This is not investment advice. The author has no position in any of the stocks mentioned. Wccftech.com has a disclosure and ethics policy. The National Aeronautics and Space Administration (NASA) has captured rare footage of a black hole eating up a start and creating a gas cloud that is as large as the solar system.
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Leading The Global Fight Against Antimicrobial Resistance (AMR) — Dr. Haileyesus Getahun, MD, MPH, Ph.D., Director of AMR Global Coordination, World Health Organization (WHO)
Dr. Haileyesus Getahun, MD, MPH, Ph.D. is Director of AMR (Antimicrobial Resistance) Global Coordination at the World Health Organization (WHO) and the Quadripartite (FAO/UNEP/WHO/WOAH) Joint Secretariat on Antimicrobial Resistance. (https://www.who.int/about/people/biography/dr-haileyesus-getahun)
Every year, the Stanford Institute for Human-Centered Artificial Intelligence (HAI) puts out its AI Index, a massive compendium of data and graphs that tries to sum up the current state of artificial intelligence. The 2022 AI Index, which came out this week, is as impressive as ever, with 190 pages covering R&D, technical performance, ethics, policy, education, and the economy. I’ve done you a favor by reading every page of the report and plucking out 12 charts that capture the state of play.
It’s worth noting that many of the trends I reported from last year’s 2021 index still hold. For example, we are still living in a golden AI summer with ever-increasing publications, the AI job market is still global, and there’s still a disconcerting gap between corporate recognition of AI risks and attempts to mitigate said risks. Rather than repeat those points here, we refer you to last year’s coverage.
Following China’s abrupt U-turn on zero-COVID policy last month, the country has seen an increase in COVID cases. A leading doctor at one of Shanghai’s top hospitals estimates that up to 70% of the city’s population has been infected with COVID-19.
A sharp-eyed developer at Krita noticed recently that, in the settings for their Adobe Creative Cloud account, the company had opted them (and everyone else) into a “content analysis” program whereby they “may analyze your content using techniques such as machine learning (e.g. for pattern recognition) to develop and improve our products and services.” Some have taken this to mean that it is ingesting your images for its AI. And … they do. Kind of? But it’s not that simple.
First off, lots of software out there has some kind of “share information with the developer” option, where it sends telemetry like how often you use the app or certain features, why it crashed, etc. Usually it gives you an option to turn this off during installation, but not always — Microsoft incurred the ire of many when it basically said telemetry was on by default and impossible to turn off in Windows 10.
That’s gross, but what’s worse is slipping a new sharing method and opting existing users into it. Adobe told PetaPixel that this content analysis thing “is not new and has been in place for a decade.” If they were using machine learning for this purpose and said so a decade ago, that’s quite impressive, as is that apparently no one noticed that whole time. That seems unlikely. I suspect the policy has existed in some form but has quietly evolved.
India will reach its population peak in 2065.
India is the second most populous country in the world. With 1,414 billion, it comes right after China. However, contrary to China’s population-reducing policy, India’s population is increasing and seems to surpass China in a few decades.
As BBC reported, China reduced its population growth rate by about half, from two percent in 1973 to 1.1 percent in 1983. According to demographers, much of this was accomplished by trampling on human rights.
Continue reading “New Study Says That World Population Will Be a Lot Lower Than Predicted by 2100” »
Musk’s response came after 57.5% Twitter users voted in favour of him stepping down as the CEO of the social media company.
Both animals and people use high-dimensional inputs (like eyesight) to accomplish various shifting survival-related objectives. A crucial aspect of this is learning via mistakes. A brute-force approach to trial and error by performing every action for every potential goal is intractable even in the smallest contexts. Memory-based methods for compositional thinking are motivated by the difficulty of this search. These processes include, for instance, the ability to: recall pertinent portions of prior experience; (ii) reassemble them into new counterfactual plans, and (iii) carry out such plans as part of a focused search strategy. Compared to equally sampling every action, such techniques for recycling prior successful behavior can considerably speed up trial-and-error. This is because the intrinsic compositional structure of real-world objectives and the similarity of the physical laws that control real-world settings allow the same behavior (i.e., sequence of actions) to remain valid for many purposes and situations. What guiding principles enable memory processes to retain and reassemble experience fragments? This debate is strongly connected to the idea of dynamic programming (DP), which using the principle of optimality significantly lowers the computing cost of trial-and-error. This idea may be expressed informally as considering new, complicated issues as a recomposition of previously solved, smaller subproblems.
This viewpoint has recently been used to create hierarchical reinforcement learning (RL) algorithms for goal-achieving tasks. These techniques develop edges between states in a planning graph using a distance regression model, compute the shortest pathways across it using DP-based graph search, and then use a learning-based local policy to follow the shortest paths. Their essay advances this field of study. The following is a summary of their contributions: They provide a strategy for long-term planning that acts directly on high-dimensional sensory data that an agent may see on its own (e.g., images from an onboard camera). Their solution blends traditional sampling-based planning algorithms with learning-based perceptual representations to recover and reassemble previously recorded state transitions in a replay buffer.
The two-step method makes this possible. To determine how many timesteps it takes for an optimum policy to move from one state to the next, they first learn a latent space where the distance between two states is the measure. They know contrastive representations using goal-conditioned Q-values acquired through offline hindsight relabeling. To establish neighborhood criteria across states, the second threshold this developed latent distance metric. They go on to design sampling-based planning algorithms that scan the replay buffer for trajectory segments—previously recorded successions of transitions—whose ends are adjacent states.
Leading Canada’s Bio-Safety & Security R&D — Dr. Loren Matheson PhD, Defence Research and Development Canada, Department of National Defence.
Dr. Loren Matheson, Ph.D. is a Portfolio Manager at the Center For Security Science, at Defence Research and Development Canada (DRDC — https://www.canada.ca/en/defence-research-development.html), which is a special operating agency of the Department of National Defence, whose purpose is to provide the Canadian Armed Forces, other government departments, and public safety and national security communities with knowledge and technology.
