Interview with Edward Cavazos, LingoLet -- Technology Innovations in E-Learning Series

Artificial intelligence is transforming many aspects of elearning as edge computing, cloud access, and speed dramatically improve. Welcome to an interview with Edward Cavazos, LingoLet, and learn how real-time translation and remote simultaneous interpretation are expanding capabilities in elearning and training. 

What is your name and your background? 

Edward Cavazos, VP Sales Operations

32 years within the language industry, focused on technology delivery platforms for communication in spoken language, Sign Language, content creation, multilingual staffing, supporting e-learning companies with translations, interpreters, translators, instructors, monitors, and facilitators.

Edward Cavazos

What is your history with artificial intelligence?

It is only over the last 5 years AI has evolved to be called intelligence and today seen as Artificial Intelligence. The Ai has evolved from speech to text, text to speech, AI transcription of documents, AI of audio files, AI interpreter a two-way communication of one spoken language to another transcribed or spoken. AI is used today in events where attendees in a language or multiple languages can access their language video channel and see the spoken word texted in their language.

What is your experience with elearning platforms? 

Our involvement is to partner and engage with elearning companies who have platform where we provide the necessary language resources and capabilities needed for the project.

How is AI used in e-learning now?  what are the advantages?

AI is not for all projects, but where the training may be general and where the communication is at 93% as acceptable then it can be of value to the client and to those embracing AI to deliver the information in language. 

The advantage is cost savings and the turnaround time of having what is needed in place when there is a short window to complete the project, or where no interpreter(s) are available to support the project.

What do you think will happen in the short and medium-term in e-learning, especially as it relates to AI?

Here is what my crystal ball states. Embrace it, learn what exist, and see how it can be part of the solution for your client and potential clients. Enterprise organization want to work with companies who lead with technology and support with human components as needed. Business is changing and those in the elearning or any business working with organizations needs to educate themselves on AI technology as a solution.

What is Lingolet? 

We are a technology software company focused in the language industry supporting companies needing technology to deliver, communicate, and to access language communication for their markets, clients, customers. Where technology is the vehicle that drives the services needed to meet client’s communication needs in language.

How does LingoLet work, and how does it relate to elearning? 

Lingolet developed the AI features not to replace linguists, interpreters, translators, they will ALWAYS be needed. Lingolet made a decision to lead the way by giving companies options of using and integrating AI technology as part of their immediate communication needs, to become  more efficient and responsive, streamlining process, versus waiting for a human(s) to facilitate the communication to move things forward

How does this relate to elearning? 

In the traditional way, we still provide the human resources needed to support the project, provide the translation of content, provide linguist via the web or in person. 

In the Artificial Intelligence way, I’m not sure if elearning companies are ready to embrace and present this as part of the capabilities?  Let’s give the audience and opportunity to speak to this question.

Please recommend a book or two. 

Blue Ocean Strategy by W. Chan Kim – Renee Mauborgne

You, Inc. by Burke Hedges “Discover the CEO within you.

Please visit the E-Learn Chat interview with Ed Cavazos here: 

https://youtu.be/3jYLVqXlKB4

What to Do when the Robot Comes for Your Job

It's not a matter of "if" but "when."  An excellent article published in Mother Jones breaks it down and provides a timeline of the kinds of jobs that will be replaced between now and 2040.  The article examples many of the arguments that have been used to make the predictions less apocalyptic.

Mother Jones breaks them all down. It's apocalyptic, indeed, but at least with warning, you can develop a plan.

Link to podcast: https://screencast-o-matic.com/watch/cqeebb0qM1 (Links to an external site.)Links to an external site.

Drum, K., and; D, A. D. (2017). You Will Lose Your Job to a Robot. Mother Jones, 42(6), 38–69  Purpose of the article:  To address the questions about when and how AI-powered machines replace jobs, and to point out that the owners of the AI-powered machines and the businesses are the ones who will benefit most.
  Useful findings:  Machines powered by artificial intelligence will become better than humans in many different tasks. The article provides a useful timeline.
  Applicability of the article:
*Routine physical tasks will be the first to be replaced, which includes packing boxes, driving trucks, etc.
*Routine cognitive tasks will be next to be replaced (teller, phone sales)

*Non-routine cognitive will be the last to be  replaced. 
 Here is a chart with a timeline showing the robot take-over.  It's not "if" but "when."

 

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Entrepreneurial Passion: Taking It to the Next Level

I found a very interesting article that explains how and why people with hobbies who are entrepreneurial will take their passion to the next level and turn it into an actual job. They take the passion, transform it into entrepreneurial intent, and then they start making concrete steps to achieve their dreams. 

But.. watch out for the robots! The second part of the video discusses another article that explains how not to lose your marketing job to a robot.

 Link to a presentation / podcast:
 https://screencast-o-matic.com/watch/cqeeDC0q7f




Biraglia, A., and; Kadile, V. (2017). The Role of Entrepreneurial Passion and Creativity in Developing Entrepreneurial Intentions: Insights from American Homebrewers. Journal of Small Business Management, 55(1), 170–188. 

  Purpose of the article:  How can we use creativity and entrepreneurship to adjust ourselves in difficult times?

  Useful findings:  We can apply Bandura’s Social Cognitive Theory (SCT) to understanding how and why people are entrepreneurs, and how and when they succeed.

  Learning, Motivational and Behavioral processes are the result of reciprocal and bidirectional interactions

  Environmental Inputs

  Personal Factors

  Behavioral Outcomes

Applicability of the article

• Entrepreneurial passion is often a characteristic of people who are able to turn hobbies into businesses, and they relate to a thorough knowledge of the topic and also skills and practice.

•Creativity helps a person solve problems, and thus build a sense of self-efficacy and confidence.

• Entrepreneurial self-efficacy and entrepreneurial intentions are very much influenced by feedback from the environment (awards, collaborations, and presumably financial rewards).

•To assure success in an entrepreneurial endeavor, it is important to build reinforcing feedback loops that strengthen cognitive, behavioral, and environmental factors. 

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Cramer, T. (2017). How Not to Lose Your Marketing Job to a Machine. EContent, 40(5), 4–8.

  Purpose of the article:  To discuss how artificial intelligence is replacing some jobs in marketing, but creating others.

  Useful findings:  Machine learning and artificial intelligence are highly effective at conducting market research and selling, but for each task for AI, there is a need to manage it.
  Applicability of the article:
-Each AI function also needs a person who can design and manage it.
-With the proliferation of websites, it is often necessary to have a human who determines if a site is fraudulent.
-Creativity is still uniquely human. To develop strategies to grow markets, and also to tell stories still requires human beings.

 

Risk Mgmt Unit 3: Predicting Risk: Approaches using Artificial Intelligence and Machine Learning

Upon successful completion of this unit, learners will be able to identify how to use artificial intelligence and machine learning to predict levels and types of risk, both known and unknown.  Links to open source platforms, languages, and computing environments are provided.  It is not necessary to learn the computing languages or to develop new code or programs; the goal of this unit is to familiarize learners in order to work effectively in teams with data scientists, domain experts, and financial decision-makers.

Unit Presentation:

Video:   https://screencast-o-matic.com/watch/cqVZht3OAh

PDF (contains links to readings, etc.) 
http://zenzebra.net/risk/risk-management-nash-pt3.pdf  

Scenario 3:  Predicting Risk: Approaches using Artificial Intelligence and Machine Learning

Julia, Patricio, and Reyna are part of a team that is tasked with classifying old shallow-water offshore wells in the Gulf of Mexico in new ways that will help them develop a plan to boost production. 

They feel very fortunate in that around a million geological and production records have been scanned, and they cover the 150 or so wells in the field.  It’s a treasure trove of data, and they want to incorporate it with the new data in order to develop a profile of the best wells, as well as the good, mediocre, and underperforming wells.

Your Task: Help Julia, Patricio, and Reyna develop a plan to analyze the data, and then help them determine where, when, and how they can use artificial intelligence and machine learning to create profiles.

Here are a few things to consider:
 How will you select the data to use?
 How will you organize it?

What does it mean for a well to be:
  Excellent
  Good
  Mediocre
  Bad
 
What are the attributes or clusters of characteristics you’ll use?
 What approach will you use to select data?
  To clean the data?
  To analyze the data?
 What kind of AI / ML approach will you use?
 How will you use the results?

Readings:
Overview thoughts / concepts

Lists of uses of AI / Machine Learning the energy industry
 Upstream
  Classify wells using your own unique set of criteria
  Identify high-value (or potential high-value) blocks
 Midstream
  Classify infrastructure (pipelines, etc) with your own criteria
  Predict overall performance and the location of bottlenecks
 Downstream
  Refining
  Retail / distribution
 Wind energy  Identify high-value, high-return new locations
  Identify small businesses that would benefit from local energy
 Solar energy
Workflow for machine learning (in general)

● Pinpoint the problem you want to solve.
● Identify the data you’ll need to use
● Collect the data
● Clean the data
● Organize your data (put into a model - if structured, may use Open Source models such as those from Apache HaDoop)
● Find a model
● Develop algorithms (May use repositories and also cloud-based interfaces)
● Train the model
● Test with data sets
● Reality check
● Decision points
 
How do I clean data?
 What is “dirty” data? 
  Does not make sense
  Bad labels
  Incorrect formatting
  Too many “nulls”
  Part of the data in a different order or different columns
Brendon Bailey’s Guide:  Use Excel or Python to Clean Data?

Use Excel if: You have fewer than 1 million records
You need to do the job quick and easy
There is a logical pattern to cleaning the data and it’s easy enough to clean using Excel functions
The logical pattern to cleaning the data is hard to define, and you need to clean the data manually
When you might use Python or another scripting language:

Use Python if: You need to document your process
You plan on doing the job on a repeat basis
There is a logical pattern to cleaning the data, but it is hard to implement with Excel functions

Brendon Bailey. “Data Cleaning 101” TowardDataScience.com
 https://towardsdatascience.com/data-cleaning-101-948d22a92e4
 
Where do you keep the data?
 cloud solutions (Google, Amazon Web Services (AWS))

Software for risk analytics (free / open source):

Spotfire (http://www.spotfire.com)
Qlik.com (free Spotfire alternative, Qlik.com)
Jupyter Notebook https://jupyter.org/
 iPython
 R
 C++
 Julia

A Gallery of interesting Jupyter Notebooks (ready to share)
https://github.com/jupyter/jupyter/wiki/A-gallery-of-interesting-Jupyter-Notebooks

How do we predict where and when high-risk situations may take place?
 Analyze data
 Probabilistic analysis (Spotfire, etc.)
 Using geospatial elements
What is the ideal combination of variable or factors to tell us when / where / how conditions are ideal for a) optimization; b) an accident or problem ?
 Use multivariate analysis
 Bring together all risk factors: geological, logistical, political, economic, legal, environmental, etc.
 Weight them by importance (assign a percentage)

https://www.kinetica.com/wp-content/uploads/2017/09/OilGas_jt1.0mn.pdf https://medium.com/syncedreview/how-ai-can-help-the-oil-industry-b853dda86be6

Learn and Use Machine Learning

Tensorflow: https://www.tensorflow.org/tutorials/keras/

Tensorflow Machine Learning Cookbook: https://github.com/nfmcclure/tensorflow_cookbook

AI and Probabilistic Models

Part I
https://medium.com/tensorflow/industrial-ai-bhges-physics-based-probabilistic-deep-learning-using-tensorflow-probability-5f215c791863

Part II
https://medium.com/tensorflow/predicting-known-unknowns-with-tensorflow-probability-industrial-ai-part-2-2fbd3522ebda

Bougher, Benjamin Bryan. (2016)  Machine Learning Applications to Geophysical Data Analysis. Open Collections. University of British Columbia.
https://open.library.ubc.ca/cIRcle/collections/ubctheses/24/items/1.0308786

Bougher, Ben B. (2016) Using the scattering transform to predict stratigraphic units from well logs. Seismic Laboratory for Imaging and Modeling (SLIM), The University of British Columbia, Vancouver

https://www.slim.eos.ubc.ca/Publications/Public/Journals/CSEGRecorder/2016/bougher2015CSEGust/bougher2015CSEGust.html

Data:  Trenton Black River gamma ray logs

Methodology:  supervised learning ("uses labelled datasets to train a classifier to make predictions about future data" (Bougher, 2016))

Methodology - what's the algorithm?  Bougher uses a scattering transform - and then it fieeds a K-Nearest Neighbours (KNN) classifier).

How can I do this?

Using convolutional neural networks to solve a mineral prospectivity mapping problem
Framing the exploration task as a supervised learning problem, the geological, geochemical and geophysical information can be used as training data, and known mineral occurrences can be used as training labels. The goal is to parameterize the complex relationships between the data and the labels such that mineral potential can be estimated in under-explored regions using available geoscience data.

Granek, Justin. (2016). Application of Machine Learning Algorithms to Mineral Prospectivity Mapping. Open Collections. University of British Columbia.
https://open.library.ubc.ca/cIRcle/collections/ubctheses/24/items/1.0340340


TAMUT  MBA in Energy Leadership: Click link to apply - more information

For more information about the courses (and this full course), please contact me. 

Machine Learning and Python: Interview with Patrick Ng

The United States is now the world's largest producer of oil and gas, and machine learning played a large role in the transformation, which has occurred because of new techniques and technologies.

Welcome to an interview with Patrick Ng, geoscientist and pioneer of innovative ways to use analytics and specifically machine learning, to find new oil and gas reserves and to produce them more efficiently and sustainably.

https://youtu.be/6uQR8PO3l3A

LIFE EDGE with Patrick Ng Chat 2018 Q&A Notes

Background - I am a geophysicist by training, and experienced A to Z in  geosciences. 1) As - AVO amplitude versus offset to reduce risk, azimuthal features to map natural fractures, 2) transform seismic to rock properties, and 3) prestack depth imaging / model building to map subsalt reservoirs leading to 3 giant discoveries total over 2.5 billion boe in the Gulf of Mexico, and 4) the Z is drilling wells and learning from the drill bit all the way to total depth (Z).

And I learn through the drill bit that we drill anything but an average well, or rather a range of IP initial productions. The risk lies in the spread, and I make it a business managing risk at Real Core Energy.

Q1: how about examples of using Python in industry?

The hackathon focus was production forecast of a well. Given the flow rate data (courtesy of Halliburton, sponsor) and Python Notebook as template, and bootcamp to bring everyone up to speed. The exercise is to try use geoscience in machine learning, and play with the number of layers and neurons in neural network, and improve the forecast accuracy.

Q2: why Python?

Python is like the foundation, that my teenage daughter uses for make up. Depending on the event, she will put on other colors and things (not sure what to call those… so I won’t).  And the real power of Python comes from a set of libraries. For example:

1) Numpy, numeric Python for vectorized numerical computation
2) Pandas for handling lots of columns and rows
3) SK learn for machine learning algorithms, ready plug-n-play.

Think of.Python example, say write a few lines of codes, in a loop do something to each element in an array one at a time.

Numpy can collapse that into a single line, operates on an entire time series as a vector all at one go.

Often we may have a thousand wells, each with its production profiles. Think of wells as columns across the top with number of barrels per day, week or month hanging down. Pandas can operate on the entire collection of series of data all at once, like getting the mean, median, statistics with one line on an entire group of data. We also get the top 25%, next 50% and bottom 25% percentiles. Quickly we get a feel for how well the producing assets perform.

Q3: why is Python so popular with  machine learning?

It has to do with the availability of powerful libraries like Keras and Tensorflow well suited for neural network and deep learning. While SK Learn has been around for some time, Tensorflow was released by Google to open source consortium in November 2017.

Lets take deep learning as example. Microsoft had success using 158 layers in a deep neural network. Using keras, we specify one layer at a time, and we’d have 158 lines of codes.

But with Tensorflow, we can do that in one line albeit a long line, by listing the number of neurons in all 158 layers all at once. Again fewer lines of codes. But if we want to customize, and tune each layer, then we can do so with Python in a more granular way.

So we go from Python (the foundation), to Numpy, Pandas, Keras and Tensorflow, each provides the tools to do more, faster with fewer line of codes. In a nutshell, Python opens up a whole new way for geoscientists to explore data, do rapid experiments and gain new insights.

Q4: can machine learning make the industry more safe and clean?

Here are two examples. First predictive maintenance, we can better anticipate and schedule downtime for routine maintenance and repairs of equipments. Just as we do annual check up for our AC in Houston and keep them running top shape. That will prevent potential leaks and minimize surprises, so keep us safe.

On cleaner environment, one possibility is that we drill fewer wells and produce the same volume, if we can better predict the outcome with machine learning. Doing so, we reduce the footprint and impact on the environment.

(One more thought came after the Chat, is refracking. If we can use machine learning to better identify refracking candidate wells, we shall improve recovery factor and may also drill fewer new wells. Again reduce footprint and lessen impact on the environment.)

Q5: is there benefit of reprocessing data and machine learning together?

Yes. It has been standard business practice that every few years, with improved algorithm, we reprocess data, get higher resolution and a more detailed look. Like going from 4K to 8K HDTV, instead of 80 to 100 feet resolution in seismic, we may get that down to 40 ft. With higher resolution data, we’d retrain machine learning and get better results. Both go hand in hand.

That brings up a good point. In the world of geoscience, if we change the model, we also get different resulting imaged data. Unlike typical data used to feed machine learning algorithm, say what I bought from Amazon or movies streamed from Netflix, what I read and watched became record. That won’t change. But when imaging seismic, the model and resulting data are tightly coupled. Change one, we change the other.

So learning with machine beats machine learning alone.

Before 1995, the thinking in Gulf of Mexico was that salt bodies would become detached because of buoyancy (density of salt is lighter than that of surrounding rocks). So over time in geologic scale (millions of years, not weeks), salt moved up from great depth and ended up what looks like cup cakes (picture inside the lava lamp). But with the Crazy Horse (now called Thunder Horse) discovery, we learn there is salt mountain that goes forty five thousand feet deep below the seafloor. No cup cakes.

Python is a tool that can geoscientists explore and test their ideas with data. Better understanding of the geology and producing more. Last but not lease, is that Python while really powerful for numerically intense applications, it can go all the way to voice. Using Python-Flask libraries, I put together numerically rigorous app and deliver via Alexa.  That I see can draw more highschool students interested in geoscience.

Closing

As a closing thought, remember the old saying “The journey of a thousand miles begins with one step.” I see learning python is the first step. Just do it!

 Thank you, Patrick! 

New Certification and Micro-Credentialing

In 2016 and continuing through the summer of 2017, a number of universities offering either a traditional face-to-face 2-year MBA, or an executive MBA, began confirming what many observed: enrollments started to drop, and students and employers commented that the cost had risen too high. Students and employers could not justify the cost due to a lack of return on investment.

People are turning to alternatives such as micro-credentialing which forms the core part of a competency-based learning program. Even Google is offering micro-credentials in its G-Suite for Education, which helps students develop skills using its cloud-based software.

Organizations are developing fast-track certification and micro-credentialing programs in response to quickly evolving industries and the need to obtain and demonstrate mastery with specific skills and knowledge.  Some of the emerging areas include new data analytics techniques, new areas of medical technology, home health care provider management, hospitality marketing, technology entrepreneurship, drones and UAV operation and analytics, urban organic farming, and more. 

Certification providers include companies with specialized experience and experts, colleges and universities, professional associations, and government agencies.

•  Assessment to determine needs for new skills and knowledge
•  Emerging needs aligned with certificates
•  Situated learning: connect knowledge and skill to real-life setting
•  Fast-Track Certification: Fewer courses, tighter timeline
•  Characteristics of a “Fast-Track” program
•  Digital badges used to motivate
•  Content quality control to assure relevance of the content
•  Assessment strategies to apply knowledge and skills in real-life situations
•  Collaboration to encourage learning from each other

Mini-credentialing and certification programs appeal to individuals who need to expand their skills, and to do It quickly. Ideally, an individual should be able to complete their training within six months. In addition, the program should be affordable so that there is a very clear positive return on investment which more than pays for itself in increased income, expanded opportunities, and enhanced adaptability.

Big Data and Machine Learning: Susan Smith Nash and seismic lines for the Gulf of Mexico

Big Data and Deep Learning: Industry Downturn Means Uptick in New Analytics

From the Midland Register Times / April 2...
 Permian Basin operators are drilling deep and long — laterals — in order to recover more of the region’s crude and natural gas.

They’re also going deep — as in deep learning — as part of those efforts.

High-tech advances such as big data, deep learning and artificial intelligence are increasingly finding their ways into upstream exploration and production operations. For example, Exxon Mobil Corp. recently set a record for high performance computing for reservoir simulation.

Big data
Technological advances have created a wide spectrum of data for operators that goes far beyond well logs, seismic surveys and pressure readings.

“(It’s) massive amounts of data generated by different methods,” said Susan Nash, director of education and professional development with the American Association of Petroleum Geologists.
 “It’s so massive it’s contained in the cloud and other ways of organizing the data.”

That data can come in structured form, as in databases, or in unstructured forms, as in emails or PDFs, anything that can be digitized, she said.

To continue, click the link: http://www.mrt.com/business/oil/article/Industry-drills-deep-to-improve-production-11039830.php