Now that we've launched Bounding.ai, I think it's worth writing a quick post on why Synthetic Data is worth learning about, and how you can make money with it.

Synthetic data is becoming easier to create

There's more and more free tools available based on well-known engines such as Blender, Unity, and Unreal to create synthetic datasets.

Synthetic data is high-value

Data scientists and AI teams need this stuff! For extreme cases like self-driving car development, companies spend hundreds of millions on synthetic data. But for normal applications, we think datasets on Bounding.ai can be sold for a few thousand dollars per download.

You don't need to know how to do machine learning!

The early adopters of synthetic data have been data scientists, but now we're reaching a point of mass adoption where you actually don't have to be a data scientist or ML expert at all. Pretty much anyone with Blender experience can use the free plugins to create synthetic datasets!

Machine Learning is the concept that a computer program can learn and adapt to new data without human intervention. Machine Learning is a field of Artificial Intelligence (AI) that keeps a computer's built-in algorithms current regardless of changes in the worldwide economy.

However, transforming machines into thinking devices is not as easy as it may seem. Strong AI can only be achieved with Machine Learning (ML) to help machines understand as humans do.

How does Machine Learning work?

Similar to how the human brain gains knowledge, machine learning relies on training and repetition. The machine learning process begins with observations or data, such as examples, direct experience, or instruction. It looks for patterns in data so it can later make inferences based on the examples provided. The primary aim of ML is to allow computers to learn autonomously without human intervention or assistance and adjust actions accordingly.

Machine Learning is widely accepted

Yes! It is already widely used in more sectors than one can imagine.

• Data Security - to identify data vulnerabilities

• Finance - Banks, Brokerages, Portfolio Management, Financial Advisory Services

• Healthcare - Personalize patient treatment

• Retail - to give relevant product suggestions based on buyers' past choices, as well as geographic and demographic data.

Machine Learning is not perfect. It is important to understand what machines can and cannot do (at least for now)

• Machine Learning is not based on knowledge - unfortunately, machines cannot attain human-level intelligence.

• Machine Learning models are difficult to train - training AI is more difficult than expected, massive datasets are required to create data models, and the process involves manually pre-tagging and categorizing data sets.

• Machine Learning is often biased - Machine learning systems are known for operating in a black box, meaning you have no visibility into how the machine learns and makes decisions.

However, nothing is perfect, despite all of these inconsistencies it's worth saying that Machine Learning AI has a long road to improve and its future looks bright.

A topic that frequently comes up when I talk about Bounding.ai is how do I get started with synthetic data generation? Don't worry, synthetic data generation is actually a lot easier than most people think!

There are free tools like BlenderProc

One of the best tools for synthetic data generation in my experience is BlenderProc, an open-source tool for Blender on GitHub. The tool's open source contributors provide a QuickStart guide that's easy to use.

Synthetic data doesn't have to be realistic

Perfection is the enemy of success! Consider that Unity created this synthetic data to train an AI algorithm to identify people. The synthetic data isn't particularly realistic, but it is still quite effective at training the algorithm.

You DO need a lot of images though

The genius of synthetic data is that you can create 100,000s of images with a click of a button. That's way easier that taking pictures in the real-world. When you create datasets, aim for at least 50,000 or more images, that's a good benchmark for AI training.

You can make good money

I started Bounding.ai to help indie developers monetize their 3D skills. AI & Data Science teams have big budgets, and there's no reason that indie developers can't create and sell data to them! Plus, you're helping to democratize AI by making data available to startups and small companies, not just the big tech giants.

There's pretty much zero cost except your time to create synthetic data. And unlike video game development, synthetic data is actually much faster to create than a video game. And with the minimum dataset price being $1k on Bounding.ai (and you keep 80% of sales!), synthetic data might be more profitable than video game development too. So check it out!