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Category: information science – Page 91

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Mentioned Videos:
AI designing Computer Chips: https://youtu.be/NeHgMaIkPuY
Deepmind AI made a Breakthrough in Math: https://youtu.be/DU6WINoehrg.

Deepmind Paper “Faster sorting algorithms discovered using deep reinforcement learning”:
https://www.nature.com/articles/s41586-023-06004-9

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Measurements conducted over an unprecedented span of conditions uncover universal behavior, but not the kind that theorists expected.

Turbulence is a mesmerizing, chaotic state of fluid motion. It occurs in natural and artificial settings whenever the Reynolds number (quantifying the relative size of inertial to viscous forces in the flow) is large. Through nonlinear coupling, kinetic energy cascades from large scales to ever smaller scales (Fig. 1) until it is dissipated by viscous effects. The fluctuations excited during this process play a crucial role in a diverse range of problems, including planetesimal formation [1], rain initiation in clouds [2], and heat transport within oceans [3]. Remarkably, a new experimental study by Christian Küchler of the Max Planck Institute for Dynamics and Self-Organization in Germany and co-workers provides compelling evidence that current theoretical models for how the fluctuations are distributed across the scales are missing some important ingredients [4].

Turbulent flows are complex. Quantitative predictions of their properties that are derived directly from the Navier-Stokes equation, without ad hoc assumptions, are accordingly scarce. Most theoretical approaches have perforce been phenomenological, the most famous being Andrey Kolmogorov’s groundbreaking 1941 theory, nicknamed K41 [5]. This mean-field theory assumes that the multiscale properties of the turbulent fluctuations are governed by the average cascade of kinetic energy passing through the scales and by the fluid viscosity. In K41 Kolmogorov went on to propose the existence of an inertial range, which corresponds to an intermediate range of scales over which viscous forces could be ignored relative to inertial forces and where the details of the large-scale forcing are unimportant.

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I hope this isn’t been posted before especially by me. I do have a bit of pre dementia but it’s not too bad. It’s from my TBI but they’re working on weeding out bias from AI and making it so it’s not bad for us or to us.

Thought-provoking TED Talk on how AI can unintentionally reinforce societal prejudices, perpetuate discrimination, and amplify toxic behaviors online. This talk is a call to action for individuals, tech companies, and policymakers alike. By addressing AI bias and toxicity head-on, we can pave the way for a future where AI systems are truly unbiased, fostering inclusivity and equality for all.

AI, Algorithm, Behavioral Economics, Discrimination, Diversity, Empathy, Engineering, Entrepreneurship, Social Entrepreneurship, Social Media, Software, Voice, Vulnerability, Women, Women in business, Women’s Rights, Work, Workplace, Writing Product leader with over 9+ years of experience in building large scale consumer Products at Yahoo, Apple. Priya is passionate about driving innovation while building dynamic & inclusive teams. During her time at MIT, she built inclusively and won prestigious funding through MIT 100K award (previous finalists include Hubspot, Akamai). She was also invited at TedX Boston and MIT Media Lab to share her work This talk was given at a TEDx event using the TED conference format but independently organized by a local community.

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A newly described type of chemistry in fungi is both surprisingly common and likely to involve highly reactive enzymes, two traits that make the genes involved useful signposts pointing to a potential treasure trove of biological compounds with medical and chemical applications.

It was also nearly invisible to scientists until now.

In the last 15 years, the hunt for molecules from living organisms—many with promise as drugs, antimicrobial agents, chemical catalysts and even food additives—has relied on trained to search the DNA of bacteria, fungi and plants for genes that produce enzymes known to drive that result in interesting compounds.

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Memories can be as tricky to hold onto for machines as they can be for humans. To help understand why artificial agents develop holes in their own cognitive processes, electrical engineers at The Ohio State University have analyzed how much a process called “continual learning” impacts their overall performance.

Continual learning is when a computer is trained to continuously learn a sequence of tasks, using its accumulated knowledge from old tasks to better learn new tasks.

Yet one major hurdle scientists still need to overcome to achieve such heights is learning how to circumvent the machine learning equivalent of memory loss—a process which in AI agents is known as “catastrophic forgetting.” As are trained on one new task after another, they tend to lose the information gained from those previous tasks, an issue that could become problematic as society comes to rely on AI systems more and more, said Ness Shroff, an Ohio Eminent Scholar and professor of computer science and engineering at The Ohio State University.

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The digital devices that we rely on so heavily in our day-to-day and professional lives today—smartphones, tablets, laptops, fitness trackers, etc.—use traditional computational technology. Traditional computers rely on a series of mathematical equations that use electrical impulses to encode information in a binary system of 1s and 0s. This information is transmitted through quantitative measurements called “bits.”

Unlike traditional computing, quantum computing relies on the principles of quantum theory, which address principles of matter and energy on an atomic and subatomic scale. With quantum computing, equations are no longer limited to 1s and 0s, but instead can transmit information in which particles exist in both states, the 1 and the 0, at the same time.

Quantum computing measures electrons or photons. These subatomic particles are known as quantum bits, or ” qubits.” The more qubits are used in a computational exercise, the more exponentially powerful the scope of the computation can be. Quantum computing has the potential to solve equations in a matter of minutes that would take traditional computers tens of thousands of years to work out.

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Artificial Intelligence (AI) has transformed our world at an astounding pace. It’s like a vast ocean, and we’re just beginning to navigate its depths.

To appreciate its complexity, let’s embark on a journey through the seven distinct stages of AI, from its simplest forms to the mind-boggling prospects of superintelligence and singularity.

Picture playing chess against a computer. Every move it makes, every strategy it deploys, is governed by a predefined set of rules, its algorithm. This is the earliest stage of AI — rule-based systems. They are excellent at tasks with clear-cut rules, like diagnosing mechanical issues or processing tax forms. But their capacity to learn or adapt is nonexistent, and their decisions are only as good as the rules they’ve been given.

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Singapore: A research paper, published in iScience, has decribed the development of a deep learning model for predicting hip fractures on pelvic radiographs (Xrays), even with the presence of metallic implants.

Yet Yen Yan of Changi General Hospital and colleagues at the Duke-NUS Medical School, Singapore, and colleagues developed the AI (artificial intelligence) algorithm using more than fortythousand pelvic radiographs from a single institution. The model demonstrated high specificity and sensitivity when applied to a test set of emergency department (ED) radiographs.

This study approximates the realworld application of a deep learning fracture detection model by including radiographs with suboptimal image quality, other nonhip fractures and meta llic implants, which were excluded from prior published work. The research team also explored the effect of ethnicity on model performance, and the accuracy of visualization algorithm for fracture localization.

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A recent paper published in Nature Aging by researchers from Integrated Biosciences, a biotechnology company combining synthetic biology and machine learning.

Machine learning is a subset of artificial intelligence (AI) that deals with the development of algorithms and statistical models that enable computers to learn from data and make predictions or decisions without being explicitly programmed to do so. Machine learning is used to identify patterns in data, classify data into different categories, or make predictions about future events. It can be categorized into three main types of learning: supervised, unsupervised and reinforcement learning.

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