learning model are a starting point for future theoretical and experimental studies.”

Department of Energy Researchers Using AI Computer Vision Techniques

Researchers at the Department of Energy’s SLAC National Accelerator Laboratory are using AI computer vision techniques to study battery life. The scientists are combining machine learning algorithms with X-ray tomography data to produce a detailed picture of degradation in one battery component, the cathode, according to an account in SciTechDaily. The referenced study was published in Nature Communications.

Dr. Yunwei Zhang, Cavendish Laboratory, Cambridge University

For cathodes made of nickel-manganese-cobalt (NMC) particles are held together by a conductive carbon matrix. Researchers have speculated that a cause of battery performance decline could be particles breaking away from that matrix. The team had access to advanced capabilities at SLAC’s Stanford Synchrotron Radiation Lightsource (SSRL), a unit of the Department of Energy operated by Stanford University, and the European Synchrotron Radiation Facility (ESRF), a European collaboration for the advancement of X-rays, based in Grenoble, France. The goal was to build a picture of how NMC particles break apart and away from the matrix, and how that relates to battery performance loss.

The team turned to computer vision with AI capability to help conduct the research. They needed a machine learning model to train the data in how to recognize different types of particles, so they could develop a three-dimensional picture of how NMC particles, large or small, break away from the cathode.

The authors encouraged more research into battery health. “Our findings highlight the importance of precisely quantifying the evolving nature of the battery electrode’s microstructure with statistical confidence, which is a key to maximize the utility of active particles towards higher battery capacity,” the authors stated.


AI is Beating the Hype With Stunning Growth

Follow the money. It is true in politics, business, and investing.

Gartner, a global IT research and advisory company, surveyed 3,000 CIOs operating in 89 countries in January. The Stamford, Conn., firm found that AI implementations grew 37% during 2018, and 270% over the last four years.
This is a trend investors should embrace. That’s because it is going to last for a while. And it’s going to make a lot of people very rich.
Investors have soured on AI recently. Self-driving cars, smart cities, and robotics keep getting smacked down as idealistic hype. That’s mostly because their implementations are decades away … or that these ideas are expensive solutions looking for problems.
So say the critics, anyway.
They point to once-high-flying stocks like Nvidia, which just saw its share price get cut in half because of slowing demand for cutting-edge gear and software.
However, that assessment is lazy. It also misses the point.
AI is a digital transformation story. Corporate managers realize AI software can help automate large parts of the enterprise, increasing productivity and saving a boatload of money.
It is true that machines will not be able to wholly replace complex human decision-making anytime soon. But the software is more than sufficient to processes mundane, repetitive tasks. And machine learning, a type of data science, can help humans see important patterns they might otherwise miss.
So companies are going all-in.
They are deploying software bots online, along with customer-relationship software to help service reps assist customers.
Executives are using integrated suites and data analytics to manage projects, workflows, payrolls and human resources.
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Devices in IOT

Components of IoT

IoT is becoming the trend of nowadays. Sooner or later it will not just take over the industrial sector but also impact the daily household. When designing an IoT ecosystem, there are certain factors which we need to bear in mind. Security, devices to be used i.e. sensors, microcontroller, gateways and cloud computing. The devices or “things” play a vital role. Right from a refrigerator door to a coffee machine; will be connected to the Internet.  With rapid advancement in technology and 5G’s demand in the future, it is believed by the year 2021, approximately 20 billion devices will be connected to the internet.

What is IoT basically? A network where not only a vehicle, a home appliance like fan or air conditioner but also embedded systems like electronics, software, sensors which enable exchange or transfer of data over wireless technologies like Wi-Fi, ZigBee, Bluetooth etc. IoT in short will reduce human effort and increase our dependency on machines.

In IoT, the major components are Sensors, Gateway or microcontrollers, connectivity, analytics or data processing and cloud computing.


iot blog1

  • Sensors: They are the “things” in an IoT system. They are responsible for collecting and transmitting real-time data to the microcontroller. Sensors are used to detect physical changes for example, temperature, humidity or pressure. Here are some features which should be the basis of a good sensor:
  • It should not be sensitive to the phenomena it measures
  • It should not detect other physical changes apart from its designation for example, DHT11 is designed to sense the temperature and humidity of its surroundings. If it starts determining the luminosity then its problem which needs attention.
  • It should not modify the readings during the measurement process.


There are several properties or characteristics one should keep in mind while selecting a sensor:


Characteristics of a sensor
Response and Recovery Time


  • Microcontrollers or Gateways as they are commonly known. Like in a human body the brain controls actions and movements, similarly microcontroller acts as the brain of an IoT system.


Why should we use a microcontroller in an IoT system?

  • Simplicity: Programming a microcontroller or a setting up is no difficult job. Also, interfacing sensors with a microcontroller is easy.
  • Security: Code is written on a “bare metal” in a microcontroller which results in very little attack and maintains a secure environment.
  • Cost: Microcontroller are very cost-effective. With minimum cost they offer you simplicity and security.

Raspberry Pi, Arduino Uno, NodeMCU are a few examples of microcontrollers.

The factors one should keep in mind while selecting a Microcontroller:

  1. Compatibility: Will our Microcontroller support the sensors/ actuators? Depending upon how sensors are being, you should decide the number ports required.
  2. Architecture: If your Microcontroller will be able to handle complex programming, what are the functional requirement of your application and also the power it should compute for the application to run.
  • Memory: Choosing the microcontroller with enough memory size is of utmost importance, in order to save time and money.
  1. Availability: A thorough research is a must about the availability of the microcontroller and the quantity. Selecting a correct microcontroller during the initial stages of your project is important which can help scale your application.
  2. Power: Energy efficiency plays a key role in designing an IoT system. How much power does it require, will it need to be wired or whether batteries are required.


Hence, this component of the IoT needs to be the most secured part as it analyses, processes data from thousands of sensors and acts a gateway to the cloud. Microcontroller (MCU) should also have the capability to host the process, store data i.e. act like a memory and provide a secured operating system.


  • Analytics or data processing or Data Analytics play another significant role in an IoT system. Drawing conclusions from big or small data is basically data analytics. It will play an integral part in the development of IoT. The following points enlist the effect it will have on the businesses.


Volume: The shear amount of data to analysed will be huge in an IoT application. Real-time data from many sensors or actuators will require data analytics.


Structure: The data coming from sensors will be structured, semi-structured or unstructured. This will require data to be analysed on a bigger scale.


Revenues: Data analytics will help us provide an insight of what the customer demands and expects from the analytics. This will increase or generate revenues for the same.


Competition: As we know IoT is going to be the future. It provides freedom and better performance. Hence, by offering data analytics, one can upgrade his or her business.



  • Cloud computing: If Microcontroller is the brain of an IoT system then Internet is the heart of an IoT system. There are many ways to connect to the Internet for example Wi-Fi, Bluetooth. Cloud computing is a sector vital in the evolution of IoT. If IoT provides the data then cloud provides the path for the data to travel.


Cloud computing’s sole motto is to enable IoT or users to access data from remote parts of the world through its storage option. Also, for the developers to work from different parts of the world. Cloud computing is also economically viable as it has minimum pay charges which depends on the cloud model. For example, Microsoft Azure Cloud Services, in their free trial provide upto 8,000 messages per day. This will encourage IoT companies or start-ups and in turn reduce costs.


Some of the cloud computing platforms are:








Book: Internet of Things by Dr. Jeeva Jose








Improving Verifiability in AI Development


We’ve contributed to a multi-stakeholder report by 58 co-authors at 30 organizations, including the Centre for the Future of IntelligenceMilaSchwartz Reisman Institute for Technology and SocietyCenter for Advanced Study in the Behavioral Sciences, and Center for Security and Emerging Technologies. This report describes 10 mechanisms to improve the verifiability of claims made about AI systems. Developers can use these tools to provide evidence that AI systems are safe, secure, fair, or privacy-preserving. Users, policymakers, and civil society can use these tools to evaluate AI development processes.


While a growing number of organizations have articulated ethics principles to guide their AI development process, it can be difficult for those outside of an organization to verify whether the organization’s AI systems reflect those principles in practice. This ambiguity makes it harder for stakeholders such as users, policymakers, and civil society to scrutinize AI developers’ claims about properties of AI systems and could fuel competitive corner-cutting, increasing social risks and harms. The report describes existing and potential mechanisms that can help stakeholders grapple with questions like:

  • Can I (as a user) verify the claims made about the level of privacy protection guaranteed by a new AI system I’d like to use for machine translation of sensitive documents?
  • Can I (as a regulator) trace the steps that led to an accident caused by an autonomous vehicle? Against what standards should an autonomous vehicle company’s safety claims be compared?
  • Can I (as an academic) conduct impartial research on the risks associated with large-scale AI systems when I lack the computing resources of industry?
  • Can I (as an AI developer) verify that my competitors in a given area of AI development will follow best practices rather than cut corners to gain an advantage?

The 10 mechanisms highlighted in the report are listed below, along with recommendations aimed at advancing each one. (See the report for discussion of how these mechanisms support verifiable claims as well as relevant caveats about our findings.)

Institutional Mechanisms and Recommendations

  1. Third party auditing. A coalition of stakeholders should create a task force to research options for conducting and funding third party auditing of AI systems.
  2. Red teaming exercises. Organizations developing AI should run red teaming exercises to explore risks associated with systems they develop, and should share best practices and tools.
  3. Bias and safety bounties. AI developers should pilot bias and safety bounties for AI systems to strengthen incentives and processes for broad-based scrutiny of AI systems.
  4. Sharing of AI incidents. AI developers should share more information about AI incidents, including through collaborative channels.

Software Mechanisms and Recommendations

  1. Audit trails. Standard setting bodies should work with academia and industry to develop audit trail requirements for safety-critical applications of AI systems.
  2. Interpretability. Organizations developing AI and funding bodies should support research into the interpretability of AI systems, with a focus on supporting risk assessment and auditing.
  3. Privacy-preserving machine learning. AI developers should develop, share, and use suites of tools for privacy-preserving machine learning that include measures of performance against common standards.

Hardware Mechanisms and Recommendations

  1. Secure hardware for machine learning. Industry and academia should work together to develop hardware security features for AI accelerators or otherwise establish best practices for the use of secure hardware (including secure enclaves on commodity hardware) in machine learning contexts.
  2. High-precision compute measurement. One or more AI labs should estimate the computing power involved in a single project in great detail and report on lessons learned regarding the potential for wider adoption of such methods.
  3. Compute support for academia. Government funding bodies should substantially increase funding for computing power resources for researchers in academia, in order to improve the ability of those researchers to verify claims made by industry.