The co-founder of DeepMind proposes that the New Turing Test should evaluate the success of AI in generating wealth.
The concept of artificial intelligence (AI) has been around for decades, but it is only in recent years that it has become a reality. With the rapid advancements in technology, AI has become an integral part of our lives, from virtual assistants to self-driving cars. However, the question of how to evaluate the success of AI remains a topic of debate.
The Turing Test, proposed by Alan Turing in 1950, is a test of a machine’s ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human. The test involves a human evaluator who engages in a natural language conversation with a machine and a human. If the evaluator cannot distinguish between the machine and the human, the machine is said to have passed the Turing Test.
However, the co-founder of DeepMind, Demis Hassabis, proposes that the New Turing Test should evaluate the success of AI in generating wealth. According to Hassabis, the ability of AI to generate wealth is a more relevant and practical measure of its success than the ability to mimic human behavior.
Hassabis argues that the primary goal of AI is to create value, and the ability to generate wealth is a clear indication of its success in achieving this goal. He suggests that the New Turing Test should evaluate the ability of AI to create new products and services, increase productivity, and drive economic growth.
Hassabis’s proposal has sparked a debate among AI experts and economists. Some argue that the ability to generate wealth is not the only measure of AI’s success, and that other factors such as social impact and ethical considerations should also be taken into account. Others argue that the ability to generate wealth is a crucial measure of AI’s success, as it is ultimately what drives innovation and progress.
One of the main advantages of using wealth generation as a measure of AI’s success is that it provides a clear and objective metric for evaluating its impact. Unlike other measures such as social impact, which can be difficult to quantify, wealth generation can be easily measured in terms of GDP growth, job creation, and other economic indicators.
Another advantage of using wealth generation as a measure of AI’s success is that it aligns with the goals of businesses and investors. Companies and investors are primarily interested in the ability of AI to create value and generate profits, and the ability to generate wealth is a clear indication of its potential to do so.
However, there are also some potential drawbacks to using wealth generation as a measure of AI’s success. One concern is that it may lead to a narrow focus on short-term profits at the expense of long-term sustainability and social impact. Another concern is that it may lead to a concentration of wealth and power in the hands of a few individuals and companies, exacerbating existing inequalities.
In conclusion, the proposal by Demis Hassabis to use wealth generation as a measure of AI’s success is an interesting and thought-provoking idea. While there are some potential drawbacks to this approach, it provides a clear and objective metric for evaluating the impact of AI on the economy and society. Ultimately, the success of AI will depend on its ability to create value and drive progress, and the ability to generate wealth is a crucial part of this equation.