Thursday, February 25, 2021
  • Setup menu at Appearance » Menus and assign menu to Top Bar Navigation
Advertisement
  • AI Development
    • Artificial Intelligence
    • Machine Learning
    • Neural Networks
    • Learn to Code
  • Data
    • Blockchain
    • Big Data
    • Data Science
  • IT Security
    • Internet Privacy
    • Internet Security
  • Marketing
    • Digital Marketing
    • Marketing Technology
  • Technology Companies
  • Crypto News
No Result
View All Result
NikolaNews
  • AI Development
    • Artificial Intelligence
    • Machine Learning
    • Neural Networks
    • Learn to Code
  • Data
    • Blockchain
    • Big Data
    • Data Science
  • IT Security
    • Internet Privacy
    • Internet Security
  • Marketing
    • Digital Marketing
    • Marketing Technology
  • Technology Companies
  • Crypto News
No Result
View All Result
NikolaNews
No Result
View All Result
Home Machine Learning

Answering the Question Why: Explainable AI

March 25, 2020
in Machine Learning
Answering the Question Why: Explainable AI
585
SHARES
3.3k
VIEWS
Share on FacebookShare on Twitter

(whiteMocca/Shutterstock)

You might also like

Zorroa Launches Boon AI; No-code Machine Learning for Media-driven Organizations

Way of using machine learning to aid mental health diagnoses developed

Machine Learning Market Size 2021

The statistical branch of artificial intelligence has enamored organizations across industries, spurred an immense amount of capital dedicated to its technologies, and entranced numerous media outlets for the past couple years. All of this attention, however, will ultimately prove unwarranted unless organizations, data scientists, and various vendors can answer one simple question: can they provide explainable AI?

Although the ability to explain the results of machine learning models—and produce consistent results from them—has never been easy, a number of emergent techniques have recently appeared to open the proverbial ‘black box’ rendering these models so difficult to explain.

One of the most useful involves modeling real world events with the adaptive schema of knowledge graphs and, via machine learning, gleaning whether they’re related and how frequently they take place together.

When the knowledge graph environment becomes endowed with an additional temporal dimension that organizations can traverse forwards and backwards with dynamic visualizations, they can understand what actually triggered these events, how one affected others, and the critical aspect of causation necessary for explainable AI.

Investments in AI may well hinge upon such visual methods for demonstrating causation between events analyzed by machine learning.

Correlation Isn’t Causation

As Judea Pearl’s renowned The Book of Why affirms, one of the cardinal statistical concepts upon which machine learning is based is that correlation isn’t tantamount to causation.

Part of the pressing need for explainable AI today is that in the zeal to operationalize these technologies, many users are mistaking correlation for causation—which is perhaps understandable because aspects of correlation can prove useful for determining causation. In ascending order of importance, an abridged hierarchy of statistical concepts contributing to explainable AI involves:

  • Co-occurrence:This basic machine learning precept indicates how often certain events occur together. For example, machine learning results might show that peanut-allergy symptoms have a high co-occurrence with asthma or other health conditions.
  • Correlation:Partially influenced by co-occurrence, correlation predominantly means there is a relationship between events. Significantly, it doesn’t denote what that relationship is.
  • Causation:This concept is essential to explainable AI in that it illustrates why events occurred, or what caused them. For instance, findings might show that web page color, rather than product placement, is causative for upselling e-commerce customers.

Causation is the foundation of explainable AI. It enables organizations to understand that when given X, they can predict the likelihood of Y. In aircraft repairs, for example, causation between events might empower organizations to know that when a specific part in an engine fails, there’s a greater probability for having to replace cooling system infrastructure.

Causation in Time

There’s an undeniable temporal element of causation readily illustrated in knowledge graphs so when depicting real world events, organizations can ascertain which took place first and how it might have affected others.

This added temporal dimension is critical in establishing causation between events, such as patients having both HIV and bi-polar disorder. In this domain, deep neural networks and other black box machine learning approaches can pinpoint any number of interesting patterns, such as the fact that there’s a high co-occurrence of these conditions in patients.

When modeling these events in graph settings alongside other relevant events—like what erratic decisions individual bi-polar patients made relating to their sexual or substance abuse activities—they might differentiate various aspects of correlation. However, the ability to dynamically visualize the sequence of those events to see which took place before what and how that contributed to other events is indispensable to finding causation.

Explainability and Accuracy

The flexibility of knowledge graph schema enables organizations to specify the start and end time of events. When leveraging speech recognition technologies in contact centers for sales opportunities, organizations can model when agents mentioned certain sales products, how long they talked about them, and the same information for customers. Visual graph mechanisms can depict these events sequentially, so organizations can see which led to what. Without this temporal method, organizations can leverage machine learning to specify co-occurrence and correlation between products.

Nevertheless, the ability to traverse these events at various points in time allows them to see which products, services, or customer prototypes generate interest in other offerings. This causation is determinate for increasing the accuracy of machine learning predictions about how to boost sales with this information. As valuable as this capacity is, the more meritorious quality of such causation is that the explanation for these predictions is not only perfectly clear, but also able to be visualized.

Explaining AI

Causation is the basis for understanding the predictions of machine learning models. Knowledge graphs have visualizations enabling organizations to go back and forth in time to see which events are causative to others. This capability is vital to solving the issue of explainable AI.

About the author: Jans Aasman is a Ph.D. psychologist, expert in cognitive science and CEO of Franz Inc., an early innovator in Artificial Intelligence and leading provider of semantic database technology and knowledge graph solutions. As both a scientist and CEO, Dr. Aasman continues to break ground in the areas of artificial intelligence and knowledge graphs as he works hand-in-hand with numerous Fortune 500 organizations as well as U.S. and foreign governments. 

Credit: Google News

Previous Post

How To Translate YouTube Videos And Dominate SEO

Next Post

An introduction to Statistical Inference and Hypothesis testing

Related Posts

Zorroa Launches Boon AI; No-code Machine Learning for Media-driven Organizations
Machine Learning

Zorroa Launches Boon AI; No-code Machine Learning for Media-driven Organizations

February 24, 2021
Machine Learning

Way of using machine learning to aid mental health diagnoses developed

February 24, 2021
Machine Learning Market Size 2021
Machine Learning

Machine Learning Market Size 2021

February 24, 2021
Market Live: Global Machine Learning Big Data Analytics Education Market Can Deliver up to High CAGR over the next Few Years | COVID19 Impact Analysis
Machine Learning

Global Machine Learning Market 2021 Size, Industry Growth and Forecast till 2025 | COVID19 Impact Analysis

February 24, 2021
Machine learning aids in simulating dynamics of interacting atoms
Machine Learning

Machine learning aids in simulating dynamics of interacting atoms

February 24, 2021
Next Post
An introduction to Statistical Inference and Hypothesis testing

An introduction to Statistical Inference and Hypothesis testing

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

Recommended

Plasticity in Deep Learning: Dynamic Adaptations for AI Self-Driving Cars

Plasticity in Deep Learning: Dynamic Adaptations for AI Self-Driving Cars

January 6, 2019
Microsoft, Google Use Artificial Intelligence to Fight Hackers

Microsoft, Google Use Artificial Intelligence to Fight Hackers

January 6, 2019

Categories

  • Artificial Intelligence
  • Big Data
  • Blockchain
  • Crypto News
  • Data Science
  • Digital Marketing
  • Internet Privacy
  • Internet Security
  • Learn to Code
  • Machine Learning
  • Marketing Technology
  • Neural Networks
  • Technology Companies

Don't miss it

Want to pass on your old PCs to good causes? Here’s how to do it while staying secure
Internet Security

Want to pass on your old PCs to good causes? Here’s how to do it while staying secure

February 24, 2021
Experts Warns of Notable Increase in QuickBooks Data Files Theft Attacks
Internet Privacy

Experts Warns of Notable Increase in QuickBooks Data Files Theft Attacks

February 24, 2021
Cutting-edge Katana Graph scores $28.5 million Series A Led by Intel Capital
Big Data

Cutting-edge Katana Graph scores $28.5 million Series A Led by Intel Capital

February 24, 2021
Assessing the rise of DeFi – and how data will drive fintech in 2021
Blockchain

Assessing the rise of DeFi – and how data will drive fintech in 2021

February 24, 2021
Zorroa Launches Boon AI; No-code Machine Learning for Media-driven Organizations
Machine Learning

Zorroa Launches Boon AI; No-code Machine Learning for Media-driven Organizations

February 24, 2021
Red Hat closes StackRox Kubernetes security acquisition
Internet Security

Red Hat closes StackRox Kubernetes security acquisition

February 24, 2021
NikolaNews

NikolaNews.com is an online News Portal which aims to share news about blockchain, AI, Big Data, and Data Privacy and more!

What’s New Here?

  • Want to pass on your old PCs to good causes? Here’s how to do it while staying secure February 24, 2021
  • Experts Warns of Notable Increase in QuickBooks Data Files Theft Attacks February 24, 2021
  • Cutting-edge Katana Graph scores $28.5 million Series A Led by Intel Capital February 24, 2021
  • Assessing the rise of DeFi – and how data will drive fintech in 2021 February 24, 2021

Subscribe to get more!

© 2019 NikolaNews.com - Global Tech Updates

No Result
View All Result
  • AI Development
    • Artificial Intelligence
    • Machine Learning
    • Neural Networks
    • Learn to Code
  • Data
    • Blockchain
    • Big Data
    • Data Science
  • IT Security
    • Internet Privacy
    • Internet Security
  • Marketing
    • Digital Marketing
    • Marketing Technology
  • Technology Companies
  • Crypto News

© 2019 NikolaNews.com - Global Tech Updates