So far, mining chip producers have delivered the promise of more efficient chips leading to an increase in the mining hashrate from 50 exahashes per second to 90 exahashes per second in the past six months. This helps miners process more hashes per second (i.e., the hashrate) to get to the right hash and attain the mining reward. The only way to keep mining profitably is to invest in better chips that produce more computing power with lower electricity consumption. Miners run not one but multiple high-end graphics processing units to mine Bitcoin, which is an electricity-intensive process. Two primary factors that compensate for the halving of rewards are an increase in the price of Bitcoin and advanced chips with high computing power. This means that following the next halving date - miners who mine Bitcoin would receive half the reward per block compared to what they do now. The bad news, however, is that the reward to mine Bitcoin is halved almost every four years. Transactions of cryptocurrencies, such as Bitcoin, are validated through a decentralized process called "mining." Mining requires miners across the world to deploy powerful computers to find the solution - or the hash - to a cryptographic puzzle that proves the legitimacy of each transaction requested on the blockchain. Similar to AI, the decentralized digital economy sector also relies on high computing power. I've observed it is driving the development in various technological landscapes, such as AI, graphics computing, 5G and cryptocurrency. The results of this investment are regularly seen in the form of advanced, more compact chips capable of producing higher computing power while consuming lesser energy.įor new technological breakthroughs, computing power itself has become the new "production material" and "energy." Computing power is the fuel of our technologically advanced society. They are constantly testing and modifying their best chips to produce more productive versions of them. It is almost as if computing power is now an asset into which investors and organizations are pouring millions of dollars. Tech companies are leaving no stone unturned to rise to this demand. The need to build better AI has made it mandatory to keep up with this requirement for more computing power. The computing power required by AI has been doubling roughly every three and a half months since 2012. The more the computing power, the faster we can feed the data to train the AI system, resulting in a shorter span for the AI to reach near-perfection, i.e., human-level intelligence. It is only possible to do that if we have powerful computers that can process millions of data points every single second. To reach that level of precision, an AI model needs to be fed a tremendous amount of data. ![]() It then learns to differentiate between two different pitches of voices or to differentiate faces based on various facial features. Consider this: For an AI system to recognize someone's voice or identify an animal or a human being, it first needs to process millions of audio, video or image samples.
0 Comments
Leave a Reply. |