AI, ML and Deep Learning “cheat sheet” for the busy professionals
July 9, 2018
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Can you distinguish between “Deep Learning” and “Machine Learning.”? What about “artificial intelligence”? Let’s start with the definition of artificial intelligence because this is where it all begins.

Artificial intelligence (AI), “the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings.”

Simply put, AI is a technology that enables a machine (computer/robot) to make an intelligent decision or take action. Academically speaking, “AI technology enables an intelligent agent to cognitively perceive its environment and correspondingly attempt to maximize its probability of success of target action.” In this context, a hardware module, software, a robot, or an application is “an intelligent agent,” The discipline of AI is probably older than you 🙂 Following excerpt is from 1953.

“We propose that a two-month, 10-man study of artificial intelligence be carried out during the summer of 1956 at Dartmouth College in Hanover, New Hampshire. The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves. We think that a significant advance can be made in one or more of these problems if a carefully selected group of scientists work on it together for a summer”.

Moving on to Machine learning (ML) which has evolved from pattern recognition and computational learning theory in AI. ML is the capability of a computer to learn without being explicitly programmed. It is functionality to learn and make predictions from data.

Simply put Machine learning, is concerned with the implementation of computer software that can learn autonomously. When provided with sufficient data, a machine learning algorithm can learn to make predictions or solve problems, such as identifying objects in pictures or winning at particular games, for example. Here is a reasonable definition of ML.

 “Machine Learning is the science of getting computers to learn and act like humans do, and improve their learning over time in autonomous fashion, by feeding them data and information in the form of observations and real-world interactions.”

  • “Machine learning is the science of getting computers to act without being explicitly programmed.” – Stanford
  •  “Machine learning is based on algorithms that can learn from data without relying on rules-based programming.”- McKinsey & Co.
  •  “Machine learning algorithms can figure out how to perform important tasks by generalizing from examples.” – University of Washington
  •  “The field of Machine Learning seeks to answer the question “How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?” – Carnegie Mellon University

A neural network is composed of simple processing nodes, or ‘artificial neurons’, which are connected to one another in layers. Each node will receive data from several nodes ‘above’ it, and give data to several nodes ‘below’ it. Nodes attach a ‘weight’ to the data they receive, and attribute a value to that data. If the data does not pass a certain threshold, it is not passed on to another node. The weights and thresholds of the nodes are adjusted when the algorithm is trained until similar data input results in consistent outputs.

Now let’s talk about “deep learning.”

“Deep learning is a machine learning technique that uses multiple internal layers of nonlinear processing units to conduct supervised or unsupervised learning from data.”

Deep learning literature borrows from neuroscience, and implemented as “a neural network.” You will find academics and practitioners of this field talk about “neurons and perceptrons.” Relax – they are not going to operate on your brain.

Simply put, the nonlinear processing units are commonly referred to as the neurons. Automation became possible due to AI technology. Machines become capable of automated learning and making decisions due to machine learning technology. Moreover, precision details are cognitively noticed in the automated learning process and used in the accurate decision making of complex problems due to deep learning technology.

A more recent variation of neural networks, which uses many layers of artificial neurons to solve more difficult problems. Its popularity as a technique increased significantly from the mid-2000s onwards, as it is behind much of the wider interest in AI today. It is often used to classify information from images, text or sound.

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From “Satisfied” to “Enthusiastic” Customers
July 9, 2018
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Increasingly, organizations are turning to contact centers, as a way to drive competitive advantage, to create a systematic and a sustainable differentiator and to improve customer loyalty, repeat business and to grow market share.

A few years back business executives told us that they saw their call center as a necessary evil. In other words, we have all those pesky customers out there, and we don’t want them bothering our salespeople, our engineers, our R&D people, our marketing people. So, we’re going to put up this call center, and they are going to become a shield between the customers and us.

In other words, call centers were there to absorb all the questions that those customers might have. So the call center was viewed as a supplement to traditional brick-and-mortar facilities. The goal was customer satisfaction.

However, in the new paradigm which has emerged, in the last 3 to 5 years, the call center is increasingly being viewed as a source of value creation. It’s a way to differentiate your company’s products and services. It’s frequently considered as a replacement for traditional brick-and-mortar facilities.

Moreover, the goal often is not to react to the customer, but instead, to stay ahead of customer needs and expectations and ultimately, to create customer enthusiasm – and customer loyalty – and to differentiate your company’s products and services from those of the competition.

Although what you’re selling – regardless of whether it’s building materials or insurance services or any other products or services, to the extent that they might be viewed as a commodity, service or product – the call center has an opportunity to differentiate your products and services from those of the competition.

So, you can think about the service you deliver at the call center at the flipside of the product that you sell within your organization. They are two sides of the same coin. Moreover, in world-class service and support organizations, in world-class call centers, the two become indistinguishable. In other words, the service becomes part of the product. Moreover, high-quality service differentiates the product and drives repeat business, drives positive word-of-mouth referrals and drives customer loyalty.

Satisfied customers are not necessarily loyal customers. They don’t necessarily want to do business with you, and they are unlikely to propagate positive word-of-mouth referrals or be loyal to your channel.

By contrast, enthusiastic customers want to do business with you; they will propagate positive word-of-mouth referrals. So eager customers are the ones that are loyal to you and keep coming back and enable you to grow your business, increase your market share and improve your revenue. Now, what we’ve seen, over time, is that call centers tend to evolve through three stages.

Call centers that are relatively new or immature tend to operate in what we would call the support stage. These are reasonably reactive call centers. They are merely trying to keep the lid on service levels, but they are forever playing catch-up with customer needs and expectations. If you have ever worked in a call center like this, you know that these call centers tend to be somewhat chaotic.

However, in most cases, the call center will begin to recognize that there must be a better way to do things. So, they start to make investments in training and tools and technology that enable them to get ahead of customer needs and expectations and start to anticipate what the customers want and to begin to get more proactive about the quality of service that they deliver and the value of service that they provide through the contact center channel.

Finally, the most successful in this evolution become what we would consider strategic stage call centers. These are the call centers that operate in this new paradigm. They know that they can create value at this interface, they see that customer contact is a potential opportunity to create a competitive advantage. The goal is to stay ahead of customer needs and expectations and to drive loyalty, enthusiasm and to differentiate the company’s products and services.

Now, unfortunately, most call centers still operate in the support stage. They are not aware of the fact that they can potentially become more strategic, more proactive, more preventive and more of a value driver within the organization.

Now, nearly two-thirds, or 64% of call centers worldwide still operate in this support stage. That is, they are very reactive. However, about 31% – nearly one-third – have entered what we call the transitional or the growth stage, where they begin to make substantial investments in training and tools and technology, in an effort to drive value at this interface, in an effort to inspire loyalty and to create a competitive advantage through the call center.

Only about 5% worldwide operate in this strategic stage. Some organizations get there and then, lose their competitive advantage and then slip back into the transitional or the support stage.

So how can you make this transition quickly and efficiently? I have seen many failed attempts and few successful ones. What about you? What do you think works?

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Data Privacy: California Leads the Way
July 9, 2018
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California passed an online-data-privacy bill granting consumers some of the most sweeping and unprecedented privacy protections in the country. The bill goes into effect in 2020,

The California Consumer Privacy Act of 2018 (AB 375) bill will apply only to California consumers. However, internet users in other states will likely see changes.

The bill gives consumers the right to have their personal data deleted; the right to know the commercial purpose for collecting their data; and the categories of sources from which the data are collected. It also prohibits a business from selling the personal data of anybody under the age of 16 unless that child agrees.

The bill gives companies the ability to offer discounts to customers who allow their data to be sold and charge those who opt out a reasonable amount based on how much the company makes selling the information.

Lawmakers say they will likely make alterations to improve the policy before it takes effect. Some privacy advocates are worried that lobbyists for business and technology groups will use that time to water it down.

TechNet, a technology lobbying group, urged lawmakers to improve the law before it takes effect “so it provides meaningful privacy protections for Californians while also allowing all the benefits and opportunities consumers expect from U.S. technology to continue.”

Policymakers around the country looking at what California has done on this issue should understand that the California Legislature’s work is far from finished and that this law remains a work in progress.

The California law is not as expansive as Europe’s General Data Protection Regulation, or G.D.P.R., a new set of laws restricting how tech companies collect, store and use personal data.

Google, Facebook, Verizon, Comcast and AT&T each contributed $200,000 to a committee opposing the proposed ballot measure, and lobbyists had estimated that businesses would spend $100 million to campaign against it before the November election.

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Artificial Intelligence: Transferring biases to machines
February 2, 2018
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Are we transferring our biases to computers? With supervised learning, machines learn what we teach them. Just like our kids often perpetuate our belief systems, biases, and preferences, will machine also carry forward our implicit biases?

Leading research on prejudice, the study of implicit social cognition, highlight the distinction between “controlled” and “automatic” information processing. Cognitive psychologists Shiffrin & Schneider in their seminal paper “Controlled and automatic human information processing: Perceptual learning, automatic attending, and a general theory,” significantly advanced the implicit bias theory. There has been a consensus that:

  • Controlled processing is “voluntary, attention-demanding, and of limited capacity.”
  • Automatic processing occurs “without attention,” has “nearly unlimited capacity,” and “hard to suppress voluntarily.”

Growing body of knowledge on implicit bias and credible studies show that awareness of stereotypes can affect social judgment and behavior in relative independence from how subjects respond to measures of their explicit attitudes. Simply put, an egalitarian, well-behaved man can display semi-sexist tendencies.

Machine learning is fundamental to artificial intelligence, or A.I. In supervised learning, computers learn like a human. AI “modeled on science’s understanding of how the human brain processes and categorizes information.”

In supervised learning, if we teach machines that name “Ibrahim” or “Mohammad” pose a higher security risk, will machine become Islamophobic?

Watch this Facebook Brand Studio production on Artificial Intelligence.


According to the U.S. Bureau of Justice Statistics (BJS) in 2013 black males accounted for 37% of the total male prison population, white males 32%, and Hispanic males 22%. White females comprised 49% of the prison population in comparison to black females who accounted for 22% of the female population. However, the imprisonment rate for black females (113 per 100,000) was 2x the rate for white females (51 per 100,000). Out of all ethnic groups, African Americans, Puerto Rican Americans, and American Indians have some of the highest rates of incarceration. Though, of these groups, the black population is the largest, and therefore make up a large portion of those incarcerated in US prisons and jails. When I look at the following chart, I wonder, if machines are learning that “Black” and “Hispanic” pose a higher risk to the society?

2010. Inmates in adult facilities, by race and ethnicity. Jails, and state and federal prisons.
Race, ethnicity % of US population % of U.S.
incarcerated population
National incarceration rate
(per 100,000 of all ages)
White (non-Hispanic) 64 39 450 per 100,000
Hispanic 16 19 831 per 100,000
Black 13 40 2,306 per 100,000
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TensorFlow is gaining currency!
January 31, 2018

This rock-paper-scissors science experiment is a novel use of TensorFlow.  Last year TensorFlow was seen automating cucumber sorting, finding sea cows in aerial imagery, sorting diced potatoes to make safer baby food, identifying skin cancer, helping to interpret bird call recordings in a New Zealand bird sanctuary, and identifying diseased plants in the most popular root crop on Earth in Tanzania!  #AI #ArtificialIntelligence

Training state-of-the-art machine learning models requires an enormous amount of computation, and researchers, engineers, and data scientists often wait weeks for results. To solve this problem, Google “designed an all-new ML accelerator from scratch — a second-generation TPU, or Tensor Processing Unit — that can accelerate both training and running ML models.”

Each device delivers up to 180 teraflops of floating-point performance, and these new TPUs are designed to be connected into even larger systems. A 64-TPU pod can apply up to 11.5 petaflops of computation to a single ML training task.

We’re extremely excited about these new TPUs, and we want to share this technology with the world so that everyone can access their benefits. That’s why we’re bringing our second-generation TPUs to Google Cloud for the first time as Cloud TPUs on GCE, the Google Compute Engine. You’ll be able to mix-and-match Cloud TPUs with Skylake CPUs, NVIDIA GPUs, and all of the rest of our infrastructure and services to build and optimize the perfect machine learning system for your needs. Best of all, Cloud TPUs are easy to program via TensorFlow, the most popular open-source machine learning framework.

Google’s new Cloud TPUs deliver up to 180 teraflops of machine learning acceleration
Amazon-Google Feud
December 6, 2017

Are you following Google-Amazon feud?

Google has decided to remove YouTube from all Fire TV products and the Echo Show apparently after Amazon’s decided to delist new Nest products.

Amazon claims that “Google is setting a disappointing precedent by selectively blocking customer access to an open website.”

Amazon has also refused to sell Google products such as Chromecast and Google Home. Fire TV owners will lose YouTube January 1.

Should Google pull YouTube from Amazon hardware? #GoogleAmazonFeud