Among the many predictions about the future of artificial intelligence is that models will one day be able to conduct scientific research on their own, leaving humans out of the equation. Already, they can write code, run experiments and search scientific literature, but carrying out open-ended research would require a significant leap in ability.Among the many predictions about the future of artificial intelligence is that models will one day be able to conduct scientific research on their own, leaving humans out of the equation. Already, they can write code, run experiments and search scientific literature, but carrying out open-ended research would require a significant leap in ability.Computer Sciences[#item_full_content]
Among the many predictions about the future of artificial intelligence is that models will one day be able to conduct scientific research on their own, leaving humans out of the equation. Already, they can write code, run experiments and search scientific literature, but carrying out open-ended research would require a significant leap in ability.Among the many predictions about the future of artificial intelligence is that models will one day be able to conduct scientific research on their own, leaving humans out of the equation. Already, they can write code, run experiments and search scientific literature, but carrying out open-ended research would require a significant leap in ability.[#item_full_content]
A new paper from Monash University and Australia’s national science agency, CSIRO, argues that effective human–robot teams depend on alignment: a shared and up-to-date understanding of each teammate’s capabilities and limitations, the task and situation, and their respective roles and timing.A new paper from Monash University and Australia’s national science agency, CSIRO, argues that effective human–robot teams depend on alignment: a shared and up-to-date understanding of each teammate’s capabilities and limitations, the task and situation, and their respective roles and timing.Robotics[#item_full_content]
Organizations can describe artificial intelligence using familiar words like “thinking,” “writing” and “learning.” But according to Carnegie Mellon University historian Christopher Phillips, those terms may tell us as much about how humans talk about technology as they do about the technology itself.Organizations can describe artificial intelligence using familiar words like “thinking,” “writing” and “learning.” But according to Carnegie Mellon University historian Christopher Phillips, those terms may tell us as much about how humans talk about technology as they do about the technology itself.Machine learning & AI[#item_full_content]
The human hand is a dexterous and versatile machine. Scientists have conventionally tried to replicate these traits in robots by copying the biological mechanics of the hand, resulting in complex and difficult-to-control structures. An alternative solution to these challenges was proposed in a new study published in Advanced Science: the BioflexBot, a novel robot that mimics and even exceeds core motions of the hand with a simple design.The human hand is a dexterous and versatile machine. Scientists have conventionally tried to replicate these traits in robots by copying the biological mechanics of the hand, resulting in complex and difficult-to-control structures. An alternative solution to these challenges was proposed in a new study published in Advanced Science: the BioflexBot, a novel robot that mimics and even exceeds core motions of the hand with a simple design.[#item_full_content]
Business leaders and investors face a deepening paradox: Companies are pouring more money into artificial intelligence than ever, but they’re not seeing the productivity gains they expect.Business leaders and investors face a deepening paradox: Companies are pouring more money into artificial intelligence than ever, but they’re not seeing the productivity gains they expect.Business[#item_full_content]
Artificial intelligence is proving to be transformative in its ability to work with language and images. Now, with a growing push to apply AI to scientific discovery, Caltech’s Anima Anandkumar says there is a crucial ingredient missing from most AI models: the ability to understand the physical world. Take, for example, weather models, says Anandkumar, Caltech’s Bren Professor of Computing and Mathematical Sciences. If you want an AI model to predict weather, it must understand chaotic physical systems, like how the atmosphere changes around the planet and over time.Artificial intelligence is proving to be transformative in its ability to work with language and images. Now, with a growing push to apply AI to scientific discovery, Caltech’s Anima Anandkumar says there is a crucial ingredient missing from most AI models: the ability to understand the physical world. Take, for example, weather models, says Anandkumar, Caltech’s Bren Professor of Computing and Mathematical Sciences. If you want an AI model to predict weather, it must understand chaotic physical systems, like how the atmosphere changes around the planet and over time.Computer Sciences[#item_full_content]
Artificial intelligence is proving to be transformative in its ability to work with language and images. Now, with a growing push to apply AI to scientific discovery, Caltech’s Anima Anandkumar says there is a crucial ingredient missing from most AI models: the ability to understand the physical world. Take, for example, weather models, says Anandkumar, Caltech’s Bren Professor of Computing and Mathematical Sciences. If you want an AI model to predict weather, it must understand chaotic physical systems, like how the atmosphere changes around the planet and over time.Artificial intelligence is proving to be transformative in its ability to work with language and images. Now, with a growing push to apply AI to scientific discovery, Caltech’s Anima Anandkumar says there is a crucial ingredient missing from most AI models: the ability to understand the physical world. Take, for example, weather models, says Anandkumar, Caltech’s Bren Professor of Computing and Mathematical Sciences. If you want an AI model to predict weather, it must understand chaotic physical systems, like how the atmosphere changes around the planet and over time.[#item_full_content]
AI data centers get a bad rap, not least because of concerns that they drive climate change by consuming massive amounts of electricity. But one potentially larger impact of AI on global carbon emissions may be that it’s helping the fossil fuel industry become more productive. That’s the main finding of a paper published in the journal npj Climate Action that looked at how boosting productivity across both clean and dirty energy sources affects net CO2 emissions.AI data centers get a bad rap, not least because of concerns that they drive climate change by consuming massive amounts of electricity. But one potentially larger impact of AI on global carbon emissions may be that it’s helping the fossil fuel industry become more productive. That’s the main finding of a paper published in the journal npj Climate Action that looked at how boosting productivity across both clean and dirty energy sources affects net CO2 emissions.Energy & Green Tech[#item_full_content]
A new semiconductor integration platform developed at the Institute of Science Tokyo combines advanced chip packaging with high-density interconnects and improved thermal management. This combination of three complementary technologies helps overcome the key challenges in building high-performance artificial intelligence (AI) hardware.A new semiconductor integration platform developed at the Institute of Science Tokyo combines advanced chip packaging with high-density interconnects and improved thermal management. This combination of three complementary technologies helps overcome the key challenges in building high-performance artificial intelligence (AI) hardware.Hardware[#item_full_content]
