The Knowledge

"Was it actually getting harder to find a taxi?", Brian wondered to himself as he found he had walked half way from Paddington to King's Cross station without seeing a single one.

And thus began one of the most fascinating, dangerous, and scandalous investigations of his long and intrepid journalistic career.

Starting with visits to Knowledge Corner, the legendary cafe and on-ramp point for all would-be Hansom Cab drivers and moving on to the hushed corridors of the hyperscale self-driving e-bike company, Fahrt, the trail would peter out, only for his pride to be piqued once more when narrowly missed by one of said companies vehicles, apparently sans rider - "ha!" he exclaimed silently to himself "this ain't no sleepy hollow".

Indeed it wasn't, as the clues led him through Limehouse past the charnel houses, to the great Koala tea warehouses of the Tai Chi Chai conglomerate.

On his mind, the constant mantra "where are all the drivers going?, as plenty of them are still dithering around town on those very e--bikes, learning the statutory 320 routes in the bluebook, and coping with the vagaries of roadworks and christmas lights and unexploded traffic cones."

As luck would have it, one day, he managed to hail a ride in a good old fashioned diesel smoke spewing black cab, and, no luck needed there, the driver wouldn't shut up about it.

"You'll never believe the signup fee they now pay - back in my day, you had to buy a moped and clipboard and waterproof all yourself, and ride up and down until you could pass the test - the inspector, no-one ever learned his name, was one hard b*rd, i can tell you. Never let you by if you made one mistake - things like thinking swiss cottage was on the way to st johns wood, or crystal palace was close to ally pally. na, pain in the backside, even more than the seats in these things"...

"so" started Brian in a millisecond gap in the constant stream of nostalgia and cursing "where are all the drivers going then, if they are paid so well?".

"now theres the thing - its a real conundrum and I can tell you " continued keef almost without taking a breath and chewing on a cheese and onion white bread sandwich whilst executing a sudden u-turn right in front of an ambulance, nearly executing a traffic warden in the same manoeuvre. "they aint going home, which just adds to the mistry. and they ain't showing up at the footie " (Brian consulted his smart watch and ascertained that gate was indeed way down at Millwall). "But" and here the cabbie touched his nose, winked and nearly took out two nuns on a pedestrian crossing "they are drinking an awful lot of tea. You can find them lined up all weekend down in the docks, you know, by that big old pagoda between limehouse basin and cannery row or wharf or whatever its called".

This was the big break Brian had been waiting for. He hotfooted it (actually pedalled) down ther right away having paid off keef for his interesting route (from paddington to kings cross via alexandra and crytal palace really showed creativity, especially in diverse use of bridges over the river thames, approaching that of US movies allegedly set in London).

And that's where he found them all, stretched out in the tea dens in the catacombs beneath the Master Ting Academy. Drinking tea, talking nonsense, but all the while, their heads in some weird contrivance that looked all the world like an old fashioned digital perm machine. 

Brian use his smart watch again to track the signals coming out of these machines and determined that, yes, indeed, it was heading back to Fahrt HQ, in grammerly square, behind, surprirse surprise, Kings Cross again. "what are they doing" he asked himself, especially as there wasn't anyone else there who would know.

Then a terrible thought struck him. Wasn't the founder of Fahrt also the guy who'd been going on about neural implants and direct mind control? Wasn't there a scandal when it was discovered that while the devices worked, they were once only use, since they erased the part of the brain that they interfaced to, in the process of sending the signals to the metacloud? What if his new outfit were crowdsourcing smart routes for their self-driving bikes, and hadn't figured out how to record the routes, so needed a constant supply of new graduates of the Knowledge, to keep the whole enterprise from collapsing? What if the answer to all these questions was "yes"?

Sadly, we will never know, as, ironically, he was knocked into the river by a black cab driven by someone the police say is probably called keef, although witnesses said it was hard to make out his features for all the blue air around the vehicle at the time.

Quantum Ransomware...or Squid Inkjection Attacks

 With apologies to whoever coined the term SQUID, here's a thought experiement.

Imagine for a moment, that I choose entangle a couple of particles, kind of QKD-style.

Now I use one of these to encode my e-mail to you. Now I can use this nearly innocently to delete your copy of my email. But Imagine, for a moment, that you are fortunate (or gullible) enough to run your email service on a Quantum Computer. I can now use my entanglement to de-cohere your processor - given it is a switched program (not really a stored program) computer, I can really spin it down. I even get notified when you try to restore it and find which e-mails are causing the problem (like when you read/delete a message with one of my QUnicode bits in, my copy gets altered too - hey, that's the physics, don't mess with that:-)

What fun!

In general, has there been much analysis of side channel attacks and denial-of-service threats to QC?

weaponising the supply chain - thanks but no thanks, mossad

By intermediating the supply chain, Mossad appears to have been able to subvert safety in various mobile devices (so far, pagers, walkie talkies, possibly some phones) - one speculation is that they undermined the current limiter or other safety fature that stop the battery overheating and catching fire - and put in some interface to allow software (e.g. via specific messages to the device) to trigger this behaviour - rather than, say, just putting a few grams of semtex in the deveice and turning it into a small IED.

Because this was done at scale, and somewhat scattershot, the normal trust in the safety of devices bought through regular supply chains has been undermined. Imagine if Mossad had decided to do this via several made up intermediaries who sold through the Amazon Market place for example, especially with relatively low value items that aren't typically checked when being shipped internationally. Great. Now the idea is out of the box, it is another extreme example of asymmetric warfare - lots of organisations could re-implement it easily[*]. 

This instantly, a lot of organisations now have to worry about people who, having innocently bought such a device, (or indeed bought one off of someone else who got one of these exploted gadgets) want to travel

We currently ban e-bikes on trains in the UK because, even without state-sponsored terrorism, safety features on e-bikes are not terribly well checked (they don't have the equivalent of a regular MoT/roadworthiness/emissions check, which might help a bit). We also don't allow really large capacity power banks on planes. In these cases, the risks are higher even if the occurance of fire/explosion is rare, because the energy in the device is so much more.  Nevertheless, the explosions seen in Lebanon would be extremely dangerous on a plane, whether in the passenger cabin or the hold. 

Or we'll only allow devices where the battery can be removed and kept seperate from any trigger circuit. (useful for consumers who want to replace old, knackered batteries too).

So maybe we will see a ban on carrying any mobile/rechargeable device on planes for a while, until some certification (including tamper proof sealing of post-certified devices) is available.

Of course, Mossad will then subvert the certification labs next, no doubt.

Just to start with I think we need to stop people with Israeli passports traveling outside of their country as they represent a clear and present danger to everyone in the world, not just to innocent bystanders in market places in lebanon (or gaza). Until they can assure us that they are not going behave so irresponsibly, and regain any possible level of trust they might once have enjoyed. Of course, most their agents will also have other passports too.

Update - this Bunniestudios blog is a very useful detailed analysis of the howto....

* footnote - why the west didn't launch cyber attacks on Russia's infrastructure (e.g. taking down all their power and comms) when they invaded Ukraine was a) revealing the tools the west has and b) inviting a retaliation which would also have succeeded, were both v. bad ideas. Mossad has just revealed that it has no clue about this type of precautionary principle. Well done. guys.

explainability, next to reversabilty?

 XAI has many flavours (includnig interpretability as well as explainability) - au fond, the idea is to shine a light into the black box, and not just say why an input produced an output, but potentially show the workings, and, in the process, quantify uncertainty in the output (confidence)- in the process of using an AI that does produce these outputs, the user can necessarily gradually construct a model of what the AI is doing (and why, given the user knows the inputs too) Hence, in a sense, this is like debugging the AI, or indeed, modelling the AI. i.e. reproducing the AI's model. In the end, the user will have reverse engineered the AI.  This is an indirect, amd possibly time consuming way of reproducing the model, effectively if not actually. Ironically, in some cases, we may end up with a more accurate, or a cheaper model, or both. 

Of course, you may dispute that the model we learn is not actually the same as the thing inside the black box - the analogy of boxes and lights is, of course, nonsense. If we were to know the actual machine learning model (linear regression, random forest, convolutional neueal net, bayesian inferencer etc, and the actual weights (model parameters etc) then it wouldn't be a black box, and we'd be able to simply copy it.  various techniques can be used even for quite complex machines, to relate the model parameters (e.g. CNN weights and clustering) to the features the model is able to detect or predict. This is the direct approach. In this approach, we are also able, potentially, to simplify the actual model, removing components that serve no useful purpose ("junk dna"?).

Either way, any sufficiently advanced and thorough explanation of an AI is going to be a copy.

I wonder if the world of LLMs is resistant to XAI techniques partly (honestly) because very large models would be very expensive to re-model these ways, but also partly because some of the proponents of GenAI technlogies like to retain the mystery -- "it's magic", or perhaps less cynically "it's commerical in confidence". 

However, if we want to depend on an AI technology for (say) safety critical activities, I think it better be fully explainable. And that means it will be transparent, actually open, and reversable (in the reverse engineering sense). 

what if cat species were named after greek food?

mossaka, the mouser

calamari, the cat of nine lives and nine tales

tsigarides, the top cat

kleftiko, the clever cat

stifado, the sedentary cat

marathopitakia, the mischief maker

dolmadakia, sleeps all day

add yours here...

Unsustainable? Inconceivable!

 It seems that people are just starting to cotton on to the fact that the new wave of giant AI is not very sustainable. Mostly they look at the cost (communications, storage, computation, electricity, water) of training. 

But there's another couple of  costs being ignored

1. it has taken 32 years (give or take) of the WWW to get to where we have all the material avaialble today, including millions of websites, blogs, scientific and other academic open access materials and wikipedia and so on, as well as huge numbers of photos, songs etc - this represents a massive investment by 100s millions of people over more than a generation.

2. really useful data out there has been curated (a.k.a. wrangled) so that it doesn't have too many lacunae or errors, and may be statistically representative - it may also be accompanied by meta data (describing its meaning, but perhaps also labelling features in the data with meaningful tags - especially useful, for example, in medical images or satellite images of earth, but also just simple stuff like names of people in pictures, and GPS/location data of a photo or movie. This also took both time&effort, but also expertise - humans spent a while using their knowledge, and possibly skills, to add that extra information.

Of course, a special class of data is code - and open source repositories have a lot of that, associated with meta data ("documentation") and labelled (e.g. with commit logs describing bug fixes or features added, by whom, and when)

While they may offer all this data for "free", using it to train an AI is being undertaken lightly as if this is the same as using an image or a blog or a piece of music for entertainment or education.  

By absorbing this mass of material into a model, what is really being done is absorbing the prior information that gives more than a slight hint about the model that was in the minds of the users who created the original content. That is to say, their labour is being appropriated, not just the fruits of their labour.

So if you want another common-crawl's worth of data, be aware a lot of people will quite like to be paid for their effort next time around. And can you afford a payroll with 100M expert employees working for 32 years? Really?

career pathways at the Turing - some ideas

 

Turing v. University v. Industry Lab v. Government

Academic classic career path

school (work experience)

undergrad (remember urop/intern) ->

masters (1-3 years)

phd (internships 2) 

post doc contract 

research fellowship (RS/RAEng, UKRI, Leverhulme etc etc 3)

faculty position (assoc prof->asst prof -> full prof (tenure)

About 30% of our students -> Masters, and 50% Masters -> PhD

Some take 1-2 years between each step , out in industry (finance or tech)

About 20-30% students switch to another discipline (CS -> finance GS/Accenture/E&Y etc)

About 30% exit at any stage to go to compute tech industry (1) including from Academic

(typical Cambridge non academic destined CS PhD CS might go to  Amazon, Microsoft, Google, Apple, etc, but also startups

Some people move from study or research to government roles (civil service or policy).

Turing 2.0

Turing 1.0 for years 1-7 approximated most of these phases with three obvious differences. (note in non AI/CS area, we have other models - e.g. CERN, Sanger, Welcome etc etc)

1. RSDE (REG) was modelled on jobs for senior software engineers at Microsoft Research, and has created an entirely new (but non industry) based career pathway (and is being adopted across UK and more widely

2. Internships (we have incoming in the Turing, as enrichment students, but not outward

3. Fellowships - here's where a nice transition from being supervised/trained in research, to writing own funding proposal to get often 5 years, of autonomous funds - uni/academic departments love it, as they get

a person who is REFable, does leading research (the funds go to people who write the very best proposals, and in some cases, e.g. ERC have to have already a cool very high publication profile already

The funds often only require someone committed 50% to research with up to 50% free to teach (or do other jobs in the uni)

The process of getting UKRI/RS type fellowships is a high quality control mechanism for some people to gain research leadership skills -some people even do several, and they can be got right up to senior level by academics.

It is the way one then learns how to write, and run larger research proposals / projects, with more junior  roles (PhDs, postdocs) for the fellow to manage to get a team to tackle larger scale problems, possibly  leading to even larger, collaborative grants (programmes of work) with other institutions (going further, possibly, adding partners from other disciplines.

Fellowships reduce, but do not eliminate the precarity of post-doctoral contract research jobs...the Turing could consider a similar role.

Moving from early career to leader

At some point in this career path, someone may get higher levels of recognition and be invited/elected 

to various scholarly bodies, to work on advice to the funding agencies, and to government departments.

And of course funds can be got from industry or philanthropic sources, but need some level of autonomy and visibility for someone to be seen as eligible (unless they move with the money to somewhere else where they would be come eligible on arriving...ERC grants sometimes have that effect).

Something not mentioned in this so far is spinout or startups. Policy/careers for this are highly variable across the UK (and EU and US) today...leaving an institution, then coming back can be done (Stanford in US and Cambridge in UK allow it - it is more common in some sectors, e.g. pharma, than others).

Being an academic almost always involves teaching duties - the major plus point of having students in 

the supply of smart, educated known quantities for the next stage of their career. Another fine point 

is that student projects are niece ways to explore ideas (especially at masters level) - the nearest the 

Turing has to this is enrichment students and the University partnership. 

However, the longitudinal relation between student and advisor is outside the Turing.

Another good thing the Turing does involving groups of people including students is data study groups.

And what support does the Turing give for these various notions of career pathway development?

Anyone should be able to aspire to one day being an S&I director, or Chief Scientific Officer

Recommends:-

Have a clear pathway for career progression, all the way to the highest level, not excluding S&I leadership, chief science etc.

Celebrate (and re-enforce) the REG/RSE model.

Deal with precarity in a way at least as good or better than current University practice.

Consider how Turing employees can be empowered to seek external funding for (e.g.) fellowships if they are planning to transition to academic posts later.

Similarly, enable and support employees wishing to spin out work and engage in startups. In that same breath, support their return from startups in appropriate ways (tech transfer achieved etc).

Consider supervisory roles and leading training tasks for the above

For career pathways with intention to transition to industry, look at senior possibilities for people liaising with such partners while at the Turing.

The Turing has strong relations with some government departments (e.g. defense&security, but also health, transport, climate etc), and so career pathways that include transition to and from government is another pathway we should curate.