There are four major areas where this technology is becoming prevalent: 1) Imagery, 2) Video, 3) Audio, 4) Writing. All
of these personally identifiable characteristics can be replicated by this technology.
The technology for creating video and imagery of real people is relatively simple to use. Anyone with a reasonably
powerful GPU can feed a few hundred images or videos of a target into a machine learning programme called Generative
Adversarial Network (GAN) to create realistic fake imagery or video of the individual. Minimum computing skills are
DeepFakes require cooperation between multiple technologies, most notably machine learning and neural networks. However,
the specific technology that enables the creation of hyper-realistic fake content is called Generative Adversarial
Networks (GAN). GANs are a class of machine learning algorithms used in unsupervised learning (a branch of machine
learning that does not require labeled data) and implemented by a system of neural networks contesting with each other.
It was created by researcher Ian Goodfellow in 2014. Goodfellow now works at Google.
The original technique was capable of generating photographs that look authentic to human observers. In 2018,
researchers at Stanford University further developed a technology called deep video portraits, which allows a computer
to transpose the expressions or actions of one person onto another person, for example making someone in a video smile
At the same time, researchers at the University of California, Berkeley used GANs to develop software that could
transpose whole actions from one person to another. A person who can’t dance could suddenly do ballet; someone who
couldn’t speak sign language can appear in a video signing perfectly.
Again in 2018, technology firm Nvidia announced that it had developed a way to create hyper-realistic imagery of
non-existent humans, using what they call a “style-based generator.”
Significant advances were made in 2018, most notably by Google, in creating human-sounding voice AI. These AIs can act
as your digital assistant, liaising with people on your behalf, without anyone knowing that it is a machine.
A number of companies have made significant advances in generating human voice outputs. There are many reasonable use
cases, most notably in digital assistants like Siri or Alexa, where the user feels more comfortable engaging with a
human voice rather than what is distinctively a robot. People engage and develop loyalty to the personality imbued in
the voice itself.
In 2016, Adobe, the creator of Photoshop, launched a new voice editing tool called VoCo. The tool needs just 20 minutes
of voice data to recreate any message in that person's voice. In effect, that means that it can generate any voice
message in the voice of prominent politicians, business people, journalists, celebrities, etc.
The company Lyrebird have taken this even further by allowing you to create your own voices. This is useful for people
who want to create chatbots, personalise audiobook readers or maintain voice privacy online, among others.
In 2015, a company called Replika (originally Luka) developed a service that uses voice data and AI to recreate loved
ones after they die. The AI uses voice samples and data inputs from e-mails and videos of the deceased person to have
conversations in their voice after death. The company recently pivoted towards creating digital companions.
There are many innovators in the space, but undoubtedly it is Google that have been working at it the longest and have
made the most meaningful advances. Google have long held a belief that voice AI is the significant chasm that needs to
be crossed for AI to really take hold of users’ lives. As far back as 2007, Google launched a directory enquiry service
called GOO411, a free tool for connecting people to phone numbers they needed. This allowed Google to analyse enormous
amounts of voice patterns and ultimately led to the creation of the Pixel phone and its voice capabilities. This
resulted in controversy, when it became clear that Android phones were recording the conversations of people close to
the device (you can delete some of these conversations in your account settings).
Google have now moved a step further by combining their digital assistant capabilities with their voice technology to
generate realistic human voice AI. The product is called Duplex and is already live. You may well be receiving calls
from robots who sound like people hoping to set up a meeting with you.
a small sample of a person's handwriting can produce a never-ending script of text in perfect replica. Signatures are
now easily fabricated. Natural Language Processing (NLP) - the science of interaction between humans and computers
through natural languages - in combination with these neural networks can now create chatbots that mimic the writing
style and logic of an individual.
Companies like Adobe have been providing real digital signatures for some time now, but they have traditionally required
you to generate it yourself and input the data through a photograph. In 2016, scientists at University College London
developed software that could mimic a person's handwriting perfectly. This would allow users to increase the
personalisation of their correspondence or work by typing in their own handwriting.
In parallel, Chicago-based company Narrative Science are developing and using NLP tools that can create text in the tone
of voice of a company or an author. This may be the only way you ever get to read an ending to the Game of Thrones
Earlier this year, researchers at OpenAI decided against releasing a chatbot due to concerns about how dangerous it may
be in the wrong hands. The AI can generate coherent paragraphs of text indistinguishable from human-created content. It
could also perform rudimentary reading comprehension, translation, question-answering and summarisation without the need
for specific training. These AIs can reasonably be expected to replace many content-creating and reporting roles in the
New and emerging technology ecosystems in imagery, audio, text, social and artificial intelligence have lowered the
barrier to entry and enabled the creation and distribution of DeepFakes at scale. Source: L’Atelier BNP Paribas.