Sorting out AI, ML, DL, and NLP

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Posted By: Roberta Mullin May 2, 2018

The alphabet soup of acronyms in the world Artificial Intelligence. What are they? What do they mean? What is the difference between them? This is our ongoing reporting on AI and how it is being integrated into healthcare technology. We are seeking out the thought leaders and innovations that are moving the needle forward using artificial intelligence. Read more posts on Artificial Intelligence in Healthcare.

Follow the hashtag #AIinHealthcare.

To Listen

Dr. Nick: The Incrementalist talks to Dr. Eric Topol on the future of digital health in context of AI and all the data.

To Read

insitro: Rethinking drug discovery using machine learning – Daphne Koller (@DaphneKoller) is an Israeli-American Professor in the Department of Computer Science at Stanford University, a MacArthur Fellowship recipient and longtime machine learning (ML) researcher of over 25 years. Read her Medium post supporting new successes in healthcare not only because there are better ML algorithms but because of the huge volume of date we have to work with today.

11 Industries Being Disrupted By AI – David Roe outlines 11 industries utilizing AI technologies to transform their deliveries. Of course Healthcare made the list. Read the article in CMS Wire.

Investment in artificial intelligence is essential for our future health – Betty Chang a researcher in gender and innovation at Unicef pens an article in the Independent bringing attention to investments in AI are coming mostly from the private sector. AI startups raised $15.2bn last year alone, adding to investments made by tech giants like Google, Facebook, and Alibaba and a host of research institutions.

 In the News

Partnership for Artificial Intelligence and Automation in Healthcare (PATH)
PATH (@P_A_T_H_HEALTH) is a new membership-based, mission-driven alliance to ensure the integration of automation, robotics, and artificial intelligence (AI) into the many components that comprise global healthcare. PATH’s forward-thinking directives help to ensure that expertise critical for decision-making and funding makes its way into the many healthcare settings. PATH members range from professionals delivering care to health system executives in industry, public policy, academia, government, and educators – all of whom have a stake in the delivery of effective healthcare around the world in both on-site and remote care environments.

Events

PATH Summit
When: September 30-October 2, 2018
Where: Omni Shoreham in Washington, DC
Networking Event for Automation and Artificial Intelligence in Healthcare – Learn More.

AI Solve: Healthcare – Webcast Replay
Recorded March 21, 2018 at UCSF in SanFrancisco
View the recording

AI is already beginning to shape the healthcare industry, Intel brought together some of the leading minds in the space. At the event called Intel SOLVE: Healthcare, in San Francisco, Intel brought together researchers from Harvard, Princeton, Stanford, GE Healthcare, Optum, Mayo Clinic, The MIT/Harvard Broad Institute and more to talk about the work they are doing with AI.

The Basics and Resources

From the leading text book around the world, Artificial Intelligence: A Modern Approach.

Artificial Intelligence is composed of six different disciplines:

  1. Natural Language Processing to enable it to communicate successfully in English
  2. Knowledge Representation to store what it knows or hears
  3. Automated Reasoning to use the stored information to answer questions and to draw new conclusions
  4. Machine Learning to adapt to new circumstances and to detect and extrapolate patterns
  5. Computer Vision to perceive objects
  6. Robotics to manipulate objects and move about

To build a generally intelligent agent, you need machine learning in addition to the other aspects mentioned above.

Machine Learning is roughly the science of prediction. Given certain knowns (features), you wish to predict some unknowns (targets). The unknown could be structured (e.g. numeric) or unstructured (e.g. a string response).

Deep Learning is a sub field of machine learning where concepts are learned hierarchically. The simplest concepts emerge first, followed by more complicated concepts that build on the simpler ones. Usually, this leads to a simple layered hierarchy of concepts.

Optum Resource Library

Natural Language Processing: AI with an ROI
Health care providers need to see a return on any analytic investment they make. Natural language processing (NLP) is one way AI can help providers convert the potential within their health data into quality improvement and cost savings. Natural language processing is an AI technology that actually makes sense for health care.