Google’s artificial intelligence computer ‘no longer constrained

Google’s artificial intelligence computer ‘no longer constrained

  • The computer that stunned humanity by beating the best mortal players at a strategy board game requiring “intuition” has become even smarter, its creators claim.
  • Even more startling, the updated version of AlphaGo is entirely self-taught — a major step towards the rise of machines that achieve superhuman abilities “with no human input”, they reported in the science journal Nature.
  • Dubbed AlphaGo Zero, the Artificial Intelligence (AI) system learnt by itself, within days, to master the ancient Chinese board game known as “Go” — said to be the most complex two-person challenge ever invented.

The computer that stunned humanity by beating the best mortal players at a strategy board game requiring “intuition” has become even smarter, its creators claim.
Even more startling, the updated version of AlphaGo is entirely self-taught — a major step towards the rise of machines that achieve superhuman abilities “with no human input”, they reported in the science journal Nature.
Dubbed AlphaGo Zero, the Artificial Intelligence (AI) system learnt by itself, within days, to master the ancient Chinese board game known as “Go” — said to be the most complex two-person challenge ever invented.
Continue reading “Google’s artificial intelligence computer ‘no longer constrained”

9 Artificial Intelligence Startups in Medical Imaging

9 Artificial Intelligence Startups in Medical Imaging

  • Just a few days ago, an artificial intelligence (AI) algorithm named Libratus beat four professional poker players at a no-limit Texas Hold ‘Em tournament played out over 20 days.
  • The AI algorithm did exceptionally well and was utilizing strategies that humans had never used before.
  • It’s no secret that AI is now performing certain medical imaging tasks better than human doctors.
  • Pundits say “well, people will always trust a human doctor over an AI” and the answer we’d have to that is “not if the AI is going to give a more accurate answer “.
  • It’s only a matter of time before every X-ray machine is connected to the cloud and one human doctor per hospital puts his hand on your shoulder when he reads you the output from the AI algorithm.

You don’t have to be a gambler to appreciate the complexities of the card game Texas Hold ‘Em. It involves a strategy that needs to evolve based on the players around the table, it takes a certain amount of intuition, and it doesn’t require the player to win every hand. Just a few days ago, an artificial intelligence (AI) algorithm named Libratus beat four professional poker players at a no-limit Texas Hold ‘Em tournament played out over 20 days.
If you have even the slightest understanding of how to write code, you would realize that it is impossible to actually code a software program to do that with such “imperfect information”. The AI algorithm did exceptionally well and was utilizing strategies that humans had never used before. Professional poker players are in no danger of losing their jobs, but the incredible capabilities of what AI is mastering these days should make everyone wonder just how safe their jobs actually are.
Let’s take the $3 billion medical imaging market. It’s no secret that AI is now performing certain medical imaging tasks better than human doctors. Pundits say “well, people will always trust a human doctor over an AI” and the answer we’d have to that is “not if the AI is going to give a more accurate answer “. It’s only a matter of time before every X-ray machine is connected to the cloud and one human doctor per hospital puts his hand on your shoulder when he reads you the output from the AI algorithm. Kind of like this:
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A Visual Introduction to Machine Learning

A Visual Introduction to Machine Learning | #DataScience #MachineLearning #RT

  • Using a data set about homes, we will create a machine learning model to distinguish homes in New York from homes in San Francisco.
  • Let’s say you had to determine whether a home is in San Francisco or in New York.
  • In machine learning terms, categorizing data points is a classification task.Since San Francisco is relatively hilly, the elevation of a home may be a good way to distinguish the two cities.
  • Based on the home-elevation data to the right, you could argue that a home above 240 ft should be classified as one in San Francisco.
  • The data suggests that, among homes at or below 240 ft, those that cost more than $1776 per square foot are in New York City.

This article was written by Stephanie and Tony on R2D3. 
In machine learning, computers apply statistical learning techniques to automatically identify pattern…
Continue reading “A Visual Introduction to Machine Learning”

A Visual Introduction to Machine Learning

A Visual Introduction to #MachineLearning #abdsc

  • Using a data set about homes, we will create a machine learning model to distinguish homes in New York from homes in San Francisco.
  • Let’s say you had to determine whether a home is in San Francisco or in New York.
  • In machine learning terms, categorizing data points is a classification task.Since San Francisco is relatively hilly, the elevation of a home may be a good way to distinguish the two cities.
  • Based on the home-elevation data to the right, you could argue that a home above 240 ft should be classified as one in San Francisco.
  • The data suggests that, among homes at or below 240 ft, those that cost more than $1776 per square foot are in New York City.

This article was written by Stephanie and Tony on R2D3. 
In machine learning, computers apply statistical learning techniques to automatically identify pattern…
Continue reading “A Visual Introduction to Machine Learning”

A Visual Introduction to Machine Learning

A Visual Introduction to #MachineLearning #abdsc

  • Using a data set about homes, we will create a machine learning model to distinguish homes in New York from homes in San Francisco.
  • Let’s say you had to determine whether a home is in San Francisco or in New York.
  • In machine learning terms, categorizing data points is a classification task.Since San Francisco is relatively hilly, the elevation of a home may be a good way to distinguish the two cities.
  • Based on the home-elevation data to the right, you could argue that a home above 240 ft should be classified as one in San Francisco.
  • The data suggests that, among homes at or below 240 ft, those that cost more than $1776 per square foot are in New York City.

This article was written by Stephanie and Tony on R2D3. 
In machine learning, computers apply statistical learning techniques to automatically identify pattern…
Continue reading “A Visual Introduction to Machine Learning”

There’s nothing artificial about intelligence

Blog: There’s nothing artificial about intelligence  #AI #technology

  • But perhaps for now we are getting ahead of ourselves perhaps we were ascribing and intelligence to our technology which at the moment does not exist.
  • Technology and artificial intelligence can and should be seen as means to an end.
  • Smart meters, data capturers, programmable devices; it seems as though everywhere we look computers are taking over our lives.
  • More importantly, does what they buy provide a genuine solution or satisfy a genuine need or is it simply the best that they can find or afford at the time.
  • And maybe our computers can churn out loyalty vouchers and incentives, but they are only responding in a set algorithm to known sets of data.

Smart meters, data capturers, programmable devices; it seems as though everywhere we look computers are taking over our lives.
Continue reading “There’s nothing artificial about intelligence”

Machine learning helps scientists discover new materials

#MachineLearning #DataScience helps scientists discover new materials

  • Researchers published their work in the journal Nature Communications last month .
  • UPI.com is your trusted source for world news, top news, science news, health news and current events.
  • LOS ALAMOS, N.M., May 9 (UPI) — Traditionally, materials scientists have used a combination of trial-and-error and intuition to discover and perfect new materials with advantageous properties.
  • To speed up the process, researchers at Los Alamos National Laboratory attempted to marry machine learning with targeted experiments.
  • “What we’ve done is show that, starting with a relatively small data set of well-controlled experiments, it is possible to iteratively guide subsequent experiments toward finding the material with the desired target,” Turab Lookman, a physicist and materials scientist at Los Alamos, said in a news release .

Read the full article, click here.


@MikeTamir: “#MachineLearning #DataScience helps scientists discover new materials”


Researchers at Los Alamos National Laboratory attempted to marry machine learning with targeted experiments to discover new materials more efficiently.


Machine learning helps scientists discover new materials