XKCD Comic : Well-Ordering Principle in XKCD Comic

Well-Ordering Principle
23 August 2019, 5:00 am



Source: xkcd.com

"AlphaZero" for find/compute the best music/voices in the Universe in General AI Discussion

What if by some analogy with AlphaZero (and AlphaGo) are try to find some patterns in is why some music more more likely for human as another - and based on this and/or with "1 billion people" evaluating  new music and/or with data from Elon Musk Neuralink (listen music and point which more likely) (or even electrodes sewn into the brain (it is clear that a small number of people) ) - and so are try to find and deep learn the best music in the World ?

I remember what's happend with me before I got into this body in childhood and I remeber some heaven music's in astral worlds - and based on this know that the limit of musical pleasure is such that you can’t even imagine ..

and it would be incredibly cool to reach it on Earth, technically there is no problem reproducing audio - the only question is to find the "desired byte sequence" (there are obviously not one) in mp3 / wav ..

your thoughts, how real is this with AI?  

chatbot tester for METAQUID in General Chatbots and Software

Chatbot name: METAQUID Hanah
location: http://www.metaquid.com/chatbot-en     <<    T E S T   C H A T B O T
NOTE: Metaquid is something beyond boundaries.
Try to think: metalanguage, metadata, metaphysics, … and then Metaquid.
The initial setting is the future, but then you move in time, as only the mind can do.
If you are passionate about: artificial intelligence, biology, unknown technologies, then Metaquid could do for you.
Metaquid is a graphic novel based on a detective, parapsychic and sci-fi theme: it’s a thriller.
A chatbot named Hanah will take you to the discovery of Metaquid, but you must know that Hanah also doesn't know exactly what Metaquid is and she is looking for it too.
To enter the scenario, you can download the first chapter of Metaquid ZERO here: https://www.metaquid.com/download/
Good reading but then you have to bombard Hanah with questions to see how she behaves!

SMZ

News: the first quantum teleportation of a qutrit in General AI Discussion

https://www.physics-astronomy.org/2019/08/quantum-teleportation-has-been-reported.html

The semi-relevance of this to AI is that quantum computers are promising to be so extremely fast that some people believe quantum computers hold the key to AI, and this new discovery that a qutrit (which holds 3 quantum states) can be quantum teleported just as well as a qubit (which holds only 2 quantum states) suggests that more information can now be quantum teleported in a single stroke, although admittedly the only practical application mentioned was creation of an unhackable communication channel.

For those unfamiliar with "quantum teleportation," the main thing you need to know is that that the term is very misleading in that physical teleportation (like you see in "Star Trek") has nothing to do with quantum teleportation; quantum teleportation cannot be used in any way (that I can imagine) for practical physical teleportation. Below is a video on that topic. However, it is still a truly amazing phenomenon in nature.

(*)
How Quantum Teleportation Works (Or Doesn't)
The Good Stuff
Published on Aug 4, 2017
https://www.youtube.com/watch?v=zoi6ffryj88

XKCD Comic : Review in XKCD Comic

Review
21 August 2019, 5:00 am



Source: xkcd.com

How artificial intelligence will revolutionize the way video games are developed in Gaming

https://www.youtube.com/watch?v=NPuYtHZud0o

If you asked video game fans what an idealized, not-yet-possible piece of interactive entertainment might look like in 10 or even 20 years from now, they might describe something eerily similar to the software featured in Orson Scott Card’s sci-fi classic Ender’s Game.

The advances of modern AI research could bring unprecedented benefits to game development.

Artificial intelligence could help data centers run far more efficiently in Robotics News

Artificial intelligence could help data centers run far more efficiently
21 August 2019, 9:31 pm

A novel system developed by MIT researchers automatically “learns” how to schedule data-processing operations across thousands of servers — a task traditionally reserved for imprecise, human-designed algorithms. Doing so could help today’s power-hungry data centers run far more efficiently.

Data centers can contain tens of thousands of servers, which constantly run data-processing tasks from developers and users. Cluster scheduling algorithms allocate the incoming tasks across the servers, in real-time, to efficiently utilize all available computing resources and get jobs done fast.

Traditionally, however, humans fine-tune those scheduling algorithms, based on some basic guidelines (“policies”) and various tradeoffs. They may, for instance, code the algorithm to get certain jobs done quickly or split resource equally between jobs. But workloads — meaning groups of combined tasks — come in all sizes. Therefore, it’s virtually impossible for humans to optimize their scheduling algorithms for specific workloads and, as a result, they often fall short of their true efficiency potential.

The MIT researchers instead offloaded all of the manual coding to machines. In a paper being presented at SIGCOMM, they describe a system that leverages “reinforcement learning” (RL), a trial-and-error machine-learning technique, to tailor scheduling decisions to specific workloads in specific server clusters.

To do so, they built novel RL techniques that could train on complex workloads. In training, the system tries many possible ways to allocate incoming workloads across the servers, eventually finding an optimal tradeoff in utilizing computation resources and quick processing speeds. No human intervention is required beyond a simple instruction, such as, “minimize job-completion times.”

Compared to the best handwritten scheduling algorithms, the researchers’ system completes jobs about 20 to 30 percent faster, and twice as fast during high-traffic times. Mostly, however, the system learns how to compact workloads efficiently to leave little waste. Results indicate the system could enable data centers to handle the same workload at higher speeds, using fewer resources.

“If you have a way of doing trial and error using machines, they can try different ways of scheduling jobs and automatically figure out which strategy is better than others,” says Hongzi Mao, a PhD student in the Department of Electrical Engineering and Computer Science (EECS). “That can improve the system performance automatically. And any slight improvement in utilization, even 1 percent, can save millions of dollars and a lot of energy in data centers.”

“There’s no one-size-fits-all to making scheduling decisions,” adds co-author Mohammad Alizadeh, an EECS professor and researcher in the Computer Science and Artificial Intelligence Laboratory (CSAIL). “In existing systems, these are hard-coded parameters that you have to decide up front. Our system instead learns to tune its schedule policy characteristics, depending on the data center and workload.”

Joining Mao and Alizadeh on the paper are: postdocs Malte Schwarzkopf and Shaileshh Bojja Venkatakrishnan, and graduate research assistant Zili Meng, all of CSAIL.

RL for scheduling

Typically, data processing jobs come into data centers represented as graphs of “nodes” and “edges.” Each node represents some computation task that needs to be done, where the larger the node, the more computation power needed. The edges connecting the nodes link connected tasks together. Scheduling algorithms assign nodes to servers, based on various policies.

But traditional RL systems are not accustomed to processing such dynamic graphs. These systems use a software “agent” that makes decisions and receives a feedback signal as a reward. Essentially, it tries to maximize its rewards for any given action to learn an ideal behavior in a certain context. They can, for instance, help robots learn to perform a task like picking up an object by interacting with the environment, but that involves processing video or images through an easier set grid of pixels.

To build their RL-based scheduler, called Decima, the researchers had to develop a model that could process graph-structured jobs, and scale to a large number of jobs and servers. Their system’s “agent” is a scheduling algorithm that leverages a graph neural network, commonly used to process graph-structured data. To come up with a graph neural network suitable for scheduling, they implemented a custom component that aggregates information across paths in the graph — such as quickly estimating how much computation is needed to complete a given part of the graph. That’s important for job scheduling, because “child” (lower) nodes cannot begin executing until their “parent” (upper) nodes finish, so anticipating future work along different paths in the graph is central to making good scheduling decisions.

To train their RL system, the researchers simulated many different graph sequences that mimic workloads coming into data centers. The agent then makes decisions about how to allocate each node along the graph to each server. For each decision, a component computes a reward based on how well it did at a specific task — such as minimizing the average time it took to process a single job. The agent keeps going, improving its decisions, until it gets the highest reward possible.

Baselining workloads

One concern, however, is that some workload sequences are more difficult than others to process, because they have larger tasks or more complicated structures. Those will always take longer to process — and, therefore, the reward signal will always be lower — than simpler ones. But that doesn’t necessarily mean the system performed poorly: It could make good time on a challenging workload but still be slower than an easier workload. That variability in difficulty makes it challenging for the model to decide what actions are good or not.

To address that, the researchers adapted a technique called “baselining” in this context. This technique takes averages of scenarios with a large number of variables and uses those averages as a baseline to compare future results. During training, they computed a baseline for every input sequence. Then, they let the scheduler train on each workload sequence multiple times. Next, the system took the average performance across all of the decisions made for the same input workload. That average is the baseline against which the model could then compare its future decisions to determine if its decisions are good or bad. They refer to this new technique as “input-dependent baselining.”

That innovation, the researchers say, is applicable to many different computer systems. “This is general way to do reinforcement learning in environments where there’s this input process that effects environment, and you want every training event to consider one sample of that input process,” he says. “Almost all computer systems deal with environments where things are constantly changing.”

Aditya Akella, a professor of computer science at the University of Wisconsin at Madison, whose group has designed several high-performance schedulers, found the MIT system could help further improve their own policies. “Decima can go a step further and find opportunities for [scheduling] optimization that are simply too onerous to realize via manual design/tuning processes,” Akella says. “The schedulers we designed achieved significant improvements over techniques used in production in terms of application performance and cluster efficiency, but there was still a gap with the ideal improvements we could possibly achieve. Decima shows that an RL-based approach can discover [policies] that help bridge the gap further. Decima improved on our techniques by a [roughly] 30 percent, which came as a huge surprise.”

Right now, their model is trained on simulations that try to recreate incoming online traffic in real-time. Next, the researchers hope to train the model on real-time traffic, which could potentially crash the servers. So, they’re currently developing a “safety net” that will stop their system when it’s about to cause a crash. “We think of it as training wheels,” Alizadeh says. “We want this system to continuously train, but it has certain training wheels that if it goes too far we can ensure it doesn’t fall over.”

Source: MIT News - CSAIL - Robotics - Computer Science and Artificial Intelligence Laboratory (CSAIL) - Robots - Artificial intelligence

Reprinted with permission of MIT News : MIT News homepage



Use the link at the top of the story to get to the original article.

beyond omega level coding in General AI Discussion

NOW ! I summon to the field my monster NecroBump !
its special ability activates : revives D thread !!!

but wait there is more !

flip ! I activate my trap card : delayed hero signal :

https://vclock.com/timer/#countdown=01:07:00&date=2019-05-17T20:01:17&title=the+LivinGrimoire+by+Moti+Barski&sound=xylophone&loop=1

I activate my spell card : swords of revealing light.

kek, it is on the way

Boosting computing power for the future of particle physics in Robotics News

Boosting computing power for the future of particle physics
19 August 2019, 3:20 pm

A new machine learning technology tested by an international team of scientists including MIT Assistant Professor Philip Harris and postdoc Dylan Rankin, both of the Laboratory for Nuclear Science, can spot specific particle signatures among an ocean of Large Hadron Collider (LHC) data in the blink of an eye.

Sophisticated and swift, the new system provides a glimpse into the game-changing role machine learning will play in future discoveries in particle physics as data sets get bigger and more complex.

The LHC creates some 40 million collisions every second. With such vast amounts of data to sift through, it takes powerful computers to identify those collisions that may be of interests to scientists, whether, perhaps, a hint of dark matter or a Higgs particle.

Now, scientists at Fermilab, CERN, MIT, the University of Washington, and elsewhere have tested a machine-learning system that speeds processing by 30 to 175 times compared to existing methods.

Such methods currently process less than one image per second. In contrast, the new machine-learning system can review up to 600 images per second. During its training period, the system learned to pick out one specific type of postcollision particle pattern.

“The collision patterns we are identifying, top quarks, are one of the fundamental particles we probe at the Large Hadron Collider,” says Harris, who is a member of the MIT Department of Physics. “It’s very important we analyze as much data as possible. Every piece of data carries interesting information about how particles interact.”

Those data will be pouring in as never before after the current LHC upgrades are complete; by 2026, the 17-mile particle accelerator is expected to produce 20 times as much data as it does currently. To make matters even more pressing, future images will also be taken at higher resolutions than they are now. In all, scientists and engineers estimate the LHC will need more than 10 times the computing power it currently has.

“The challenge of future running,” says Harris, “becomes ever harder as our calculations become more accurate and we probe ever-more-precise effects.”  

Researchers on the project trained their new system to identify images of top quarks, the most massive type of elementary particle, some 180 times heavier than a proton. “With the machine-learning architectures available to us, we are able to get high-grade scientific-quality results, comparable to the best top-quark identification algorithms in the world,” Harris explains. “Implementing core algorithms at high speed gives us the flexibility to enhance LHC computing in the critical moments where it is most needed.”

Source: MIT News - CSAIL - Robotics - Computer Science and Artificial Intelligence Laboratory (CSAIL) - Robots - Artificial intelligence

Reprinted with permission of MIT News : MIT News homepage



Use the link at the top of the story to get to the original article.

Project Thread: building Blinky in Home Made Robots

Time to try and implement some of the things we have been theorizing about. Here I will be detailing my first attempt at assembling something that is more than the sum of its parts.