Google making it possible to securely stop AI robots from causing harm

#google making it possible to securely stop #ai robots from causing harm

  • This “framework” makes it so that a “human operator” can easily and safely interrupt whatever an AI robot is doing.
  • Disobeying you would be one of the very few things these AI agents can’t learn.
  • Google making it possible to securely stop AI robots from causing harm
  • I am not too freaked out by the idea of a robot apocalypse, but I do say having a kill switch is necessary.
  • And because the agents can also learn, DeepMind is making sure the AI can’t learn to prevent interruption.

Read the full article, click here.


@AndroidAuth: “#google making it possible to securely stop #ai robots from causing harm”


Worried about the robot apocalypse? Some may think it’s nothing to worry about, but Google is making sure we can stop AI agents from making bad decisions.


Google making it possible to securely stop AI robots from causing harm

Machine Learning Is Redefining The Enterprise In 2016

#MachineLearning Is Redefining The Enterprise In 2016by @LouisColumbus  #DataScience #BigData

  • “Generating creative work is not for the faint of heart.”
  • Quote of the Day
  • Julien Jarreau

Read the full article, click here.


@marcusborba: “#MachineLearning Is Redefining The Enterprise In 2016by @LouisColumbus #DataScience #BigData”


Bottom line: Machine learning is providing the needed algorithms, applications, and frameworks to bring greater predictive accuracy and value to enterprises’ data, leading to diverse company-wide strategies succeeding faster and more profitably than before. Industries Where Machine Learning Is Making An Impact   The good news for businesses is that all the data […]


Machine Learning Is Redefining The Enterprise In 2016

Artificial Intelligence: Don’t Fear It, Embrace It

Artificial Intelligence: Don't Fear It, Embrace It | #BigData #Artificialintelligence #RT

  • Many intermediary steps had to be taken to teach machine learning systems.
  • “We’re seeing the point at which data-driven deep learning systems are starting to overtake systems that we’ve engineered ourselves,” said Coates. “
  • Big data could drive the next big security strategy shift.
  • But traditional machine learning hit a wall, Coates said.
  • Adam Coates, director of Baidu Research’s Silicon Valley AI Lab , isn’t worried about artificial intelligence taking over the world.

Read the full article, click here.


@Ronald_vanLoon: “Artificial Intelligence: Don’t Fear It, Embrace It | #BigData #Artificialintelligence #RT”


Adam Coates, the director of the Baidu Research’s Silicon Valley AI Lab, says don’t fear artificial intelligence. Instead, look to it to save lives. He spoke at the InformationWeek Elite 100 Conference this week.


Artificial Intelligence: Don’t Fear It, Embrace It

The Really Big Data Weekly

The Really #BigData Weekly!  #DataScience #Spark #Hadoop #MachineLearning via @ratzesberger

  • The Really Big Data Weekly, by Oliver Ratzesberger: updated automatically with a curated selection of articles, blog posts, videos and photos.
  • Please enable Javascript to correctly display the content on Paper.li
  • The Really Big Data Weekly
  • © SmallRivers 2016

Read the full article, click here.


@KirkDBorne: “The Really #BigData Weekly! #DataScience #Spark #Hadoop #MachineLearning via @ratzesberger”


The Really Big Data Weekly, by Oliver Ratzesberger: updated automatically with a curated selection of articles, blog posts, videos and photos.


The Really Big Data Weekly

Integer Sequence Learning

Check out @kaggle's new Integer Sequence Learning competition:  #MachineLearning #math

  • The competition challenges you create a machine learning algorithm capable of guessing the next number in an integer sequence.
  • The On-Line Encyclopedia of Integer Sequences is a 50+ year effort by mathematicians the world over to catalog sequences of integers.
  • We thank the OEIS and its contributors for cataloging this data.
  • Started: 6:37 pm, Thursday 2 June 2016 UTC Ends: 11:59 pm, Friday 30 September 2016 UTC (120 total days) Points: this competition does not award ranking points Tiers: this competition does not count towards tiers
  • Kaggle is hosting the competition for the data science community to use for fun and education.

Read the full article, click here.


@KirkDBorne: “Check out @kaggle’s new Integer Sequence Learning competition: #MachineLearning #math”


Kaggle is your home for data science. Learn new skills, build your career, collaborate with other data scientists, and compete in world class machine learning challenges.


Integer Sequence Learning

Will a Conscious, Intelligent AI Emerge in Our Lifetimes?

Will a Conscious, Intelligent #AI Emerge in Our Lifetimes?  #infographic RT @KhalidHamdan0

  • The accompanying graphic gives a visual timeline layout of where each researcher fell in belief to those timeframes, along with an expanded response on why they do or do not believe that AI will achieve consciousness.
  • The majority of responses received (24.24 percent) fell in the 2036-2060 time frame, the next 20 to 40 years; the second highest response rate (18.18 percent) was “no timeframe given”, a safe alternative for researchers who still wanted to respond to the survey but weren’t willing to give a set timeframe.
  • They received over 30 responses that fell across an entire spectrum, from the next five years to never.
  • If you know researchers, you know they don’t like to prognosticate about future outcomes.
  • I am disappointed, because “consciousness” (whatever that is) is unnecessary for ASI (artificial super intelligence).

Read the full article, click here.


@TamaraMcCleary: “Will a Conscious, Intelligent #AI Emerge in Our Lifetimes? #infographic RT @KhalidHamdan0”


How likely is it that artificial intelligence will achieve a human-level intelligence in the next 10 years, 20 years, 100 years, or for that matter ever? If you know researchers, you know they don’t like to prognosticate about future outcomes.

Yet it’s undeniable that humans have a unique talent for envisioning future possibilities and even whole worlds, and doing so helps drive product development, research directions, and ultimately the face of future societies.

Typically, the more you know on a particular subject, the better qualified you are to articulate the “what if’s” and “might be’s” of the future of a particular domain. When it comes to artificial intelligence with a form of consciousness or human-level intelligent awareness (often termed artificial general intelligence (AGI)), TechEmergence decided to go straight to the source. They asked over 30 researchers about when they thought might be a reasonable timeframe to expect an AI of this sort to emerge. They received over 30 responses that fell across an entire spectrum, from the next five years to never.

The majority of responses received (24.24 percent) fell in the 2036-2060 time frame, the next 20 to 40 years; the second highest response rate (18.18 percent) was “no timeframe given”, a safe alternative for researchers who still wanted to respond to the survey but weren’t willing to give a set timeframe. The accompanying graphic gives a visual timeline layout of where each researcher fell in belief to those timeframes, along with an expanded response on why they do or do not believe that AI will achieve consciousness.

[![zoom](https://tctechcrunch2011.files.wordpress.com/2016/02/conscious-machines-2-01.jpg?w=923&h=4115)](https://tctechcrunch2011.files.wordpress.com/2016/02/conscious-machines-2-01.jpg?w=923&h=4115)


Will a Conscious, Intelligent AI Emerge in Our Lifetimes?

h2o-3/gbmTuning.ipynb at master · h2oai/h2o-3 · GitHub

H2O GBM Tuning Guide for #Python  and #rstats  @h2oai #machinelearning

  • Please note that GitHub will soon be dropping support for Internet Explorer 10.
  • If you are using IE 11, make sure you turn off “Compatibility View” .
  • We recommend upgrading to the latest Internet Explorer , Google Chrome , or Firefox .
  • Personal Open source Business Explore
  • Pull requests 0 Pulse Graphs

Read the full article, click here.


@ArnoCandel: “H2O GBM Tuning Guide for #Python and #rstats @h2oai #machinelearning”


h2o-3 – Fast Scalable Machine Learning API For Smarter Applications (Deep Learning, GBM, GLM…)


h2o-3/gbmTuning.ipynb at master · h2oai/h2o-3 · GitHub