AI News, Wanted
Artificial Intelligence Will Do What We Ask. That’s a Problem.
Uncertainty about our preferences may be key, as demonstrated by the off-switch game, a formal model of the problem involving Harriet the human and Robbie the robot.
“It turns out that uncertainty about the objective is essential for ensuring that we can switch the machine off,” Russell wrote in Human Compatible, “even when it’s more intelligent than us.” These and other partial-knowledge scenarios were developed as abstract games, but Scott Niekum’s lab at the University of Texas, Austin is running preference-learning algorithms on actual robots.
“What’s happening more often as the demonstrations get better, what happens less often?” The latest version of the learning algorithm, called Bayesian T-REX (for “trajectory-ranked reward extrapolation”), finds patterns in the ranked demos that reveal possible reward functions that humans might be optimizing for.
A robot running Bayesian T-REX can efficiently infer the most likely rules of place settings, or the objective of an Atari game, Niekum said, “even if it never saw the perfect demonstration.” Russell’s ideas are “making their way into the minds of the AI community,” said Yoshua Bengio, the scientific director of Mila, a top AI research institute in Montreal.
He said Russell’s approach, where AI systems aim to reduce their own uncertainty about human preferences, can be achieved with deep learning — the powerful method behind the recent revolution in artificial intelligence, where the system sifts data through layers of an artificial neural network to find its patterns.
Artificial Intelligence Processes Provide Solutions to Gadolinium Retention Concerns
Gadolinium-based contrast agents (GBCAs) are widely used in medical imaging to provide essential diagnostic information from magnetic resonance imaging (MRI) scans.
However, recent research has shown that gadolinium agents may remain in the body — including the liver, bone and brain — for months or even years after injection.1 This has stoked fears of adverse clinical outcomes from the use of GBCAs.
It remains unclear if there are any neurological signs or symptoms associated with gadolinium retention although it must be considered that up to now, no negative health effects can be attributed to gadolinium retention in the brain.2,3 Despite agreement among the American College of Radiology (ACR) and the U.S. Food and Drug Administration (FDA) that gadolinium retention is not known to have adverse outcomes in patients,2,3 some practitioners still have concerns about the use of GBCAs.
used its advanced, layered machine learning system tools to verify that equimolar doses of the standard concentration gadoteridol (ProHance), a macrocyclic contrast agent which has been shown from animal studies to result in less retention one month after injection compared to other agents,4,5 is non-inferior to the more concentrated gadolinium agent gadobutrol (Gadavist).6 In the GBCA comparison study, 32 patients with glioblastoma (GBM) received both ProHance and Gadavist using double-blind, randomized crossover methodology.
Analysis, Inc., and three blinded readers confirmed that the low dose eGad images achieved image quality equal or greater than the cumulative full dose images. In light of concerns about gadolinium retention, these findings will help radiologists make the best decisions with and for their patients with confidence.
As these studies demonstrate, the use of AI in research can rapidly produce findings that are both transparent and replicable, the hallmarks of scientific validity. There is more in a medical image than the human eye can understand, and AI can help us make better decisions and better treatment plans for our patients.
Artificial intelligence-created medicine to be used on humans for first time
A drug molecule 'invented' by artificial intelligence (AI) will be used in human trials in a world first for machine learning in medicine.
The molecule - known as DSP-1181 - was created by using algorithms that sifted through potential compounds, checking them against a huge database of parameters.
The firm is already working on potential drugs for the treatment of cancer and cardiovascular disease and hopes to have another molecule ready for clinical trials by the end of the year.
'I'm excited to see what I believe is the first example of a new drug now entering human clinical trials, that was created by scientists using AI in a major way to guide and speed up discovery.'
- On 22. januar 2021
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