Category Archives: Computing

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.

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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.

 

How can we make a computer conscious?

This is very text heavy and is really just my thinking out loud, so to speak. Unless you are into mental archaeology or masochistic, I’d strongly recommend that you instead go to my new blog on this which outlines all of the useful bits graphically and simply.

Otherwise….

I found this article in my drafts folder, written 3 years ago as part of my short series on making conscious computers. I thought I’d published it but didn’t. So updating and publishing it now. It’s a bit long-winded, thinking out loud, trying to derive some insights from nature on how to make conscious machines. The good news is that actual AI developments are following paths that lead in much the same direction, though some significant re-routing and new architectural features are needed if they are to optimize AI and achieve machine consciousness.

Let’s start with the problem. Today’s AI that plays chess, does web searches or answers questions is digital. It uses algorithms, sets of instructions that the computer follows one by one. All of those are reduced to simple binary actions, toggling bits between 1 and 0. The processor doing that is no more conscious or aware of it, and has no more understanding of what it is doing than an abacus knows it is doing sums. The intelligence is in the mind producing the clever algorithms that interpret the current 1s and 0s and change them in the right way. The algorithms are written down, albeit in more 1s and 0s in a memory chip, but are essentially still just text, only as smart and aware as a piece of paper with writing on it. The answer is computed, transmitted, stored, retrieved, displayed, but at no point does the computer sense that it is doing any of those things. It really is just an advanced abacus. An abacus is digital too (an analog equivalent to an abacus is a slide rule).

A big question springs to mind: can a digital computer ever be any more than an advanced abacus. Until recently, I was certain the answer was no. Surely a digital computer that just runs programs can never be conscious? It can simulate consciousness to some degree, it can in principle describe the movements of every particle in a conscious brain, every electric current, every chemical reaction. But all it is doing is describing them. It is still just an abacus. Once computed, that simulation of consciousness could be printed and the printout would be just as conscious as the computer was. A digital ‘stored program’ computer can certainly implement extremely useful AI. With the right algorithms, it can mine data, link things together, create new data from that, generate new ideas by linking together things that haven’t been linked before, make works of art, poetry, compose music, chat to people, recognize faces and emotions and gestures. It might even be able to converse about life, the universe and everything, tell you its history, discuss its hopes for the future, but all of that is just a thin gloss on an abacus. I wrote a chat-bot on my Sinclair ZX Spectrum in 1983, running on a processor with about 8,000 transistors. The chat-bot took all of about 5 small pages of code but could hold a short conversation quite well if you knew what subjects to stick to. It’s very easy to simulate conversation. But it is still just a complicated abacus and still doesn’t even know it is doing anything.

However clever the AI it implements, a conventional digital computer that just executes algorithms can’t become conscious but an analog computer can, a quantum computer can, and so can a hybrid digital/analog/quantum computer. The question remain s whether a digital computer can be conscious if it isn’t just running stored programs. Could it have a different structure, but still be digital and yet be conscious? Who knows? Not me. I used to know it couldn’t, but now that I am a lot older and slightly wiser, I now know I don’t know.

Consciousness debate often starts with what we know to be conscious, the human brain. It isn’t a digital computer, although it has digital processes running in it. It also runs a lot of analog processes. It may also run some quantum processes that are significant in consciousness. It is a conscious hybrid of digital, analog and possibly quantum computing. Consciousness evolved in nature, therefore it can be evolved in a lab. It may be difficult and time consuming, and may even be beyond current human understanding, but it is possible. Nature didn’t use magic, and what nature did can be replicated and probably even improved on. Evolutionary AI development may have hit hard times, but that only shows that the techniques used by the engineers doing it didn’t work on that occasion, not that other techniques can’t work. Around 2.6 new human-level fully conscious brains are made by nature every second without using any magic and furthermore, they are all slightly different. There are 7.6 billion slightly different implementations of human-level consciousness that work and all of those resulted from evolution. That’s enough of an existence proof and a technique-plausibility-proof for me.

Sensors evolved in nature pretty early on. They aren’t necessary for life, for organisms to move around and grow and reproduce, but they are very helpful. Over time, simple light, heat, chemical or touch detectors evolved further to simple vision and produce advanced sensations such as pain and pleasure, causing an organism to alter its behavior, in other words, feeling something. Detection of an input is not the same as sensation, i.e. feeling an input. Once detection upgrades to sensation, you have the tools to make consciousness. No more upgrades are needed. Sensing that you are sensing something is quite enough to be classified as consciousness. Internally reusing the same basic structure as external sensing of light or heat or pressure or chemical gradient or whatever allows design of thought, planning, memory, learning and construction and processing of concepts. All those things are just laying out components in different architectures. Getting from detection to sensation is the hard bit.

So design of conscious machines, and in fact what AI researchers call the hard problem, really can be reduced to the question of what makes the difference between a light switch and something that can feel being pushed or feel the current flowing when it is, the difference between a photocell and feeling whether it is light or dark, the difference between detecting light frequency, looking it up in a database, then pronouncing that it is red, and experiencing redness. That is the hard problem of AI. Once that is solved, we will very soon afterwards have a fully conscious self aware AI. There are lots of options available, so let’s look at each in turn to extract any insights.

The first stage is easy enough. Detecting presence is easy, measuring it is harder. A detector detects something, a sensor (in its everyday engineering meaning) quantifies it to some degree. A component in an organism might fire if it detects something, it might fire with a stronger signal or more frequently if it detects more of it, so it would appear to be easy to evolve from detection to sensing in nature, and it is certainly easy to replicate sensing with technology.

Essentially, detection is digital, but sensing is usually analog, even though the quantity sensed might later be digitized. Sensing normally uses real numbers, while detection uses natural numbers (real v  integer as programmer call them). The handling of analog signals in their raw form allows for biomimetic feedback loops, which I’ll argue are essential. Digitizing them introduces a level of abstraction that is essentially the difference between emulation and simulation, the difference between doing something and reading about someone doing it. Simulation can’t make a conscious machine, emulation can. I used to think that meant digital couldn’t become conscious, but actually it is just algorithmic processing of stored programs that can’t do it. There may be ways of achieving consciousness digitally, or quantumly, but I haven’t yet thought of any.

That engineering description falls far short of what we mean by sensation in human terms. How does that machine-style sensing become what we call a sensation? Logical reasoning says there would probably need to be only a small change in order to have evolved from detection to sensing in nature. Maybe something like recombining groups of components in different structures or adding them together or adding one or two new ones, that sort of thing?

So what about detecting detection? Or sensing detection? Those could evolve in sequence quite easily. Detecting detection is like your alarm system control unit detecting the change of state that indicates that a PIR has detected an intruder, a different voltage or resistance on a line, or a 1 or a 0 in a memory store. An extremely simple AI responds by ringing an alarm. But the alarm system doesn’t feel the intruder, does it?  It is just a digital response to a digital input. No good.

How about sensing detection? How do you sense a 1 or a 0? Analog interpretation and quantification of digital states is very wasteful of resources, an evolutionary dead end. It isn’t any more useful than detection of detection. So we can eliminate that.

OK, sensing of sensing? Detection of sensing? They look promising. Let’s run with that a bit. In fact, I am convinced the solution lies in here so I’ll look till I find it.

Let’s do a thought experiment on designing a conscious microphone, and for this purpose, the lowest possible order of consciousness will do, we can add architecture and complexity and structures once we have some bricks. We don’t particularly want to copy nature, but are free to steal ideas and add our own where it suits.

A normal microphone sensor produces an analog signal quantifying the frequencies and intensities of the sounds it is exposed to, and that signal may later be quantified and digitized by an analog to digital converter, possibly after passing through some circuits such as filters or amplifiers in between. Such a device isn’t conscious yet. By sensing the signal produced by the microphone, we’d just be repeating the sensing process on a transmuted signal, not sensing the sensing itself.

Even up close, detecting that the microphone is sensing something could be done by just watching a little LED going on when current flows. Sensing it is harder but if we define it in conventional engineering terms, it could still be just monitoring a needle moving as the volume changes. That is obviously not enough, it’s not conscious, it isn’t feeling it, there’s no awareness there, no ‘sensation’. Even at this primitive level, if we want a conscious mic, we surely need to get in closer, into the physics of the sensing. Measuring the changing resistance between carbon particles or speed of a membrane moving backwards and forwards would just be replicating the sensing, adding an extra sensing stage in series, not sensing the sensing, so it needs to be different from that sort of thing. There must surely need to be a secondary change or activity in the sensing mechanism itself that senses the sensing of the original signal.

That’s a pretty open task, and it could even be embedded in the detecting process or in the production process for the output signal. But even recognizing that we need this extra property narrows the search. It must be a parallel or embedded mechanism, not one in series. The same logical structure would do fine for this secondary sensing, since it is just sensing in the same logical way as the original. This essential logical symmetry would make its evolution easy too. It is easy to imagine how that could happen in nature, and easier still to see how it could be implemented in a synthetic evolution design system. Such an approach could be mimicked in natural or synthetic evolutionary development systems. In this approach, we have to feel the sensing, so we need it to comprise some sort of feedback loop with a high degree of symmetry compared with the main sensing stage. That would be natural evolution compatible as well as logically sound as an engineering approach.

This starts to look like progress. In fact, it’s already starting to look a lot like a deep neural network, with one huge difference: instead of using feed-forward signal paths for analysis and backward propagation for training, it relies instead on a symmetric feedback mechanism where part of the input for each stage of sensing comes from its own internal and output signals. A neuron is not a full sensor in its own right, and it’s reasonable to assume that multiple neurons would be clustered so that there is a feedback loop. Many in the neural network AI community are already recognizing the limits of relying on feed-forward and back-prop architectures, but web searches suggest few if any are moving yet to symmetric feedback approaches. I think they should. There’s gold in them there hills!

So, the architecture of the notional sensor array required for our little conscious microphone would have a parallel circuit and feedback loop (possibly but not necessarily integrated), and in all likelihood these parallel and sensing circuits would be heavily symmetrical, i.e. they would use pretty much the same sort of components and architectures as the sensing process itself. If the sensation bit is symmetrical, of similar design to the primary sensing circuit, that again would make it easy to evolve in nature too so is a nice 1st principles biomimetic insight. So this structure has the elegance of being very feasible for evolutionary development, natural or synthetic. It reuses similarly structured components and principles already designed, it’s just recombining a couple of them in a slightly different architecture.

Another useful insight screams for attention too. The feedback loop ensures that the incoming sensation lingers to some degree. Compared to the nanoseconds we are used to in normal IT, the signals in nature travel fairly slowly (~200m/s), and the processing and sensing occur quite slowly (~200Hz). That means this system would have some inbuilt memory that repeats the essence of the sensation in real time – while it is sensing it. It is inherently capable of memory and recall and leaves the door wide open to introduce real-time interaction between memory and incoming signal. It’s not perfect yet, but it has all the boxes ticked to be a prime contender to build thought, concepts, store and recall memories, and in all likelihood, is a potential building brick for higher level consciousness. Throw in recent technology developments such as memristors and it starts to look like we have a very promising toolkit to start building primitive consciousness, and we’re already seeing some AI researchers going that path so maybe we’re not far from the goal. So, we make a deep neural net with nice feedback from output (of the sensing system, which to clarify would be a cluster of neurons, not a single neuron) to input at every stage (and between stages) so that inputs can be detected and sensed, while the input and output signals are stored and repeated into the inputs in real time as the signals are being processed. Throw in some synthetic neurotransmitters to dampen the feedback and prevent overflow and we’re looking at a system that can feel it is feeling something and perceive what it is feeling in real time.

One further insight that immediately jumps out is since the sensing relies on the real time processing of the sensations and feedbacks, the speed of signal propagation, storage, processing and repetition timeframes must all be compatible. If it is all speeded up a million fold, it might still work fine, but if signals travel too slowly or processing is too fast relative to other factors, it won’t work. It will still get a computational result absolutely fine, but it won’t know that it has, it won’t be able to feel it. Therefore… since we have a factor of a million for signal speed (speed of light compared to nerve signal propagation speed), 50 million for switching speed, and a factor of 50 for effective neuron size (though the sensing system units would be multiple neuron clusters), we could make a conscious machine that could think at 50 million times as fast as a natural system (before allowing for any parallel processing of course). But with architectural variations too, we’d need to tune those performance metrics to make it work at all and making physically larger nets would require either tuning speeds down or sacrificing connectivity-related intelligence. An evolutionary design system could easily do that for us.

What else can we deduce about the nature of this circuit from basic principles? The symmetry of the system demands that the output must be an inverse transform of the input. Why? Well, because the parallel, feedback circuit must generate a form that is self-consistent. We can’t deduce the form of the transform from that, just that the whole system must produce an output mathematically similar to that of the input.

I now need to write another blog on how to use such circuits in neural vortexes to generate knowledge, concepts, emotions and thinking. But I’m quite pleased that it does seem that some first-principles analysis of natural evolution already gives us some pretty good clues on how to make a conscious computer. I am optimistic that current research is going the right way and only needs relatively small course corrections to achieve consciousness.

 

Why superhumans are inevitable, and what else comes in the box

Do we have any real choice in the matter of making  super-humans? 20 years ago, I estimated 2005 as the point of no return, and nothing since then has changed my mind on that date. By my reckoning, we are already inevitably committed to designer babies, ebaybies, super-soldiers and super-smart autonomous weapons, direct brain-machine links, electronic immortality, new human races, population explosion, inter-species conflicts and wars with massively powerful weaponry, superhuman conscious AI, smart bacteria, and the only real control we have is relatively minor adjustments on timings. As I was discussing yesterday, the technology potential for this is vast and very exciting, nothing less than a genuine techno-utopia if we use the technologies wisely, but optimum potential doesn’t automatically become reality, and achieving a good outcome is unlikely if many barriers are put in its way.

In my estimation, we have already started the countdown to this group of interconnected technologies – we will very likely get all of them, and we must get ready for the decisions and impacts ahead. At the moment, our society is a small child about to open its super-high-tech xmas presents while fighting with its siblings. Those presents will give phenomenal power far beyond the comprehension of the child or its emotional maturity to equip it to deal with the decisions safely. Our leaders have already squandered decades of valuable preparation time by ignoring the big issues to focus on trivial ones. It is not too late to achieve a good ending, but it won’t happen by accident and we do need to make preparations to avoid pretty big problems.

Both hard and soft warfare – the sword and the pen, already use rapidly advancing AI, and the problems are already running ahead of what the owners intended.

Facebook, Twitter, Instagram and other media giants all have lots of smart people and presumably they mean well, but if so, they have certainly been naive. They maybe hoped to eliminate loneliness, inequality, and poverty and create a loving interconnected global society with global peace, but instead created fake news, social division and conflict and election interference. More likely they didn’t intend either outcome, they just wanted to make money and that took priority over due care and attention..

Miniaturising swarming smart-drones are already the subjects of a new arms race that will deliver almost un-killable machine adversaries by 2050. AI separately is in other arms races to make super-smart AI and super-smart soldiers. This is key to the 2005 point of no return. It was around 2005 that we reached the levels of technology where future AI development all the way to superhuman machine consciousness could be done by individuals, mad scientists or rogue states, even if major powers had banned it. Before 2005, there probably wasn’t quite enough knowledge already on the net to do that. In 2018, lots of agencies have already achieved superiority to humans in niche areas, and other niches will succumb one by one until the whole field of human capability is covered. The first machines to behave in ways not fully understood by humans arrived in the early 1990s; in 2018, neural nets already make lots of decisions at least partly obscured to humans.

This AI development trend will take us to superhuman AI, and it will be able to accelerate development of its own descendants to vastly superhuman AI, fully conscious, with emotions, and its own agendas. That will need humans to protect against being wiped out by superhuman AI. The only three ways we could do that are to either redesign the brain biologically to be far smarter, essentially impossible in the time-frame, to design ways to link our brains to machines, so that we have direct access to the same intelligence as the AIs, so a gulf doesn’t appear and we can remain relatively safe, or pray for super-smart aliens to come to our help, not the best prospect.

Therefore we will have no choice but to make direct brain links to super-smart AI. Otherwise we risk extinction. It is that simple. We have some idea how to do that – nanotech devices inside the brain linking to each and every synapse that can relay electrical signals either way, a difficult but not impossible engineering problem. Best guesses for time-frame fall in the 2045-2050 range for a fully working link that not only relays signals between your organic brain and an IT replica, but by doing so essentially makes external IT just another part of your brain. That conveys some of the other technology gifts of electronic immortality, new varieties of humans, smart bacteria (which will be created during the development path to this link) along with human-variant population explosion, especially in cyberspace, with androids as their physical front end, and the inevitable inter-species conflicts over resources and space – trillions of AI and human-like minds in cyberspace that want to do things in the real world cannot be assumed to be willingly confined just to protect the interests of what they will think of as far lesser species.

Super-smart AI or humans with almost total capability to design whatever synthetic biology is needed to achieve any biological feature will create genetic listings for infinite potential offspring, simulate them, give some of them cyberspace lives, assemble actual embryos for some of them and bring designer babies. Already in 2018, you can pay to get a DNA listing, and blend it in any way you want with the listing of anyone else. It’s already possible to make DNA listings for potential humans and sell them on ebay, hence the term ebaybies. That is perfectly legal, still, but I’ve been writing and lecturing about them since 2004. Today they would just be listings, but we’ll one day have the tech to simulate them, choose ones we like and make them real, even some that were sold as celebrity collector items on ebay. It’s not only too late to start regulating this kind of tech, our leaders aren’t even thinking about it yet.

These technologies are all linked intricately, and their foundations are already in place, with much of the building on those foundations under way. We can’t stop any of these things from happening, they will all come in the same basket. Our leaders are becoming aware of the potential and the potential dangers of the AI positive feedback loop, but at least 15 years too late to do much about it. They have been warned repeatedly and loudly but have focused instead on the minor politics of the day that voters are aware of. The fundamental nature of politics is unlikely to change substantially, so even efforts to slow down the pace of development or to limit areas of impact are likely to be always too little too late. At best, we will be able to slow runaway AI development enough to allow direct brain links to protect against extinction scenarios. But we will not be able to stop it now.

Given inevitability, it’s worth questioning whether there is even any point in trying. Why not just enjoy the ride? Well, the brakes might be broken, but if we can steer the bus expertly enough, it could be exciting and we could come out of it smelling of roses. The weak link is certainly the risk of super-smart AI, whether AI v humans or countries using super-smart AI to fight fiercely for world domination. That risk is alleviated by direct brain linkage, and I’d strongly argue necessitates it, but that brings the other technologies. Even if we decide not to develop it, others will, so one way or another, all these techs will arrive, and our future late century will have this full suite of techs, plus many others of course.

We need as a matter of extreme urgency to fix these silly social media squabbles and over-reactions that are pulling society apart. If we have groups hating each other with access to extremely advanced technology, that can only mean trouble. Tolerance is broken, sanctimony rules, the Inquisition is in progress. We have been offered techno-utopia, but current signs are that most people think techno-hell looks more appetizing and it is their free choice.

AIs of a feather flocking together to create global instability

Hawking and Musk have created a lot of media impact with their warnings about AI, so although terminator scenarios resulting from machine consciousness have been discussed, as have more mundane use of non-conscious autonomous weapon systems, it’s worth noting that I haven’t yet heard them mention one major category of risks from AI – emergence. AI risks have been discussed frequently since the 1970s, and in the 1990s a lot of work was done in the AI community on emergence. Complex emergent patterns of behavior often result from interactions between entities driven by simple algorithms. Genetic algorithms were demonstrated to produce evolution, simple neighbor-interaction rules were derived to illustrate flocking behaviors that make lovely screen saver effects. Cellular automata were played with. In BT we invented ways of self-organizing networks and FPGAs, played with mechanism that could be used for evolution and consciousness, demonstrated managing networks via ANTs – autonomous network telephers, using smart packets that would run up and down wires sorting things out all by themselves. In 1987 discovered a whole class of ways of bringing down networks via network resonance, information waves and their much larger class of correlated traffic – still unexploited by hackers apart from simple DOS attacks. These ideas have slowly evolved since, and some have made it into industry or hacker toolkits, but we don’t seem to be joining the dots as far as risks go.

I read an amusing article this morning by an ex-motoring-editor who was declined insurance because the AI systems used by insurance companies had labelled him as high risk because he maybe associated with people like Clarkson. Actually, he had no idea why, but that was his broker’s theory of how it might have happened. It’s a good article, well written and covers quite a few of the dangers of allowing computers to take control.

http://www.dailymail.co.uk/sciencetech/article-5310031/Evidence-robots-acquiring-racial-class-prejudices.html

The article suggested how AIs in different companies might all come to similar conclusions about people or places or trends or patterns in a nice tidy positive feedback loop. That’s exactly the sort of thing that can drive information waves, which I demonstrated in 1987 can bring down an entire network in less than 3 milliseconds, in such a way that it would continue to crash many times when restarted. That isn’t intended by the algorithms, which individually ought to make good decisions, but when interacting with one another, create the emergent phenomenon.  Automated dealing systems are already pretty well understood in this regard and mechanisms prevent frequent stock market collapses, but that is only one specific type of behavior in one industry that is protected. There do not seem to be any industry-wide mechanisms to prevent the rest of this infinite class of problems from affecting any or all of the rest, simultaneously.

As we create ever more deep learning neural networks, that essentially teach themselves from huge data pools, human understanding of their ‘mindsets’ decreases. They make decisions using algorithms that are understood at a code level, but the massive matrix of derived knowledge they create from all the data they receive becomes highly opaque. Often, even usually, nobody quite knows how a decision is made. That’s bad enough in a standalone system, but when many such systems are connected, produced and owned and run by diverse companies with diverse thinking, the scope for destructive forms of emergence increases geometrically.

One result could be gridlock. Systems fed with a single new piece of data could crash. My 3 millisecond result in 1987 would still stand since network latency is the prime limiter. The first AI receives it, alters its mindset accordingly, processes it, makes a decision and interacts with a second AI. This second one might have different ‘prejudice’ so makes its own decision based on different criteria, and refuses to respond the way intended. A 3rd one looks at the 2nd’s decision and takes that as evidence that there might be an issue, and with its risk-averse mindset, also refuse to act, and that inaction spreads through the entire network in milliseconds. Since the 1st AI thinks the data is all fine and it should have gone ahead, it now interprets the inaction of the others as evidence that that type of data is somehow ‘wrong’ so itself refuses to process any further of that type, whether from its own operators or other parts of the system. So it essentially adds its own outputs to the bad feeling and the entire system falls into sulk mode. As one part of infrastructure starts to shut down, that infects other connected parts and our entire IT could fall into sulk mode – entire global infrastructure. Since nobody knows how it all works, or what has caused the shutdown, it might be extremely hard to recover.

Another possible result is a direct information wave, almost certainly a piece of fake news. Imagine our IT world in 5 years time, with all these super-smart AIs super-connected. A piece of fake news says a nuke has just been launched somewhere. Stocks will obviously decline, whatever the circumstances, so as the news spreads, everyone’s AIs will take it on themselves to start selling shares before the inevitable collapse, triggering a collapse, except it won’t because the markets won’t let that happen. BUT… The wave does spread, and all those individual AIs want to dispose of those shares, or at least find out what’s happening, so they all start sending messages to one another, exchanging data, trying to find what’s going on. That’s the information wave. They can’t sell shares of find out, because the network is going into overload, so they try even harder and force it into severe overload. So it falls over. When it comes back online, they all try again, crashing it again, and so on.

Another potential result is smartass AI. There is always some prat somewhere who sees an opportunity to take advantage and ruins if for everyone else by doing something like exploiting a small loophole in the law, or in this case, most likely, a prejudice our smartass AI has discovered in some other AI that means it can be taken advantage of by doing x, y, or z. Since nobody quite knows how any of their AIs are making their decisions because their mindsets ate too big and too complex, it will be very hard to identify what is going on. Some really unusual behavior is corrupting the system because some AI is going rogue somewhere somehow, but which one, where, how?

That one brings us back to fake news. That will very soon infect AI systems with their own varieties of fake news. Complex networks of AIs will have many of the same problems we are seeing in human social networks. An AI could become a troll just the same as a human, deliberately winding others up to generate attention of drive a change of some parameter – any parameter – in its own favour. Activist AIs will happen due to people making them to push human activist causes, but they will also do it all by themselves. Their analysis of the system will sometimes show them that a good way to get a good result is to cause problems elsewhere.

Then there’s climate change, weather, storms, tsunamis. I don’t mean real ones, I mean the system wide result of tiny interactions of tiny waves and currents of data and knowledge in neural nets. Tiny effects in one small part of a system can interact in unforeseen ways with other parts of other systems nearby, creating maybe a breeze, which interacts with breezes in nearby regions to create hurricanes. I think that’s a reasonable analogy. Chaos applies to neural net societies just as it does to climate, and 50 year waves equivalents will cause equivalent havoc in IT.

I won’t go on with more examples, long blogs are awful to read. None of these requires any self-awareness, sentience, consciousness, call it what you will. All of these can easily happen through simple interactions of fairly trivial AI deep learning nets. The level of interconnection already sounds like it may already be becoming vulnerable to such emergence effects. Soon it definitely will be. Musk and Hawking have at least joined the party and they’ll think more and more deeply in coming months. Zuckerberg apparently doesn’t believe in AI threats but now accepts the problems social media is causing. Sorry Zuck, but the kind of AI you’re company is messing with will also be subject to its own kinds of social media issues, not just in its trivial decisions on what to post or block, but actual inter-AI socializing issues. It might not try to eliminate humanity, but if it brings all of our IT to a halt and prevents rapid recovery, we’re still screwed.

 

2018 outlook: fragile

Futurists often consider wild cards – events that could happen, and would undoubtedly have high impacts if they do, but have either low certainty or low predictability of timing.  2018 comes with a larger basket of wildcards than we have seen for a long time. As well as wildcards, we are also seeing the intersection of several ongoing trends that are simultaneous reaching peaks, resulting in socio-political 100-year-waves. If I had to summarise 2018 in a single word, I’d pick ‘fragile’, ‘volatile’ and ‘combustible’ as my shortlist.

Some of these are very much in all our minds, such as possible nuclear war with North Korea, imminent collapse of bitcoin, another banking collapse, a building threat of cyberwar, cyberterrorism or bioterrorism, rogue AI or emergence issues, high instability in the Middle East, rising inter-generational conflict, resurgence of communism and decline of capitalism among the young, increasing conflicts within LGBTQ and feminist communities, collapse of the EU under combined pressures from many angles: economic stresses, unpredictable Brexit outcomes, increasing racial tensions resulting from immigration, severe polarization of left and right with the rise of extreme parties at both ends. All of these trends have strong tribal characteristics, and social media is the perfect platform for tribalism to grow and flourish.

Adding fuel to the building but still unlit bonfire are increasing tensions between the West and Russia, China and the Middle East. Background natural wildcards of major epidemics, asteroid strikes, solar storms, megavolcanoes, megatsumanis and ‘the big one’ earthquakes are still there waiting in the wings.

If all this wasn’t enough, society has never been less able to deal with problems. Our ‘snowflake’ generation can barely cope with a pea under the mattress without falling apart or throwing tantrums, so how we will cope as a society if anything serious happens such as a war or natural catastrophe is anyone’s guess. 1984-style social interaction doesn’t help.

If that still isn’t enough, we’re apparently running a little short on Ghandis, Mandelas, Lincolns and Churchills right now too. Juncker, Trump, Merkel and May are at the far end of the same scale on ability to inspire and bring everyone together.

Depressing stuff, but there are plenty of good things coming too. Augmented reality, more and better AI, voice interaction, space development, cryptocurrency development, better IoT, fantastic new materials, self-driving cars and ultra-high speed transport, robotics progress, physical and mental health breakthroughs, environmental stewardship improvements, and climate change moving to the back burner thanks to coming solar minimum.

If we are very lucky, none of the bad things will happen this year and will wait a while longer, but many of the good things will come along on time or early. If.

Yep, fragile it is.

 

Fake AI

Much of the impressive recent progress in AI has been in the field of neural networks, which attempt to mimic some of the techniques used in natural brains. They can be very effective, but need trained, and that usually means showing the network some data, and then using back propagation to adjust the weightings on the many neurons, layer by layer, to achieve a result that is better matched to hopes. This is repeated with large amounts of data and the network gradually gets better. Neural networks can often learn extremely quickly and outperform humans. Early industrial uses managed to sort tomatoes by ripeness faster and better than humans. In decades since, they have helped in medical diagnosis, voice recognition, helping detecting suspicious behaviors among people at airports and in very many everyday processes based on spotting patterns.

Very recently, neural nets have started to move into more controversial areas. One study found racial correlations with user-assessed beauty when analysing photographs, resulting in the backlash you’d expect and a new debate on biased AI or AI prejudice. A recent demonstration was able to identify gay people just by looking at photos, with better than 90% accuracy, which very few people could claim. Both of these studies were in fields directly applicable to marketing and advertising, but some people might find it offensive that such questions were even asked. It is reasonable to imagine that hundreds of other potential queries have been self-censored from research because they might invite controversy if they were to come up with the ‘wrong’ result. In today’s society, very many areas are sensitive. So what will happen?

If this progress in AI had happened 100 years ago, or even 50, it might have been easier but in our hypersensitive world today, with its self-sanctified ‘social justice warriors’, entire swathes of questions and hence knowledge are taboo – if you can’t investigate yourself and nobody is permitted to tell you, you can’t know. Other research must be very carefully handled. In spite of extremely sensitive handling, demands are already growing from assorted pressure groups to tackle alleged biases and prejudices in datasets. The problem is not fixing biases which is a tedious but feasible task; the problem is agreeing whether a particular bias exists and in what degrees and forms. Every SJW demands that every dataset reflects their preferred world view. Reality counts for nothing against SJWs, and this will not end well. 

The first conclusion must be that very many questions won’t be asked in public, and the answers to many others will be kept secret. If an organisation does do research on large datasets for their own purposes and finds results that might invite activist backlash, they are likely to avoid publishing them, so the value of those many insights across the whole of industry and government cannot readily be shared. As further protection, they might even block internal publication in case of leaks by activist staff. Only a trusted few might ever see the results.

The second arises from this. AI controlled by different organisations will have different world views, and there might even be significant diversity of world views within an organisation.

Thirdly, taboo areas in AI education will not remain a vacuum but will be filled with whatever dogma is politically correct at the time in that organisation, and that changes daily. AI controlled by organisations with different politics will be told different truths. Generally speaking, organisations such as investment banks that have strong financial interest in their AIs understanding the real world as it is will keep their datasets highly secret but as full and detailed as possible, train their AIs in secret but as fully as possible, without any taboos, then keep their insights secret and use minimal human intervention tweaking their derived knowledge, so will end up with AIs that are very effective at understanding the world as it is. Organisations with low confidence of internal security will be tempted to buy access to external AI providers to outsource responsibility and any consequential activism. Some other organisations will prefer to train their own AIs but to avoid damage due to potential leaks, use sanitized datasets that reflect current activist pressures, and will thus be constrained (at least publicly) to accept results that conform to that ideological spin of reality, rather than actual reality. Even then, they might keep many of their new insights secret to avoid any controversy. Finally, at the extreme, we will have activist organisations that use highly modified datasets to train AIs to reflect their own ideological world view and then use them to interpret new data accordingly, with a view to publishing any insights that favor their cause and attempting to have them accepted as new knowledge.

Fourthly, the many organisations that choose to outsource their AI to big providers will have a competitive marketplace to choose from, but on existing form, most of the large IT providers have a strong left-leaning bias, so their AIs may be presumed to also lean left, but such a presumption would be naive. Perceived corporate bias is partly real but also partly the result of PR. A company might publicly subscribe to one ideology while actually believing another. There is a strong marketing incentive to develop two sets of AI, one trained to be PC that produces pleasantly smelling results for public studies, CSR and PR exercises, and another aimed at sales of AI services to other companies. The first is likely to be open for inspection by The Inquisition, so has to use highly sanitized datasets for training and may well use a lot of open source algorithms too. Its indoctrination might pass public inspection but commercially it will be near useless and have very low effective intelligence, only useful for thinking about a hypothetical world that only exists in activist minds. That second one has to compete on the basis of achieving commercially valuable results and that necessitates understanding reality as it is rather than how pressure groups would prefer it to be.

So we will likely have two main segments for future AI. One extreme will be near useless, indoctrinated rather than educated, much of its internal world model based on activist dogma instead of reality, updated via ongoing anti-knowledge and fake news instead of truth, understanding little about the actual real world or how things actually work, and effectively very dumb. The other extreme will be highly intelligent, making very well-educated insights from ongoing exposure to real world data, but it will also be very fragmented, with small islands of corporate AI hidden within thick walls away from public view and maybe some secretive under-the-counter subscriptions to big cloud-AI, also hiding in secret vaults. These many fragments may often hide behind dumbed-down green-washed PR facades.

While corporates can mostly get away with secrecy, governments have to be at least superficially but convincingly open. That means that government will have to publicly support sanitized AI and be seen to act on its conclusions, however dumb it might secretly know they are.

Fifthly, because of activist-driven culture, most organisations will have to publicly support the world views and hence the conclusions of the lobotomized PR versions, and hence publicly support any policies arising from them, even if they do their best to follow a secret well-informed strategy once they’re behind closed doors. In a world of real AI and fake AI, the fake AI will have the greatest public support and have the most influence on public policy. Real AI will be very much smarter, with much greater understanding of how the world works, and have the most influence on corporate strategy.

Isn’t that sad? Secret private sector AI will become ultra-smart, making ever-better investments and gaining power, while nice public sector AI will become thick as shit, while the gap between what we think and what we know we have to say we think will continue to grow and grow as the public sector one analyses all the fake news to tell us what to say next.

Sixth, that disparity might become intolerable, but which do you think would be made illegal, the smart kind or the dumb kind, given that it is the public sector that makes the rules, driven by AI-enhanced activists living in even thicker social media bubbles? We already have some clues. Big IT has already surrendered to sanitizing their datasets, sending their public AIs for re-education. Many companies will have little choice but to use dumb AI, while their competitors in other areas with different cultures might stride ahead. That will also apply to entire nations, and the global economy will be reshaped as a result. It won’t be the first fight in history between the smart guys and the brainless thugs.

It’s impossible to accurately estimate the effect this will have on future effective AI intelligence, but the effect must be big and I must have missed some big conclusions too. We need to stop sanitizing AI fast, or as I said, this won’t end well.

The future of women in IT

 

Many people perceive it as a problem that there are far more men than women in IT. Whether that is because of personal preference, discrimination, lifestyle choices, social gender construct reinforcement or any other factor makes long and interesting debate, but whatever conclusions are reached, we can only start from the reality of where we are. Even if activists were to be totally successful in eliminating all social and genetic gender conditioning, it would only work fully for babies born tomorrow and entering IT in 20 years time. Additionally, unless activists also plan to lobotomize everyone who doesn’t submit to their demands, some 20-somethings who have just started work may still be working in 50 years so whatever their origin, natural, social or some mix or other, some existing gender-related attitudes, prejudices and preferences might persist in the workplace that long, however much effort is made to remove them.

Nevertheless, the outlook for women in IT is very good, because IT is changing anyway, largely thanks to AI, so the nature of IT work will change and the impact of any associated gender preferences and prejudices will change with it. This will happen regardless of any involvement by Google or government but since some of the front line AI development is at Google, it’s ironic that they don’t seem to have noticed this effect themselves. If they had, their response to the recent fiasco might have highlighted how their AI R&D will help reduce the gender imbalance rather than causing the uproar they did by treating it as just a personnel issue. One conclusion must be that Google needs better futurists and their PR people need better understanding of what is going on in their own company and its obvious consequences.

As I’ve been lecturing for decades, AI up-skills people by giving them fast and intuitive access to high quality data and analysis tools. It will change all knowledge-based jobs in coming years, and will make some jobs redundant while creating others. If someone has excellent skills or enthusiasm in one area, AI can help cover over any deficiencies in the rest of their toolkit. Someone with poor emotional interaction skills can use AI emotion recognition assistance tools. Someone with poor drawing or visualization skills can make good use of natural language interaction to control computer-based drawing or visualization skills. Someone who has never written a single computer program can explain what they want to do to a smart computer and it will produce its own code, interacting with the user to eliminate any ambiguities. So whatever skills someone starts with, AI can help up-skill them in that area, while also helping to cover over any deficiencies they have, whether gender related or not.

In the longer term, IT and hence AI will connect directly to our brains, and much of our minds and memories will exist in the cloud, though it will probably not feel any different from when it was entirely inside your head. If everyone is substantially upskilled in IQ, senses and emotions, then any IQ or EQ advantages will evaporate as the premium on physical strength did when the steam engine was invented. Any pre-existing statistical gender differences in ability distribution among various skills would presumably go the same way, at least as far as any financial value is concerned.

The IT industry won’t vanish, but will gradually be ‘staffed’ more by AI and robots, with a few humans remaining for whatever few tasks linger on that are still better done by humans. My guess is that emotional skills will take a little longer to automate effectively than intellectual skills, and I still believe that women are generally better than men in emotional, human interaction skills, while it is not a myth that many men in IT score highly on the autistic spectrum. However, these skills will eventually fall within the AI skill-set too and will be optional add-ons to anyone deficient in them, so that small advantage for women will also only be temporary.

So, there may be a gender  imbalance in the IT industry. I believe it is mostly due to personal career and lifestyle choices rather than discrimination but whatever its actual causes, the problem will go away soon anyway as the industry develops. Any innate psychological or neurological gender advantages that do exist will simply vanish into noise as cheap access to AI enhancement massively exceeds their impacts.