We are Building an A.I. system that Learned to Lie to Us?

DeepFakes = DeepLearning + Fake

Trung Anh Dang
Nov 5 · 3 min read
Illustrated by Shelly Palmer

I have been hearing about concerns over deepfakes in recent years. Facebook is teaming up with Microsoft, the Partnership on A.I. coalition and academics from several universities to launch a contest (from late 2019 to spring of 2020) to better detect deepfakes. The social media giant spends $10 million on this contest.

What is DeepFakes?

Nicolas Cage Can Now Be Put Into Any Movie in History

The danger of this technique is “the technology can be used to make people believe something is real when it is not. It can be used to weaken the reputation of a political candidate by making the candidate appear to say or do things that never actually occurred.

What does the Tech behind DeepFakes?

GANS help us to build an A.I. system that learned to lie to us.

GANs were not the first computer algorithm used to generate data, but their results and versatility set them apart from all the rest such as the ability to generate fake images with real like quality.

Increasingly realistic synthetic faces generated by variations on Generative Adversarial Networks (GANs). In order, the images are from papers by Goodfellow et al. (2014), Radford et al. (2015), Liu and Tuzel (2016), and Karras et al. (2017)³

How GANS work?

  • The generator: trained to generate fake data.
  • The discriminator: trained to discern the fake data from real examples.
the objectives of generator and discriminator

GANs introduced a competition between a generator and a discriminator. They perform a min-max game together, where the generator's objective is to generate data which can fool the discriminator and the discriminators' objective is to accurately distinguish the generated data from real.

In the not so distant future, A.I. tools will help us to make all types of decisions and it also learned to lies to us. Don’t worry because we also can use A.I to detect manipulated contents.

References

[2] Nick Bostrom, 2016, Oxford University Press. Superintelligence: Paths, Dangers, Strategies.

[3] The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation, 2018. Malicious AI Report.

Data Driven Investor

from confusion to clarity, not insanity

Trung Anh Dang

Written by

I write about things that I like and things that I don’t, mainly in the business, art and tech sphere

Data Driven Investor

from confusion to clarity, not insanity

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