Is High Computing Power A Roadblock in the Path to AI Systems Deployment? Probably Not.

AI and hardware

Artificial intelligence has progressed by leaps and bounds. Naturally, this has been enabled by the increase in investment in research and development in both AI programming and computer hardware. As AI becomes more complex and versatile, it becomes increasingly difficult for the hardware to keep up if there are no parallel improvements.

Computing power and its growth

There is a definite need for improving the computing power in order to sustain the rapid growth and development in AI. Increasing sophistication and gathering of data requires a corresponding growth in the ability of the hardware to tackle the demands of AI. Basically, computing power is the ability of a computer to perform a certain task with speed and accuracy. And as it happens, the computing power required for training the largest AI models, as found by OpenAI, has doubled by a rate of every 3.4 months since 2012. This was not the case before 2012, where computing power doubled at the rate of 2 years, on average. This means that resources used today are doubling at a rate seven times faster than before.

To put this in another perspective, on a linear scale, the compute usage has increased by 300,000 fold until 2019. This points to the fact that there is an exponentially growing demand for AI specific hardware and that this hardware comes at a high cost. An increase in computational costs directly translates into increased carbon emissions, as pointed out in a research from University of Massachusetts, Amherst.

Computing power and AI

According to an IDC whitepaper, economic growth directly correlates with the development of computing. One point of growth in the computing index results into a 3.3% rise in the digital economy and a 1.8% rise in the GDP. As it happens, the development of emerging technologies and computing are mutually beneficial. Therefore, an improvement in the computing power drives an improvement in AI, which in turn drives the improvement in computing power. It highlights the fact that improvement in computing power is an indicator of productivity. Computing power also becomes the determining factor in the growth of AI.

AI Hardware

AI specific hardware is a bit different from the general computer hardware. This hardware, comprising of microprocessors or microchips, is designed for enabling faster processing of AI applications. These applications include machine learning, neural networks and computer vision. One of the most common hardware for AI applications is the GPU which is one of the biggest drivers of progress in AI. GPUs were not really intended for AI specific work but rather for better graphic output for games. However, owing to their massively parallel architecture, GPUs are suited for performing calculations required by machine learning algorithms. GPUs are also high in demand due to their ability to mine cryptocurrencies. The versatility of uses makes GPUs very desirable, so much so that there is a shortage of supply for the most powerful GPU made by Nvidia.

AI Hardware market trends

The market for AI hardware is growing every day. As the demand soars, the industry is also waiting for a new generation of AI hardware which would have improved capabilities. The most obvious capability is the need for more computational power and lower cost, which is in line with the current trends of hardware development. New materials to manufacture the hardware, new architecture, and faster insights are among the desirable capabilities of the AI hardware. With advancements in the hardware come the corresponding advancement in AI technology, which would only propel the deployment of AI algorithms for multiple purposes and tasks.

Computing power today

Computing power is undeniably the reason why AI has become as powerful and versatile as it is today. Only a few years ago, AI was still rudimentary enough that it was almost difficult to imagine using it in our day to day lives. Our phones could not support AI, the chips in computers could not handle as many calculations as they can today, and technology was lagging, obviously. The development of new technologies such as the optic fiber cable and the 3G and 4G wireless over the course of time have made it possible for us to reap the benefits of AI. Computing power has improved exponentially, so much so that the modern chips in today’s computers can perform trillions of calculations per second. It is astounding as to how far ahead technology has come where chips smaller than our fingers perform at unimaginable speeds, all while consuming a fraction of the energy. And this only makes one optimistic about the future of computing power.

Challenges with computing power

But in any case, computing power remains a challenge, both in terms of cost and in terms of efficiency. Modern AI algorithms require high levels of power which are not yet within the reach of everyone, owing mainly due to cost constraints. And AI has also recently come under fire along with cryptocurrencies for its carbon footprint. Therefore, it becomes necessary that the available computing power is utilized optimally, both in terms of cost and its environmental impact.

Mindsync.ai: your answer to computing power needs

Under such circumstances, a smart thing to do would be to utilize the computing power of devices of a group of users by sharing it on a platform. This would achieve a couple of goals. Firstly, it would significantly reduce the computing power cost because one would not need to invest in AI hardware or pay for cloud computing at their high rates and secondly, users who share their computing power would be able to earn money for doing so, thereby providing an incentive for sharing. And Mindsync.ai is a platform which facilitates just that. It is a decentralized, community driven platform of AI/ML experts and data scientists who can utilize the resources of GPU miners, who are members of the platform as well. By exchanging computing power with the members of Mindsync, it can potentially reduce the computational cost by threefold in comparison to cloud computing. This is in line with the objective of Mindsync, which is to make AI solutions widely accessible, economical and easier for a variety of customers. Cloud services like AWS, Google Cloud and Microsoft Azure can definitely provide the required computing power but Mindsync provides an economical solution compared to them, thereby delivering on its objective for more accessible and economical AI solutions. Head on over to Mindsync.ai to learn more about us and step into the world of decentralized AI solutions!