Cognitive Services with Dr. Jerry Smith

02/18/22 • 31 min

Dr. Jerry Smith welcomes you to another episode of AI Live and Unbiased to explore the breadth and depth of Artificial Intelligence and to encourage you to change the world, not just observe it!

Dr. Jerry is talking today about Cognitive Services, which is the second part of the evolutionary optimized digital surrogate-based predictions causal AI-driven digital transformation life cycle. Listen to this episode to find what Cognitive Services are, why they are important to businesses, how to identify four key cognitive attributes to your services, and three of the most important services you should start using today.

Key Takeaways:

Before starting with today’s cognitive services, Dr. Jerry goes back a few years to lead us to where we are now.
- In 2016, cognitive services started, but a lot of them were just promises that did not bring economic value.
- Cognitive services missed the connection with the business side, but causality changed that.
What are Cognitive Services?
- Cognitive relates to conscious and mental activities.
- If you are using AI to help you make decisions that relate to your customers, you need to have someone that relates to Cognitive Psychology and understands the psychology of people and the sociology of groups.
- Examples of cognitive services are Speech and Image recognition, Text to Speech, Speech to Text, and searching through vast amounts of information.
What is Cognitive Computing and what are some of its benefits?
- Cognitive computing is the use of computerized cognitive models to simulate human thought processes.
- One benefit of cognitive computing is that it performs well in complex situations where answers and data are ambiguous and uncertain.
What constitutes cognitive computer systems? Four primary characteristics:
- Cognitive computer systems have to be highly adaptive, they have to be flexible enough to learn as information changes.
- Cognitive computer systems have to be interactive.
- Iterative and stateful. Cognitive computer technology can identify problems by asking questions if the state of the problem is vague or incomplete.
- Cognitive computer systems have to be contextual since understanding the context is probably the most critical process in the causal AI digital transformation cycle.
- Cognitive computing is not AI; it uses AI and AI uses cognitive computing.
- Cognitive computing is used when you are dealing with human characteristics in industries like health care and services. Cognitive computing is more about being human than being a machine.
What is data science?
- Data science is the process of studying data, you always get value out of it.
- Machine learning learns the characteristics of the data in different variables in order to make predictions.
- AI is all about making decisions based on the machine learning models and the predictions of tomorrow and asking what are we going to be doing tomorrow as a consequence of that.
What are some of the top cognitive services today?
- Computer vision: consists of pulling out actual information from images.
- Emotion: Analyzing faces and bodies to detect emotional ranges of mood.
- Face: Identify similar faces.
- Content moderation is automatically moderating text, images, or videos, and has profound importance to our society.
Why are cognitive services important?
- Cognitive services allow us to drive behavioral insights from data.
- Data has no intrinsic value; the value of data comes in how we process it, how we look at it, and what questions we ask (which are very subjective and will give different outcomes).
- Behavior, behavior, behavior.

Stay Connected with AI Live and Unbiased:

Visit our website AgileThought.com

Email your thoughts or suggestions to [email protected] or Tweet @AgileThought using #AgileThoughtPodcast!

Learn more about Dr. Jerry Smith

Dr. Jerry Smith welcomes you to another episode of AI Live and Unbiased to explore the breadth and depth of Artificial Intelligence and to encourage you to change the world, not just observe it!

Key Takeaways:

Before starting with today’s cognitive services, Dr. Jerry goes back a few years to lead us to where we are now.
- In 2016, cognitive services started, but a lot of them were just promises that did not bring economic value.
- Cognitive services missed the connection with the business side, but causality changed that.
What are Cognitive Services?
- Cognitive relates to conscious and mental activities.
- If you are using AI to help you make decisions that relate to your customers, you need to have someone that relates to Cognitive Psychology and understands the psychology of people and the sociology of groups.
- Examples of cognitive services are Speech and Image recognition, Text to Speech, Speech to Text, and searching through vast amounts of information.
What is Cognitive Computing and what are some of its benefits?
- Cognitive computing is the use of computerized cognitive models to simulate human thought processes.
- One benefit of cognitive computing is that it performs well in complex situations where answers and data are ambiguous and uncertain.
What constitutes cognitive computer systems? Four primary characteristics:
- Cognitive computer systems have to be highly adaptive, they have to be flexible enough to learn as information changes.
- Cognitive computer systems have to be interactive.
- Iterative and stateful. Cognitive computer technology can identify problems by asking questions if the state of the problem is vague or incomplete.
- Cognitive computer systems have to be contextual since understanding the context is probably the most critical process in the causal AI digital transformation cycle.
- Cognitive computing is not AI; it uses AI and AI uses cognitive computing.
- Cognitive computing is used when you are dealing with human characteristics in industries like health care and services. Cognitive computing is more about being human than being a machine.
What is data science?
- Data science is the process of studying data, you always get value out of it.
- Machine learning learns the characteristics of the data in different variables in order to make predictions.
- AI is all about making decisions based on the machine learning models and the predictions of tomorrow and asking what are we going to be doing tomorrow as a consequence of that.
What are some of the top cognitive services today?
- Computer vision: consists of pulling out actual information from images.
- Emotion: Analyzing faces and bodies to detect emotional ranges of mood.
- Face: Identify similar faces.
- Content moderation is automatically moderating text, images, or videos, and has profound importance to our society.
Why are cognitive services important?
- Cognitive services allow us to drive behavioral insights from data.
- Data has no intrinsic value; the value of data comes in how we process it, how we look at it, and what questions we ask (which are very subjective and will give different outcomes).
- Behavior, behavior, behavior.

Stay Connected with AI Live and Unbiased:

Visit our website AgileThought.com

Email your thoughts or suggestions to [email protected] or Tweet @AgileThought using #AgileThoughtPodcast!

Learn more about Dr. Jerry Smith

Previous Episode

AI Digital Transformation with Dr. Jerry Smith

Dr. Jerry Smith welcomes you to another episode of AI Live and Unbiased to explore the breadth and depth of Artificial Intelligence and to encourage you to change the world, not just observe it!

In today’s episode, Dr. Jerry is going to talk about AI digital transformation, a process that has evolved into some capabilities that are believed to be transformational. Dr. Jerry is going through the story circle, outlining the reasons why digital transformation is important and its several components.

Key Takeaways:

Challenges in traditional digital transformation:
- Transforming the company digitally (which is not the same as digitally transforming you).
- Digital transformation is a continuous adapting process.
First problems that all organizations have:
- Organizations collect information and store it without seeing the immediate value in storing data.
- What can data tell me? Does any of this data tell me about the behavior of the customer?
The cognitive phase is about the human cognition applied to data; Dr. Jerry explains how it is done through our five senses:
- Vision.
- Voice (we can tell how someone is feeling by their different tones).
- Language processing.
- Sentiment analysis.
- Cognitive search on decision-making.
Causality: All data is interesting, but it is not all important.
- Certain kinds of data are causal to certain kinds of problems.
- Find what is causal to your business concern.
Levers of change:
- If you can find a way to change a variable to go from A to B then you are going to change the output.
- We move from a passive understanding of the world (collecting data) to a deep insight phase where we can now change the variables and learn deeply.
A digital surrogate is a predictor with causal inputs, it is a digital representation of the entity that is important to us.
- We can predict how the world is going to change.
The Optimization Stage:
- Evolutionary computing is an optimization technique that allows us to seek in the complete search base in a very time-friendly way, and find the optimal result.
- We apply evolutionary computing to figure out how to best treat inputs to achieve an optimal outcome.
The Implementation stage:
- We take these inputs and transition them into real-life programs in the real world.
- If you apply this program, you will create change, it is unavoidable.

Stay Connected with AI Live and Unbiased:

Visit our website AgileThought.com

Email your thoughts or suggestions to [email protected] or Tweet @AgileThought using #AgileThoughtPodcast!

Learn more about Dr. Jerry Smith

Next Episode

Four Most Commonly Asked Questions About AI with Dr. Jerry Smith

Dr. Jerry Smith welcomes you to another episode of AI Live and Unbiased to explore the breadth and depth of Artificial Intelligence and to encourage you to change the world, not just observe it!

Dr. Jerry is talking today about questions and answers in the world of data science machinery and artificial intelligence.

Key Takeaways:

What are Dr. Jerry’s favorite AI design tools? Dr, Jerry shares his four primary tools:
- MATLAB. Is a commercial product. It has a home, academic, and enterprise version. MATLAB has toolkits and applications. The Predictive Maintenance Toolbox at MATLAB, especially the preventive failure model is of great value when we want to know why things fail, also by measuring systems performance and predicting the useful life of a product.
- Mathematical Modeling with Symbolic Math Toolbox is useful for algorithm-based environments. It is built on solid mathematics.
- R Programming is Dr. Jerry’s favorite free tool for programming with statistical and math perspectives. R is an open and free source and comes with a lot of applications.
- Python is a great tool for programming and is as capable as R programming to assist us in problem-solving. Python is very useful when you know your work is directed to an enterprise level.
Does Dr. Jerry have any recommended books for causality?
- The Book of Why is foundational for both the businessperson and the data scientist. It provides a historical review of what causality is and why it is important.
- For a deeper understanding of causality, Dr. Jerry recommends Causal Inference in Statistics: A Primer.
Counterfactuals and Causal Inferences: Methods and Principles it is a great tool to think through the counterfactual analysis.
- Behavioral Data Analysis with R and Python is an awesome book for the practitioner who wants to know what behaviors are, how they show up in data, the causal characteristics, and how to abstract behavioral aspects from data.
- Dr. Jerry recommends Designing for Behavior Change, it talks about the three main strategies that we use to help people change their behaviors.
- The seven rules of human behavior can be found in Eddie Rafii’s latest book: Behaviology, New Science of Human Behavior.
Dr. Jerry shares his favorite tools for casual analysis:
- Compellon allows us to do performance analysis, showing the fundamental causal chains in your target of interest. It can be used by analysts. It allows users to do “what-if” analysis. Compellon is a commercial product.
Causal Nexus is an open-source package in Python that has a much deeper look at causal models than Compellon.
BayesiaLab is a commercial tool that is one of the higher-end tools an organization can have. It allows you to work on casual networks and counterfactual events. It is used in AI research.
What skills are needed for data science machinery and AI developers?
- Capabilities can be segmented into Data-oriented, Information-oriented, Knowledge, and Intelligence. These different capabilities are used in many roles according to several levels of maturity.