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
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