Lifeboat Foundation

Love: The Glue That Holds the Universe Together. “Love contrasts with fear, light with dark, black implies white, self implies other, suffering implies ecstasy, death implies life. We can devise and apprehend something only in terms of what it is not. This is the cosmic binary code: Ying/Yang, True/False, Infinite/Finite, Masculine/Feminine, On/Off, Yes/No… There are really only two opposing forces at play: love as universal integrating force and fear as universal disintegrating force… Like in Conway’s Game of Life information flows along the path of the least resistance influenced by the bigger motivator – either love factor of fear factor (or, rather, their sophisticated gradients like pleasure and pain) – Go or No go. Love and its contrasting opposite fear is what makes us feel alive… Love is recognized self-similarity in the other, a fractal algorithm of the least resistance. And love, as the finest intelligence, is obviously an extreme form of collaboration… collectively ascending to higher love, “becoming one planet of love.” Love is the glue that holds the Universe together…” –Excerpt from ‘The Syntellect Hypothesis: Five Paradigms of the Mind’s Evolution’ by Alex Vikoulov, available now on Amazon.

#SyntellectHypothesis #AlexVikoulov #Love

P.S. Extra For Digitalists: “In this quantum [computational] multiverse the essence of digital IS quantum entanglement. The totality of your digital reality is what your conscious mind implicitly or explicitly chooses to experience out of the infinite -\-\ a cocktail of love response and fear response.”

Smartphones aren’t just for selfies anymore. A novel cell phone imaging algorithm can now analyze assays typically evaluated via spectroscopy, a powerful device used in scientific research. Researchers analyzed more than 10,000 images and found that their method consistently outperformed existing algorithms under a wide range of operating field conditions. This technique reduces the need for bulky equipment and increases the precision of quantitative results.

Accessible, connected, and computationally powerful, smartphones aren’t just for “selfies” anymore. They have emerged as powerful evaluation tools capable of diagnosing medical conditions in point-of-care settings. Smartphones also are a viable solution for health care in the developing world because they allow untrained users to collect and transmit data to medical professionals.

Although smartphone camera technology today offers a wide range of medical applications such as microscopy and cytometric analysis, in practice, cell phone image tests have limitations that severely restrict their utility. Addressing these limitations requires external smartphone hardware to obtain quantitative results – imposing a design tradeoff between accessibility and accuracy.

Continue reading “Selfies to Self-Diagnosis: Algorithm ‘Amps Up’ Smartphones to Diagnose Disease” »

Built on thousands of retina images, algorithm helps diagnose eye problem caused by diabetes.

A team of researchers including Neil Johnson, a professor of physics at the George Washington University, has discovered that decentralized systems work better when the individual parts are less capable.

Dr. Johnson was interested in understanding how systems with many moving parts can reach a desired target or goal without centralized control. This explores a common theory that decentralized systems, those without a central brain, would be more resilient against damage or errors.

This research has the potential to inform everything from how to effectively structure a company, build a better autonomous vehicle, optimize next-generation artificial intelligence algorithms—and could even transform our understanding of evolution. The key lies in understanding how the “sweet spot” between decentralized and centralized systems varies with how clever the pieces are, Dr. Johnson said.

Continue reading “Decentralized systems are more efficient at reaching a target when its components are not overly capable” »

Forensics is on the cusp of a third revolution in its relatively young lifetime. The first revolution, under the brilliant but complicated mind of J. Edgar Hoover, brought science to the field and was largely responsible for the rise of criminal justice as we know it today. The second, half a century later, saw the introduction of computers and related technologies in mainstream forensics and created the subfield of digital forensics.

We are now hurtling headlong into the third revolution with the introduction of Artificial Intelligence (AI) – intelligence exhibited by machines that are trained to learn and solve problems. This is not just an extension of prior technologies. AI holds the potential to dramatically change the field in a variety of ways, from reducing bias in investigations to challenging what evidence is considered admissible.

AI is no longer science fiction. A 2016 survey conducted by the National Business Research Institute (NBRI) found that 38% of enterprises are already using AI technologies and 62% will use AI technologies by 2018. “The availability of large volumes of data—plus new algorithms and more computing power—are behind the recent success of deep learning, finally pulling AI out of its long winter,” writes Gil Press, contributor to Forbes.com.

Continue reading “The Coming AI Revolution in Digital Forensics” »

Researchers develop an app that can estimate the biological age of a rodent from its mug shot, and could give a boost to the science of human aging in the process.

AI may quickly point out a corrupt official, but it is not very good at explaining the process it has gone through to reach such a conclusion.

“We just use the machine’s result as reference,” Zhang Yi, an official in a province that’s still using the software, told the SCMP. “We need to check and verify its validity. The machine cannot pick up the phone and call the person with a problem. The final decision is always made by humans.”

Algorithmic Justice

Continue reading “China Built an AI to Detect Corruption and Officials Shut it Down” »

AI farms are well suited to impoverished regions like Guizhou, where land and labor are cheap and the climate temperate enough to enable the running of large machines without expensive cooling systems. It takes only two days to train workers like Yin in basic AI tagging, or a week for the more complicated task of labeling 3D pictures.

A battle for AI supremacy is being fought one algorithm at a time.

A new breed of algorithms has mastered Atari video games 10 times faster than state-of-the-art AI, with a breakthrough approach to problem solving.

Designing AI that can negotiate planning problems, especially those where rewards are not immediately obvious, is one of the most important research challenges in advancing the field.

A famous 2015 study showed Google DeepMind AI learnt to play Atari video games like Video Pinball to human level, but notoriously failed to learn a path to the first key in 1980s video game Montezuma’s Revenge due to the game’s complexity.

Continue reading “Atari master: New AI smashes Google DeepMind in video game challenge” »