That is all.

Who decides the interests of a country? If you think its just the president or just the department of defence then you don't understand democracy.

Many smart people believe its in the best interests of their country (and the human race) that we don't race to weaponize AI.

Shall I fact check this for you, or would you like to take a go?

There's nothing inherently wrong in publishing in incremental steps. In fact, it can be useful to get peer review at different stages in a project.

The problem here is that the article overstates the achievement, seemingly intentionally misleading the reader into believing more has been done than actually has been.

I'd upgrade my other machines if my Windows 10 machine didn't BSOD every couple days.

Yea, it says, "Examples of inputs [to the AI system] include all positional, velocity, and acceleration states, estimated missile range data, visibility of each platform, bogey ratio, and number of shots taken by hostile platforms", which seems to be saying it has direct access to the information in the simulation.

Yep, from TFA, "Examples of inputs [to the AI system] include all positional, velocity, and acceleration states, estimated missile range data, visibility of each platform, bogey ratio, and number of shots taken by hostile platforms..."

Basically, it knows everything about the current state of the dog fight at all times (without having to parse any visual information). If the human pilot had access to this information, s/he would probably perform much better also. Basically, the NPC cheated.

While I'm sure its a nice AI system, its a LONG way from an autonomous fighter pilot.

I remember playing a few different games at school. Off the top of my head: Math Circus, The Incredible Machine and we even did a 5th grade project involving Sim Tower. This was all back in the mid-90's. Video games have been used in schools for decades now.

For anyone interested, the "quantized inertia" explanation was proposed by McCulloch, and is here: http://dx.doi.org/10.1209/0295-5075/111/60005

I agree completely. One hint at where this problem stems from is the fact that scientists often even have trouble communicating with other scientists outside of their own field (and these are the people writing the wikipedia articles). The goal of research is to go very deep, so scientists tend to get entrenched in their own specialized terminology and way of looking at the world. Furthermore, in my experience as a PhD student, being able to communicate scientific ideas with the broader public is not a skill that is trained or even all that valued among most scientists (because it typically doesn't help get grants), so it's not something that many scientists get good at.

Note to self, don't start a long-term indie game project on whatever Microsoft's next game dev platform is.

"Deep learning" refers a family of machine learning techniques (such as neural-networks, convolutional neural-networks, stacked-autoencoders, etc.) that have a multi-layer architechture, typically allowing the system to learn highly non-linear functions of many variables. Each layer can be thought of as a simple learned function whose output is fed into the next layer. Such systems can often have thousands or millions of parameters to learn and thus require a LOT of training data and a fair bit of computing power/ runtime to train. But if you look at some area (e.g. object reccognition in computer vision), deep networks are currently the top techniques by a fair margin.

The devil is in the details. How do you best represent learning mathematically and computationally? What are mistakes and or what are the objectives? How do you encode these and how to you penalize making these mistakes in the future? These are all challenging questions.

Machine learning approaches have often taken inspiration from biology, however the exact neurological mechanisms of learning are not yet entirely understood. Its difficult to replicate nature. Its even more difficult when you don't yet understand nature.

In this case, it seems like the details matter but when you say "went something like this", its unclear if you've actually got their argument correct.
1) Had there been attacks on women on campus or in the area?
2) Was the closure actually to discourage anyone (including women) from hanging around the labs late at night when the crime rate is higher (before a camera was installed)?
3) Was the workload for the courses that used the labs such that it likely required late night working?

I'm guessing the answer to some or all of those questions is actually yes. You make it sound as if this was unnecessary complaining by some women, but is this the case? Furthermore, have you ever walked around the campus late at night as a woman? (or at least talked to women about this issue)?

Everyone else sees a blue pill and a black pill, right?

In Canada, if you aren't using a cloth/reusable bag, you're just that fucking dude who didn't bring a bag. Almost everywhere asks and charges before handing you one. The only exception, as others have pointed out, is if you just need garbage bags and want them cheap.