Lifeboat Foundation: Safeguarding Humanity
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Category: robotics/AI – Page 2272

AI good for internal back office and some limited front office activities; however, still need to see more adoption of QC in the Net and infrastructure in companies to expose their services and information to the public net & infrastructure.

Deep learning, as explained by tech journalist Michael Copeland on Blogs.nvidia.com, is the newest and most powerful computational development thus far. It combines all prior research in artificial intelligence (AI) and machine learning. At its most fundamental level, Copeland explains, deep learning uses algorithms to peruse massive amounts of data, and then learn from that data to make decisions or predictions. The Defense Agency Advanced Project Research (DARPA), as Wired reports, calls this method “probabilistic programming.”

Mimicking the human brain’s billions of neural connections by creating artificial neural networks was thought to be the path to AI in the early days, but it was too “computationally intensive.” It was the invention of Nvidia’s powerful graphics processing unit (GPU), that allowed Andre Ng, a scientist at Google, to create algorithms by “building massive artificial neural networks” loosely inspired by connections in the human brain. This was the breakthrough that changed everything. Now, according to Thenextweb.com, Google’s Deep Mind platform has been proven to teach itself, without any human input.

Continue reading “AI takeover: Google’s ‘DeepMind’ platform can learn and think on it’s own without human input” | >

This is what scares me; autonomous warfare.

WASHINGTON: DARPA is taking another step toward building autonomous electronic warfare systems with a small contract award to BAE Systems.

Artificial intelligence and autonomy loom large in the Pentagon these days. And electronic warfare, much more quietly, dominates a great deal of thinking across the services these days after we’ve watched how the Russians operate against Ukraine and in Syria. So DARPA’s additional $13.3 million award announced today is worth noting.

Why does all this matter? One of the biggest challenges facing the F-35 program, for example, is the creation of a huge digital threat library (known as mission data files) for the airplane. It includes electronic spectrum information for a wide array of emitters — radar, radio and other sources.

Continue reading “DARPA Ups Funding For Autonomous Electronic Warfare Work « Breaking Defense” | >

In recent years, the best-performing systems in artificial-intelligence research have come courtesy of neural networks, which look for patterns in training data that yield useful predictions or classifications. A neural net might, for instance, be trained to recognize certain objects in digital images or to infer the topics of texts.

But neural nets are black boxes. After training, a network may be very good at classifying data, but even its creators will have no idea why. With visual data, it’s sometimes possible to automate experiments that determine which visual features a neural net is responding to. But text-processing systems tend to be more opaque.

At the Association for Computational Linguistics’ Conference on Empirical Methods in Natural Language Processing, researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) will present a new way to train so that they provide not only predictions and classifications but rationales for their decisions.

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Light-sheet microscopy is one of the most powerful method for imaging the development and function of whole living organisms. However, achieving high-resolution images with these microscopes requires manual adjustments during imaging. Researchers of the Max Planck Institute of Molecular Cell Biology and Genetics in Dresden together with colleagues at Janelia Research Campus (HHMI) have developed a new kind of light-sheet microscope that can ‘drive’ itself automatically by adapting to the challenging and dynamic optical conditions of large living specimens. This new smart microscope combines a novel hardware design and a smart ‘AutoPilot’ system that can analyze images and automatically adjust and optimize the microscope. This framework enables for the first time long-term adaptive imaging of entire developing embryos and improves the resolution of light-sheet microscopes up to five-fold.

Light sheet microscopy is a novel microscopy technique developed in the last ten years that is uniquely suited to image large . In a light-sheet microscope, a laser light sheet illuminates the sample perpendicularly to the observation along a thin plane within the sample. Out-of-focus and scattered light from other planes—which often impair image quality—is largely avoided because only the observed plane is illuminated.

The long-standing goal of microscopy is to achieve ever-sharper images deep inside of living samples. For light-sheet microscopes this requires to perfectly maintain the careful alignments between imaging and light-sheet illumination planes. Mismatches between these planes arise from the optical variability of living tissues across different locations and over time. Tackling this challenge is essential to acquire the necessary to decipher the biology behind organism development and morphogenesis. “So far, researchers had to sit at their microscope and tweak things manually—our system puts an end to this: it is like a self-driving car: it functions autonomously”, says Loïc Royer, first author of the study. This smart autonomous microscope can in real-time analyze and optimize the spatial relationship between light-sheets and detection planes across the specimen volume.

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Computronium is defined by some as a substance which approaches the theoretical limit of computational power that we can achieve through engineering of the matter around us. It would mean that every atom of a piece of matter would be put to useful work doing computation. Such a system would reside at the ultimate limits of efficiency, and the smallest amount of energy possible would be wasted through the generation of heat. Computronium crops up in science fiction a lot, usually as something that advanced civilizations have created, occasionally causing conflicts due to intensive harvesting of matter from their galaxy to further their processing power. The idea is also also linked with advanced machine intelligence: A block of matter which does nothing other than compute could presumably would be incredibly sought after by any artificial intelligence looking to get the most compact and powerful brain for its money!

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