During an internship almost ten years ago, I was working on a component of the ESA Exomars 2020 mission. After leaving the company, I occasionally checked the progress of the project. First, there was a delay for the launch of two years. Later, Russia invaded Ucraine. As ESA was cooperating with the Russian space agency, the project was stopped. It has since been rescheduled to 2028.
It was a great learning experience, but I’m still not sure whether the stuff that I worked on at that time will ever be used.
Interesting paper, thanks for bringing this up! I have been working on methods for trajectory optimization using both, analytic gradient computations and black box stochastic gradient approximations (proximal policy optimization).
I was always wondering about a question that is touched in the paper: despite the analytic gradient computation being intuitively more efficient and mathematically correct, it is much harder to learn a policy with it than with the “brute force trial-and-error” black box methods.
My guess: the cart pole is an inverted pendulum, and requires multiple left-right-swing-up movements to bring it from the “hanging” position to the “standing” position.
Finding this action sequence using gradients of “where is the tip” vs “where should it be” is very hard, as swinging the pendulum to the left and right goes against the gradient.
Instead, using stochastic gradient approximations (policy gradient method such as proximal policy optimization) might be better suited to solving these kinds of problems. Effectively, they do not compute the exact gradient locally, but rather kind of a global approximation by trying out random sequences of actions and determining which of them are closest to the desired outcome.
Hence, stochastic gradient approximations might be considered some kind of hybrid between greedy local optimization (such as following the exact gradient) and global optimization.
Wow, thanks for pointing out the international Spiegel.
It seems much more substantial than the free German counterpart, at least in my first impression.
Realized profit? Or is the current value just above the value of the purchase date? I would believe the latter, as the value is still quite high.
However, realizing the profit is a different thing. That will work only for a few who invested early and deinvest early. Basically, btc is a pyramid scheme, the only difference to other pyramid schemes is that this fact is not obfuscated. Every investor knows that he is buying a fundamentally worthless good, but is hoping that someone else will eventually pay him a higher price.
That works well until the market is saturated and no one is willing to buy btc any more. Then, the price collapses.
Thus: even if most investors today have made profit, that simply means that the bottom of the pyramid has not been reached. At that point, most investors will have made loss.
I‘m on windows, and here it is still possible. Try checking your settings again, it is a bit hidden: goto settings/sync, then file-sync -> via WebDAV is available in a Dropdown menu.
I have the same issue with a keyboard that I love. I simply bought two of them and use one for the left and one for the right hand. Certainly not the sleekest solution, but it works.
Having accustomed to using a Thinkpad Trackpoint, I am always struggling with separate keyboard and mouses. I mean, I rarely use the mouse, but cannot completely ditch it.
The ergonomics of a split keyboard is very appealing. For the last months, I’ve simply used two thinkpad trackpoint usb keyboards as a cheap but working substitute :-D
The Tex Yoda II might be of interest, its just a small mechboard with a trackpoint and of course mouse buttons. Even more so, maybe the (also Tex) Kodachi or Shinobi, which are basically just thinkpad keyboards but mechanical
I don’t get the benefits of the articles vision of 6G. There is a good reason for why we use the OSI Mode and separate problems that appear at the physical layer from those that appear at the application layer. I think of it as a powerful and universal interface specification.
I couldn’t find a single argument in the article that even justifies mingling some general machine learning with the actual physical data transmission.
Please, 6G people, more bandwidth and lower latency is all we want.
