The Growing Computation Ceiling Affecting Machine Learning Models Globally; and How to Fix It

Machine learning is an exciting industry that will ultimately pave the way for global automation. However, it is also an expensive process due to the growing computational cost affecting this industry vertical. Therefore, finding solutions to that pressing problem remains paramount in 2022 and beyond.

The Growing Cost of Machine Learning

It is appealing to think of machine learning – and artificial intelligence – as processes that do not involve humans. But unfortunately, that is not entirely accurate. Building a machine learning algorithm requires tremendous input and computing power. Those aspects have to be taken care of by humans who “feed” these algorithms new data so they can become smarter, better, and more advanced.

As an algorithm becomes smarter, it will require more powerful hardware. Having access to petabytes of data is intriguing, but that information needs to be stored somewhere. Moreover, it needs to be accessible, requiring robust hardware with multiple redundancies. It is a very cost-intensive aspect of automating business workflow, although costs will come down eventually. 

Combined with the cost of integrating AI and machine learning for specific business models, the costs currently do not outweigh the benefits for most companies. Technology giants like Google, NVIDIA, Meta, and others can find ways to keep their overall costs down. However, a smaller company or new business will not have that option right away, delaying their integration of these exciting technologies. 

Solving this issue of “diminishing returns” requires a very different approach altogether. No one questions the potential of machine learning and AI; improving performance requires more data points and better hardware. Bringing down the overall costs is mandatory to make this business model sustainable. 

A Decentralized Approach Is A Solution

Acquiring more computational power for machine learning or AI development is a painstaking process. More often than not, researchers have to rely on conglomerates providing the necessary hardware, inflating overall costs, and introducing potential restrictions. Moreover, using large third-party providers introduces a layer of centralization, which acts as a point of failure. 

Decentralizing access to vast amounts of computing power can provide much-needed relief. However, it is easier said than done, even though there is tremendous computing power in the hands of everyday consumers, small businesses, and so forth. Advances in technology make smartphones more powerful than home computers, yet there needs to be an incentive for device owners to share their spare resources. 

A peer-to-peer network, such as provided by Morphware, may be the catalyst to make computational power more accessible. Video game players often have the latest and most expensive hardware in their machines. Moreover, these are the people who often possess idle processing capacity, which they can monetize through Morphware. Gamers can use idle power to train models, enhance machine learning, and much more. 

As a two-sided marketplace, Morphware can serve the needs of data scientists. These scientists can access remote computing power shared by owners of computers – similar to AWS – but at much more democratic prices and through a better user interface. Moreover, owners of excess computing power can sell their excess capacity at a preferred price and reap the rewards accordingly. 

Closing Thoughts

There is much computing power in the world that doesn’t see much use during most hours of the day. Gaming enthusiasts build incredibly powerful rigs yet struggle to monetize their idle power. Morphware creates an abridge between users looking to make some money and researchers needing democratically-priced hardware. Furthermore, the remote hardware approach foregoes setting up data centers and ensures geographical decentralization. 

Peer-to-peer interaction applies to many business models, including the distribution of computing power. It is a big step forward to reducing overall machine learning and AI development costs. Additionally, it enables other high-intensity computational tasks to be “outsourced” through financial incentives without a hefty price tag.