How Crypto + AI Are Taking Down the Black Box Monopolies

These days, you can’t even seem to go to the toilet without someone stopping to tell you about the wonders of AI. It’s everywhere. But in truth, the proliferation of AI in public consciousness is unsurprising. In just a few short years, its rapid evolution has reshaped industries and revolutionized processes. It’s doing no less than completely redefining what we thought was possible — both on Earth and in space. 

Yet, beneath the glitzy glamour of AI and all its promises lies a troubling reality — and no, we don’t mean the 5% chance it will go full Skynet and end us all. The scarier reality (at present) is the dominance of a few powerful corporations whose AI systems function as opaque “black boxes.” These centralized systems are characterized by inscrutable decision-making processes, raising profound ethical, practical, and regulatory concerns. Some frightening reports include discriminatory hiring algorithms, the misuse of AI for surveillance, and even the Orwellian manipulation of human behavior. 

So…is AI Good or Bad?

The hard truth is that for every wide-eyed AI enthusiast touting its merits, there are as many — if not more — people with genuine concerns. The general consensus is that public trust is being sandpapered away by the near-monopolistic control exercised by these tech giants. These organizations stifle AI training competition and innovation, all while harvesting and hoarding personal data like some sort of starving digital squirrel. Who can blame people for being concerned by the pervading black box mentality of these same centralized AI systems? After all, if we can’t see what is going on behind the scenes, how can we know that it’s for the benefit of all? 

Fortunately, another technology has been growing increasingly popular over the last few years. And it’s through the convergence of these two mighty giants that we can finally see a light at the end of the tunnel.

Cryptocurrency.

Decentralized AI: The Better Way

As most know, cryptocurrencies live on blockchains. It’s this underlying framework that is getting so many people excited. Why? Because the key hallmarks of blockchain networks are threefold:

• They’re decentralized, meaning that they can’t be owned or controlled by big companies or governments.
• They’re immutable, meaning that any records made on them cannot be changed.
• They’re transparent, meaning that any transaction or action can be seen by everyone. For these reasons, integrating blockchain technology with AI is an absolute game-changer.

Indeed, numerous high-profile cryptocurrency AI projects have emerged in the past few years. These leverage the blockchain’s core attributes of immutability, transparency, and decentralization to produce more equitable and ethical solutions. Of course, there are always the inevitable counterarguments of profiteering, market saturation, and scams. Many argue that the AI crypto surge is no better than the memecoin rise. But — as with anything — the key is to locate the good among the bad (and the meh). 

The AI/Crypto Surge

For example, the huge AI training platform NetMind.AI has become understandably popular in a short time due to its decentralization of AI training itself. It leverages idle GPU power from participating global users, rather than resource-hungry centralized data centers. Cleverly, NetMind features its NMT cryptocurrency as both access key and credits, keeping the spotlight on it in the crypto space. 

There’s also Chromia, a relational blockchain platform designed to handle the complex data demands of AI applications. Chromia’s SQL-based infrastructure simplifies the development of decentralized AI models. This makes it accessible to data scientists and machine learning engineers — by extension lowering barriers against entry and driving a healthy ecosystem of collaboration and innovation. There are so many more too, with Fetch.ai, Quasar, and Ocean Protocol being just a handful. These platforms interact with AI in unique ways, but with the full scrutiny that decentralized platforms provide. 

Indeed, the rise of decentralized inference frameworks themselves is another promising development within the space. Technologies such as zkML (zero-knowledge machine learning) and opML (optimistic machine learning) enable users to verify AI model execution without compromising privacy or efficiency. This offers a practical solution to one of the core challenges of decentralized AI, emphasizing both transparency and user empowerment. 

The (Possible) Future of Decentralized AI 

Despite these advancements, the path to widespread adoption of decentralized AI is going to be like playing hopscotch on a minefield. It will be fraught with missteps and dangers until the end is finally reached. 

Why? The real concern lies beyond the scams, bad actors, and all the tangential stuff that detracts from the true value of decentralized and transparent AI. Properly integrating blockchain with AI, in the long run, requires a balancing act between innovation and practicality. In essence, while the idea of decentralization is a powerful ethos, it needs to be increasingly accompanied by tangible benefits that actually enhance user experience. 

Simply put, any solution needs to address real-world problems — rather than just marketing itself as a solution. Presenting a decentralized blockchain AI project as the next step is just lip service if there’s no clear evidence of how it benefits everyday users. Words alone won’t drive the adoption of blockchain as AI’s new home. The value proposition in each instance needs to be clear and compelling.

Still, the potential of decentralized AI to dismantle the black box monopolies is both immense and exciting. But, realizing this potential will require a concerted effort to prioritize responsible innovation. Developers need to ensure that these technologies are designed with transparency, ethics, and user empowerment at their core. In turn, policymakers need to create regulatory frameworks that support decentralization while safeguarding against misuse. 

Then — and only then — can we start loving AI again.