Tag Archives: AI

How can we make a computer conscious?

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.


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.


Emotion maths – A perfect research project for AI

I did a maths and physics degree, and even though I have forgotten much of it after 36 years, my brain is still oriented in that direction and I sometimes have maths dreams. Last night I had another, where I realized I’ve never heard of a branch of mathematics to describe emotions or emotional interactions. As the dream progressed, it became increasingly obvious that the most suited part of maths for doing so would be field theory, and given the multi-dimensional nature of emotions, tensor field theory would be ideal. I’m guessing that tensor field theory isn’t on most university’s psychology syllabus. I could barely cope with it on a maths syllabus. However, I note that one branch of Google’s AI R&D resulted in a computer architecture called tensor flow, presumably designed specifically for such multidimensional problems, and presumably being used to analyse marketing data. Again, I haven’t yet heard any mention of it being used for emotion studies, so this is clearly a large hole in maths research that might be perfectly filled by AI. It would be fantastic if AI can deliver a whole new branch of maths. AI got into trouble inventing new languages but mathematics is really just a way of describing logical reasoning about numbers or patterns in formal language that is self-consistent and reproducible. It is ideal for describing scientific theories, engineering and logical reasoning.

Checking Google today, there are a few articles out there describing simple emotional interactions using superficial equations, but nothing with the level of sophistication needed.


an example from this:

Disappointment = Expectations – Reality

is certainly an equation, but it is too superficial and incomplete. It takes no account of how you feel otherwise – whether you are jealous or angry or in love or a thousand other things. So there is some discussion on using maths to describe emotions, but I’d say it is extremely superficial and embryonic and perfect for deeper study.

Emotions often behave like fields. We use field-like descriptions in everyday expressions – envy is a green fog, anger is a red mist or we see a beloved through rose-tinted spectacles. These are classic fields, and maths could easily describe them in this way and use them in equations that describe behaviors affected by those emotions. I’ve often used the concept of magentic fields in some of my machine consciousness work. (If I am using an optical processing gel, then shining a colored beam of light into a particular ‘brain’ region could bias the neurons in that region in a particular direction in the same way an emotion does in the human brain. ‘Magentic’ is just a playful pun given the processing mechanism is light (e.g. magenta, rather than electrics that would be better affected by magnetic fields.

Some emotions interact and some don’t, so that gives us nice orthogonal dimensions to play in. You can be calm or excited pretty much independently of being jealous. Others very much interact. It is hard to be happy while angry. Maths allows interacting fields to be described using shared dimensions, while having others that don’t interact on other dimensions. This is where it starts to get more interesting and more suited to AI than people. Given large databases of emotionally affected interactions, an AI could derive hypotheses that appear to describe these interactions between emotions, picking out where they seem to interact and where they seem to be independent.

Not being emotionally involved itself, it is better suited to draw such conclusions. A human researcher however might find it hard to draw neat boundaries around emotions and describe them so clearly. It may be obvious that being both calm and angry doesn’t easily fit with human experience, but what about being terrified and happy? Terrified sounds very negative at first glance, so first impressions aren’t favorable for twinning them, but when you think about it, that pretty much describes the entire roller-coaster or extreme sports markets. Many other emotions interact somewhat, and deriving the equations would be extremely hard for humans, but I’m guessing, relatively easy for AI.

These kinds of equations fall very easily into tensor field theory, with types and degrees of interactions of fields along alternative dimensions readily describable.

Some interactions act like transforms. Fear might transform the ways that jealousy is expressed. Love alters the expression of happiness or sadness.

Some things seem to add or subtract, others multiply, others act more like exponential or partial derivatives or integrations, other interact periodically or instantly or over time. Maths seems to hold innumerable tools to describe emotions, but first-person involvement and experience make it extremely difficult for humans to derive such equations. The example equation above is easy to understand, but there are so many emotions available, and so many different circumstances, that this entire problem looks like it was designed to challenge a big data mining plant. Maybe a big company involved in AI, big data, advertising and that knows about tensor field theory would be a perfect research candidate. Google, Amazon, Facebook, Samsung….. Has all the potential for a race.

AI, meet emotions. You speak different languages, so you’ll need to work hard to get to know one another. Here are some books on field theory. Now get on with it, I expect a thesis on emotional field theory by end of term.


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.

Guest Post: Blade Runner 2049 is the product of decades of fear propaganda. It’s time to get enlightened about AI and optimistic about the future

This post from occasional contributor Chris Moseley

News from several months ago that more than 100 experts in robotics and artificial intelligence were calling on the UN to ban the development and use of killer robots is a reminder of the power of humanity’s collective imagination. Stimulated by countless science fiction books and films, robotics and AI is a potent feature of what futurist Alvin Toffler termed ‘future shock’. AI and robots have become the public’s ‘technology bogeymen’, more fearsome curse than technological blessing.

And yet curiously it is not so much the public that is fomenting this concern, but instead the leading minds in the technology industry. Names such as Tesla’s Elon Musk and Stephen Hawking were among the most prominent individuals on a list of 116 tech experts who have signed an open letter asking the UN to ban autonomous weapons in a bid to prevent an arms race.

These concerns appear to emanate from decades of titillation, driven by pulp science fiction writers. Such writers are insistent on foretelling a dark, foreboding future where intelligent machines, loosed from their binds, destroy mankind. A case in point – this autumn, a sequel to Ridley Scott’s Blade Runner has been released. Blade Runner,and 2017’s Blade Runner 2049, are of course a glorious tour de force of story-telling and amazing special effects. The concept for both films came from US author Philip K. Dick’s 1968 novel, Do Androids Dream of Electric Sheep? in which androids are claimed to possess no sense of empathy eventually require killing (“retiring”) when they go rogue. Dick’s original novel is an entertaining, but an utterly bleak vision of the future, without much latitude to consider a brighter, more optimistic alternative.

But let’s get real here. Fiction is fiction; science is science. For the men and women who work in the technology industry the notion that myriad Frankenstein monsters can be created from robots and AI technology is assuredly both confused and histrionic. The latest smart technologies might seem to suggest a frightful and fateful next step, a James Cameron Terminator nightmare scenario. It might suggest a dystopian outcome, but rational thought ought to lead us to suppose that this won’t occur because we have historical precedent on our side. We shouldn’t be drawn to this dystopian idée fixe because summoning golems and ghouls ignores today’s global arsenal of weapons and the fact that, more 70 years after Hiroshima, nuclear holocaust has been kept at bay.

By stubbornly pursuing the dystopian nightmare scenario, we are denying ourselves from marvelling at the technologies which are in fact daily helping mankind. Now frame this thought in terms of human evolution. For our ancient forebears a beneficial change in physiology might spread across the human race over the course of a hundred thousand years. Today’s version of evolution – the introduction of a compelling new technology – spreads throughout a mass audience in a week or two.

Curiously, for all this light speed evolution mass annihilation remains absent – we live on, progressing, evolving and improving ourselves.

And in the workplace, another domain where our unyielding dealers of dystopia have exercised their thoughts, technology is of course necessarily raising a host of concerns about the future. Some of these concerns are based around a number of misconceptions surrounding AI. Machines, for example, are not original thinkers and are unable to set their own goals. And although machine learning is able to acquire new information through experience, for the most part they are still fed information to process. Humans are still needed to set goals, provide data to fuel artificial intelligence and apply critical thinking and judgment. The familiar symbiosis of humans and machines will continue to be salient.

Banish the menace of so-called ‘killer robots’ and AI taking your job, and a newer, fresher world begins to emerge. With this more optimistic mind-set in play, what great feats can be accomplished through the continued interaction between artificial intelligence, robotics and mankind?

Blade Runner 2049 is certainly great entertainment – as Robbie Collin, The Daily Telegraph’s film critic writes, “Roger Deakins’s head-spinning cinematography – which, when it’s not gliding over dust-blown deserts and teeming neon chasms, keeps finding ingenious ways to make faces and bodies overlap, blend and diffuse.” – but great though the art is, isn’t it time to change our thinking and recast the world in a more optimistic light?


Just a word about the film itself. Broadly, director Denis Villeneuve’s done a tremendous job with Blade Runner 2049. One stylistic gripe, though. While one wouldn’t want Villeneuve to direct a slavish homage to Ridley Scott’s original, the alarming switch from the dreamlike techno miasma (most notably, giant nude step-out-the-poster Geisha girls), to Mad Max II Steampunk (the junkyard scenes, complete with a Fagin character) is simply too jarring. I predict that there will be a director’s cut in years to come. Shorter, leaner and sans Steampunk … watch this space!

Author: Chris Moseley, PR Manager, London Business School


Tel +44 7511577803

The age of dignity

I just watched a short video of robots doing fetch and carry jobs in an Alibaba distribution centre:


There are numerous videos of robots in various companies doing tasks that used to be done by people. In most cases those tasks were dull, menial, drudgery tasks that treated people as machines. Machines should rightly do those tasks. In partnership with robots, AI is also replacing some tasks that used to be done by people. Many are worried about increasing redundancy but I’m not; I see a better world. People should instead be up-skilled by proper uses of AI and robotics and enabled to do work that is more rewarding and treats them with dignity. People should do work that uses their human skills in ways that they find rewarding and fulfilling. People should not have to do work they find boring or demeaning just because they have to earn money. They should be able to smile at work and rest at the end of the day knowing that they have helped others or made the world a better place. If we use AI, robots and people in the right ways, we can build that world.

Take a worker in a call centre. Automation has already replaced humans in most simple transactions like paying a bill, checking a balance or registering a new credit card. It is hard to imagine that anyone ever enjoyed doing that as their job. Now, call centre workers mostly help people in ways that allow them to use their personalities and interpersonal skills, being helpful and pleasant instead of just typing data into a keyboard. It is more enjoyable and fulfilling for the caller, and presumably for the worker too, knowing they genuinely helped someone’s day go a little better. I just renewed my car insurance. I phoned up to cancel the existing policy because it had increased in price too much. The guy at the other end of the call was very pleasant and helpful and met me half way on the price difference, so I ended up staying for another year. His company is a little richer, I was a happier customer, and he had a pleasant interaction instead of having to put up with an irate customer and also the job satisfaction from having converted a customer intending to leave into one happy to stay. The AI at his end presumably gave him the information he needed and the limits of discount he was permitted to offer. Success. In billions of routine transactions like that, the world becomes a little happier and just as important, a little more dignified. There is more dignity in helping someone than in pushing a button.

Almost always, when AI enters a situation, it replaces individual tasks that used to take precious time and that were not very interesting to do. Every time you google something, a few microseconds of AI saves you half a day in a library and all those half days add up to a lot of extra time every year for meeting colleagues, human interactions, learning new skills and knowledge or even relaxing. You become more human and less of a machine. Your self-actualisation almost certainly increases in one way or another and you become a slightly better person.

There will soon be many factories and distribution centres that have few or no people at all, and that’s fine. It reduces the costs of making material goods so average standard of living can increase. A black box economy that has automated mines or recycling plants extracting raw materials and uses automated power plants to convert them into high quality but cheap goods adds to the total work available to add value; in other words it increases the size of the economy. Robots can make other robots and together with AI, they could make all we need, do all the fetching and carrying, tidying up, keeping it all working, acting as willing servants in every role we want them in. With greater economic wealth and properly organised taxation, which will require substantial change from today, people could be freed to do whatever fulfills them. Automation increases average standard of living while liberating people to do human interaction jobs, crafts, sports, entertainment, leading, inspiring, teaching, persuading, caring and so on, creating a care economy. 

Each person knows what they are good at, what they enjoy. With AI and robot assistance, they can more easily make that their everyday activity. AI could do their company set-up, admin, billing, payments, tax, payroll – all the crap that makes being an entrepreneur a pain in the ass and stops many people pursuing their dreams.  Meanwhile they would do that above a very generous welfare net. Many of us now are talking about the concept of universal basic income, or citizen wage. With ongoing economic growth at the average rate of the last few decades, the global economy will be between twice and three times as big as today in the 2050s. Western countries could pay every single citizen a basic wage equivalent to today’s average wage, and if they work or run a company, they can earn more.

We will have an age where material goods are high quality, work well and are cheap to buy, and recycled in due course to minimise environmental harm. Better materials, improved designs and techniques, higher efficiency and land productivity and better recycling will mean that people can live with higher standards of living in a healthier environment. With a generous universal basic income, they will not have to worry about paying their bills. And doing only work that they want to do that meets their self-actualisation needs, everyone can live a life of happiness and dignity.

Enough of the AI-redundancy alarmism. If we elect good leaders who understand the options ahead, we can build a better world, for everyone. We can make real the age of dignity.

Google and the dangerous pursuit of ‘equality’

The world just got more dangerous, and I’m not talking about N Korea and Trump.

Google just sacked an employee because he openly suggested that men and women, (not all, but some, and there is an overlap, and …) might tend to have different preferences in some areas and that could (but not always, and only in certain cases, and we must always recognize and respect everyone and …) possibly account for some of the difference in numbers of men and women in certain roles (but there might be other causes too and obviously lots of discrimination and …. )

Yes, that’s what he actually said, but with rather more ifs and buts and maybes. He felt the need to wrap such an obvious statement in several kilometers thick of cotton wool so as not to offend the deliberately offended but nonetheless deliberate offense was taken and he is out on his ear.

Now, before you start thinking this is some right-wing rant, I feel obliged to point out just how progressive Futurizon is: 50% of all Futurizon owners and employees are female, all employees and owners have the same voting rights, 50% are immigrants and all are paid exactly the same and have the same size offices, regardless of dedication, ability, nature or quality or volume of output and regardless of their race, religion, beauty, shape, fitness, dietary preferences, baldness, hobbies or political views, even if they are Conservatives. All Futurizon offices are safe zones where employees may say anything they want of any level of truth, brilliance or stupidity and expect it to be taken as absolute fact and any consequential emotional needs to be fully met. No employee may criticize any other employee’s mouse mat, desk personalisation or screen wallpaper for obvious lack of taste. All employees are totally free to do anything they choose 100% of the time and can take as much leave as they want. All work is voluntary. All have the same right to respectfully request any other employee to make them coffee, tea or Pimms. All employees of all genders real or imagined are entitled to the same maternity and paternity rights, and the same sickness benefits, whether ill or not. In fact, Futurizon does not discriminate on any grounds whatsoever. We are proud to lead the world in non-discrimination. Unfortunately, our world-leading terms of employment mean that we can no longer afford to hire any new employees.

However, I note that Google has rather more power and influence than Futurizon so their policies count more. They appear (Google also has better lawyers than I can afford, so I must stress that all that follows is my personal opinion) to have firmly decided that diversity is all-important and they seem to want total equality of outcome. The view being expressed not just by Google but by huge swathes of angry protesters seems to be that any difference in workforce representation from that of the general population must arise from discrimination or oppression so must be addressed by positive action to correct it. There are apparently no statistically discernible differences in behavior between genders, or in job or role preference, so any you may have noticed over the time you’ve been alive is just your prejudice. Google says they fully support free speech and diversity of views, but expression of views is apparently only permitted as long as those views are authorized, on penalty of dismissal.

So unless I’m picking up totally the wrong end of the stick here, and I don’t do that often, only 13% of IT engineers are women, but internal policies must ensure that the proportion rises to 50%, whether women want to do that kind of work or not. In fact, nobody may question whether as many women want to work as IT engineers as men; it must now be taken as fact. By extension, since more women currently work in marketing, HR and PR, they must be substituted by men via positive action programs until men fill 50% of those roles. Presumably similar policies must also apply in medical bays for nursing and other staff there, and in construction teams for their nice new buildings. Ditto all other genders, races, religions; all groups must be protected and equalized to USA population proportions, apparently except those that don’t claim to hold sufficiently left-wing views, in which case it is seemingly perfectly acceptable to oppress, ostracize and even expel them.

In other words, freedom of choice and difference in ability, and more importantly freedom from discrimination, must be over-ruled in favor of absolute equality of diversity, regardless of financial or social cost, or impact on product or service quality. Not expressing full and enthusiastic left-wing compliance is seemingly just cause for dismissal.

So, why does this matter outside Google? Well, AI is developing very nicely. In fact, Google is one of the star players in the field right now. It is Google that will essentially decide how much of the AI around us is trained, how it learns, what it learns, what ‘knowledge’ it has of the world. Google will pick the content the AI learns from, and overrule or reeducate it if it draws any ‘wrong’ conclusions about the world, such as that more women than men want to be nurses or work in HR, or that more men than women want to be builders or engineers. A Google AI must presumably believe that the only differences between men and women are physical, unless their AI is deliberately excluded from the loudly declared corporate values and belief sets.

You should be very worried. Google’s values really matter. They have lots of influence on some of the basic tools of everyday life. Even outside their company, their AI tools and approaches will have strong influence on how other AI develops, determining operating systems and platforms, languages, mechanisms, interfaces, filters, even prejudices and that reach and influence is likely to increase. Their AI may well be in many self-driving cars, and if they have to make life or death decisions, the underlying value assumptions must feature in the algorithms. Soon companies will need AI that is more emotionally compliant. AI will use compliments or teasing or seduction or sarcasm or wit as marketing tools as well as just search engine positioning. Soon AI will use highly expressive faces with attractive voices, with attractive messages, tailored to appeal to you by pandering to your tastes and prejudices while thinking something altogether different. AI might be the person at the party that is all smiles and compliments, before going off to tell everyone else how awful it thinks you are. If you dare to say something not ‘authorized’, the ultra-smart AI all around you might treat you condescendingly, making you feel ashamed, ostracized, a dinosaur. Then it might secretly push you down a few pages in search results, or put a negative spin on text summaries about you, or exclude you from recommendations. Or it might do all the secret stuff while pretending it thinks you’re fantastic. Internal cultural policies in companies like Google today could soon be external social engineering to push the left-wing world the IT industry believes in – it isn’t just Google; Facebook and Twitter are also important and just as Left, though Amazon, Samsung, IBM and other AI players are less overtly politically biased, so far at least. Left wing policies generally cost a lot more, but Google and Facebook will presumably still expect other companies and people to pay the taxes to pay for it all. As their female staff gear up to fight them over pay differences between men and women for similar jobs, it often seems that Google’s holier-than-thou morality doesn’t quite make it as far as their finances.

Then it really starts being fun. We’ll soon have bacteria that can fabricate electronic circuits within themselves. Soon they’ll be able to power them too, giving the concept of smart yogurt. These bacteria could also have nanotechnology flagella to help them get around. We’ll soon have bacterial spies all over our environment, even on our skin, intercepting electronic signals that give away our thoughts. They’ll bring in data on everything that is said, everything that everyone even thinks or feels. Those bacteria will be directly connected into AI, in fact they’ll be part of it. They’ll be able to change things, to favor or punish according to whether they like what someone believes in or how they behave.

It isn’t just right-wing extremists that need to worry. I’m apparently Noveau Left – I score slightly left of center on political profiling tests, but I’m worried. A lot of this PC stuff seems extreme to me, sometimes just nonsense. Maybe it is, or maybe I should be lefter. But it’s not my choice. I don’t make the rules. Companies like Google make the rules, they even run the AI ethics groups. They decide much of what people see online, and even the meaning of the words. It’s very 1984-ish.

The trouble with the ‘echo chambers’ we heard about is that they soon normalize views to the loudest voices in those groups, and they don’t tend to be the moderates. We can expect it will go further to the extreme, not less. You probably aren’t left enough either. You should also be worried.

AI Activism Part 2: The libel fields

This follows directly from my previous blog on AI activism, but you can read that later if you haven’t already. Order doesn’t matter.


Older readers will remember an emotionally powerful 1984 film called The Killing Fields, set against the backdrop of the Khmer Rouge’s activity in Cambodia, aka the Communist Part of Kampuchea. Under Pol Pot, the Cambodian genocide of 2 to 3 million people was part of a social engineering policy of de-urbanization. People were tortured and murdered (some in the ‘killing fields’ near Phnom Penh) for having connections with former government of foreign governments, for being the wrong race, being ‘economic saboteurs’ or simply for being professionals or intellectuals .

You’re reading this, therefore you fit in at least the last of these groups and probably others, depending on who’s making the lists. Most people don’t read blogs but you do. Sorry, but that makes you a target.

As our social divide increases at an accelerating speed throughout the West, so the choice of weapons is moving from sticks and stones or demonstrations towards social media character assassination, boycotts and forced dismissals.

My last blog showed how various technology trends are coming together to make it easier and faster to destroy someone’s life and reputation. Some of that stuff I was writing about 20 years ago, such as virtual communities lending hardware to cyber-warfare campaigns, other bits have only really become apparent more recently, such as the deliberate use of AI to track personality traits. This is, as I wrote, a lethal combination. I left a couple of threads untied though.

Today, the big AI tools are owned by the big IT companies. They also own the big server farms on which the power to run the AI exists. The first thread I neglected to mention is that Google have made their AI an open source activity. There are lots of good things about that, but for the purposes of this blog, that means that the AI tools required for AI activism will also be largely public, and pressure groups and activist can use them as a start-point for any more advanced tools they want to make, or just use them off-the-shelf.

Secondly, it is fairly easy to link computers together to provide an aggregated computing platform. The SETI project was the first major proof of concept of that ages ago. Today, we take peer to peer networks for granted. When the activist group is ‘the liberal left’ or ‘the far right’, that adds up to a large number of machines so the power available for any campaign is notionally very large. Harnessing it doesn’t need IT skill from contributors. All they’d need to do is click a box on a email or tweet asking for their support for a campaign.

In our new ‘post-fact’, fake news era, all sides are willing and able to use social media and the infamous MSM to damage the other side. Fakes are becoming better. Latest AI can imitate your voice, a chat-bot can decide what it should say after other AI has recognized what someone has said and analysed the opportunities to ruin your relationship with them by spoofing you. Today, that might not be quite credible. Give it a couple more years and you won’t be able to tell. Next generation AI will be able to spoof your face doing the talking too.

AI can (and will) evolve. Deep learning researchers have been looking deeply at how the brain thinks, how to make neural networks learn better and to think better, how to design the next generation to be even smarter than humans could have designed it.

As my friend and robotic psychiatrist Joanne Pransky commented after my first piece, “It seems to me that the real challenge of AI is the human users, their ethics and morals (Their ‘HOS’ – Human Operating System).” Quite! Each group will indoctrinate their AI to believe their ethics and morals are right, and that the other lot are barbarians. Even evolutionary AI is not immune to religious or ideological bias as it evolves. Superhuman AI will be superhuman, but might believe even more strongly in a cause than humans do. You’d better hope the best AI is on your side.

AI can put articles, blogs and tweets out there, pretending to come from you or your friends, colleagues or contacts. They can generate plausible-sounding stories of what you’ve done or said, spoof emails in fake accounts using your ID to prove them.

So we’ll likely see activist AI armies set against each other, running on peer to peer processing clouds, encrypted to hell and back to prevent dismantling. We’ve all thought about cyber-warfare, but we usually only think about viruses or keystroke recorders, or more lately, ransom-ware. These will still be used too as small weapons in future cyber-warfare, but while losing files or a few bucks from an account is a real nuisance, losing your reputation, having it smeared all over the web, with all your contacts being told what you’ve done or said, and shown all the evidence, there is absolutely no way you could possible explain your way convincingly out of every one of those instances. Mud does stick, and if you throw tons of it, even if most is wiped off, much will remain. Trust is everything, and enough doubt cast will eventually erode it.

So, we’ve seen  many times through history the damage people are willing to do to each other in pursuit of their ideology. The Khmer Rouge had their killing fields. As political divide increases and battles become fiercer, the next 10 years will give us The Libel Fields.

You are an intellectual. You are one of the targets.

Oh dear!


AI and activism, a Terminator-sized threat targeting you soon

You should be familiar with the Terminator scenario. If you aren’t then you should watch one of the Terminator series of films because you really should be aware of it. But there is another issue related to AI that is arguably as dangerous as the Terminator scenario, far more likely to occur and is a threat in the near term. What’s even more dangerous is that in spite of that, I’ve never read anything about it anywhere yet. It seems to have flown under our collective radar and is already close.

In short, my concern is that AI is likely to become a heavily armed Big Brother. It only requires a few components to come together that are already well in progress. Read this, and if you aren’t scared yet, read it again until you understand it 🙂

Already, social media companies are experimenting with using AI to identify and delete ‘hate’ speech. Various governments have asked them to do this, and since they also get frequent criticism in the media because some hate speech still exists on their platforms, it seems quite reasonable for them to try to control it. AI clearly offers potential to offset the huge numbers of humans otherwise needed to do the task.

Meanwhile, AI is already used very extensively by the same companies to build personal profiles on each of us, mainly for advertising purposes. These profiles are already alarmingly comprehensive, and increasingly capable of cross-linking between our activities across multiple platforms and devices. Latest efforts by Google attempt to link eventual purchases to clicks on ads. It will be just as easy to use similar AI to link our physical movements and activities and future social connections and communications to all such previous real world or networked activity. (Update: Intel intend their self-driving car technology to be part of a mass surveillance net, again, for all the right reasons: http://www.dailymail.co.uk/sciencetech/article-4564480/Self-driving-cars-double-security-cameras.html)

Although necessarily secretive about their activities, government also wants personal profiles on its citizens, always justified by crime and terrorism control. If they can’t do this directly, they can do it via legislation and acquisition of social media or ISP data.

Meanwhile, other experiences with AI chat-bots learning to mimic human behaviors have shown how easily AI can be gamed by human activists, hijacking or biasing learning phases for their own agendas. Chat-bots themselves have become ubiquitous on social media and are often difficult to distinguish from humans. Meanwhile, social media is becoming more and more important throughout everyday life, with provably large impacts in political campaigning and throughout all sorts of activism.

Meanwhile, some companies have already started using social media monitoring to police their own staff, in recruitment, during employment, and sometimes in dismissal or other disciplinary action. Other companies have similarly started monitoring social media activity of people making comments about them or their staff. Some claim to do so only to protect their own staff from online abuse, but there are blurred boundaries between abuse, fair criticism, political difference or simple everyday opinion or banter.

Meanwhile, activists increasingly use social media to force companies to sack a member of staff they disapprove of, or drop a client or supplier.

Meanwhile, end to end encryption technology is ubiquitous. Malware creation tools are easily available.

Meanwhile, successful hacks into large company databases become more and more common.

Linking these various elements of progress together, how long will it be before activists are able to develop standalone AI entities and heavily encrypt them before letting them loose on the net? Not long at all I think.  These AIs would search and police social media, spotting people who conflict with the activist agenda. Occasional hacks of corporate databases will provide names, personal details, contacts. Even without hacks, analysis of publicly available data going back years of everyone’s tweets and other social media entries will provide the lists of people who have ever done or said anything the activists disapprove of.

When identified, they would automatically activate armies of chat-bots, fake news engines and automated email campaigns against them, with coordinated malware attacks directly on the person and indirect attacks by communicating with employers, friends, contacts, government agencies customers and suppliers to do as much damage as possible to the interests of that person.

Just look at the everyday news already about alleged hacks and activities during elections and referendums by other regimes, hackers or pressure groups. Scale that up and realize that the cost of running advanced AI is negligible.

With the very many activist groups around, many driven with extremist zeal, very many people will find themselves in the sights of one or more activist groups. AI will be able to monitor everyone, all the time.  AI will be able to target each of them at the same time to destroy each of their lives, anonymously, highly encrypted, hidden, roaming from server to server to avoid detection and annihilation, once released, impossible to retrieve. The ultimate activist weapon, that carries on the fight even if the activist is locked away.

We know for certain the depths and extent of activism, the huge polarization of society, the increasingly fierce conflict between left and right, between sexes, races, ideologies.

We know about all the nice things AI will give us with cures for cancer, better search engines, automation and economic boom. But actually, will the real future of AI be harnessed to activism? Will deliberate destruction of people’s everyday lives via AI be a real problem that is almost as dangerous as Terminator, but far more feasible and achievable far earlier?