How blockchain technology can save AI - CryptoSlate | The CYDigital Blog | Scoop.it
Much of what we term AI today results from the application of Machine Learning to extraordinarily large amounts of data. To be precise, it is the application of so-called Deep (Machine) Learning techniques that has enabled the rise of voice search and voice-activated assistants such as Siri, healthcare innovations in areas such as cancer diagnosis and treatment, face recognition such as AWS Rekognition and the broader areas of image and video analysis and recognition, machine translation including tools like Bing Translator, speech recognition tools and the emergence of the so-called self-driving automobiles and more. Technically, we should call this the Deep Learning resurgence, and not the AI resurgence.

Blockchain platforms have led to incredible advances in the design and development of decentralized applications and systems and have been applied to domains ranging from cryptocurrencies to enterprise supply chains. More importantly, there are two capabilities that blockchains enable due to their inherent decentralized implementation.

First, blockchains provide the ability for users to be in control of their data and to decide when, where, to whom, and for how long to provide access to their data i.e. blockchains are the anti-thesis of systems that intrinsically and automatically exploit the user’s private data. Further, with the advent of Zero-knowledge proofs, blockchains now have the ability to reveal nothing about a transaction except that it is valid.

Second, blockchains are designed without a central authority or system. Therefore, in order to achieve agreement on both data and transactions, blockchains use a variety of fault-tolerant consensus algorithms. While there is an assortment of consensus algorithms, all of them share similar characteristics with respect to achieving agreement across a decentralized set of nodes (or systems). In particular, a variant called Byzantine Consensus addresses the Byzantine Fault Tolerance problem referred to earlier. Blockchains enable the development of AI applications that are not reliant on a single-vendor implementation with all of their concomitant risks and faults.

Together, these two critical capabilities have the potential to enable today’s Machine Learning implementations to address their Achilles Heel and to enable AI applications that are both not privacy intrusive and not susceptible to the single-vendor Byzantine Faults.