DarkNightoftheSoul

It's homework help, but I'm not asking for the solution. The problem only asks for cos, sin, tan, cot, csc given sec. I found those pretty quickly on my own, and confirmed solutions with the back of the book.

Where I run into confusion is when I try to find angle theta on my own. Arccos of found cos gives 2.06, arcsin of found sin gives 1.08, and arctan of found tan gives -1.08. Problem givens exclude possibility of the negative angle found by arctan(-15/8), but the other two are possible and conflicting. And why wouldn't they all be the same? I reattempted because there were so many erase marks from trying to figure this out that it was almost illegible.

Am I wrong? Did the book give me a point not on the unit circle or something, assuming I wouldn't try to find theta on my own? Have I used arcfuncs wrong- I checked the domains against the function definitions? Have I found a hole in math?

The VA has approved me for community care! Instead of a revolving door of unprofessional incompetent clowns who specialize in PTSD, I can select one of the incompetent unprofessional clowns from my community who specialize in BPD!

How should I look for a psychologist? There's like, a lot to choose from here. Most everyone mentions "specialties" like bipolar on their site, in among a bunch of stuff like ptsd and depression and lgbtq issues- Which makes me think they aren't actually specialists, which gives me the impression they'll say just about anything to get me in the door. It's all a bit overwhelming and I basically only have one shot. Once I engage with someone, if they aren't a good fit (my history suggests it will take several sessions before I know if they'll be a good fit, so...) I have to go through the several months approval process for community care again before I can make another selection, assuming I'm approved. How do you weed out assholes, idiots, and the incompetent before you see them?

What treatments have you found to be effective in your therapy?

Also, I'm much better on Lithium than I ever have been, but I'm already coming close to the maximum safe dose- if there is such a thing. The psychiatrist mentioned depakote, but I've tried that before and had severe gastrointestinal problems. Are there any other medications I should be looking out for or asking about here?

Abstract:

Rapid progress in machine learning and artificial intelligence (AI) has brought increasing attention to the potential impacts of AI technologies on society. In this paper we discuss one such potential impact: the problem of accidents in machine learning systems, defined as unintended and harmful behavior that may emerge from poor design of real-world AI systems. We present a list of five practical research problems related to accident risk, categorized according to whether the problem originates from having the wrong objective function ("avoiding side effects" and "avoiding reward hacking"), an objective function that is too expensive to evaluate frequently ("scalable supervision"), or undesirable behavior during the learning process ("safe exploration" and "distributional shift"). We review previous work in these areas as well as suggesting research directions with a focus on relevance to cutting-edge AI systems. Finally, we consider the high-level question of how to think most productively about the safety of forward-looking applications of AI.

We can expect AI systems to accidentally create serious negative side effects - how can we avoid that? The first of several videos about the paper "Concrete Problems in AI Safety".

A brief overview of the concept of generality in AI systems.

Abstract

For stable and efficient fusion energy production using a tokamak reactor, it is essential to maintain a high-pressure hydrogenic plasma without plasma disruption. Therefore, it is necessary to actively control the tokamak based on the observed plasma state, to manoeuvre high-pressure plasma while avoiding tearing instability, the leading cause of disruptions. This presents an obstacle-avoidance problem for which artificial intelligence based on reinforcement learning has recently shown remarkable performance. However, the obstacle here, the tearing instability, is difficult to forecast and is highly prone to terminating plasma operations, especially in the ITER baseline scenario. Previously, we developed a multimodal dynamic model that estimates the likelihood of future tearing instability based on signals from multiple diagnostics and actuators. Here we harness this dynamic model as a training environment for reinforcement-learning artificial intelligence, facilitating automated instability prevention. We demonstrate artificial intelligence control to lower the possibility of disruptive tearing instabilities in DIII-D, the largest magnetic fusion facility in the United States. The controller maintained the tearing likelihood under a given threshold, even under relatively unfavourable conditions of low safety factor and low torque. In particular, it allowed the plasma to actively track the stable path within the time-varying operational space while maintaining H-mode performance, which was challenging with traditional preprogrammed control. This controller paves the path to developing stable high-performance operational scenarios for future use in ITER.

It's been a long time since I've had a good week, but this one was alright. Not amazing or anything, but just... good?

I had only one major episode, it was entirely private, I went into my car to scream for a bit then went in and felt better. The entire rest of the week went... pretty smoothly? I didn't sit inside all day. I got out and did things. When things didn't go exactly how I wanted to, I didn't immediately seethe or mope. Get this: I worked with people, found solutions, and got shit done. I had fun without overdoing it. I was still pretty impulsive with my food spending, and still struggled with cleaning up and cooking for myself. The two are very much correlated, it's a vicious cycle and the target of my next goal for self-improvement.

I started a "mood journal." It's helping. I should have done this years ago. I should have been given Lithium years ago.

This video discusses the first stage of the machine learning process [identified in the previous post]: (1) formulating a problem to model. There are lots of opportunities to incorporate physics into this process, and learn new physics by applying ML to the right problem.

This video describes how to incorporate physics into the machine learning process. The process of machine learning is broken down into five stages: (1) formulating a problem to model, (2) collecting and curating training data to inform the model, (3) choosing an architecture with which to represent the model, (4) designing a loss function to assess the performance of the model, and (5) selecting and implementing an optimization algorithm to train the model. At each stage, we discuss how prior physical knowledge may be embedding into the process.

Physics informed machine learning is critical for many engineering applications, since many engineering systems are governed by physics and involve safety critical components. It also makes it possible to learn more from sparse and noisy data sets.

Abstract

There is no proof that black holes contain singularities when they are generated by real physical bodies. Roger Penrose claimed sixty years ago that trapped surfaces inevitably lead to light rays of finite affine length (FALL’s). Penrose and Stephen Hawking then asserted that these must end in actual singularities. When they could not prove this they decreed it to be self evident. It is shown that there are counterexamples through every point in the Kerr metric. These are asymptotic to at least one event horizon and do not end in singularities.

It's not too often that a giant of physics threatens to overturn an idea held to be self-evident by generations of physicists. Well, that may be the fate of the famous Penrose Singularity Theorem if we're to believe a recent paper by Roy Kerr. Long story short, the terrible singularity at the heart of the black hole may be no more.

3D prints still suffer from bad layer adhesion due to their 2.5D slicing and printing approach. I investigated if a novel slicing method that interleaves the layer could improve the strength of 3D prints.