“Fake Data” makes Surveillance AI Stronger. Learn How

Let’s know the why companies need fake data to develop surveillance AI

Companies are building software that uses AI to monitor people in real life, virtually, and even in the metaverse to develop this AI needs synthetic data. So, companies are stepping in to supply it. Fake data is sample data that looks as much as possible like real data but has no relationship at all with the real data; it is generated entirely randomly.

It requires no advanced setup or extensive configuration when you use it for the first time. It can reduce bias in artificial intelligence, but it also helps build controversial technologies used to monitor people’s behavior and interpret emotions and body language. It is a highly customizable and programmable form filler that can be used on any form, no matter how complex it is. This type of development and test data is sometimes also called 'spoof' data, generated data, random data, or dummy data.

Synthetic data makes AI stronger:

Synthetic data organizations are giving a great many pictures, videos, and audio data samples. It is to be generated to further the development of AI models in controversial forms of AI such as facial recognition, emotion AI, and other algorithmic systems used to monitor individuals' way of behaving.

Simply having bunches of faces across societies may not be sufficient to prepare AI that additionally needs to realize what individuals resemble when they are wearing a baseball hat when they are ready or snoozing or in conditions with low or splendid light. Nowadays AI developers customized synthetic data to address more and more domain-specific problems that have zero data you can actually access.

Algorithmic systems might be monitoring people's facial expressions. Synthesis AI is making data for use in an AR environment, the system will produce multiple versions of images of people. Synthetic data comes with details about what images and videos represent that are necessary to help AI models learn. Affectiva has used fake data to increase the diversity of its dataset representing people across age ranges and ethnicities.

Spil.ly company was developing an AR app, for this, they need to train machine learning algorithms to closely track human bodies in the video. It gets really complex and expensive. So their engineers began creating their own labeled images to train the algorithms, by adapting techniques of fake data. The models they train on purely synthetic data are pretty much equivalent to models we train on actual data.

One of the organizations to seize the means of fake data production is Tonic.ai. With the data generated by this company these folks can get realistic data sets that have all the qualities of real data, but without the possibility of accidentally disclosing somebody’s personal information. Tonic.ai also has data scientists in its sights.

Fake data is actually preferred over real data because it doesn’t carry the same privacy and security risks. Fake data is in a growth stage at the moment, and a number of startups are targeting the space. The market for synthetic data will grow as companies explore the benefits of the synthetic data approach, particularly when paired with pre-trained models.