For the last day of this week I would like to share a post from Trent McConaghy in BigChainDB‘s Medium page. It was posted last 3rd january. It is a long read, but a very complete, worthwhile readership for anyone interested in both Blockchain and Artificial Intelligence (AI) technologies.
This post is quite suited to this blog alma mater: Information Technology cutting-edge most significant developments. The long essay by Trent MacConaghy is full of detailed analysis about the potential of Blockchain technologies, BigChainDB as a new kind of decentralized database, the implications of the new open source and open data paradigms and finally the connection of all this with the trends in Artificial Intelligence and automation technologies. It lists a number of advantages of adopting these technologies as compared to current traditional paradigms.
By traditional database standards, traditional blockchains like Bitcoin are terrible: low throughput, low capacity, high latency, poor query support, and so on. But in blue-ocean thinking, that’s ok, because blockchains introduced three new characteristics: decentralized / shared control, immutable / audit trails, and native assets / exchanges. People inspired by Bitcoin were happy to overlook the traditional database-centric shortcomings, because these new benefits had potential to impact industries and society at large in wholly new ways.
These three new “blockchain” database characteristics are also potentially interesting for AI applications. But most real-world AI works on large volumes of data, such as training on large datasets or high-throughput stream processing. So for applications of blockchain to AI, you need blockchain technology with big-data scalability and querying. Emerging technologies like BigchainDB, and its public network IPDB do exactly that. You no longer need to compromise on the benefits of traditional big-data databases in order to have the benefits of blockchains.
The paragraph above is of particular relevance, outlining in a few dozens words almost all of the value in the rest of the post: how the new database paradigm brought by Blockchains will interact with the increasing data needs of AI algorithms without a need to compromise with centralized data stores.
I will skip the rest of points detailed by the author in this short post. But I highly encourage it to be read through with attentive appraisal.
These blockchain benefits lead to the following opportunities for AI practitioners:
Decentralized / shared control encourages data sharing:
- (1) Leads to more data, and therefore better models.
- (2) Leads to qualitatively new data, and therefore qualitatively new models.
- (3) Allows for shared control of AI training data & models.
Immutability / audit trail:
- (4) Leads to provenance on training/testing data & models, to improve the trustworthiness of the data & models. Data wants reputation too.
Native assets / exchanges:
- (5) Leads to training/testing data & models as intellectual property (IP) assets, which leads to decentralized data & model exchanges. It also gives better control for upstream usage of your data.
A final note: this post is not intended to say anything definitive about the technologies involved in Blockchains and Artificial Intelligence. These are technologies currently undergoing developments, research and further improvements. There have been already some successes, sure, but the challenges that remain should be viewed with due respect. Not least the economic, regulatory, political and social challenges, that are of a more uncertain or slow nature than the scientific/technological ones.