As industries race to find a competitive advantage – any edge to beat out the competition – AI and machine learning have emerged as key strategic tools– but one of the greatest weaknesses of AI and specifically, autonomous machine learning, is a lack of affordable computing power. Large technology companies often spend millions on warehouses filled with servers to achieve more power. These “server farms” often cost millions more when companies must add in the additional expenses of providing energy, cooling, technicians, upgrades, etc. And according to industry experts, this will only get worse. According to AI Impacts, “the cost per unit of computation is decreasing by an order of magnitude every 4-12 years”, but, “the cost of the largest experiments is increasing by an order of magnitude every 1.1 – 1.4 years”.
So clearly, demand for AI computing power is outstripping supply with current usage. Without major changes in the AI Computing trendlines, the trend becomes unsustainable within 3.5 – 10 years. Large investors and users like the US Government and private businesses (Amazon, Google, etc.) will control more and more of available computing power. Small businesses will have little hope of accessing affordable computing power in necessary quantities.
Titan’s distributed neural network (DNN) will help solve these issues by leveraging the power of the shared economy. By linking thousands of privately-owned computers via idle CPUs and GPUs across the globe, Titan aims to harness a massive amount of available computing power, and in the process, create new, competitive revenue streams for the owners of these assets while providing businesses with affordable cloud computing solutions.
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