Researchers were able to find new indicators of heart disease risk present in pictures of retinas by analyzing them with artificial intelligence, according to a paper published Thursday by researchers at Google and Verily, which has not been peer reviewed. Doctors today rely heavily on blood tests to determine risk of heart disease; a potential test based on retinal images would be less invasive, easier to obtain, and faster to analyze with AI.
Author: Michael Harries
Chomsky critiqued the field of AI for adopting an approach reminiscent of behaviorism, except in more modern, computationally sophisticated form. Chomsky argued that the field’s heavy use of statistical techniques to pick regularities in masses of data is unlikely to yield the explanatory insight that science ought to offer. For Chomsky, the “new AI” — focused on using statistical learning techniques to better mine and predict data — is unlikely to yield general principles about the nature of intelligent beings or about cognition.
Very few occupations will be automated in their entirety in the near or medium term. Rather, certain activities are more likely to be automated, requiring entire business processes to be transformed, and jobs performed by people to be redefined, much like the bank teller’s job was redefined with the advent of ATMs.
More specifically, our research suggests that as many as 45 percent of the activities individuals are paid to perform can be automated by adapting currently demonstrated technologies.4 In the United States, these activities represent about $2 trillion in annual wages. Although we often think of automation primarily affecting low-skill, low-wage roles, we discovered that even the highest-paid occupations in the economy, such as financial managers, physicians, and senior executives, including CEOs, have a significant amount of activity that can be automated.
Conceptual graphs (CGs) are a formalism for knowledge representation. In the first published paper on CGs, John F. Sowa (Sowa 1976) used them to represent the conceptual schemas used in database systems. The first book on CGs (Sowa 1984) applied them to a wide range of topics in artificial intelligence, computer science, and cognitive science.
Well worth taking a look at Conceptual Graphs — in large part, Kyndi’s (http://kyndi.com) technology uses conceptual graphs as one of the pillars for natural language understanding.
This post is an attempt to bridge the gap between the elementary and advanced understandings of tensors. We’ll start with the elementary (axiomatic) approach, just to get a good feel for the objects we’re working with and their essential properties. Then we’ll transition to the “universal” mode of thought, with the express purpose of enlightening us as to why the properties are both necessary and natural.
1. Loop Support is human-powered AI for customer support. Existing tickets and conversations provide the data to train AI how to handle easy requests and then humans in the middle handle the tougher tasks. I like that there is a closed loop for machine learning and a clear ROI story in a big existing market. For now, Loop Support differentiates itself from a crowded landscape (Digital Genius, ASAPP and many others) by bringing the reps as opposed to just powering the software.
Nice positioning around human in the loop. Reminds me of the way we used RippleDownRules back in the day at http://pks.com.
By unifying what data scientists, data engineers,
and the business are doing, we enable enterprises
to focus on the actual problems they want to solve.
In the near future, you might ask your email client how to get an introduction to a new professional contact. That’s the idea behind the latest updates to Trove, a service that’s designed to analyze users’ email messages and provide them with insights about their professional network.
A study published in July in the journal Neurobiology of Aging found that artificial intelligence could detect signs of the disease in patient brain scans before physicians. The computer-based algorithm was able to correctly predict if a person would develop Alzheimer’s disease up to two years before he or she actually displayed symptoms. It was correct 84 percent of the time.
It’s been a while, but I’m back to technoist.
My blogging is a little rusty – and my intent is frequency over refinement, so don’t expect too much, and much here could be excerpted insights from others.
I’m also intending to create a series of pages exploring major themes. These will link to pages, and content from elsewhere, but build up some theses.
Feedback always welcome.