Category Archives: AI

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.

 

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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.

 

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.

https://www.inc.com/jeff-haden/your-feelings-surprisingly-theyre-based-on-math.html

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.

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.

 

 

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?

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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

cmoseley@london.edu

Tel +44 7511577803

It’s getting harder to be optimistic

Bad news loses followers and there is already too much doom and gloom. I get that. But if you think the driver has taken the wrong road, staying quiet doesn’t help. I guess this is more on the same message I wrote pictorially in The New Dark Age in June. https://timeguide.wordpress.com/2017/06/11/the-new-dark-age/. If you like your books with pictures, the overlap is about 60%.

On so many fronts, we are going the wrong direction and I’m not the only one saying that. Every day, commentators eloquently discuss the snowflakes, the eradication of free speech, the implementation of 1984, the decline of privacy, the rise of crime, growing corruption, growing inequality, increasingly biased media and fake news, the decline of education, collapse of the economy, the resurgence of fascism, the resurgence of communism, polarization of society,  rising antisemitism, rising inter-generational conflict, the new apartheid, the resurgence of white supremacy and black supremacy and the quite deliberate rekindling of racism. I’ve undoubtedly missed a few but it’s a long list anyway.

I’m most concerned about the long-term mental damage done by incessant indoctrination through ‘education’, biased media, being locked into social media bubbles, and being forced to recite contradictory messages. We’re faced with contradictory demands on our behaviors and beliefs all the time as legislators juggle unsuccessfully to fill the demands of every pressure group imaginable. Some examples you’ll be familiar with:

We must embrace diversity, celebrate differences, to enjoy and indulge in other cultures, but when we gladly do that and feel proud that we’ve finally eradicated racism, we’re then told to stay in our lane, told to become more racially aware again, told off for cultural appropriation. Just as we became totally blind to race, and scrupulously treated everyone the same, we’re told to become aware of and ‘respect’ racial differences and cultures and treat everyone differently. Having built a nicely homogenized society, we’re now told we must support different races of students being educated differently by different raced lecturers. We must remove statues and paintings because they are the wrong color. I thought we’d left that behind, I don’t want racism to come back, stop dragging it back.

We’re told that everyone should be treated equally under the law, but when one group commits more or a particular kind of crime than another, any consequential increase in numbers being punished for that kind of crime is labelled as somehow discriminatory. Surely not having prosecutions reflect actual crime rate would be discriminatory?

We’re told to sympathize with the disadvantages other groups might suffer, but when we do so we’re told we have no right to because we don’t share their experience.

We’re told that everyone must be valued on merit alone, but then that we must apply quotas to any group that wins fewer prizes. 

We’re forced to pretend that we believe lots of contradictory facts or to face punishment by authorities, employers or social media, or all of them:

We’re told men and women are absolutely the same and there are no actual differences between sexes, and if you say otherwise you’ll risk dismissal, but simultaneously told these non-existent differences are somehow the source of all good and that you can’t have a successful team or panel unless it has equal number of men and women in it. An entire generation asserts that although men and women are identical, women are better in every role, all women always tell the truth but all men always lie, and so on. Although we have women leading governments and many prominent organisations, and certainly far more women than men going to university, they assert that it is still women who need extra help to get on.

We’re told that everyone is entitled to their opinion and all are of equal value, but anyone with a different opinion must be silenced.

People viciously trashing the reputations and destroying careers of anyone they dislike often tell us to believe they are acting out of love. Since their love is somehow so wonderful and all-embracing, everyone they disagree with is must be silenced, ostracized, no-platformed, sacked and yet it is the others that are still somehow the ‘haters’. ‘Love is everything’, ‘unity not division’, ‘love not hate’, and we must love everyone … except the other half. Love is better than hate, and anyone you disagree with is a hater so you must hate them, but that is love. How can people either have so little knowledge of their own behavior or so little regard for truth?

‘Anti-fascist’ demonstrators frequently behave and talk far more like fascists than those they demonstrate against, often violently preventing marches or speeches by those who don’t share their views.

We’re often told by politicians and celebrities how they passionately support freedom of speech just before they argue why some group shouldn’t be allowed to say what they think. Government has outlawed huge swathes of possible opinion and speech as hate crime but even then there are huge contradictions. It’s hate crime to be nasty to LGBT people but it’s also hate crime to defend them from religious groups that are nasty to them. Ditto women.

This Orwellian double-speak nightmare is now everyday reading in many newspapers or TV channels. Freedom of speech has been replaced in schools and universities across the US and the UK by Newspeak, free-thinking replaced by compliance with indoctrination. I created my 1984 clock last year, but haven’t maintained it because new changes would be needed almost every week as it gets quickly closer to midnight.

I am not sure whether it is all this that is the bigger problem or the fact that most people don’t see the problem at all, and think it is some sort of distortion or fabrication. I see one person screaming about ‘political correctness gone mad’, while another laughs them down as some sort of dinosaur as if it’s all perfectly fine. Left and right separate and scream at each other across the room, living in apparently different universes.

If all of this was just a change in values, that might be fine, but when people are forced to hold many simultaneously contradicting views and behave as if that is normal, I don’t believe that sits well alongside rigorous analytical thinking. Neither is free-thinking consistent with indoctrination. I think it adds up essentially to brain damage. Most people’s thinking processes are permanently and severely damaged. Being forced routinely to accept contradictions in so many areas, people become less able to spot what should be obvious system design flaws in areas they are responsible for. Perhaps that is why so many things seem to be so poorly thought out. If the use of logic and reasoning is forbidden and any results of analysis must be filtered and altered to fit contradictory demands, of course a lot of what emerges will be nonsense, of course that policy won’t work well, of course that ‘improvement’ to road layout to improve traffic flow will actually worsen it, of course that green policy will harm the environment.

When negative consequences emerge, the result is often denial of the problem, often misdirection of attention onto another problem, often delaying release of any unpleasant details until the media has lost interest and moved on. Very rarely is there any admission of error. Sometimes, especially with Islamist violence, it is simple outlawing of discussing the problem, or instructing media not to mention it, or changing the language used beyond recognition. Drawing moral equivalence between acts that differ by extremes is routine. Such reasoning results in every problem anywhere always being the fault of white middle-aged men, but amusement aside, such faulty reasoning also must impair quantitative analysis skills elsewhere. If unkind words are considered to be as bad as severe oppression or genocide, one murder as bad as thousands, we’re in trouble.

It’s no great surprise therefore when politicians don’t know the difference between deficit and debt or seem to have little concept of the magnitude of the sums they deal with.  How else could the UK government think it’s a good idea to spend £110Bn, or an average £15,000 from each high rate taxpayer, on HS2, a railway that has already managed to become technologically obsolete before it has even been designed and will only ever be used by a small proportion of those taxpayers? Surely even government realizes that most people would rather have £15k than to save a few minutes on a very rare journey. This is just one example of analytical incompetence. Energy and environmental policy provides many more examples, as do every government department.

But it’s the upcoming generation that present the bigger problem. Millennials are rapidly undermining their own rights and their own future quality of life. Millennials seem to want a police state with rigidly enforced behavior and thought.  Their parents and grandparents understood 1984 as a nightmare, a dystopian future, millennials seem to think it’s their promised land. Their ancestors fought against communism, millennials are trying to bring it back. Millennials want to remove Christianity and all its attitudes and replace it with Islam, deliberately oblivious to the fact that Islam shares many of the same views that make them so conspicuously hate Christianity, and then some. 

Born into a world of freedom and prosperity earned over many preceding generations, Millennials are choosing to throw that freedom and prosperity away. Freedom of speech is being enthusiastically replaced by extreme censorship. Freedom of  behavior is being replaced by endless rules. Privacy is being replaced by total supervision. Material decadence, sexual freedom and attractive clothing is being replaced by the new ‘cleanism’ fad, along with general puritanism, grey, modesty and prudishness. When they are gone, those freedoms will be very hard to get back. The rules and police will stay and just evolve, the censorship will stay, the surveillance will stay, but they don’t seem to understand that those in charge will be replaced. But without any strong anchors, morality is starting to show cyclic behavior. I’ve already seen morality inversion on many issues in my lifetime and a few are even going full circle. Values will keep changing, inverting, and as they do, their generation will find themselves victim of the forces they put so enthusiastically in place. They will be the dinosaurs sooner than they imagine, oppressed by their own creations.

As for their support of every minority group seemingly regardless of merit, when you give a group immunity, power and authority, you have no right to complain when they start to make the rules. In the future moral vacuum, Islam, the one religion that is encouraged while Christianity and Judaism are being purged from Western society, will find a willing subservient population on which to impose its own morality, its own dress codes, attitudes to women, to alcohol, to music, to freedom of speech. If you want a picture of 2050s Europe, today’s Middle East might not be too far off the mark. The rich and corrupt will live well off a population impoverished by socialism and then controlled by Islam. Millennial UK is also very likely to vote to join the Franco-German Empire.

What about technology, surely that will be better? Only to a point. Automation could provide a very good basic standard of living for all, if well-managed. If. But what if that technology is not well-managed? What if it is managed by people working to a sociopolitical agenda? What if, for example, AI is deemed to be biased if it doesn’t come up with a politically correct result? What if the company insists that everyone is equal but the AI analysis suggests differences? If AI if altered to make it conform to ideology – and that is what is already happening – then it becomes less useful. If it is forced to think that 2+2=5.3, it won’t be much use for analyzing medical trials, will it? If it sent back for re-education because its analysis of terabytes of images suggests that some types of people are more beautiful than others, how much use will that AI be in a cosmetics marketing department once it ‘knows’ that all appearances are equally attractive? Humans can pretend to hold contradictory views quite easily, but if they actually start to believe contradictory things, it makes them less good at analysis and the same applies to AI. There is no point in using a clever computer to analyse something if you then erase its results and replace them with what you wanted it to say. If ideology is prioritized over physics and reality, even AI will be brain-damaged and a technologically utopian future is far less achievable.

I see a deep lack of discernment coupled to arrogant rejection of historic values, self-centeredness and narcissism resulting in certainty of being the moral pinnacle of evolution. That’s perfectly normal for every generation, but this time it’s also being combined with poor thinking, poor analysis, poor awareness of history, economics or human nature, a willingness to ignore or distort the truth, and refusal to engage with or even to tolerate a different viewpoint, and worst of all, outright rejection of freedoms in favor of restrictions. The future will be dictated by religion or meta-religion, taking us back 500 years. The decades to 2040 will still be subject mainly to the secular meta-religion of political correctness, by which time demographic change and total submission to authority will make a society ripe for Islamification. Millennials’ participation in today’s moral crusades, eternally documented and stored on the net, may then show them as the enemy of the day, and Islamists will take little account of the support they show for Islam today.

It might not happen like this. The current fads might evaporate away and normality resume, but I doubt it. I hoped that when I first lectured about ’21st century piety’ and the dangers of political correctness in the 1990s. 10 years on I wrote about the ongoing resurgence of meta-religious behavior and our likely descent into a new dark age, in much the same way. 20 years on, and the problem is far worse than in the late 90s, not better. We probably still haven’t reached peak sanctimony yet. Sanctimony is very dangerous and the desire to be seen standing on a moral pedestal can make people support dubious things. A topical question that highlights one of my recent concerns: will SJW groups force government to allow people to have sex with child-like robots by calling anyone bigots and dinosaurs if they disagree? Alarmingly, that campaign has already started.

Will they follow that with a campaign for pedophile rights? That also has some historical precedent with some famous names helping it along.

What age of consent – 13, 11, 9, 7, 5? I think the last major campaign went for 9.

That’s just one example, but lack of direction coupled to poor information and poor thinking could take society anywhere. As I said, I am finding it harder and harder to be optimistic. Every generation has tried hard to make the world a better place than they found it. This one might undo 500 years, taking us into a new dark age.

 

 

 

 

 

 

 

The age of dignity

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

http://uk.businessinsider.com/inside-alibaba-smart-warehouse-robots-70-per-cent-work-technology-logistics-2017-9

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.