AI Live and Unbiased, hosted by Dr. Jerry Smith, is the podcast for practitioners and leaders seeking new ways to apply AI, create better outcomes, find new growth for their businesses, and explore AI's impact on society and our lives.
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Digital Transformation in the World of Causal AI with Dr. Jerry Smith
lundi 21 mars 2022 • Durée 35:51
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 Digital Transformation in the new world of Causal AI. Dr. Jerry has spent many years within the area of computer science, technology, data science, machine learning, and AI, seeking always a way to do a better job of decoupling the brilliant successes of marketing and making them even more usable.
Key Takeaways:
Digital transformation is failing business expectations.
More than 60% of CEOs believe that the digital transformation did not meet their expectations.
Digital Transformation is not the same thing as transforming you digitally.
Digital transformation needs to be driven by the will to change a company by changing aspects of its people.
If you don’t like what you see in the company’s data, to see a change in that data, you need to change people.
The most important is not about the products a company sells but about the behavior people have with those products.
Causal AI asks: Why do people do what they do?
Why don’t people buy a company’s products or services?
Dr. Jerry shares the example of why people buy certain Spaghetti Sauce.
To change human behavior the first step is to collect information, which is a cost for any company that decides to do this.
Secondly, what to do with that data? Cognitive and emotional services are what come next in order to interpret that data by creating columns of variables and rows of observations.
What can be done with that digital surrogate that was created? Optimization!
How to Optimize:
Two ways to optimize models are Particle Swarm Optimization or Evolutionary Computing.
Evolutionary computing has the capacity to take suboptimal solutions and combine them to produce an optimal response.
Data Science, Machine Learning, and AI are different.
Data Science is the science of data, it is about how we look at data from a scientific perspective to get insights and learn from it.
Machine learning is predicting the future state for something based upon the current state of that matter.
AI is artificial intelligence; artificial to human beings. AI is about the decision-making process based on predictions of an outcome.
Not all data is the same.
Digital psychologists, sociologists, and anthropologists help transform and codify human behavior.
Causality and Artificial Intelligence with Arni Steingrimsson
Épisode 6
vendredi 11 mars 2022 • Durée 36:40
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 joined today by Arni Steingrimsson, a Data Science Machine Learning and Artificial Intelligence in the U.S. and Mexico. He is a senior-level Data Scientist, who comes from a biomedical field. Arnie and Dr. Jerry are talking today about Causality and the crucial role it plays in the AI space.
Key Takeaways:
What is Causality? Why is it important to Artificial Intelligence?
Causality is what is causing the outcome; from a data perspective there are certain features that will be causal to the outcome but there is no guarantee that you can change the outcome by changing those features.
Defining causality is less important than knowing what is capable.
Granger causality is defined as a statistical dependence.
Judea Pearl proposes three levels of causality: Association, Intervention, and Counterfactual.
Why it is important to actually know the cause of something?
People who want to be ahead and business leaders need to know how they can influence their decisions and make a change, that is why knowing the causality is crucial.
Counterfactual Causality explains the connection between x and y, but y does not really change the possibility for x to occur or not to occur.
What are counterfactuals? They are a comparison of different states in the same world, but how do you quantitatively compute these two states? It is done by holding to a variable.
Simpson’s paradox: Something observed at a high level is counter to the thing observed at a low level.
Simpson’s paradox is usually overlooked.
, by Judea Pearl
AI and the Democratization of Data of with Alonso Castañeda Andrade
Épisode 5
vendredi 4 mars 2022 • Durée 26:48
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 joined today by Alonso Castañeda Andrade, who is the Managing Director of Data Engineering and Analytics at Agile Thought. Dr. Jerry and Alonso are talking today about the role that Data Engineering and Analytics play in AI.
Key Takeaways:
Why is it a challenge today to create quality data products?
Technically a lot of tools are available today for databases, the cloud allows us to scale quickly to be able to manage all the data, but most of the challenges come from the organizational aspect and processes, which involve the dynamic nature of the data.
You can do AI without having data. Where do we start to create good quality data?
Data is available in a variety of forms and places.
Organizing data is a challenging job and tools are needed to assist the Data Engineer to perform his role, like having a good architecture platform for data and having a well-defined flow of information.
Once we have the organized data, analytics can be run on them.
What are customers looking for out of their dashboards? What are they really looking to get out of their analytic solutions?
Data Engineering and Analytics are asked to work on the integration of systems.
Customers expect their business to gain more visibility.
Customers want to receive trusted data in a timely manner.
The analytic team, dashboard engineers, and data scientists need to work together for better outcomes.
The democratization of the data: How do we enable everyone in the company to have access to the data that they require, and do that by themselves without depending on others?
Four Most Commonly Asked Questions About AI with Dr. Jerry Smith
Épisode 4
vendredi 25 février 2022 • Durée 43:02
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.
Cognitive Services with Dr. Jerry Smith
Épisode 3
vendredi 18 février 2022 • Durée 31:06
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.
AI Digital Transformation with Dr. Jerry Smith
Épisode 2
vendredi 11 février 2022 • Durée 29:11
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.
AI Improving Health Care with Tammy Siragusano
Épisode 1
vendredi 4 février 2022 • Durée 31:07
Dr. Jerry Smith welcomes you to the inaugural 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 joined today by Tammy Siragusano who is a veteran in U.S. healthcare reform whose focus has been on the enterprise approach to some of the most complex health care problems facing the industry today. She is experienced in healthcare IT, managed care operations, is a PBM-PBM migration specialist, and takes care of a lot of operational issues. Tammy has also led the digital transformation for clinical logistics for drug and utilization review.
In today’s episode, she shares her vast knowledge and experience in the field of AI and shares great ideas and suggestions for better AI implementation in the healthcare field.
Key Takeaways:
Tammy shares why she got involved in the healthcare field
Tammy belonged to the debate team and healthcare was at the center of their attention, a reform was desperately needed due to many delays in the care.
Tammy didn’t join the healthcare field as a software engineer but as a sports medicine practitioner.
Three of the biggest aspects that drive decisions in healthcare:
Watch the pennies, the dollars will take care of themselves.
What is the most convenient way that the patient can receive the care he/she needs?
Where should the focus be to make the biggest long-term impact?
Why is AI needed in healthcare?
Technology has to find a way to disrupt the status quo.
Health practitioners need to focus on patient care.
One-third of healthcare practitioners are already using AI and almost one-half of the other companies are currently exploring it.
What kind of problems is healthcare trying to solve through AI?
Upcoding and regulatory sides are two of the major fields where AI is needed.
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The study of data is an important part of the causality world.
Using machine learning in the world of causality:
There are some data scientists that didn’t study causality, and they think that they can just use classical machine learning, isolating features, and feature reduction and that means using causality… but that is not the way of “changing the world”; you need to know why certain inputs changed and what caused this change.
A reported driver is different than a causal driver.
The application of Evolutionary principles in the AI world:
The predictors are the blocks that put those inputs which are causal; this way we know the causal input to then create the machine learning model that will tell what will happen as a result of the given inputs but it does not tell us what we should set those inputs to.
First, we figure out what is causal and make a model for that, then once we have this model of the world, we tell people what conditions need to be set to get the best chances of achieving your outcome.
What kinds of tools are used for evolutionary computing? Python and their library called Deep.
What can be done after simulation? What is next?
After simulation, we need to take the inputs that represent causal drivers and put them into action in the field to monitor the change.
If you want to improve your product you need to put programs (such as marketing and sales efforts) out and collect the data on them, how they are improving and what are the changes.
Trends for 2022: The continuous migration to the cloud.
Clouds play a very important role in the data modernization of platforms since they allow businesses to deploy data products faster (up to 50% velocity increase).
Services become really significant, especially the cognitive services and analytical databases.
Business requires their data as soon as possible when something happens, for example, five minutes in the banking industry for fraud can cost millions of dollars.
What is going on today in the world of Data apps?
One of the challenges is to provide the data that the business requires in a timely manner, generally traditional analytics have been waterfall in nature, bringing all the data to create a massive data model; many fail in this process since it is time-consuming and expensive, and once they are ready the data may be obsolete.
Data is an asset of an organization and being able to make that into a competitive advantage is key.
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.
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).
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.
There are three obstacles to AI implementation in the healthcare industry:
limited resources,
different data that is unconnected,
and lack of tools and platforms to develop solutions.
CMS (Centers for Medicare and Medicaid Services) set aside a considerable amount of money for technology companies to use for innovations.
Budget is not an issue; the government will fund projects that develop platforms to create solutions.
What are the biggest problems to focus attention on?
There are performance improvement plans for clinical groups that will be based on outcomes; not only they will have to be doing better but be able to prove it since the reimbursements will depend on it.
Call centers collect very important patient information that could be utilized more effectively with the help of AI.
AI could identify how the patient feels when calling and identify the level of urgency of the call.
What does the future of wearable medical devices look like?
Wearable medical devices will get better and better.
Wearables will help those who are currently restricted in their abilities because of a disease or characteristic of their body.
Tammy talks about the possible uses of AI for prescription monitoring.
There are outreach programs to follow up on how patients are taking their prescription medicine, for them to stay on track with their medication, and find out the reasons why some people are not.
Where do we go from here?
The government is looking to fund great ideas, they are looking for creativity and new technology from the AI world.
