AI can now predict whether or not humans will think your photo is awesome

#AI can now predict whether or not humans will think your photo is awesome

  • The Aesthetics tool, still in beta testing, allows users to upload a photo and get an auto-generated list of tags, as well as a percentage rate on the “chance that this image is awesome.”
  • According to developers, the neural network was trained to view an image much in the same way a human photo editor would, looking at factors such as color, sharpness, and subject.
  • As early users report, the system seems to be fairly good at recognizing factors like whether or not the image is sharp and if the composition is interesting, but it is certainly far from a pair of human eyes.
  • While the results of just how “awesome” a photo is may not be accurate for every image, the auto-tagging tool could prove useful, generating a list of keywords from object recognition as well as less concrete terms, like love, happiness, and teamwork.
  • Clicking on a keyword will bring up an Everypixel search for other images with that same tag, or users can copy and paste the list of keywords.

Can a computer judge art? A new neural network program will rank photos by their probability of being awesome.
Continue reading “AI can now predict whether or not humans will think your photo is awesome”

Try The Everypixel Tool To See What A Computer Thinks Of Your Best Shot

#AI can now predict whether or not humans will think your photo is awesome

  • The Aesthetics tool, still in beta testing, allows users to upload a photo and get an auto-generated list of tags, as well as a percentage rate on the “chance that this image is awesome.”
  • According to developers, the neural network was trained to view an image much in the same way a human photo editor would, looking at factors such as color, sharpness, and subject.
  • As early users report, the system seems to be fairly good at recognizing factors like whether or not the image is sharp and if the composition is interesting, but it is certainly far from a pair of human eyes.
  • While the results of just how “awesome” a photo is may not be accurate for every image, the auto-tagging tool could prove useful, generating a list of keywords from object recognition as well as less concrete terms, like love, happiness, and teamwork.
  • Clicking on a keyword will bring up an Everypixel search for other images with that same tag, or users can copy and paste the list of keywords.

Can a computer judge art? A new neural network program will rank photos by their probability of being awesome.
Continue reading “Try The Everypixel Tool To See What A Computer Thinks Of Your Best Shot”

Book: Machine Learning Algorithms From Scratch

Book: #MachineLearning Algorithms From Scratch #abdsc

  • From First Principles With Pure Python and

    Use them on Real-World Datasets

    You must understand algorithms to get good at machine learning.

  • In this mega Ebook written in the friendly Machine Learning Mastery style that you’re used to, finally cut through the math and learn exactly how machine learning algorithms work.
  • I’ve written books on algorithms, won and ranked in the top 10% in machine learning competitions, consulted for startups and spent a long time working on systems for forecasting tropical cyclones.
  • (yes I have written tons of code that runs operationally)

    I get a lot of satisfaction helping developers get started and get really good at machine learning.

  • I teach an unconventional top-down and results-first approach to machine learning where we start by working through tutorials and problems, then later wade into theory as we need it.

Discover How to Code Machine Algorithms
From First Principles With Pure Python and
Use them on Real-World Datasets

$37 USD
You must understand algorithms t…
Continue reading “Book: Machine Learning Algorithms From Scratch”

Artificial Intelligence: Removing The Human From Fintech

Artificial intelligence: Removing the human from fintech

  • News announced this week also suggests that artificial intelligence will become a central part of anything a technology organisation will do in the future.
  • Chief technology officer Paul Daugherty highlighted that “AI is poised to transform business in ways we’ve not seen since the impact of computer technology in the late 20th century.”
  • The UK government’s AI research will be led by Benevolent Tech’s CEO Jérôme Pesenti who explored how despite the “negative hype” around artificial intelligence, it has the ability to create jobs and transform industries.
  • This seems a little too optimistic to me as perhaps, AI will create new jobs, but it will remove people from careers that they know how to do and it will take time, years or decades in fact, for those people to learn new skills and then take on the jobs that artificial intelligence has ‘created’.
  • On the other hand, the millennial generation seem to welcome and encourage new technology – cellphone apps are a perfect example of how quickly new systems can enter the marketplace, so it could be said that this is the area in which AI could potentially blossom.

As I’m sure many in the technology industry have thought today, there should have been a way to avoid the Oscars Envelopegate. But, is artificial intelligence the answer to all of our human error problems?
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What Does Artificial Intelligence See In A Quarter Billion Global News Photographs?

What does artificial intelligence see in a quarter billion global news photographs?

  • Google’s Cloud Vision API is a commercial cloud service that accepts as input any arbitrary photograph and uses deep learning algorithms to catalog a wealth of data about each image, including a list of objects and activities it depicts, recognizable logos, OCR text recognition in almost 80 languages, levels of violence, an estimate of visual sentiment and even the precise location on earth the image appears to depict.
  • In total, the Vision API applied 9,853 unique labels to the images, with the most popular being “person” (27% of images), “profession” (14%), “vehicle” (10%), “sports” (7%), “speech” (6%), and “people” (5%).
  • The Vision API appears to apply the “person” label primarily in cases where a single person or a small number of people are the primary object of the photograph, such as a speaker standing at a podium.
  • The map below colors each country by the density of human faces in all imagery monitored by GDELT from news media in that country – ie, the total number of recognized human faces in all images from that country is divided by the total count of all images from that country.
  • It also reinforces why only deep learning systems with large numbers of category labels like Google’s Cloud Vision API are sufficient to work with news imagery – a more simplistic system designed to recognize just a few classes of imagery would struggle to provide much utility when applied to the incredible diversity of the world’s news imagery.

What deep learning algorithms can tell us about the visual narratives of the world’s news imagery, from depictions of violence to the importance of people to visual context – a look inside what we see about the world around us
Continue reading “What Does Artificial Intelligence See In A Quarter Billion Global News Photographs?”

Book: Machine Learning Algorithms From Scratch

Book: Machine Learning Algorithms From Scratch | #BigData #MachineLearning #RT

  • From First Principles With Pure Python and

    Use them on Real-World Datasets

    You must understand algorithms to get good at machine learning.

  • In this mega Ebook written in the friendly Machine Learning Mastery style that you’re used to, finally cut through the math and learn exactly how machine learning algorithms work.
  • I’ve written books on algorithms, won and ranked in the top 10% in machine learning competitions, consulted for startups and spent a long time working on systems for forecasting tropical cyclones.
  • (yes I have written tons of code that runs operationally)

    I get a lot of satisfaction helping developers get started and get really good at machine learning.

  • I teach an unconventional top-down and results-first approach to machine learning where we start by working through tutorials and problems, then later wade into theory as we need it.

Discover How to Code Machine Algorithms
From First Principles With Pure Python and
Use them on Real-World Datasets

$37 USD
You must understand algorithms t…
Continue reading “Book: Machine Learning Algorithms From Scratch”

Brian Roemmele_The FinTech Voice_Episode-26

@sammaule interviews @BrianRoemmele  on @Fintech5Podcast
Listen Now:  
#Payments #Voice #AI

  • Brian is also a long time and repeat company founder.
  • Brian Roemmele has a natural affinity for the audio realm – whether this be through his love of music or through his belief that voice would become the next operating system way before this was the “in thing”.
  • Subscribe: iTunes | Android | Google Play | Stitcher | RSS
  • January 18, 2017 usarsenalfan Leave a comment
  • And last but not least, he is one of the hosts of one of my one personal favorite podcasts Around the Coin .

Brian Roemmele has a natural affinity for the audio realm – whether this be through his love of music or through his belief that voice would become the next operating system way before this was the “in thing”.  Brian is also a long time and repeat company founder.  He began 1st American Card Service back in 1986, Multiplex Media Corporation in 1995, and now Payfinders in 2014.  And last but not least, he is one of the hosts of one of my one personal favorite podcasts Around the Coin.
Continue reading “Brian Roemmele_The FinTech Voice_Episode-26”