econintersect.com
       
  

FREE NEWSLETTER: Econintersect sends a nightly newsletter highlighting news events of the day, and providing a summary of new articles posted on the website. Econintersect will not sell or pass your email address to others per our privacy policy. You can cancel this subscription at any time by selecting the unsubscribing link in the footer of each email.



posted on 16 August 2016

What If Intelligent Machines Could Learn From Each Other?

from The Conversation

-- this post authored by Raja Jurdak, CSIRO

Take a look around and you'll see evidence of the widespread adoption of wearable sensors for health and fitness, such as the Fitbit, Garmin or other devices.

What many people many not know is that we are also using sensors to monitor the structural integrity of bridges and buildings, as well as tracking the movements of insects and other animals.

With the rapid growth of the Internet of Things (IoT), tens of billions of sensor devices are projected to connect in the next decade. These connected sensor devices will automate processes across a broad range of economic sectors, from industrial plants to healthcare management, delivering productivity gains and hopefully quality-of-life improvements.

The core of these sensor devices that will be deployed across this broad range of applications is largely the same, featuring a microprocessor, memory and a wired or wireless communication interface to the internet, along with a battery or other energy source.

Each application and IoT device will bring its own unique context, such as its location, the conditions of the surrounding environment and the behaviour of people in the area. Individual devices will observe and adapt to their unique contexts.

Enter artificial intelligence

So what happens when we introduce artificial intelligence (AI) into the mix? With AI, these devices can evolve their behaviour in response to changing contexts. Just like how living beings optimise their behaviour to their surroundings, even smaller IoT devices around us can run AI machines that evolve their software over time.

Consider a portable mobile device, such as a smartwatch or a smartphone, that typically ships in large volumes with one-size-fits-all features and apps for all users.

To personalise them, users have to manually configure each app individually, and keep updating these configurations as their preferences change over time.

What if the device itself could learn our preferences, simply by observing our usage patterns? This could help automate the personalisation process.

What about situations that our device has not yet experienced? Is it possible for this device to learn what our preferences could be in an unknown situation?

This is where AI machines can help each other learn faster, effectively by sharing information from each other's experience, resulting in a multiplier effect for how quickly these devices can learn.

Talking smartphones

As an example, we have demonstrated how smartphones that are in proximity to each other can both run their own AI machines and share logic blocks from their programs to accelerate learning how to maintain battery life.

There are two reasons behind these benefits. First, each phone learns independently, developing its own genetic material of program logic - an evolution of sorts.

This is known as the "island model" in evolutionary computing. In the IoT, each device becomes its own "island". Occasionally, the devices share what they've learned.

This adds to the diversity of their genetic pool, which can be beneficial in a system that learns or evolves. It also means that both devices know how to react better to new contexts that may have originally been observed by other collaborating devices.

Animal tracking provides a similar driver from collaborative AI among IoT devices. Devices are frequently placed on collars or ear tags to track the position and activities of livestock, pets, or wildlife.

In order to deliver accurate tracking information, each device needs to learn the specific movement features of the animal it is tracking - such as the species, age and gender - which AI can help with.

Then, when two or more animals meet, the IoT devices can share what they've learned about their animal's movement, which can speed up the learning process for other devices on animals with similar features.

Predicting faults

The benefits of shared learning in IoT goes beyond devices on animals and people. Take devices that are placed for monitoring the structural health of bridges or roads.

In many instances, these devices would not have a communication link to the internet due to cost and remoteness, but they can gather information locally and learn the specific patterns in observed sensor data that may predict faults.

Because faults are relatively rare, shared learning with neighbouring devices provides a larger pool for training IoT devices that may have not yet encountered a fault, on what to look out for.

Some open questions remain on the road to making shared IoT device learning a reality. Does a device compromise the privacy of its owner if it participates in a shared learning environment? The answer is it depends on whether the AI approach shares information that has intrinsic meaning or not, such as in genetic programming.

An IoT device also needs to ensure that it continues to deliver on its day-to-day tasks as it learns how to respond to new situations. Appropriate safety controls would need to be devised, such as placing hard constraints on what a device can learn and what should not change in response to learning.

Another question is how does a device know which neighbouring devices to trust when deciding which ones to collaborate with? What if a malicious entity enters a network with the aim of injecting disruptive logic into a shared IoT learning environment? Methods still need to be created to fully address these issues.

So where are we headed with IoT devices that can potentially learn from each other? While their applications are still considered to be in their infancy, the potential opportunities warrant attention, debate and investigation.

The ConversationRaja Jurdak, Research Group Leader, Distributed Sensing Systems, CSIRO

This article was originally published on The Conversation. Read the original article.

>>>>> Scroll down to view and make comments <<<<<<

Click here for Historical News Post Listing










Make a Comment

Econintersect wants your comments, data and opinion on the articles posted.  As the internet is a "war zone" of trolls, hackers and spammers - Econintersect must balance its defences against ease of commenting.  We have joined with Livefyre to manage our comment streams.

To comment, using Livefyre just click the "Sign In" button at the top-left corner of the comment box below. You can create a commenting account using your favorite social network such as Twitter, Facebook, Google+, LinkedIn or Open ID - or open a Livefyre account using your email address.



You can also comment using Facebook directly using he comment block below.





Econintersect Contributors


search_box

Print this page or create a PDF file of this page
Print Friendly and PDF


The growing use of ad blocking software is creating a shortfall in covering our fixed expenses. Please consider a donation to Econintersect to allow continuing output of quality and balanced financial and economic news and analysis.


Take a look at what is going on inside of Econintersect.com
Main Home
Analysis Blog
The Destruction of the Existing Workforce
Finance and Growth: The Direction of Causality
News Blog
Redneck Inventions
How Repealing Portions Of The Affordable Care Act Would Affect Health Insurance Coverage And Premiums
Grassroots Terrorism In 2017: A Small But Stubborn Threat
Earthquake Risk: Spotlight On Canada
Federal Income Taxes By Income Bracket
Infographic Of The Day: Guide To Caring For Your First Dog
Early Headlines: Migrants Incr. 41 Pct This Century, Women's March Largest Ever?, GOP ACA Disarray, Trump Hit With Ethics Complaint, Trump Back To '29?, May And Nieto To Visit Trump And More
New Seasonal Outlook Updates from NOAA and JAMSTEC Disagree Dramatically
Earnings And Economic Reports: Week Starting 23 January 2017
France And Germany Differ Starkly On Strong Leaders
Most Flags Combine Red, White And Blue
Electroconvulsive Therapy: A History Of Controversy, But Also Of Help
Super Bowl Ad Prices Doubled In A Decade
Investing Blog
The Week Ahead: Political Uncertainty And Market Volatility
Investors: How Not To Lose Everything And Die Broke
Opinion Blog
Retailing In America: Bricks And Torture
Economics, Society, And The Environment: What's Wrong With This Picture?
Precious Metals Blog
Four Catalysts Drive Gold And Silver For 2017
Live Markets
20Jan2017 Market Close: U.S. Stocks Were Up But Off Their Highs Of The Session, Crude Prices Continue To Climb, Next Week May Be Volatile
Amazon Books & More






.... and keep up with economic news using our dynamic economic newspapers with the largest international coverage on the internet
Asia / Pacific
Europe
Middle East / Africa
Americas
USA Government































 navigate econintersect.com

Blogs

Analysis Blog
News Blog
Investing Blog
Opinion Blog
Precious Metals Blog
Markets Blog
Video of the Day
Weather

Newspapers

Asia / Pacific
Europe
Middle East / Africa
Americas
USA Government
     

RSS Feeds / Social Media

Combined Econintersect Feed
Google+
Facebook
Twitter
Digg

Free Newsletter

Marketplace - Books & More

Economic Forecast

Content Contribution

Contact

About

  Top Economics Site

Investing.com Contributor TalkMarkets Contributor Finance Blogs Free PageRank Checker Active Search Results Google+

This Web Page by Steven Hansen ---- Copyright 2010 - 2017 Econintersect LLC - all rights reserved