We have recently announced an important strategic partnership: Neuromation has joined forces with Longenesis, a startup that promises to develop a “decentralized ecosystem for exchange and utilization of human life data using the latest advances in blockchain and artificial intelligence”. Sounds like a winning entry for last year’s bullshit bingo, right? Well, in this case we actually do believe in Longenesis, understand very well what they are trying to do, and feel that together we have all the components needed for success. Let’s see why.

A match made in heaven

I will begin with the obvious: Longenesis is all about the data, and Neuromation is all about processing this data, training state-of-the-art AI models. This makes us ideal partners: Longenesis needs a computational framework to train AI models and highly qualified people to make use of this framework, and Neuromation needs interesting and useful datasets to make the platform more attractive to both customers and vendors.

This is especially important for us because Longenesis will bring not just any data but data related to medicine and pharmaceutics. I have recently written here about AI in medicine, but this is an endless topic: we are at the brink of an AI revolution in medicine, both in terms of developing generic tools and in terms of personalized medicine. Longenesis will definitely be on the frontier of this revolution.

I have personally collaborated with Longenesis CSO Alex Zhavoronkov and his colleagues in Longenesis’ parent company, Insilico Medicine, especially Arthur Kadurin (he is my Ph.D. student and my co-author in our recently published book, Deep Learning). Together, we have been working on frontier problems related to drug discovery: researchers at Insilico have been applying generative adversarial networks (GANs) to generate promising molecules with desired properties. Check out, e.g., our recent joint paper about the DruGAN model (a name that rings true to a Russian ear). Hence, I can personally vouch that not only Alex himself is one of the most energetic and highly qualified people I have ever met, but that his team in both Insilico and Longenesis is made of great people. And this is the best predictor for success of all.

The NeuroPlatform problem

The basic idea of Longenesis blends together perfectly with what is actually an open problem for the NeuroPlatform so far. On the recently released NeuroPlatform, AI researchers and practitioners will be able to construct AI models (especially deep neural networks that could be trained on the GPUs provided by mining pools), upload them in a standardized form into the NeuroMarket, our marketplace of AI models and datasets, and then rent out either the model architectures or already pretrained models.

To train a neural network, you need a dataset. And if you want to use the GPUs on mining pools, you need to transfer the data to these GPUs. The transfer itself also presents a technical problem for very large datasets, but the main question is: how are you going to trust some unknown mining pool from Inner Mongolia with your sensitive and/or valuable data? It’s not a problem when you train on standard publicly available datasets such as ImageNet, the key dataset for modern computer vision, but to develop a customized solution you will still need to fine-tune on your own data.

We at Neuromation have an interesting solution for this problem: we plan to use synthetic data to train neural networks, creating, e.g., generators for rendered photos based on 3D models. In this case, we solve two problems at once: synthetic data is very unlikely to be sensitive, and there is no transfer of huge files because you only transfer the generator and the full dataset does not even have to be stored at any time. But still, you can’t really synthetize the MRI of a specific person or make up pictures of faces of specific people you want to recognize. In many applications, you have to use real data, and it could be sensitive.

Here is where Longenesis comes in. Longenesis is developing a solution for blockchain-based safe storage for the most sensitive data of all: personal medical records. If hospitals and individuals trust Longenesis’ solution with medical data, you can definitely trust this solution with any kind of sensitive or valuable data you might have. Their solution also has to be able to handle large datasets: CT or MRI scans are pretty hefty. Therefore, we are eagerly awaiting news from them on this front.

But this is still only the beginning.

The human data economy

The ultimate goal of Longenesis is to create a marketplace of personal medical records, a token economy where you can safely store and sell access to your medical records to interested parties such as research hospitals, medical researchers, pharmaceutical companies and so on.

Over his or her lifetime, a modern person accumulates a huge amount of medical records: X-rays, disease histories, CT scans, MRIs, you name it. All of this is very sensitive information that should rightly belong to a person — but also very useful information.

Imagine, God forbid, that you are diagnosed with a rare disease. This is a very unfortunate turn of events, but it also means that your medical records suddenly increase in value: now doctors could get not just yet another MRI of some healthy average Joe but the MRI of a person who has developed or will later develop this disease. The human data economy that Longenesis plans to build will literally sweeten the pill in this case: yes, it’s a bad thing to get this disease but it also means you can cash out on your now-interesting medical records. And let’s face it, in most cases people won’t mind to share their MRIs or CT scans with medical researchers at all, especially for a price.

But the price in this case is much less important than the huge possibilities that open up for the doctors to develop new drugs and better treat this rare disease. With the human data economy in place, people will actually be motivated to bring their records to medical researchers, not the other way around.

This could be another point where we can collaborate; here at Neuromation, we are also building a token economy for useful computing, so this is a natural point where we could join forces as well. In any case, the possibilities are endless, and the journey to better medicine for all and for every one in particular is only beginning. Let’s see where this path takes us.

Sergey Nikolenko

Chief Research Officer, Neuromation