Abb. 9 Im Normalfall erarbeitete man jedoch eine detaillierte interne Feinsortierung des Belegmaterials häufiger Wörter. Naturgemäß hätte jede Dimension der Analyse (chronologisch, grammatisch, semantisch, graphisch) die Grundlage einer eigenen Sortierordnung bilden können.

Folgezettel

rule-based algorithms like decision trees are not affected by feature scaling.

Isaac and I uh with another colleague we did a little bit of work trying to look at what would the Swedish policy or the UK policy indeed look like if it was carried out globally and it would look at something like two and a half degrees Centigrade of warming if 00:31:58 not more

if if one looks around and on the web and elsewhere there are numerous uh kind of experiments going on in in a 00:40:13 variety of things in the and new forms of representative democracy new forms of decision making new forms of economies in the sense of 00:40:26 you know local digital currencies and things like that i think all of those you know all of those are are excellent um you know a resource to draw from 00:40:43 uh of the the task is then is to take these ideas these ideas that are springing up all over and put integrate them in a way that is functional you know 00:40:57 can serve a community initially maybe the community is small just a few thousand but the idea is that it would that it would grow over time exponentially grow to who knows you know hundreds of thousands millions i don't know 00:41:09 so how can you take all those ideas and actually make them work sometimes i i liken it to you know that you might tinker in the garage with a with an airplane you know you might 00:41:21 build a two-seater in the garage and that's totally cool you know you can you know that maybe what the wright brothers did or something that's fantastic but i'm really interested in building a jumbo jet that takes you know 500 00:41:33 passengers at a time in an hour and a half to from new york to london or whatever and doesn't fall in the ocean you know like so how do you do that how do you how do you build a integrated system that is safe that is 00:41:47 resilient that is that it that has metrics that you can monitor progress that has good anticipation so you know where the you know that where you're going tomorrow you know where this 00:42:00 where is this going you know what's going to happen tomorrow and sort of what's going to happen to me you know that's that's part of the question so uh so the so i think the challenge is to take all these ideas that are 00:42:11 that are popping up all over which some really great ideas and then to integrate them into uh into a coherent hole that that spans every one of the i think maybe is it six 00:42:23 systems that i talk about so that so that they're not designed in silos we're not just building the new economic system we're not just building a new educational system we're building this we're building a 00:42:36 a cognitive architecture that includes all of those

scaling fractally

It says the world's wealthiest 1% produce double the combined carbon emissions of the poorest 50%, according to the UN.

gsettings set org.gnome.mutter experimental-features "['scale-monitor-framebuffer']"

Scale, inevitably leads to power-law distributed outcomes, leading to the inevitable concentration of talent and resources among a few investigators pursuing a few lines of inquiry, and their pale second-rate imitators. Through this mechanism science at scale reinforces (and in fact, under sufficient political capture imposes) consensus, further annihilating the possibility of the necessary revolutionary synthesis of ideas.

Popular architectures deal with heavy traffic loads by adding logic to cache popular views and resources.

WhyGeneral infrastructure simply takes time to build. You have to carefully design interfaces, write documentation and tests, and make sure that your systems will handle load. All of that is rival with experimentation, and not just because it takes time to build: it also makes the system much more rigid.Once you have lots of users with lots of use cases, it’s more difficult to change anything or to pursue radical experiments. You’ve got to make sure you don’t break things for people or else carefully communicate and manage change.Those same varied users simply consume a great deal of time day-to-day: a fault which occurs for 1% of people will present no real problem in a small prototype, but it’ll be high-priority when you have 100k users.Once this playbook becomes the primary goal, your incentives change: your goal will naturally become making the graphs go up, rather than answering fundamental questions about your system.

This is obviously a powerful playbook, but it should be deployed with careful timing because it tends to freeze the conceptual architecture of the system.

“scaling out is the only cost-effective thing”, but plenty of successful companies managed to scale up with a handful of large machines or VMs

Scaling is hard if you try do it yourself, so absolutely don’t try do it yourself. Use vendor provided, cloud abstractions like Google App Engine, Azure Web Apps or AWS Lambda with autoscaling support enabled if you can possibly avoid it.

I would like to make an appeal to core developers: all design decisions involving involuntary session creation MUST be made with a great caution. In case of a high-load project, avoiding to create a session for non-authenticated users is a vital strategy with a critical influence on application performance. It doesn't really make a big difference, whether you use a database backend, or Redis, or whatever else; eventually, your load would be high enough, and scaling further would not help anymore, so that either network access to the session backend or its “INSERT” performance would become a bottleneck. In my case, it's an application with 20-25 ms response time under a 20000-30000 RPM load. Having to create a session for an each session-less request would be critical enough to decide not to upgrade Django, or to fork and rewrite the corresponding components.

how the features are all on the same relative scale. The relative spaces between each feature’s values have been maintained.

However, this doesn’t mean that Min-Max scaling is not useful at all! A popular application is image processing, where pixel intensities have to be normalized to fit within a certain range (i.e., 0 to 255 for the RGB color range). Also, typical neural network algorithm require data that on a 0-1 scale.

To solve the problem of ‘scaling up’ requires ‘scaling in’ –by this we mean developing the designs and infrastructure needed to support effective use of an innovation.

As student numbers have increased, teaching has regressed for a variety of reasons to a greater focus on information transmission and less focus on questioning, exploration of ideas, presentation of alternative viewpoints, and the development of critical or original thinking. Yet these are the very skills needed by students in a knowledge-based society.