Principles of Communications -- Michaelmas Term 2017 - Nov 29, L24

Finished Ad Hoc/Mesh/Network Coding, and
Systems Design + Wrap Up/Summary of Course (29th Nov 2017).

Principles of Communications -- Michaelmas Term 2017 - Nov 24, L22

Having covered switches & data centers, this friday (24th) and monday (27th will look at Mesh Wireless networks - then next wednesday (29th) we wrap up with systems & course overview.

Principles of Communications -- Michaelmas Term 2017 - Nov 17, L19

This week, we shall finish with Scheduling() and Queue_Management().
Next week, on to switches, data centers (for real) and thence, to mesh...

Principles of Communications -- Michaelmas Term 2017 - Nov 10, L16

This week sees us complete the control theory section of the course, and cover the optimisation model of end-to-end congestion control + traffic engineering/routing as a joint optimal solution...on friday 10th nov.

Will contrast the optimization model of TCP with a very large scale practical measurement based study of real world traces of TCP, on monday 13th....

Principles of Communications -- Michaelmas Term 2017 - Nov 3, L13

This week, we looked at errors (coding for TCP)
flow control (open loop, token bucket etc, forward reference to schedulers)
closed loop (TCP equation, explicit v. implicit feedback), and will just start on
control theory....

if people want a lookaside at transforms, see
basis functions + also Markus Kuhn's excelent Digital Signal Processing course/notes.

Brexit & Principles of Communications

i'd just like to say that graph theory, compact routing, BGP, multicast, and the Erlang equation for call blocking probability have absolutely no bearing on whether brexit is a bad or good idea. The latter is entirely obvious, whereas the stuff I teach in this course is (hopefully) subtle, complex, and useful.

Principles of Communications -- Michaelmas Term 2017 - Oct 27, L10

By today, we've reached the outlimits of routing, having gone from compact, through centralized, via policy, multicast and mobile, to sticky random (DAR).

nb. material on information centric networking skipped/elided - not examinable:-)
plus didn't cover the tiny bit of mobile ip (but we do mesh networks later:)

Next week (from mon oct 30), we make a start on error, flow, and congestion control.

Principles of Communications -- Michaelmas Term 2017 - Oct 20, L7

By friday, oct 20, should have covered most of the inter-domain routing material including key core attributes, decision process, what BGP really computes. Roughly on schedule. Have also put supervisor material up for people to start using...

Principles of Communications -- Michaelmas Term 2017 - Oct 6, L1

Introduction&Outline of Course
  tangentially relevant cartoon of communication failure

Start on Graphs
  Next week (Oct 9&11) Graph Representations + Small Worlds, Clustering, Power Laws

CFI- Myth&Reality - Sci-Fi Dreams

How does reading literature, and in particular, SF, influence AI researchers (and I assume developers)?

My take on this was to look at Robots (and disembodied robots) that care  - ranging from positive role models, e.g.
Robbie the Robot (originally in Forbidden Planet)
R Daneel Olivaw (I robot, and all the way up to the 4th law in much later foudnation&robots series by Asimov),
Data, in star trek
the Synths (in the TV series but also in the Alien movies)
the replicants, in blade runner,
but also HAL and Roderick....

So these stories all feature moral tales and ethical dilemmas - sometimes, the resolution is bad, but often it is in favour of humanity. What is interesting is what "goes wrong" is often the result of a paradox, which would also be a problem for a human. There are simple examples (Alien's first movie synth has confluct in mission parameters, as does HAL), and many of the early asimov I robot stories feature Susan Calvin, RObot Psychologist "debugging" the way the 3 laws interact with the mission and humans orders and so on (c.f. funny why the laws are in that order )


but a more subtle problem arises from experience (i.e. training humans and AIs, whether simple learning or deep) which is that data and choices may conflate both bias and policy.....two examples
1. if we use re-offending probabilty as a guide to deciding in court whether to give a custodial sentence or a fine/payback, we include the social, police, jury and court biases (which are many) in the data - we will re-enforce things (as bad as the probabilty an african american is more likely to be found guilty than a european weather that's true or not, but also that the sample is bias and the root cause may be the opposite direction to the inference - i.e. jail causes re-office, not being drawn from a subset of the population who show up more often in jail for social reasons etc etc). De-biasing is tricky, but do-able through running natural experiments and multiple competing ML/AIs and having a meta-AI look at ground truth and (maybe) humans (like Dr Susan Calvin) at mechanism
2. we may decided to disciminate on age for car insurance, but not for gender (as is the case in the EU) because we want to encourage safer driving by young people (policy) but not assumptions about women driving...we need to be careful that this is explicit and transparent, and that acquired rules (e.g. through model inference) dont undermine this implicity...

These sorts of questions havn't shown up much in the SF/Tech/Geek literature so much as in classic novels such as Ralph Ellison's The Invisible Man (not to be confused with HG wells book of same name:-)

Finally, if we are thinking about influence between literature (or other media - music, dance, architecture, visual arts, movies) and tech creativity/development, never forget that much SF is written by scientists or engineers (Clark, Asimov,
Stephenson,  Chiang etc) so the ordering (a causes b) may not be obvious....and movies often have a different narrative arc than novels for many reasons. Someone asked if there's an equivalent to the myths/motifs/archetype (this teaching material for example,.). analysis done for movies for AI-based literature - that'd be a fine thing

Of course, myths and stories for moral education go back to ancient times, and who knows if 50,000 year old cave art drawn with the use of pre-Promethean fire didn't have some societal lesson to impart...if only we had a time machine to go back and ask

shadows and ghosts and computer philosophy

a lot of systems work in computer science is about virtualisation, which is basically the way to hide details of gritty reality from people (programmers) and processes.  so plato spoke about us witnessing the universe as if sitting in a cave by the fire trying to work out how the cosmos works via shadows on the wall. a lot of hacking is like this.

a lot of security and usability is about trying to make a computer that interacts with you like a real person. to make the machine a ghost, at least, partially convincing. we now even find ourselves trying to convince computers that we are not machines. we, the cpu god creators, are now failing to live up to our own creation's expectations.

how the hell did we get to this juncture?

PrinComm revison info

just put online linked from top line of course materials web pages:-

http://www.cl.cam.ac.uk/teaching/1617/PrincComm/materials.html

there's also a link to the review paper on compact routing for anyone interested..

academia uber alles

i hope the eponymous taxi company hasn't got a "business process" Patent on it because there's prior art - the 100% hollowed out notion (gives a whole new life to the idea of shell company or the emperor's new clothes metaphor) has been alive and well in academia for many decades

you know the script, right - you get a missive saying
"here's a paper, we need reviewed - oh, and if you can't do it now, we'll out you on our books for later, and can you recommend someone who can?"

1. the paper was written by an academic, who type set it using software freely avaialble, designed by researchers, and is probably on a web site run by academics, running on software written by researchers, and maintained by academics, etc

2. when the paper is published, good chance some private company will make money by sending it to libraries curated by academics, and it will be read by researchers,

3. we can extend this to MOOCs

4. and graduate students (here's a student, wanna supervise them, then we'll hire them at Company X, having had them trained by you) etc

the "gift" economics is ok when everyone is (as we say in what Tom Lehrer used to call the Ed Biz,I think in that memorable song about Ivan Lobachevsky) collegiate.

but the reality is that there's a mix of behaviours (I'm sure its heavy tailed - like a fox, or whatever) where a few people do a zillion amounts of stuff, and most do doodly squat - after all, most papers are read by between 0 and 1 people (not even the reviewers in some cases) - its true - look at google scholar stats

I'm beginning to believe we are in the world described in Theodore Sturgeon's brilliant story "It Wasn't Syzygy" (see collection e pluribus unicorn) - a wonderful author who should be as well known as Ray Bradbury...

there really should only be 3 universities and 3 computers and 3 queens.

Principles of Communications -- Michaelmas Term 2016....weak ate (to Nov 30)

This week, wra up with Ad hoc Net capacity&coding tricks
+ systems structures
+ Course Overview - including 2 missing pieces
1/ didnt cover shared media (as was done in 1b)
2/ didn't cover traffic engineering&signaling (rsvp) as most the principles already covered in other lectures earlier in term (open & closed loop control, optimisation and fibbing).

Principles of Communications -- Michaelmas Term 2016....week 7 (to Nov 25)

scheduling & switching last week and this week - some associated info:

sorting queues

huis clos shows up in multicores, routers, and data centers:-)

will not cover shared media, friday, as was done in 1b physical & dala link layer really nicely already, so revise that - 

instead, will move on to ad hoc/mobile networking capacity *might talk about opportunistic networks and firechat too:-)

Principles of Communications -- Michaelmas Term 2016....week 5 (to Nov 11)

This week was feedback control, theory &
optimization (routing and congestion pricing)...a bit math/algebra/calculus heavy methinks (supervisions should work through one or two examples of a PID controller for different systems
and how you show stability&long term operating point) - next week, real world TCP, then scheduling.

Principles of Communications -- Michaelmas Term 2016....week 4 (to Nov 4)

Have  covered Sticky Random Routing (DAR material here) + Network Coding for TCP

see wikipedia for Gaussian Elimination, + Linear Network Coding articles - best source/explanation I can find

And Open Loop Flow Control (including leaky bucket regulators/policiers)

Next week, closed loop/feedback control, and underlying theory for controller design and stability/efficiency analysis.

Principles of Communications -- Michaelmas Term 2016....week 3 (to Oct 21)

Done centralised/hybrid routing/fibbing -- as someone pointed out, forwarding continues if a central controler caches - depends on timeout in openflow added state/fib entries - with fibbing, the timeout will be whatever OSPF Or equiv does - so a comparison is potentially a bit more subtle than as presented.
Also, SDN/Openflow lets you add entries based on 5-tuple, whereas fibbing lets you add by destination only.....so potentially more fine grain choices in SDN, even if at the cost of more state - so the "prefix hijack" in fibbing is neat, but not the last word in adding custom routes...(e.g. if you wanted by source, needs more state than can be added by fake Link State Advert - as far as I can see)

rest of week was on BGP - why, what, how, where, when, and why not!

Principles of Communications -- Michaelmas Term 2016....

Finishing up end of week 2 (lecture 4) with Compact routing
having done background graphs & reminder of routing basics.

See the  slides page for lecture material + links to papers with more detail if you want (where not covered in books -

I've also added some more pointers to background book like reading on the course materials page for people that want to read more around the area, as there's no specific single book that covers all the course materials.

ttfn

fairness, machine learning, versus optimal stopping and cognitive bias

There's a bunch of work in making sure that machine learning systems are, in some carefully defined sense, fair - see for example the MPI work by Krishna Gummadi, in removing biases in various ML use cases (e.g. gender as an explicit or implicit discriminator).

For me there's a really subtle problem here which links between this work and other problems of Optimal Stopping and Cognitive Biases, and how one choose to define fairness in ML and the feedback loop between this and human society and the views we take on each other.

So lets take two simple use cases:

1. Admissions to University and Gender

Imagine a Computer Science department has 100 applicants a month, over 3 months for 50 places and wants to pick the best 50 people. Naive use of Optimal stopping would say wait til you have 37% of the applicants (111 people), then pick. What if the population is drawn differently by gender - e.g. out of every 100 applicants, only 1 is female. Lets say this is because applicants are self selecting based on the position in the ability of their own sub-population.. You have about a 1/2 chance of having 0 women in the admissions. The feedback to the population in society is you have to be in the top 1% of female applicants, but in the top 18% of men. Assuming their isn't actually a gender basis for ability distribution. You've just built a system that re-enforces it. TO get out of this, you have to run a two-factor optimal stopping scheme. If you want to do this for other groups in society, it will get more complex too...

2. Stop&Search and Race

It may be the case that you stop and search people in safeguarding society by profiling individuals based on past cases of stopping and successfully apprehending miscreants. Lets say this leads to a higher probability of stopping people who "look middle eastern". Again there's a feedback loop between your "correct" but naive selection scheme, and how people behave - in this case, various cognitive biases in how society will regard the group you target, may lead to the group being marginalised, out of proportion to even your allegedly accurate statistical model. e.g. anchorism....or many others, will lead to over-weighting by society, especially since humans are risk averse.