wydrukuj poleć znajomym zamów materiały
Od ilu lat pracuje Pani/Pan na różnych stanowiskach menedżerskich:

powyżej 20 lat
powyżej 15 lat
powyżej 10 lat
powyżej 5 lat
poniżej 5 lat
jeszcze nie byłam/-em menedżerem
nie chcę być menedżerem


Subskrypcja najnowszych ofert pracy





Nasi partnerzy:

rp.pl
gazeta.pl
onet.pl
interia.pl
wp.pl

How Facebook Uses Data Analytics To Understand Your Posts And Recognize Your Face 2017.01.31

LinkedIn - January 26, 2017 - With 1.2 billion people uploading 136,000 photos and updating their status 293,000 times per minute, until recently Facebook could only hope to draw value from a tiny fraction of its unstructured data - information which isn't easily quantified and put into rows and tables for computer analysis. Facebook uses a DL application called DeepFace to teach it to recognize people in photos. It says that its most advanced image recognition tool is more successful than humans in recognizing whether two different images are of the same person or not - with DeepFace scoring a 97% success rate compared to humans with 96%. Facebook is a strong supporter of Open Source and makes most of the work of its AI labs Facebook Artificial Intelligence Research (FAIR) freely available for anyone to use or modify however they like. Most of Facebook's Deep Learning is built on the Torch platform, a development environment focused on the development of deep learning technologies and neural networks.

Photo courtesy of Bernard Marr / LinkedIn / Shutterstock
 

LinkedIn – January 26, 2017

By Bernard Marr
Chief Executive
Advanced Performance Institute


After writing several articles intended to act as basic introductions to Artificial Intelligence such as Machine Learning and Deep Learning, I thought it would be good to look at some examples of how they are being used in practice.

http://www.forbes.com/sites/bernardmarr/2016/12/08/what-is-the-difference-between-deep-learning-machine-learning-and-ai  

Facebook builds its business by learning about its users and packaging their data for advertisers.
It then reinvests this money into offering us new, useful functionality – currently video and shopping - which it also uses to learn even more about us.

As the way it enables communication and conversation between people has proven to be hugely valuable to us, it has become a magnet for a huge amount of data about us – who we are, where we spend our time and what we like.

The problem for Facebook’s data scientists who have to try to make sense of this is that much of this data is very messily unstructured.

With 1.2 billion people uploading 136,000 photos and updating their status 293,000 times per minute, until recently Facebook could only hope to draw value from a tiny fraction of its unstructured data – information which isn’t easily quantified and put into rows and tables for computer analysis.

Bernard Marr
Best-Selling Author, Keynote Speaker and Leading Business and Data Expert.
Photo courtesy of LinkedIn
 
Deep Learning is helping to play a part in changing that.

Deep Learning techniques enables machines to learn to classify data by themselves.
A simple example is a deep learning image analysis tool which would learn to recognize images which contain cats, without specifically being told what a cat looks like.

By analyzing a large number of images, it can learn from the context of the image – what else is likely to be present in an image of a cat?
What text or metadata might suggest that an image contains a cat?

This helps it to give structure to unstructured data, by quantifying it and representing it in a form from which analytical tools can derive insights.

They try to answer questions such as -how often does a company’s products appear in pictures which also contain cats?
Should we focus on displaying our ads to people who like cats, or not?

That’s the basic principle of why Deep Learning (DL) is useful to Facebook, and as DL algorithms become more sophisticated they can increasingly be applied to more data that we share, from text to pictures to videos.

So here’s a couple of specific use cases where DL is used to gain value and help Facebook achieve its goals of providing greater convenience to users, and enabling them to learn more about us.


1. Textual analysis

A large proportion of the data shared on Facebook is still text.

Video may involve larger data volumes in terms of megabytes, but in terms of insights, text can still be just as rich
.

A picture may paint 1,000 words, but if you just want to answer a simple question, you often don’t need 1,000 words.
Every bit of data which isn’t essential to answering your question is just noise, and more importantly, a waste of resources to store and analyze.

Facebook uses a tool it developed itself called DeepText to extract meaning from words we post by learning to analyze them contextually.

https://code.facebook.com/posts/181565595577955/introducing-deeptext-facebook-s-text-understanding-engine/  

Neural networks analyze the relationship between words to understand how their meaning changes depending on other words around them.
Because this is semi-unsupervised learning, the algorithms do not necessarily have reference data – for example a dictionary – explaining the meaning of every word.
Instead, it learns for itself based on how words are used.

This means that it won’t be tripped up by variations in spelling, slang or idiosyncrasies of language use.
In fact, Facebook say the technology is “language agnostic” – due to the way it assigns labels to words, it can easily switch between working across different human languages and apply what it has learned from one to another.

At present the tool is used to direct people towards products they may want to purchase based on conversations they are having – this video gives an example of how it decides whether providing a user with a shopping link is appropriate or not, depending on the context.

https://www.facebook.com/Engineering/videos/10154132641047200/  


2. Facial recognition

Facebook uses a DL application called DeepFace to teach it to recognize people in photos.

It says that its most advanced image recognition tool is more successful than humans in recognizing whether two different images are of the same person or not – with DeepFace scoring a 97% success rate compared to humans with 96%.

It’s fair to say that use of this technology has proven controversial.

https://www.bloomberg.com/news/articles/2016-10-26/is-facebook-s-facial-scanning-technology-invading-your-privacy-rights  

Privacy campaigners said it went too far as it would allow Facebook – based on a high resolution photograph of a crowd - to put names to many of the faces which is clearly an obstacle to our freedom to move in public anonymously.

EU legislators agreed and persuaded Facebook to remove the functionality from European citizens’ accounts in 2013.

Back then the social media giant was using an earlier version of the facial recognition tool which did not use Deep Learning.

Facebook has been somewhat quiet about the development of this technology since it first hit headlines, and can be assumed to be waiting on the outcome of pending privacy cases before saying more about their plans to roll it out.


3. Targeted advertising

Facebook uses deep neural networks – the foundation stones of deep learning – to decide which adverts to show to which users.

This has always been the cornerstone of its business, but by tasking machines themselves to find out as much as they can about us, and to cluster us together in the most insightful ways when serving us ads, it hopes to maintain a competitive edge against other high-tech competitors such as Google who are fighting for supremacy of the same market.


4. Designing AI applications

Facebook has even decided that the task of deciding which processes can be improved by AI and Deep Learning can be handled by machines.
A system called Flow has been implemented which uses Deep Learning analysis to run simulations of 300,000 machine learning models every month, to allow engineers to test ideas and pinpoint opportunities for efficiency.

https://www.wired.com/2016/05/facebook-trying-create-ai-can-create-ai/  


Open source

Facebook is a strong supporter of Open Source and makes most of the work of its AI labs Facebook Artificial Intelligence Research (FAIR) freely available for anyone to use or modify however they like.

https://research.fb.com/fair-open-sources-deep-learning-modules-for-torch/  

Most of Facebook’s Deep Learning is built on the Torch platform, a development environment focused on the development of deep learning technologies and neural networks.

http://torch.ch/  

It has even open sourced the design of its graphics processer unit (GPU)-driven AI hardware – super-fast computers optimized for carrying out Deep Learning tasks, which are often processing-power intensive due to the vast numbers of calculations involved and the speed of the incoming data they are built to handle.


Looking to the future

Deep Learning is likely to continue to play a key part in the future development of Facebook.

Although it is tight-lipped about potential new applications at the moment, ideas which have been suggested include automatically generating audio descriptions of pictures to assist the visually-impaired, and to predict where greater coverage is required in its mission to roll out internet access to poorly served parts of the world.

https://www.wired.com/2016/11/inside-facebooks-first-efforts-rain-internet-sky/  

In the long-run work of their well-resourced AI and Deep Learning labs is likely to provide benefit to countless other organizations too, either directly through use of their services, or indirectly thanks to their support of open source principles.


As always, I’d love to read your thoughts and comments on the topic.

Thank you for reading my post
.

Here at LinkedIn and at Forbes I regularly write about management, technology and Big Data.
If you would like to read my future posts then simply join my network here or click 'Follow'.

https://www.linkedin.com/today/posts/bernardmarr  

http://www.forbes.com/sites/bernardmarr  

http://www.ap-institute.com/big-data-possibilities.aspx  


Also feel free to connect on Twitter, Facebook or Slideshare.

https://twitter.com/BernardMarr  

https://www.facebook.com/BernardWMarr/  

http://www.slideshare.net/BernardMarr  


You might also be interested in my new and free ebook on Big Data in Practice, which includes 3 Amazing use cases from NASA, Dominos Pizza and the NFL.
You can download the ebook from here: Big Data in Practice eBook.

http://www.ap-institute.com/big-data-possibilities.aspx  


Bernard Marr

Best-Selling Author, Keynote Speaker and Leading Business and Data Expert

https://www.linkedin.com/in/bernardmarr  


Source: LinkedIn

https://www.linkedin.com/pulse/  



Editor-in-Chief of ASTROMAN magazine: Roman Wojtala, Ph.D.


wydrukuj ten artykuł
  strona: 1 z 1
polecamy artykuły
Airbus: Indigo Partners doubles existing A320neo Family order with commitment for additional 430 aircraft valued at USD49.5 billion
Fusion Genomics Turns to IBM Cloud to Help Support Advances Designed to Conquer Global Pandemics
Boeing, flydubai Sign Historic Deal for 225 737 MAX Airplanes Valued at USD27 billion
Cray Advanced Supercomputer to Assist Samsung's Research on Artificial Intelligence and Deep Learning
Rada Ministrów przyjęła Konstytucję Biznesu
Polski Fundusz Rozwoju nowym inwestorem w Grupie WB Electronics
Intelligent World Drive - five continents in five months. On the road to autonomous driving: Mercedes-Benz on automated worldwide test drive
BMW Connected further expands its digital ecosystem. BMW is now integrated with the Google Assistant
EICMA 2017: How Bosch is taking the motorcycle into the future. Bosch's two-wheeler and powersports business is growing twice as fast as the market
Raytheon Immersive Design Center wins Aviation Week 2017 Program Excellence award
Lockheed Martin Receives Contract to Develop Compact Airborne High Energy Laser Capabilities
Fujitsu, Lenovo and Development Bank of Japan form PC Joint Venture
Newest Cisco tech for the future of work will keep you occupied. Daylight Saving Time: Putting one extra hour to good use
Boeing HorizonX Invests in Advanced Materials Producer Gamma Alloys
For 10 years running, the Family Office Club is the premier community for more than 1,500 registered family offices who manage USD1 trillion+ in assets
strona główna  |  oferty pracy  |  executive search  |  ochrona prywatności  |  warunki używania  |  kontakt     RSS feed subskrypcja RSS
Copyright ASTROMAN © 1995-2017. Wszelkie prawa zastrzeżone.
Projekt i wykonanie: TAU CETI.