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Difficult to tell what's real

This issue of the newsletter was supposed to be sent on Friday, due to some technical problems, it is
July 1 · Issue #37 · View online
iAfrikan Daily Brief
This issue of the newsletter was supposed to be sent on Friday, due to some technical problems, it is only going out now. Apologies. - Tefo Mohapi

If you have been following deepfakes for some years (originally starting and spreading from the now banned /r/deepfakes Reddit community) you will know how they’ve improved and it is becoming extremely difficult to tell what’s real with the human eye. The Artificial Intelligence (AI) technique used to generate deepfakes has improved so much that it is only through the use of other algorithms that it can possibly be detected.
Deepfakes, the name is derived from deep learning (an AI learning technique) and fake (well, given that the results are fake photos and videos), are generated using an AI technique known as GAN, short for Generative Adversarial Networks. A GAN is a deep learning technique which learns any skill (in this case, the skill is to learn how to reproduce real-looking human faces) by getting feedback on what is still missing. It then relearns the missing skill based on feedback, unlearns what is not necessary to master the core skill, get feedback, and repeat until it keeps getting better. In the case of deepfakes, the training “data” is images and videos of real humans it is supposed to mimic
A screenshot from a deepfake video that mimics how former US President Barack Obama looks and speaks. In the video, Obama says some things he normally wouldn't say (in public).
I was reminded of deepfakes given the uproar that has been happening over the past few days over the now shut down DeepNude application. Just like deepfakes, DeepNude used AI to generate realistic looking nude photos of women after you upload a clothed version of the photo. The main argument against the application (which was available on Windows and Linux) was that it targeted women and there was no version targeting men. The other argument is around the invasion of privacy.
I fully agree with both arguments. The only thing I couldn’t get my head around is why now? Deepfakes have been around for over a year, and as I write this, there are numerous deepfake adult websites that use AI to generate pornograpic movies using faces of well-known people, all women. There’s also been numerous Nicholas Cage deepfakes and lately a realistic-looking fake video of Mark Zuckerberg.
Several people I spoke to are calling for regulation of such AI. Some are calling for an outright ban. However, as much as I disagree with how it is being used, it is near impossible to regulate software. How do you stop anyone from developing another DeepNude?
What does worry me, despite some researchers developing algorithms to detect deepfakes, is how easily this can be used a mass of people to sway opinion or decisions. Already, fake news (written text) spread through instant messaging platforms such as WhatsApp has led to propaganda and in some country killings, what more a realistic-looking video of someone like Barack Obama saying something to sway your opinion?
Perhaps one of the ways is to develop software that can run on smartphones and computers, similar to how anti-virus works, that is able to detect deepfakes and alert you immediately when it detects one.
I don’t really know what the ultimate solution is but I do know it is becoming difficult to tell what is real, and what is not.
🤖 Most deepfakes are made by showing a computer algorithm many images of a person, and then having it use what it saw to generate new face images. Researchers can now identify the manipulation of a video by looking closely at the pixels of specific frames. Link
👁️ DeepNude, an app that used an Artificial Intelligence technique to generate realistic looking nude photos of women has been shut down by the creator. In a statement, the creator has said that the world is not yet ready for DeepNude. Link
🏦 Standard Chartered has expanded its digital banking offering to Botswana, Zambia and Zimbabwe. This follows on the bank launching its digital-only banks in Côte d'Ivoire, Uganda, Tanzania, Ghana and Kenya. Link
🎓 4IR is a fact and nations need to act or risk being left behind. This is particularly important for African manufacturers, which can use the jump to leap into the frontline of modern manufacturing. But to do so, we shouldn’t just fall for the idea of 4IR. Link
Read This Book
This week’s recommended book is the story of Alibaba. Titled Alibaba: The House that Jack Ma Built, the book is written by Duncan Clark and it takes a look back at Ma’s life before the formation of Alibaba and how the company was formed. I found it to be a good story that illustrates how closely tied Jack Ma’s life is tied to Alibaba’s “life”.
Without revealing some spoilers, a few things struck me.
There was a lot of what you can call “luck” and helping hands who were they not there, Ma might have not even taken the entrepreneurial route and started Alibaba. Having said that, his tenacity and “happy go lucky” attitude I think helped him seize those “lucky” opportunities as they presented themselves.
All in all, I found it a great read that also provides some context to the Internet and technology businesses in China, along with the people who fund them.
Alibaba: The House That Jack Ma Built
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