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The long-standing argument against AI fully replacing humans has been that the technology doesn’t yet possess the emotional bandwidth and cognitive ability that humans have. But we are a daily witness to the growth of artificial intelligence in areas of art, medicine, and scientific research.
In light of these changes, there is growing concern about the future of employment worldwide. Surveys suggest that one-fourth of all jobs are at risk of being automated, which understandably makes people worry about job security. However, there is evidence to suggest that the impact of automation may not be as dire as some may fear.
Contrary to popular belief, the automation of jobs is not necessarily synonymous with the elimination of jobs. Instead, it is likely to change the nature of occupations by taking over easy and repetitive tasks, which will free up employees to focus on work that requires higher-level interpersonal skills. This shift is expected to create a demand for workers who are skilled in areas such as communication, problem-solving, and critical thinking.
In conclusion, the nature of labor is evolving at an unprecedented pace due to the rise of technology. Automation and AI are transforming the types of jobs available in many industries, creating new opportunities for workers with higher-level skills. Although there may be concerns about job security, the impact of automation is expected to change rather than eliminate occupations, providing a chance for workers to develop new skills and remain relevant in an ever-changing job market.
The quarterly reports by these tech behemoths show their efforts to increase AI productivity in the face of growing economic worries.
The US tech giants like Alphabet, Microsoft, Amazon, and Meta are increasing their large language model (LLM) investments as a show of their dedication to utilizing the power of artificial intelligence (AI) while cutting costs and jobs.
Since the launch of OpenAI’s ChatGPT chatbot in late 2022, these businesses have put their artificial intelligence AI models on steroids to compete in the market, CNBC reported on Friday.
The IT behemoths Alphabet, Microsoft, Amazon, and Meta are increasing their large language model (LLM) investments as a show of their dedication to utilizing the power of artificial intelligence (AI) while cutting costs and jobs.
As highlighted in a recent article, the release of ChatGPT in its various guises, along with numerous other generative AI-based technologies, has heralded a flurry of articles, studies, and headlines lauding the often catastrophic impact such technologies will have on jobs and society more broadly.
It’s the kind of simplistic and often doom-laden narrative that so often thrives on social media. As Greg Berman and Aubrey Fox remind us in their recent book Gradual, however, change seldom happens rapidly and almost never happens in such a linear fashion.
The study surveyed executives from 200 large companies and found that while most recognized the importance of new technologies, many were unrealistic about their ability to transform their businesses. The survey revealed that companies that took a more measured and realistic approach to technology adoption tended to be more successful.
Overall, these studies suggest that technological predictions are often overly optimistic and that many new technologies fail to meet their initial expectations.
So while many technologies are portrayed as being rapidly adopted, the reality is usually very different. The challenges are perhaps best summed up by Daniel Patrick Moynihan, who famously remarked that when considering change, “we constantly underestimate difficulties, overpromise results, and avoid any evidence of incompatibility and conflict, thus repeatedly creating the conditions of failure out of our desperate need for success.”
In Silicon Valley, some of the brightest minds believe a universal basic income (UBI) that guarantees people unrestricted cash payments will help them to survive and thrive as advanced technologies eliminate more careers as we know them, from white collar and creative jobs — lawyers, journalists, artists, software engineers — to labor roles. The idea has gained enough traction that dozens of guaranteed income programs have been started in U.S. cities since 2020.
Yet even Sam Altman, the CEO of OpenAI and one of the highest-profile proponents of UBI, doesn’t believe that it’s a complete solution. As he said during a sit-down earlier this year, “I think it is a little part of the solution. I think it’s great. I think as [advanced artificial intelligence] participates more and more in the economy, we should distribute wealth and resources much more than we have and that will be important over time. But I don’t think that’s going to solve the problem. I don’t think that’s going to give people meaning, I don’t think it means people are going to entirely stop trying to create and do new things and whatever else. So I would consider it an enabling technology, but not a plan for society.”
The question begged is what a plan for society should then look like, and computer scientist Jaron Lanier, a founder in the field of virtual reality, writes in this week’s New Yorker that “data dignity” could be an even bigger part of the solution.
Now, we live in a time in which AI research and technology is advancing exponentially. In the coming years, AI — and ultimately AGI — has the potential to drive one of the greatest social, economic and scientific transformations in history.
That’s why today Sundar is announcing that DeepMind and the Brain team from Google Research will be joining forces as a single, focused unit called Google DeepMind. Combining our talents and efforts will accelerate our progress towards a world in which AI helps solve the biggest challenges facing humanity, and I’m incredibly excited to be leading this unit and working with all of you to build it. Together, in close collaboration with our fantastic colleagues across the Google Product Areas, we have a real opportunity to deliver AI research and products that dramatically improve the lives of billions of people, transform industries, advance science, and serve diverse communities.
By creating Google DeepMind, I believe we can get to that future faster. Building ever more capable and general AI, safely and responsibly, demands that we solve some of the hardest scientific and engineering challenges of our time. For that, we need to work with greater speed, stronger collaboration and execution, and to simplify the way we make decisions to focus on achieving the biggest impact.
