Lifeboat Foundation

Computer scientists at New York University and Michigan State University have trained an artificial neural network to create fake digital fingerprints that can bypass locks on cell phones. The fakes are called “DeepMasterPrints”, and they present a significant security flaw for any device relying on this type of biometric data authentication. After exploiting the weaknesses inherent in the ergonomic needs of cellular devices, DeepMasterPrints were able to imitate over 70% of the fingerprints in a testing database.

An artificial neural network is a type of artificial intelligence comprising computer algorithms modeled after the human brain’s ability to recognize patterns. The DeepMasterPrints system was trained to analyze sets of fingerprint images and generate a new image based on the features that occurred most frequently. This “skeleton key” could then be used to exploit the way cell phones authenticate user fingerprints.

In cell phones, the necessarily small size of fingerprint readers creates a weakness in the way they verify a print. In general, phone sensors only capture a partial image of a print when a user is attempting to unlock the device, and that piece is then compared to the phone’s authorized print image database. Since a partial print means there are fewer characteristics to distinguish it than a full print, a DeepMasterPrint needs to match fewer features to imitate a fingerprint. It’s worth noting that the concept of exploiting this flaw is not unique to this particular study; however, generating unique images rather than using actual or synthesized images is a new development.

“Mortal computation” means analog computers marrying AI closely to hardware will put GPT-3 in your toaster for $1 running on a few watts of power.

Researchers at Penn Medicine and Intel Corporation led the largest-to-date global machine learning effort to securely aggregate knowledge from brain scans of 6,314 glioblastoma (GBM) patients at 71 sites around the globe and develop a model that can enhance identification and prediction of boundaries in three tumor sub-compartments, without compromising patient privacy. Their findings were published today in Nature Communications.

“This is the single largest and most diverse dataset of glioblastoma patients ever considered in the literature, and was made possible through federated learning,” said senior author Spyridon Bakas, Ph.D., an assistant professor of Pathology & Laboratory Medicine, and Radiology, at the Perelman School of Medicine at the University of Pennsylvania. “The more data we can feed into machine learning models, the more accurate they become, which in turn can improve our ability to understand, treat, and remove glioblastoma in patients with more precision.”

Researchers studying rare conditions, like GBM, an aggressive type of brain tumor, often have patient populations limited to their own institution or geographical location. Due to privacy protection legislation, such as the Health Insurance Portability and Accountability Act of 1996 (HIPAA) in the United States, and General Data Protection Regulation (GDPR) in Europe, data sharing collaborations across institutions without compromising patient privacy data is a major obstacle for many healthcare providers.

These fundamental units of the brain and nervous system – composed of the cell body, the dendrites and the axon (a long, thin extension responsible for communicating with other cells) – receive sensory input from the external world, send motor commands to our muscles and for transform and relay the electrical signals at every step in between.

“Our novel method of creating ‘mini-brains’ opens the door to finding solutions for various neurological impairments”

Prof. Orit Shefi and doctoral student Reut Plen from the Kofkin Faculty of Engineering at Bar-Ilan University (BIU) have developed a novel technique to overcome this challenge using nanotechnology and magnetic manipulations – one of the most innovative approaches to creating neural networks. Their research was recently published in the peer-reviewed journal Advanced Functional Materials under the title “Bioengineering 3D Neural Networks Using Magnetic Manipulations.”

For decades, the growth of artificial intelligence has fascinated scholars and scientists alike. Today, intelligent machines aid and streamline our lives, but the next fifty or a hundred years may yield even more powerful AI, which could elevate or transform our species. If humans do create sophisticated, super-intelligent machines, how will the growth of artificial intelligence affect the future of humanity?
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In this video, you will see#Elon Musk on Artificial Intelligence and how #A.I am going to change the world in the #future.

Carboncopies attended the Future of Neurotechnology With: Human Intelligence + Artificial Intelligence open discussion with Bryan Johnson and David Eagleman. A picture at our annual Whole Brain Emulation dinner in San Diego. Randal and Jevon after the discussion.

Ray Kurzweil is an author, computer scientist, inventor, futurist and a director of engineering at Google. Kurzweil is a public advocate for the futurist and transhumanist movements, and gives public talks to share his optimistic outlook on life extension technologies and the future of nanotechnology, robotics, and biotechnology.

Recorded 2013

A team of researchers at DeepMind Technologies Ltd., has created an AI application called “DeepNash” that is able to play the game Stratego at an expert level. In their paper published in the journal Science, the group describes the unique approach they took to improve the app’s level of play.

Stratego is a two-player board game and is considered to be difficult to master. The goal for each player is to capture their opponent’s flag, which is hidden among their initial 40 game pieces. Each of the game pieces is marked with a power ranking—higher-ranked players defeat lower-ranked players in face-offs. Making the game more difficult is that neither player can see the markings on the opponent’s game pieces until they meet face-to-face.

Prior research has shown that the complexity of the game is higher than that of chess or go, with 10⁵³⁵ possible game scenarios. This level of complexity makes it extremely challenging for computer experts attempting to create Stratego-playing AI systems. In this new effort, the researchers took a different approach, creating an app capable of beating most human and other AI systems.