Category Archives: Computing

Optical computing

A few nights ago I was thinking about the optical fibre memories that we were designing in the late 1980s in BT. The idea was simple. You transmit data into an optical fibre, and if the data rate is high you can squeeze lots of data into a manageable length. Back then the speed of light in fibre was about 5 microseconds per km of fibre, so 1000km of fibre, at a data rate of 2Gb/s would hold 10Mbits of data, per wavelength, so if you can multiplex 2 million wavelengths, you’d store 20Tbits of data. You could maintain the data by using a repeater to repeat the data as it reaches one end into the other, or modify it at that point simply by changing what you re-transmit. That was all theory then, because the latest ‘hero’ experiments were only just starting to demonstrate the feasibility of such long lengths, such high density WDM and such data rates.

Nowadays, that’s ancient history of course, but we also have many new types of fibre, such as hollow fibre with various shaped pores and various dopings to allow a range of effects. And that’s where using it for computing comes in.

If optical fibre is designed for this purpose, with optimal variable refractive index designed to facilitate and maximise non-linear effects, then the photons in one data stream on one wavelength could have enough effects of photons in another stream to be used for computational interaction. Computers don’t have to be digital of course, so the effects don’t have to be huge. Analog computing has many uses, and analog interactions could certainly work, while digital ones might work, and hybrid digital/analog computing may also be feasible. Then it gets fun!

Some of the data streams could be programs. Around that time, I was designing protocols with smart packets that contained executable code, as well as other packets that could hold analog or digital data or any mix. We later called the smart packets ANTs – autonomous network telephers, a contrived term if ever there was one, but we wanted to call them ants badly. They would scurry around the network doing a wide range of jobs, using a range of biomimetic and basic physics techniques to work like ant colonies and achieve complex tasks using simple means.

If some of these smart packets or ANTs are running along a fibre, changing the properties as they go to interact with other data transmitting alongside, then ANTs can interact with one another and with any stored data. ANTs could also move forwards or backwards along the fibre by using ‘sidings’ or physical shortcuts, since they can route themselves or each other. Data produced or changed by the interactions could be digital or analog and still work fine, carried on the smart packet structure.

(If you’re interested my protocol was called UNICORN, Universal Carrier for an Optical Residential Network, and used the same architectural principles as my previous Addressed Time Slice invention, compressing analog data by a few percent to fit into a packet, with a digital address and header, or allowing any digital data rate or structure in a payload while keeping the same header specs for easy routing. That system was invented (in 1988) for the late 1990s when basic domestic broadband rate should have been 625Mbit/s or more, but we expected to be at 2Gbit/s or even 20Gbit/s soon after that in the early 2000s, and the benefit as that we wouldn’t have to change the network switching because the header overheads would still only be a few percent of total time. None of that happened because of government interference in the telecoms industry regulation that strongly disincentivised its development, and even today, 625Mbit/s ‘basic rate’ access is still a dream, let alone 20Gbit/s.)

Such a system would be feasible. Shortcuts and sidings are easy to arrange. The protocols would work fine. Non-linear effects are already well known and diverse. If it were only used for digital computing, it would have little advantage over conventional computers. With data stored on long fibre lengths, external interactions would be limited, with long latency. However, it does present a range of potentials for use with external sensors directly interacting with data streams and ANTs to accomplish some tasks associated with modern AI. It ought to be possible to use these techniques to build the adaptive analog neural networks that we’ve known are the best hope of achieving strong AI since Hans Moravek’s insight, coincidentally also around that time. The non-linear effects even enable ideal mechanisms for implementing emotions, biasing the computation in particular directions via intensity of certain wavelengths of light in much the same way as chemical hormones and neurotransmitters interact with our own neurons. Implementing up to 2 million different emotions at once is feasible.

So there’s a whole mineful of architectures, tools and techniques waiting to be explored and mined by smart young minds in the IT industry, using custom non-linear optical fibres for optical AI.

AI could use killer drone swarms to attack people while taking out networks

In 1987 I discovered a whole class of security attacks that could knock out networks, which I called correlated traffic attacks, creating particular patterns of data packet arrivals from particular sources at particular times or intervals. We simulated two examples to successfully verify the problem. One example was protocol resonance. I demonstrated that it was possible to push a system into a gross overload state with a single call, by spacing the packets precise intervals apart. Their arrival caused a strong resonance in the bandwidth allocation algorithms and the result was that network capacity was instantaneously reduced by around 70%. Another example was information waves, whereby a single piece of information appearing at a particular point could, by its interaction with particular apps on mobile devices (the assumption was financially relevant data that would trigger AI on the devices to start requesting voluminous data, triggering a highly correlated wave of responses, using up bandwidth and throwing the network into overload, very likely crashing due to initiation of rarely used software. When calls couldn’t get through, the devices would wait until the network recovered, then they would all simultaneously detect recovery and simultaneously try again, killing the net again, and again, until people were asked to turn  their devices off and on again, thereby bringing randomness back into the system. Both of these examples could knock out certain kinds of networks, but they are just two of an infinite set of possibilities in the correlated traffic attack class.

Adversarial AI pits one AI against another, trying things at random or making small modifications until a particular situation is achieved, such as the second AI accepting an image is acceptable. It is possible, though I don’t believe it has been achieved yet, to use the technique to simulate a wide range of correlated traffic situations, seeing which ones achieve network resonance or overloads, which trigger particular desired responses from network management or control systems, via interactions with the network and its protocols, commonly resident apps on mobile devices or computer operating systems.

Activists and researchers are already well aware that adversarial AI can be used to find vulnerabilities in face recognition systems and thereby prevent recognition, or to deceive autonomous car AI into seeing fantasy objects or not seeing real ones. As Noel Sharkey, the robotics expert, has been tweeting today, it will be possible to use adversarial AI to corrupt recognition systems used by killer drones, potentially to cause them to attack their controllers or innocents instead of their intended targets. I have to agree with him. But linking that corruption to the whole extended field of correlated traffic attacks extends the range of mechanisms that can be used greatly. It will be possible to exploit highly obscured interactions between network physical architecture, protocols and operating systems, network management, app interactions, and the entire sensor/IoT ecosystem, as well as software and AI systems using it. It is impossible to check all possible interactions, so no absolute defence is possible, but adversarial AI with enough compute power could randomly explore across these multiple dimensions, stumble across regions of vulnerability and drill down until grand vulnerabilities are found.

This could further be linked to apps used as highly invisible Trojans, offering high attractiveness to users with no apparent side effects, quietly gathering data to help identify potential targets, and simply waiting for a particular situation or command before signalling to the attacking system.

A future activist or terrorist group or rogue state could use such tools to make a multidimensional attack. It could initiate an attack, using its own apps to identify and locate targets, control large swarms of killer drones or robots to attack them, simultaneously executing a cyberattack that knocks out selected parts of the network, crashing or killing computers and infrastructure. The vast bulk of this could be developed, tested and refined offline, using simulation and adversarial AI approaches to discover vulnerabilities and optimise exploits.

There is already debate about killer drones, mainly whether we should permit them and in what circumstances, but activists and rogue states won’t care about rules. Millions of engineers are technically able to build such things and some are not on your side. It is reasonable to expect that freely available AI tools will be used in such ways, using their intelligence to design, refine, initiate and control attacks using killer drones, robots and self-driving cars to harm us, while corrupting systems and infrastructure that protect us.

Worrying, especially since the capability is arriving just as everyone is starting to consider civil war.

 

 

Some trees just don’t get barked up

Now and then, someone asks me for an old document and as I search for it, I stumble across others I’d forgotten about. I’ve been rather frustrated that AI progress hasn’t kept up with its development rate in the 90s, so this was fun to rediscover, highlighting some future computing directions that offered serious but uncertain potential exactly 20 years ago, well 20 years ago 3 weeks ago. Here is the text, and the Schrodinger’s Computer was only ever intended to be silly (since renamed the Yonck Processor):

Herrings, a large subset of which are probably red

Computers in the future will use a wide range of techniques, not just conventional microprocessors. Problems should be decomposed and the various components streamed to the appropriate processing engines. One of the important requirements is therefore some means of identifying automatically which parts of a problem could best be tackled by which techniques, though sometimes it might be best to use several in parallel with some interaction between them.

 Analogs

We have a wider variety of components available to be used in analog computing today than we had when it effectively died out in the 80s. With much higher quality analog and mixed components, and additionally micro-sensors, MEMs, simple neural network components, and some imminent molecular capability, how can we rekindle the successes of the analog domain. Nature handles the infinite body problem with ease! Things just happen according to the laws of physics. How can we harness them too? Can we build environments with synthetic physics to achieve more effects? The whole field of non-algorithmic computation seems ripe for exploitation.

 Neural networks

  • Could we make neural microprocessor suspensions, using spherical chips suspended in gel in a reflective capsule and optical broadcasting. Couple this with growing wires across the electric field. This would give us both electrical and optical interconnection that could be ideal for neural networks with high connectivity. Could link this to gene chip technology to have chemical detection and synthesis on the chips too, so that we could have close high speed replicas of organic neural networks.
  • If we can have quantum entanglement between particles, might this affect the way in which neurons in the brain work? Do we have neural entanglement and has this anything to do with how our brain works. Could we create neural entanglement or even virtual entanglement and would it have any use?
  • Could we make molecular neurons (or similar) using ordinary chemistry? And then form them into networks. Might need nanomachines and bottom-up assembly.
  • Could we use neurons as the first stage filters to narrow down the field to make problems tractable for other techniques
  • Optical neurons
  • Magnetic neurons

Electromechanical, MEMS etc

  • Micromirror arrays as part of optical computers, perhaps either as data entry, or as part of the algorithm
  • Carbon fullerene balls and tubes as MEM components
  • External fullerene ‘décor’ as a form of information, cf antibodies in immune system
  • Sensor suspensions and gels as analog computers for direct simulation

Interconnects

  • Carbon fullerene tubes as on chip wires
  • Could they act as electron pipes for ultra-high speed interconnect
  • Optical or radio beacons on chip

Software

  • Transforms – create a transform of every logic component, spreading the functionality across a wide domain, and construct programs using them instead. Small perturbation is no longer fatal but just reduces accuracy
  • Filters – nature works often using simple physical effects where humans design complex software. We need to look at hard problems to see how we might make simple filters to narrow the field before computing final details and stages conventionally.
  • Interference – is there some form of representation that allows us to compute operations by means of allowing the input data to interact directly, i.e. interference, instead of using tedious linear computation. Obviously only suited to a subset of problems.

And finally, the frivolous

  • Schrodinger’s computer – design of computer and software, if any, not determined until box is opened. The one constant is that it destroys itself if it doesn’t finding the solution. All possible computers and all possible programs exist and if there is a solution, the computer will pop out alive and well with the answer. Set it the problem of answering all possible questions too, working out which ones have the most valuable answers and using up all the available storage to write the best answers.

Your phone is wasted on you

Between 1983 and 1985, the fastest computer on Earth was the Cray X-MP. Its two 105MHz processors and 16MB of memory provided a peak performance of 400MFLOPS. It cost around $15M + disks.

The Apple iPhone XS is 1500 times faster and 15000 times cheaper.

In 1985, our division of 50 people ran all of its word processing on a VAX 11- 780, that produced 0.5MIPS (32bit). On equivalent instructions per second basis, the iPhone XS is 2.5M times faster, so ought to be able to run word processing for a country of 125M people.

Think about that next time you’re typing a text.

 

Augmented reality will objectify women

Microsoft Hololens 2 Visor

The excitement around augmented reality continues to build, and I am normally  enthusiastic about its potential, looking forward to enjoying virtual architecture, playing immersive computer games, or enjoying visual and performance artworks transposed into my view of the high street while I shop.

But it won’t all be wonderful. While a few PR and marketing types may worry a little about people overlaying or modifying their hard-won logos and ads, a bigger issue will be some people choosing to overlay people in the high street with ones that are a different age or gender or race, or simply prettier. Identity politics will be fought on yet another frontier.

In spite of waves of marketing hype and misrepresentation, AR is really only here in primitive form outside the lab. Visors fall very far short of what we’d hoped for by now even a decade ago, even the Hololens 2 shown above. But soon AR visors and eventually active contact lenses will enable fully 3D hi-res overlays on the real world. Then, in principle at least, you can make things look how you want, with a few basic limits. You could certainly transform a dull shop, cheap hotel room or an office into an elaborate palace or make it look like a spaceship. But even if you change what things look like, you still have to represent nearby physical structures and obstacles in your fantasy overlay world, or you may bump into them, and that includes all the walls and furniture, lamp posts, bollards, vehicles, and of course other people. Augmented reality allows you to change their appearance thoroughly but they still need to be there somehow.

When it comes to people, there will be some battles. You may spend ages creating a wide variety of avatars, or may invest a great deal of time and money making or buying them. You may have a digital aura, hoping to present different avatars to different passers-by according to their profiles. You may want to look younger or thinner or as a character you enjoy playing in a computer game. You may present a selection of options to the AIs controlling the passer person’s view and the avatar they see overlaid could be any one of the images you have on offer. Perhaps some privileged people get to pick from a selection you offer, while others you wish to privilege less are restricted to just one that you have set for their profile. Maybe you’d have a particularly ugly or offensive one to present to those with opposing political views.

Except that you can’t assume you will be in control. In fact, you probably won’t.

Other people may choose not to see your avatar, but instead to superimpose one of their own choosing. The question of who decides what the viewer sees is perhaps the first and most important battle in AR. Various parties would like to control it – visor manufacturers, O/S providers, UX designers, service providers, app creators, AI providers, governments, local councils, police and other emergency services, advertisers and of course individual users. Given market dynamics, most of these ultimately come down to user choice most of the time, albeit sometimes after paying for the privilege. So it probably won’t be you who gets to choose how others see you, via assorted paid intermediary services, apps and AI, it will be the other person deciding how they want to see you, regardless of your preferences.

So you can spend all the time you want designing your avatar and tweaking your virtual make-up to perfection, but if someone wants to see their favorite celebrity walking past instead of you, they will. You and your body become no more than an object on which to display any avatar or image someone else chooses. You are quite literally reduced to an object in the AR world. Augmented reality will literally objectify women, reducing them to no more than a moving display space onto which their own selected images are overlaid. A few options become obvious.

Firstly they may just take your actual physical appearance (via a video camera built into their visor for example) and digitally change it,  so it is still definitely you, but now dressed more nicely, or dressed in sexy lingerie, or how you might look naked, using the latest AI to body-fit fantasy images from a porn database. This could easily be done automatically in real time using some app or other. You’ve probably already seen recent AI video fakery demos that can present any celebrity saying anything at all, almost indistinguishable from reality. That will soon be pretty routine tech for AR apps. They could even use your actual face as input to image-matching search engines to find the most plausible naked lookalikes. So anyone could digitally dress or undress you, not just with their eyes, but with a hi-res visor using sophisticated AI-enabled image processing software. They could put you in any kind of outfit, change your skin color or make-up or age or figure, and make you look as pretty and glamorous or as slutty as they want. And you won’t have any idea what they are seeing. You simply won’t know whether they are respectfully celebrating your inherent beauty, or flattering you by making you look even prettier, which you might not mind at all, or might object to strongly in the absence of explicit consent, or worse still, stripping or degrading you to whatever depths they wish, with no consent or notification, which you probably will mind a lot.

Or they can treat you as just an object on which to superimpose some other avatar, which could be anything or anyone – a zombie, favorite actress or supermodel. They won’t need your consent and again you won’t have any idea what they are seeing. The avatar may make the same gestures and movements and even talk plausibly, saying whatever their AI thinks they might like, but it won’t be you. In some ways this might not be so bad. You’d still be reduced to an object but at least it wouldn’t be you that they’re looking at naked. To most strangers on a high street most of the time, you’re just a moving obstacle to avoid bumping into, so being digitally transformed into a walking display board may worry you. Most people will cope with that bit. It is when you stop being just a passing stranger and start to interact in some way that it really starts to matter. You probably won’t like it if someone is chatting to you but they are actually looking at someone else entirely, especially if the viewer is one of your friends or your partner. And if your partner is kissing or cuddling you but seeing someone else, that would be a strong breach of trust, but how would you know? This sort of thing could and probably will damage a lot of relationships.

Most of the software to do most of this is already in development and much is already demonstrable. The rest will develop quickly once AR visors become commonplace.

In the office, in the home, when you’re shopping or at a party, you soon won’t have any idea what or who someone else is seeing when they look at you. Imagine how that would clash with rules that are supposed to be protection from sexual harassment  in the office. Whole new levels of harassment will be enabled, much invisible. How can we police behaviors we can’t even detect? Will hardware manufacturers be forced to build in transparency and continuous experience recording

The main casualty will be trust.  It will make us question how much we trust each of our friends and colleagues and acquaintances. It will build walls. People will often become suspicious of others, not just strangers but friends and colleagues. Some people will become fearful. You may dress as primly or modestly as you like, but if the viewer chooses to see you wearing a sexy outfit, perhaps their behavior and attitude towards you will be governed by that rather than reality. Increased digital objectification might lead to increase physical sexual assault or rape. We may see more people more often objectifying women in more circumstances.

The tech applies equally to men of course. You could make a man look like a silverback gorilla or a zombie or fake-naked. Some men will care more than others, but the vast majority of real victims will undoubtedly be women. Many men objectify women already. In the future AR world , they’ll be able to do so far more effectively, more easily.

 

Future AI: Turing multiplexing, air gels, hyper-neural nets

Just in time to make 2018 a bit less unproductive, I managed to wake in the middle of the night with another few inventions. I’m finishing the year on only a third as many as 2016 and 2017, but better than some years. And I quite like these new ones.

Gel computing is a very old idea of mine, and I’m surprised no company has started doing it yet. Air gel is different. My original used a suspension of processing particles in gel, and the idea was that the gel would hold the particles in fixed locations with good free line of sight to neighbor devices for inter-device optical comms, while acting also as a coolant.

Air gel uses the same idea of suspending particles, but does so by using ultrasound, standing waves holding the particles aloft. They would form a semi-gel I suppose, much softer. The intention is that they will be more easily movable than in a gel, and maybe rotate. I imagine using rotating magnetic fields to rotate them, and use that mechanism to implement different configurations of inter-device nets. That would be the first pillar of running multiple neural nets in the same space at the same time, using spin-based TDM (time division multiplexing), or synchronized space multiplexing if you prefer. If a device uses on board processing that is fast compared to the signal transmission time to other devices (the speed of light may be fast but can still be severely limiting for processing and comms), then having the ability to deal with processing associated with several other networks while awaiting a response allows a processing network to be multiplied up several times. A neural net could become a hyper-neural net.

Given that this is intended for mid-century AI, I’m also making the assumption that true TDM can also be used on each net, my second pillar. Signals would carry a stream of slots holding bits for each processing instance. Since this allows a Turing machine to implement many different processes in parallel, I decided to call it Turing multiplexing. Again, it helps alleviate the potential gulf between processing and communication times. Combining Turing and spin multiplexing would allow a single neural net to be multiplied up potentially thousands or millions of times – hyper-neurons seems as good a term as any.

The third pillar of this system is that the processing particles (each could contain a large number of neurons or other IT objects) could be energized and clocked using very high speed alternating EM fields – radio, microwaves, light, even x-rays. I don’t have any suggestions for processing mechanisms that might operate at such frequencies, though Pauli switches might work at lower speeds, using Pauli exclusion principle to link electron spin states to make switches. I believe early versions of spin cubits use a similar principle. I’m agnostic whether conventional Turing machine or quantum processing would be used, or any combination. In any case, it isn’t my problem, I suspect that future AIs will figure out the physics and invent the appropriate IT.

Processing devices operating at high speed could use a lot of energy and generate a lot of heat, and encouraging the system to lase by design would be a good way to cool it as well as powering it.

A processor using such mechanisms need not be bulky. I always assumed a yogurt pot size for my gel computer before and an air gel processor could be the same, about 100ml. That is enough to suspend a trillion particles with good line of sight for optical interconnections, and each connection could utilise up to millions of alternative wavelengths. Each wavelength could support many TDM channels and spinning the particles multiplies that up again. A UV laser clock/power source driving processors at 10^16Hz would certainly need to use high density multiplexing to make use of such a volume, with transmission distances up to 10cm (but most sub-mm) otherwise being a strongly limiting performance factor, but 10 million-fold WDM/TDM is attainable.

A trillion of these hyper-neurons using that multiplexing would act very effectively as 10 million trillion neurons, each operating at 10^16Hz processing speed. That’s quite a lot of zeros, 35 of them, and yet each hyperneuron could have connections to thousands of others in each of many physical configurations. It would be an obvious platform for supporting a large population of electronically immortal people and AIs who each want a billion replicas, and if it only occupies 100ml of space, the environmental footprint isn’t an issue.

It’s hard to know how to talk to a computer that operates like a brain, but is 10^22 times faster, but I’d suggest ‘Yes Boss’.

 

Enhanced cellular blockchain

I thought there was a need for a cellular blockchain variant, and a more sustainable alternative to cryptocurrencies like Bitcoin that depend on unsustainable proofs-of-work. So I designed one and gave it a temporary project name of Grapevine. I like biomimetics, which I used for both the blockchain itself and its derivative management/application/currency/SW distribution layer. The ANTs were my invention in 1993 when I was with BT, along with Chris Winter. BT never did anything with it, and I believe MIT later published some notes on the idea too. ANTs provide an ideal companion to blockchain and together, could be the basis of some very secure IT systems.

The following has not been thoroughly checked so may contain serious flaws, but hopefully contain some useful ideas to push the field a little in the right direction.

A cellular, distributed, secure ledger and value assurance system – a cheap, fast, sustainable blockchain variant

  • Global blockchain grows quickly to enormous size because all transactions are recorded in single chain – e.g. bitcoin blockchain is already >100GB
  • Grapevine (temp project name) cellular approach would keep local blocks small and self-contained but assured by blockchain-style verification during growth and protected from tampering after block is sealed and stripped by threading with a global thread
  • Somewhat analogous to a grape vine. Think of each local block as a grape that grow in bunches. Vine links bunches together but grapes are all self-contained and stay small in size. Genetics/nutrients/materials/processes all common to entire vine.
  • Grape starts as a flower, a small collection of unverified transactions. All stamens listen to transactions broadcast via any stamen. Flower is periodically (every minute) frozen (for 2 seconds) while pollen is emitted by each stamen, containing stamen signature, previous status verification and new transactions list. Stamens check the pollen they receive for origin signature and previous growth verification and then check all new transactions. If valid, they emit a signed pollination announcement. When each stamen has received signed pollination announcements from the majority of other stamens, that growth stage is closed, (all quite blockchain-like so far), stripped of unnecessary packaging such as previous hash, signatures etc) to leave a clean record of validated transactions, which is then secured from tampering by the grape signature and hash. The next stage of growth then begins, which needs another pollination process (deviating from biological analogy here). Each grape on the bunch grows like this throughout the day. When the grapes are all fully grown, and the final checks made by each grape, the grapes are stripped again and the whole bunch is signed onto the vine using a highly secure bunch signature and hash to prevent any later tampering. Grapes are therefore collections of verified local transactions that have grown in many fully verified stages during the day but are limited in size and stripped of unnecessary packaging. The bunch is a verified global record of all of the grapes grown that day that remains the same forever. The vine is a growing collection of bunches of grapes, but each new grape and bunch starts off fresh each day so signalling and the chain never grow significantly. Each transaction remains verified and recorded forever but signalling is kept minimal. As processing power increases, earlier bunches can be re-secured using a new bunch signature.

Key Advantages

  • Grape vine analogy is easier for non-IT managers to understand than normal blockchain.
  • Unlike conventional blockchains, blocks grow in stages so transactions don’t have to wait long to be verified and sealed.
  • Cellular structure means signalling is always light, with just a few nearby nodes checking a few transactions and keeping short records.
  • Ditto bunching, each day’s records start from zero and bunch is finished and locked at end of day.
  • Cellular structure allows sojourn time for signalling to be kept low with potentially low periods for verification and checking. Will scale well with improving processing speed, less limited by signal propagation time than non-cellular chains.
  • Global all-time record is still complete, duplicated, distributed, but signalling for new transactions always starts light and local every new day.
  • Cellular approach allows easy re-use of globally authenticated tokens within each cell. This limits cost of token production.
  • Cells may be either geographic or logical/virtual. Virtual cells can be geographically global (at penalty of slower comms), but since each is independent until the end of the day, virtual cell speed will not affect local cell speed.
  • Protocols can be different for different cells, allowing cells with higher value transactions to use tighter security.

Associated mechanisms

  • Inter-cell transactions can be implemented easily by using logical/virtual cell that includes both parties. Users may need to be registered for access to multiple cells. If value is being transferred, it is easy to arrange clearing of local cell first (1 minute overhead) and then check currency hasn’t already been spent before allowing transaction on another cell.
  • Grapes are self-contained and data is held locally, duplicated among several stamens. Once sealed for the day, the grape data remains in place, signed off with the appropriate grape signature and the bunch signature verifies it with an extra lock that prevents even a future local majority from being able to tamper with it later. To preserve data in the very long-term against O/S changes, company failure etc, subsequent certified copies may be distributed and kept updated.
  • Signalling during the day can be based on ANT (autonomous network telepher) protocols. These use a strictly limited variety of ANT species that are authenticated and shared at the start of a period (a day or a week perhaps), using period lifetime encryption keys. Level of encryption is determined by ensuring that period is much smaller than the estimated time to crack on current hardware at reasonable cost. All messages use this encryption and ANT mechanisms therefore chances of infiltration or fraudulent transaction is very low so associated signalling and time overhead costs are kept low.
  • ANTs may include transaction descriptor packets, signature distribution packets, new key distribution packets, active (executable code) packets, new member verification packets, software distribution, other admin data, performance maintenance packets such as load distribution, RPCs and many others. Overall, perhaps 64 possible ANT species may be allowed at any one time. This facility makes the system ideal for secure OS and software distribution/maintenance.

Financial use

  • ANTs can contain currency to make valuable packets, or an ANT variant could actually be currency.
  • Optional coins could be made for privacy, otherwise transactions would use real world accounts. A coin-based system can be implemented simply by using the grape signature and coin number. Coins could be faked by decrypting the signature but that signature only lasts one period so by then they will be invalid. Remember, encryption level is set according to cost to decrypt during a period. Coins are globally unique due to different cells having different signatures. Once grapes are sealed no tampering is possible.
  • One mechanism is that coins are used as temporary currency that only lasts one period. Coins are bought using any currency immediately before transactions. At end of day, coins are converted back to desired currency. Any profits/losses due to conversion differences during day accrue to user at point of conversion.
  • A lingering cybercurrency can be made that renews its value to live longer than one period. It simply needs conversion to a new coin at the start of the new day, relying on signature security and short longevity to protect.
  • ANTs can alternatively carry real currency value by direct connection to any account. At end of each growth stage or end of day, transaction clearing debits and deposits in each respective account accordingly.
  • Transaction fees can be implemented easily and simply debited at either or both ends.
  • No expensive PoW is needed. Wasteful mining and PoW activity is unnecessary. Entire system relies only on using encryption signatures that are valid for shorter times than their cost-effective decryption times. Tamper-resistance avoids decryption of earlier signatures being useful.

With thanks to my good friend Prof Nick Colosimo for letting me bounce the ideas off him.

Biomimetic insights for machine consciousness

About 20 years ago I gave my first talk on how to achieve consciousness in machines, at a World Future Society conference, and went on to discuss how we would co-evolve with machines. I’ve lectured on machine consciousness hundreds of times but never produced any clear slides that explain my ideas properly. I thought it was about time I did. My belief is that today’s deep neural networks using feed-forward processing with back propagation training can not become conscious. No digital algorithmic neural network can, even though they can certainly produce extremely good levels of artificial intelligence. By contrast, nature also uses neurons but does produce conscious machines such as humans easily. I think the key difference is not just that nature uses analog adaptive neural nets rather than digital processing (as I believe Hans Moravec first insighted, a view that I readily accepted) but also that nature uses large groups of these analog neurons that incorporate feedback loops that act both as a sort of short term memory and provide time to sense the sensing process as it happens, a mechanism that can explain consciousness. That feedback is critically important in the emergence of consciousness IMHO. I believe that if the neural network AI people stop barking up the barren back-prop tree and start climbing the feedback tree, we could have conscious machines in no time, but Moravec is still probably right that these need to be analog to enable true real-time processing as opposed to simulation of that.

I may be talking nonsense of course, but here are my thoughts, finally explained as simply and clearly as I can. These slides illustrate only the simplest forms of consciousness. Obviously our brains are highly complex and evolved many higher level architectures, control systems, complex senses and communication, but I think the basic foundations of biomimetic machine consciousness can be achieved as follows:

That’s it. I might produce some more slides on higher level processing such as how concepts might emerge, and why in the long term, AIs will have to become hive minds. But they can wait for later blogs.

AI that talks to us could quickly become problematic

Google’s making the news again adding evidence to the unfortunate stereotype of the autistic IT nerd that barely understands normal people, and they have therefore been astonished at the backlash that normal people would all easily have predicted. (I’m autistic and work in IT mostly too, and am well used to the stereotype it so it doesn’t bother me, in fact it is a sort of ‘get out of social interactions free’ card). Last time it was Google Glass, where it apparently didn’t occur to them that people may not want other people videoing them without consent in pubs and changing rooms. This time it is Google Duplex, that makes phone calls on your behalf to arrange appointment using voice that is almost indistinguishable from normal humans. You could save time making an appointment with a hairdresser apparently, so the Googlanders decided it must be a brilliant breakthrough, and expected everyone to agree. They didn’t.

Some of the objections have been about ethics: e.g. An AI should not present itself as human – Humans have rights and dignity and deserve respectful interactions with other people, but an AI doesn’t and should not masquerade as human to acquire such privilege without knowledge of the other party and their consent.

I would be more offended by the presumed attitude of the user. If someone thinks they are so much better then me that they can demand my time and attention without the expense of any of their own, delegating instead to a few microseconds of processing time in a server farm somewhere, I’ll treat them with the contempt they deserve. My response will not be favourable. I am already highly irritated by the NHS using simple voice interaction messaging to check I will attend a hospital appointment. The fact that my health is on the line and notices at surgeries say I will be banned if I complain on social media is sufficient blackmail to ensure my compliance, but it still comes at the expense of my respect and goodwill. AI-backed voice interaction with better voice wouldn’t be any better, and if it asking for more interaction such as actually booking an appointment, it would be extremely annoying.

In any case, most people don’t speak in fully formed grammatically and logically correct sentences. If you listen carefully to everyday chat, a lot of sentences are poorly pronounced, incomplete, jumbled, full of ums and er’s, likes and they require a great deal of cooperation by the listener to make any sense at all. They also wander off topic frequently. People don’t stick to a rigid vocabulary list or lists of nicely selected sentences.  Lots of preamble and verbal meandering is likely in a response that is highly likely to add ambiguity. The example used in a demo, “I’d like to make a hairdressing appointment for a client” sounds fine until you factor in normal everyday humanity. A busy hairdresser or a lazy receptionist is not necessarily going to cooperate fully. “what do you mean, client?”, “404 not found”, “piss off google”, “oh FFS, not another bloody computer”, “we don’t do hairdressing, we do haircuts”, “why can’t your ‘client’ call themselves then?” and a million other responses are more likely than “what time would you like?”

Suppose though that it eventually gets accepted by society. First, call centers beyond the jurisdiction of your nuisance call blocker authority will incessantly call you at all hours asking or telling you all sorts of things, wasting huge amounts of your time and reducing quality of life. Voice spam from humans in call centers is bad enough. If the owners can multiply productivity by 1000 by using AI instead of people, the result is predictable.

We’ve seen the conspicuous political use of social media AI already. Facebook might have allowed companies to use very limited and inaccurate knowledge of you to target ads or articles that you probably didn’t look at. Voice interaction would be different. It uses a richer emotional connection that text or graphics on a screen. Google knows a lot about you too, but it will know a lot more soon. These big IT companies are also playing with tech to log you on easily to sites without passwords. Some gadgets that might be involved might be worn, such as watches or bracelets or rings. They can pick up signals to identify you, but they can also check emotional states such as stress level. Voice gives away emotion too. AI can already tell better then almost all people whether you are telling the truth or lying or hiding something. Tech such as iris scans can also tell emotional states, as well as give health clues. Simple photos can reveal your age quite accurately to AI, (check out how-old.net).  The AI voice sounds human, but it is better then even your best friends at guessing your age, your stress and other emotions, your health, whether you are telling the truth or not, and it knows far more about what you like and dislike and what you really do online than anyone you know, including you. It knows a lot of your intimate secrets. It sounds human, but its nearest human equivalent was probably Machiavelli. That’s who will soon be on the other side of the call, not some dumb chatbot. Now re-calculate political interference, and factor in the political leaning and social engineering desires of the companies providing the tools. Google and Facebook and the others are very far from politically neutral. One presidential candidate might get full cooperation, assistance and convenient looking the other way, while their opponent might meet rejection and citation of the official rules on non-interference. Campaigns on social issues will also be amplified by AI coupled to voice interaction. I looked at some related issue in a previous blog on fake AI (i.e. fake news type issues): https://timeguide.wordpress.com/2017/11/16/fake-ai/

I could but won’t write a blog on how this tech could couple well to sexbots to help out incels. It may actually have some genuine uses in providing synthetic companionship for lonely people, or helping or encouraging them in real social interactions with real people. It will certainly have some uses in gaming and chatbot game interaction.

We are not very far from computers that are smarter then people across a very wide spectrum, and probably not very far from conscious machines that have superhuman intelligence. If we can’t even rely on IT companies to understand likely consequences of such obvious stuff as Duplex before thy push it, how can we trust them in other upcoming areas of AI development, or even closer term techs with less obvious consequences? We simply can’t!

There are certainly a few such areas where such technology might help us but most are minor and the rest don’t need any deception, but they all come at great cost or real social and political risk, as well as more abstract risks such as threats to human dignity and other ethical issues. I haven’t give this much thought yet and I am sure there must be very many other consequences I have not touched on yet. Google should do more thinking before they release stuff. Technology is becoming very powerful, but we all know that great power comes with great responsibility, and since most people aren’t engineers so can’t think through all the potential technology interactions and consequences, engineers such as Google’s must act more responsibly. I had hoped they’d started, and they said they had, but this is not evidence of that.

 

Beyond VR: Computer assisted dreaming

I first played with VR in 1983/1984 while working in the missile industry. Back then we didn’t call it VR, we just called it simulation but it was actually more intensive than VR, just as proper flight simulators are. Our office was a pair of 10m wide domes onto which video could be projected, built decades earlier, in the 1950s I think. One dome had a normal floor, the other had a hydraulic platform that could simulate being on a ship. The subject would stand on whichever surface was appropriate and would see pretty much exactly what they would see in a real battlefield. The missile launcher used for simulation was identical to a real one and showed exactly the same image as a real one would. The real missile was not present of course but its weight was simulated and when the fire button was pressed, a 140dB bang was injected into the headset and weights and pulleys compensated for the 14kg of weight, suddenly vanishing from the shoulder. The experience was therefore pretty convincing and with the loud bang and suddenly changing weight, it was almost as hard to stand steady and keep the system on target as it would be in real life – only the presumed fear and knowledge of the reality of the situation was different.

Back then in 1983, as digital supercomputers had only just taken over from analog ones for simulation, it was already becoming obvious that this kind of computer simulation would one day allow ‘computer assisted dreaming’. (That’s one of the reasons I am irritated when Jaron Lanier is credited for inventing VR – highly realistic simulators and the VR ideas that sprung obviously from them had already been around for decades. At best, all he ‘invented’ was a catchy name for a lower cost, lower quality, less intense simulator. The real inventors were those who made the first generation simulators long before I was born and the basic idea of VR had already been very well established.)

‘Computer assisted dreaming’ may well be the next phase of VR. Today in conventional VR, people are immersed in a computer generated world produced by a computer program (usually) written by others. Via trial and feedback, programmers make their virtual worlds better. As AI and sensor technology continue rapid progress, this is very likely to change to make worlds instantly responsive to the user. By detecting user emotions, reactions, gestures and even thoughts and imagination, it won’t be long before AI can produce a world in real time that depends on those thoughts, imagination and emotions rather than putting them in a pre-designed virtual world. That world would depend largely on your own imagination, upskilled by external AI. You might start off imagining you’re on a beach, then AI might add to it by injecting all sorts of things it knows you might enjoy from previous experiences. As you respond to those, it picks up on the things you like or don’t like and the scene continues to adapt and evolve, to make it more or less pleasant or more or less exciting or more or less challenging etc., depending on your emotional state, external requirements and what it thinks you want from this experience. It would be very like being in a dream – computer assisted lucid dreaming, exactly what I wanted to make back in 1983 after playing in that simulator.

Most people enjoy occasional lucid dreams, where they realise they are dreaming and can then decide what happens next. Making VR do exactly that would be better than being trapped in someone else’s world. You could still start off with whatever virtual world you bought, a computer game or training suite perhaps, but it could adapt to you, your needs and desires to make it more compelling and generally better.

Even in shared experiences like social games, experiences could be personalised. Often all players need to see the same enemies in the same locations in the same ways to make it fair, but that doesn’t mean that the situation can’t adapt to the personalities of those playing. It might actually improve the social value if each time you play it looks different because your companions are different. You might tease a friend if every time you play with them, zombies or aliens always have to appear somehow, but that’s all part of being friends. Exploring virtual worlds with friends, where you both see things dependent on your friend’s personality would help bonding. It would be a bit like exploring their inner world. Today, you only explore the designer’s inner world.

This sort of thing would be a superb development and creativity tool. It could allow you to explore a concept you have in your head, automatically feeding in AI upskilling to amplify your own thoughts and ideas, showing you new paths to explore and helping you do so. The results would still be extremely personal to you, but you on a good day. You could accomplish more, have better visions, imagine more creative things, do more with whatever artistic talent you have. AI could even co-create synthetic personas, make virtual friends you can bond with, share innermost thoughts with, in total confidence (assuming the company you bought the tool from is trustworthy and isn’t spying on you or selling your details, so maybe best not to buy it from Facebook then).

And it would have tremendous therapeutic potential too. You could explore and indulge both enjoyable and troublesome aspects of your inner personality, to build on the good and alleviate or dispel the bad. You might become less troubled, less neurotic, more mentally healthy. You could build your emotional and creative skills. You could become happier and more fulfilled. Mental health improvement potential on its own makes this sort of thing worth developing.

Marketers would obviously try to seize control as they always do, and advertising is already adapting to VR and will continue into its next phases of development. Your own wants and desires might help guide the ‘dreaming’, but marketers will inevitably have some control over what else is injected, and will influence algorithms and AI in how it chooses how to respond to your input. You might be able to choose much of the experience, but others will still want and try to influence and manipulate you, to change your mindset and attitudes in their favour. That will not change until the advertising business model changes. You might be able to buy devices or applications that are entirely driven by you and you alone, but it is pretty certain that the bulk of products and services available will be at least partly financed by those who want to have some control of what you experience.

Nevertheless, computer-assisted dreaming could be a much more immersive and personal experience than VR, being more like an echo of your own mind and personality than external vision, more your own creation, less someone else’s. In fact, echo sounds a better term too. Echo reality, ER, or maybe personal reality, pereal, or mental echo, ME. Nah, maybe we need Lanier to invent a catchy name again, he is good at that. That 1983 idea could soon become reality.