Among the rapidly advancing frontiers of data science are two critical yet distinct concepts: synthetic data and generative AI (Gen AI). These are two different approaches to the manipulation and generation of information, yet are often erroneously labelled together as the same thing.

Synthetic Data refers to the creation of data that imitates real-world data; in the case of computer vision, synthetic data is created using computer graphics techniques. It’s a powerful tool used to augment datasets, enabling more robust models, especially when real-world data is scarce or sensitive. Its applications are vast, ranging from healthcare simulations to the training of autonomous vehicles such as warehouse robots.

Its closeness to the real world has allowed it to find success in AI algorithm development and testing (for example, with autonomous vehicle companies “driving” billions of simulated miles). And the success of this approach is driving (pun intended!) its rapid uptake. A recent Gartner report has predicted that most data used to train machine learning models will be synthetic and automatically generated by the end of 2024, potentially over 60%.

Gen AI, on the other hand – a technology with diverse applications across industries such as art, writing, software, product design, healthcare, finance, and gaming – involves the creation of content by AI systems. Major players like Microsoft, Google, and Baidu, along with smaller companies, have significantly invested in its development, but concerns over its misuse remain strong.

Though there is an intersection between these two realms, understanding the nuanced differences is crucial for both practitioners and decision-makers leveraging these technologies.

Defining distinctions

There is a lot of AI hype fuelling misconceptions about its use. This hype has a tendency to merge the various subsets of AI together, removing the different nuances of how these technologies work. It is this generalisation that is leading to Gen AI being equated with synthetic data.

A key distinction lies in how data is created and used. Synthetic data is artificially created using algorithms, statistical models and real-world data and looks to mimic real-world patterns. Gen AI models learn the patterns and structure of their input training data (gathered by scraping any sources of data such as social media content). and then generate new data that has similar characteristics.They use advanced machine learning components such as ‘transformers’ that allow them to place content in its given context.

Assessing the ability to model specific data between synthetic and generative output can illustrate the difference between the two. Synthetic data fills in the gaps where real-world data is lacking, by creating specific scenarios and mimicking with CGI. For example, when it comes to automotive in-cabin testing, it is not feasible to test extreme weather events and all edge-case scenarios in the real world. Synthetic data can create virtual environments that reflect the real-world simulating camera, sensor and lens types not available to generative AI and test a variety of interactions within this. Not only does this environment allow engineers to recreate real-world situations, but also accurately predicts them as if using real-world data.

A synthetic Image for in cabin monitoring from the Mindtech Chameleon Synthetic Data platform.  The platform is able to mimic complex real world systems such as NiR cameras, with precise camera positioning that is not possible with Generative AI.

Gen AI, on the other hand, creates “new” content in itself by learning from the data it is fed, such as images, text and music. This data doesn’t directly reflect real-world data but works to use the same statistical qualities as its real-world equivalent to generate new content.

Gen AI’s achilles’ heels

The internet can be home to a disorderly range of inaccurate datasets, which can also carry legal and privacy implications. This can create a limited amount of usable real-world data for AI training. With scarcer and less diverse datasets, the likelihood for bias and generalisation substantially increases, and this scarcity can also produce an insensitivity by AI models to rare occurrences and scenarios.

Likewise, if the data generated is suboptimal, and this data then enters the world AI models continue to learn off, this creates a domino effect of poorer data being generated each time that moves further away from its real-world counterpart. Given Gen AI learns off the data it is fed, using it from the outset for AI training does not represent the optimal approach, especially if it uses such suboptimal data.

One of the major issues is that Gen AI is learning from datasets off the internet that uphold many human biases prevalent in our society. The new content created perpetuates and even worsens stereotypes, such as with race and gender, that currently exist in the data sources available for machine learning training. This is creating major ethical issues in safely and effectively developing Gen AI models.

The other major issue for Generative AI, is its inability to create any genuinely new data.  It is simply manipulating the data it has sponged whilst learning, but cannot create new scenarios and items – for example if a generative AI system has never seen a tin of a supermarket own brand baked beans, it will not be able to create it in an image representing that

Image from Stable Diffusion text to Image generator – prompt of “a tin of Branston® baked beans in a man’s hand” – Note the incorrect text, the very odd looking hand, and the incorrect tin.

The final issue that really hinders the usage of generative AI for any form of computer vision development compared to synthetic data, is the lack of any available annotation.  Annotations are critical to both model development and evaluation. Synthetic data from platforms like MIndtech’s Chameleon offer a rich set of annotation, including such elements as full instance segmentation, facial and skeleton keypoints, and per pixel depth and surface normal information.

Navigating ethical waters

Synthetic data is created in controlled environments with human input to directly reflect specific real-world data, as opposed to Gen AI’s self-learning process, which can be unhinged, produce inaccurate content and uphold bias, especially as the data used to train generative solutions is both untracked and exhibits the bias of the data sources such as the social networks data is often scraped from.. Therefore, using synthetic data for AI training could offer innovative solutions to foster the emergence of robust, unbiased AI systems.

While synthetic data utilises real-world data  to ensure it matches the real world structurally and statistically, it can test infinitely more scenarios, be modified to mitigate bias, and overcome data privacy issues, such as not using personal identifiable information (PII). This data can then be used as a trusted dataset to train AI algorithms. It’s an approach that could shape the future of AI development and deployment, forming clear waters for responsible and ethically sound AI advancements.

While major players continue to invest heavily in Gen AI, nonetheless, there are apprehensions surrounding its potential misuse, ranging from cybercrime to the creation of deceptive fake news and deepfake content designed to manipulate individuals. Therefore it is paramount that the tech community ensures PII is not exposed in training and that it adheres to regulations that prioritise its responsible development. Synthetic data represents one clear route to achieving this.

Who comes out on top?

GenAI has been taking the tech world by storm. Its growing use has created much hype over AI’s use and role in society, with such discussion often equating synthetic data as the same thing. But knowing the differences between the two is key – there is a reason why the use of synthetic data is making rapid progress. Generative AI is great to make a greetings card, or a small graphic in a presentation, but for anything related to AI model development …

    Generative AI – great for a greetings card image not for AI model development and testing

… that’s where we turn to Synthetic data.  As concerns rightly build surrounding AI’s various achilles’ heels, including bias and data scarcity, synthetic data provides a route to forming a safe and responsible passage in the next stage of its development. In this respect, there is one clear winner , if you are developing and testing AI models, Synthetic data holds the key.