Stanford-hosted study examines how AI might affect urban life in 2030 Stanford News

#artificialintelligence

A panel of academic and industrial thinkers has looked ahead to 2030 to forecast how advances in artificial intelligence (AI) might affect life in a typical North American city – in areas as diverse as transportation, health care and education – and to spur discussion about how to ensure the safe, fair and beneficial development of these rapidly emerging technologies. Titled "Artificial Intelligence and Life in 2030," this year-long investigation is the first product of the One Hundred Year Study on Artificial Intelligence (AI100), an ongoing project hosted by Stanford to inform societal deliberation and provide guidance on the ethical development of smart software, sensors and machines. "We believe specialized AI applications will become both increasingly common and more useful by 2030, improving our economy and quality of life," said Peter Stone, a computer scientist at the University of Texas at Austin and chair of the 17-member panel of international experts. "But this technology will also create profound challenges, affecting jobs and incomes and other issues that we should begin addressing now to ensure that the benefits of AI are broadly shared." The new report traces its roots to a 2009 study that brought AI scientists together in a process of introspection that became ongoing in 2014, when Eric and Mary Horvitz created the AI100 endowment through Stanford.


How artificial intelligence will augment the typical North American city of 2030

#artificialintelligence

Artificial intelligence (AI) is no longer the stuff of science fiction books and movies. It is a reality that is already permeating society and is affecting our daily lives. If you use facebook or google, artificial intelligence enables machines to virtually understand what you're looking for or to augment your network. In some countries and states in the US, self-driving cars have already taken to the streets. Elsewhere, companies are experimenting with bots that can act like lawyers, tellers, or doctors.


Experts Forecast the Changes Artificial Intelligence Could Bring by 2030

#artificialintelligence

Titled "Artificial Intelligence and Life in 2030," this year-long investigation is the first product of the One Hundred Year Study on Artificial Intelligence (AI100), an ongoing project hosted by Stanford University to inform societal deliberation and provide guidance on the ethical development of smart software, sensors and machines. "We believe specialized AI applications will become both increasingly common and more useful by 2030, improving our economy and quality of life," said Peter Stone, a computer scientist at The University of Texas at Austin and chair of the 17-member panel of international experts. "But this technology will also create profound challenges, affecting jobs and incomes and other issues that we should begin addressing now to ensure that the benefits of AI are broadly shared." The new report traces its roots to a 2009 study that brought AI scientists together in a process of introspection that became ongoing in 2014, when Eric and Mary Horvitz created the AI100 endowment through Stanford's School of Engineering. AI100 formed a standing committee of scientists and charged it with commissioning reports on different aspects of AI over the ensuing century.


What is AI in business and why does it matter?

#artificialintelligence

Nearly 70 years since Alan Turing posed the question "Can machines think?" artificial intelligence (AI) is finally beginning to have an impact on the global economy. Proponents of AI believe that it has the potential to transform the world as we know it. So what is AI in business and what are the main themes? Within the AI industry, there are seven key technology categories: machine learning, data science, conversational platforms, computer vision, AI chips, smart robots and context-aware computing. Machine learning (ML) is an application of AI that gives computer systems the ability to learn and improve from data without being explicitly programmed.