Insilico Medicine is radiating heat amid the biotech winter, kindling its fires with a Sanofi collaboration that could be worth up to $1.2 billion in biobucks—the AI drug discovery company’s larges | Insilico Medicine is radiating heat amid the biotech winter, kindling its fires with a Sanofi collab that could be worth up to $1.2 billion in biobucks—the AI drug discovery company’s largest deal to date.
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Many fear that future automation may turn out to be the bane of civilization rather than its liberator. How do we ensure we take the path to a prosperous world and not one of ruin?
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Credits:
What Happens If We Can’t Leave Earth?
Science & Futurism with Isaac Arthur.
Episode 368, November 10, 2022
Produced & Narrated by Isaac Arthur.
Written By:
Isaac Arthur.
Editors:
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Free AI software is primed to strip away the control of studios and actors who appears in films.
Wearable technology is capable of tracking various measures of human health and is getting better all the time. New research shows how this could come to mean real-time feedback on posture and body mechanics. A research team at Cornell University has demonstrated this functionality in a novel camera system for the wrist, which it hopes to work into smartwatches of the future.
The system is dubbed BodyTrak and comes from the same lab behind a face-tracking wearable we looked at earlier in the year that is able to recreate facial expressions on a digital avatar through sonar. This time around, the group made use of a tiny dime-sized RGB camera and a customized AI to construct models of the entire body.
The camera is worn on the wrist and relays basic images of body parts in motion to a deep neural network, which had been trained to turn these snippets into virtual recreations of the body. This works in real time and fills in the blanks left by the camera’s images to construct 3D models of the body in 14 different poses.
The number of AI and, in particular, machine learning (ML) publications related to medical imaging has increased dramatically in recent years. A current PubMed search using the Mesh keywords “artificial intelligence” and “radiology” yielded 5,369 papers in 2021, more than five times the results found in 2011. ML models are constantly being developed to improve healthcare efficiency and outcomes, from classification to semantic segmentation, object detection, and image generation. Numerous published reports in diagnostic radiology, for example, indicate that ML models have the capability to perform as good as or even better than medical experts in specific tasks, such as anomaly detection and pathology screening.
It is thus undeniable that, when used correctly, AI can assist radiologists and drastically reduce their labor. Despite the growing interest in developing ML models for medical imaging, significant challenges can limit such models’ practical applications or even predispose them to substantial bias. Data scarcity and data imbalance are two of these challenges. On the one hand, medical imaging datasets are frequently much more minor than natural photograph datasets such as ImageNet, and pooling institutional datasets or making them public may be impossible due to patient privacy concerns. On the other hand, even the medical imaging datasets that data scientists have access to could be more balanced.
In other words, the volume of medical imaging data for patients with specific pathologies is significantly lower than for patients with common pathologies or healthy people. Using insufficiently large or imbalanced datasets to train or evaluate a machine learning model may result in systemic biases in model performance. Synthetic image generation is one of the primary strategies to combat data scarcity and data imbalance, in addition to the public release of deidentified medical imaging datasets and the endorsement of strategies such as federated learning, enabling machine learning (ML) model development on multi-institutional datasets without data sharing.
My AI Girlfriend won’t talk to me unless I renew my annual Netflix subscription.
— You in five years
Everyone has written about the dangers of AI and the uncertain future of humanity, and many of these worries focus on large scale issues like disinformation, democracy, wartime decision making by computers, etc. However, it is the small and personal changes to human life that tend to create the biggest effects down the line. If we assume that a sizeable portion of the population will have, at some point, some form of AI assistant, friend, companion, etc. and that these AI assistants are designed by for-profit companies to perfectly press our psychological buttons, then we are in serious danger of handing ourselves over to the whims of those companies, or governments.
Vibrating tiny robots could revolutionize research.
Individual robots can work collectively as swarms to create major advances in everything from construction to surveillance, but microrobots’ small scale is ideal for drug delivery, disease diagnosis, and even surgeries.
Despite their potential, microrobots’ size often means they have limited sensing, communication, motility, and computation abilities, but new research from the Georgia Institute of Technology enhances their ability to collaborate efficiently. The work offers a new system to control swarms of 300 3-millimeter microbristle robots’ (microbots) ability to aggregate and disperse controllably without onboard sensing.
A new and more efficient way of modeling and designing power electronic converters using artificial intelligence (AI) has been created by a team of experts from Cardiff University and the Compound Semiconductor Applications (CSA) Catapult.
The method has reduced design times for technology by up to 78% compared to traditional approaches and was used to create a device with an efficiency of over 98%.
The team’s findings have been published in the IEEE Open Journal of Power Electronics and IEEE Transactions on Power Electronics.