New book: Fashion Tomorrow

I finally finished the book I started 2 years ago on future fashion, or rather future technologies relevant to the fashion industry.

It is a very short book, more of a quick guide at 40k words, less than half as long as my other books and covers women’s fashion mostly, though some applies to men too. I would never have finished writing a full-sized book on this topic and I’d rather put out something now, short and packed full of ideas that are (mostly) still novel than delay until they are commonplace. It is aimed at students and people working in fashion design, who have loads of artistic and design talent, but want to know what technology opportunities are coming that they could soon exploit, but anyone interested in fashion who isn’t technophobic should find it interesting. Some sections discussing intimate apparel contain adult comments so the book is unsuitable for minors.

It started as a blog, then I realised I had quite a bit more stuff I could link together, so I made a start, then go sidetracked, for 20 months! I threw away 75% of the original contents list and tidied it up to release a short guide instead. I wanted to put it out for free but 99p or 99c seems to be the lowest price you can start at, but I doubt that would put anyone off except the least interested readers. As with my other books, I’ll occasionally make it free.

Huge areas I left out include swathes of topics on social, political, environmental and psychological fashions, impacts of AI and robots, manufacturing, marketing, distribution and sales. These are all big topics, but I just didn’t have time to write them all up so I just stuck to the core areas with passing mentions of the others. In any case, much has been written on these areas by others, and my book focuses on things that are unique, embryonic or not well covered elsewhere. It fills a large hole in fashion industry thinking.

 

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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.

 

Artificial muscles using folded graphene

Slide1

Folded Graphene Concept

Two years ago I wrote a blog on future hosiery where I very briefly mentioned the idea of using folded graphene as synthetic muscles:

https://timeguide.wordpress.com/2015/11/16/the-future-of-nylon-ladder-free-hosiery/

Although I’ve since mentioned it to dozens of journalists, none have picked up on it, so now that soft robotics and artificial muscles are in the news, I guess it’s about time I wrote it up myself, before someone else claims the idea. I don’t want to see an MIT article about how they have just invented it.

The above pic gives the general idea. Graphene comes in insulating or conductive forms, so it will be possible to make sheets covered with tiny conducting graphene electromagnet coils that can be switched individually to either polarity and generate strong magnetic forces that pull or push as required. That makes it ideal for a synthetic muscle, given the potential scale. With 1.5nm-thick layers that could be anything from sub-micron up to metres wide, this will allow thin fibres and yarns to make muscles or shape change fabrics all the way up to springs or cherry-picker style platforms, using many such structures. Current can be switched on and off or reversed very rapidly, to make continuous forces or vibrations, with frequency response depending on application – engineering can use whatever scales are needed. Natural muscles are limited to 250Hz, but graphene synthetic muscles should be able to go to MHz.

Uses vary from high-rise rescue, through construction and maintenance, to space launch. Since the forces are entirely electromagnetic, they could be switched very rapidly to respond to any buckling, offering high stabilisation.

Slide2

The extreme difference in dimensions between folded and opened state mean that an extremely thin force mat made up of many of these cherry-picker structures could be made to fill almost any space and apply force to it. One application that springs to mind is rescues, such as after earthquakes have caused buildings to collapse. A sheet could quickly apply pressure to prize apart pieces of rubble regardless of size and orientation. It could alternatively be used for systems for rescuing people from tall buildings, fracking or many other applications.

Slide3

It would be possible to make large membranes for a wide variety of purposes that can change shape and thickness at any point, very rapidly.

Slide4

One such use is a ‘jellyfish’, complete with stinging cells that could travel around in even very thin atmospheres all by itself. Upper surfaces could harvest solar power to power compression waves that create thrust. This offers use for space exploration on other planets, but also has uses on Earth of course, from surveillance and power generation, through missile defense systems or self-positioning parachutes that may be used for my other invention, the Pythagoras Sling. That allows a totally rocket-free space launch capability with rapid re-use.

Slide5

Much thinner membranes are also possible, as shown here, especially suited for rapid deployment missile defense systems:

Slide6

Also particularly suited to space exploration o other planets or moons, is the worm, often cited for such purposes. This could easily be constructed using folded graphene, and again for rescue or military use, could come with assorted tools or lethal weapons built in.

Slide7

A larger scale cherry-picker style build could make ejector seats, elevation platforms or winches, either pushing or pulling a payload – each has its merits for particular types of application.  Expansion or contraction could be extremely rapid.

Slide8

An extreme form for space launch is the zip-winch, below. With many layers just 1.5nm thick, expanding to 20cm for each such layer, a 1000km winch cable could accelerate a payload rapidly as it compresses to just 7.5mm thick!

Slide9

Very many more configurations and uses are feasible of course, this blog just gives a few ideas. I’ll finish with a highlight I didn’t have time to draw up yet: small particles could be made housing a short length of folded graphene. Since individual magnets can be addressed and controlled, that enables magnetic powders with particles that can change both their shape and the magnetism of individual coils. Precision magnetic fields is one application, shape changing magnets another. The most exciting though is that this allows a whole new engineering field, mixing hydraulics with precision magnetics and shape changing. The powder can even create its own chambers, pistons, pumps and so on. Electromagnetic thrusters for ships are already out there, and those same thrust mechanisms could be used to manipulate powder particles too, but this allows for completely dry hydraulics, with particles that can individually behave actively or  passively.

Fun!

 

 

BAE Systems & Futurizon share thoughts on the future

I recently visited BAE Systems to give a talk on future tech, including the Pythagoras Sling concept. It was a great place to visit. Afterwards, their Principal Technologist Nick Colosimo and I gave a joint interview on future technologies.

Here is the account from their internal magazine:

The Next Chapter

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.

 

 

We need to stop xenoestrogen pollution

Endocrine disruptors in the environment are becoming more abundant due to a wide variety of human-related activities over the last few decades. They affect mechanisms by which the body’s endocrine system generates and responds to hormones, by attaching to receptors in similar ways to natural hormones. Minuscule quantities of hormones can have very substantial effects on the body so even very diluted pollutants may have significant effects. A sub-class called xenoestrogens specifically attach to estrogen receptors in the body and by doing so, can generate similar effects to estrogen in both women and men, affecting not just women’s breasts and wombs but also bone growth, blood clotting, immune systems and neurological systems in both men and women. Since the body can’t easily detach them from their receptors, they can sometimes exert a longer-lived effect than estrogen, remaining in the body for long periods and in women may lead to estrogen dominance. They are also alleged to contribute to prostate and testicular cancer, obesity, infertility and diabetes. Most notably, mimicking sex hormones, they also affect puberty and sex and gender-specific development.

Xenoestrogens can arise from breakdown or release of many products in the petrochemical and plastics industries. They may be emitted from furniture, carpets, paints or plastic packaging, especially if that packaging is heated, e.g. in preparing ready-meals. Others come from women taking contraceptive pills if drinking water treatment is not effective enough. Phthalates are a major group of synthetic xenoestrogens – endocrine-disrupting estrogen-mimicking chemicals, along with BPA and PCBs. Phthalates are present in cleaning products, shampoos, cosmetics, fragrances and other personal care products as well as soft, squeezable plastics often used in packaging but some studies have also found them in foodstuffs such as dairy products and imported spices. There have been efforts to outlaw some, but others persist because of lack of easy alternatives and lack of regulation, so most people are exposed to them, in doses linked to their lifestyles. Google ‘phthalates’ or ‘xenoestrogen’ and you’ll find lots of references to alleged negative effects on intelligence, fertility, autism, asthma, diabetes, cardiovascular disease, neurological development and birth defects. It’s the gender and IQ effects I’ll look at in this blog, but obviously the other effects are also important.

‘Gender-bending’ effects have been strongly suspected since 2005, with the first papers on endocrine disrupting chemicals appearing in the early 1990s. Some fish notably change gender when exposed to phthalates while human studies have found significant feminizing effects from prenatal exposure in young boys too (try googling “human phthalates gender” if you want references).  They are also thought likely to be a strong contributor to greatly reducing sperm counts across the male population. This issue is of huge importance because of its effects on people’s lives, but its proper study is often impeded by LGBT activist groups. It is one thing to champion LGBT rights, quite another to defend pollution that may be influencing people’s gender and sexuality. SJWs should not be advocating that human sexuality and in particular the lifelong dependence on medication and surgery required to fill gender-change demands should be arbitrarily imposed on people by chemical industry pollution – such a stance insults the dignity of LGBT people. Any exposure to life-changing chemicals should be deliberate and measured. That also requires that we fully understand the effects of each kind of chemical so they also should not be resisting studies of these effects.

The evidence is there. The numbers of people saying they identify as the opposite gender or are gender fluid has skyrocketed in the years since these chemicals appeared, as has the numbers of men describing themselves as gay or bisexual. That change in self-declared sexuality has been accompanied by visible changes. An AI recently demonstrated better than 90% success at visually identifying gay and bisexual men from photos alone, indicating that it is unlikely to be just a ‘social construct’. Hormone-mimicking chemicals are the most likely candidate for an environmental factor that could account for both increasing male homosexuality and feminizing gender identity.

Gender dysphoria causes real problems for some people – misery, stress, and in those who make a full physical transition, sometimes post-op regrets and sometimes suicide. Many male-to-female transsexuals are unhappy that even after surgery and hormones, they may not look 100% feminine or may require ongoing surgery to maintain a feminine appearance. Change often falls short of their hopes, physically and psychologically. If xenoestrogen pollution is causing severe unhappiness, even if that is only for some of those whose gender has been affected, then we should fix it. Forcing acceptance and equality on others only superficially addresses part of their problems, leaving a great deal of their unhappiness behind.

Not all affected men are sufficiently affected to demand gender change. Some might gladly change if it were possible to change totally and instantly to being a natural woman without the many real-life issues and compromises offered by surgery and hormones, but choose to remain as men and somehow deal with their dysphoria as the lesser of two problems. That impacts on every individual differently. I’ve always kept my own feminine leanings to being cyber-trans (assuming a female identity online or in games) with my only real-world concession being wearing feminine glasses styles. Whether I’m more feminine or less masculine than I might have been doesn’t bother me; I am happy with who I am; but I can identify with transgender forces driving others and sympathize with all the problems that brings them, whatever their choices.

Gender and sexuality are not the only things affected. Xenoestrogens are also implicated in IQ-reducing effects. IQ reduction is worrying for society if it means fewer extremely intelligent people making fewer major breakthroughs, though it is less of a personal issue. Much of the effect is thought to occur while still in the womb, though effects continue through childhood and some even into adulthood. Therefore individuals couldn’t detect an effect of being denied a potentially higher IQ and since there isn’t much of a link between IQ and happiness, you could argue that it doesn’t matter much, but on the other hand, I’d be pretty miffed if I’ve been cheated out of a few IQ points, especially when I struggle so often on the very edge of understanding something. 

Gender and IQ effects on men would have quite different socioeconomic consequences. While feminizing effects might influence spending patterns, or the numbers of men eager to join the military or numbers opposing military activity, IQ effects might mean fewer top male engineers and top male scientists.

It is not only an overall IQ reduction that would be significant. Studies have often claimed that although men and women have the same average IQ, the distribution is different and that more men lie at the extremes, though that is obviously controversial and rapidly becoming a taboo topic. But if men are being psychologically feminized by xenoestrogens, then their IQ distribution might be expected to align more closely with female IQ distributions too, the extremes brought closer to centre.  In that case, male IQ range-compression would further reduce the numbers of top male scientists and engineers on top of any reduction caused by a shift. 

The extremes are very important. As a lifelong engineer, my experience has been that a top engineer might contribute as much as many average ones. If people who might otherwise have been destined to be top scientists and engineers are being prevented from becoming so by the negative effects of pollution, that is not only a personal tragedy (albeit a phantom tragedy, never actually experienced), but also a big loss for society, which develops slower than should have been the case. Even if that society manages to import fine minds from elsewhere, their home country must lose out. This matters less as AI improves, but it still matters.

Looking for further evidence of this effect, one outcome would be that women in affected areas would be expected to account for a higher proportion of top engineers and scientists, and a higher proportion of first class degrees in Math and Physical Sciences, once immigrants are excluded. Tick. (Coming from different places and cultures, first generation immigrants are less likely to have been exposed in the womb to the same pollutants so would not be expected to suffer as much of the same effects. Second generation immigrants would include many born to mothers only recently exposed, so would also be less affected on average. 3rd generation immigrants who have fully integrated would show little difference.)

We’d also expect to see a reducing proportion of tech startups founded by men native to regions affected by xenoestrogens. Tick. In fact, 80% of Silicon Valley startups are by first or second generation immigrants. 

We’d also expect to see relatively fewer patents going to men native to regions affected by xenoestrogens. Erm, no idea.

We’d also expect technology progress to be a little slower and for innovations to arrive later than previously expected based on traditional development rates. Tick. I’m not the only one to think engineers are getting less innovative.

So, there is some evidence for this hypothesis, some hard, some colloquial. Lower inventiveness and scientific breakthrough rate is a problem for both human well-being and the economy. The problems will continue to grow until this pollution is fixed, and will persist until the (two) generations affected have retired. Some further outcomes can easily be predicted:

Unless AI proceeds well enough to make a human IQ drop irrelevant, and it might, then we should expect that having enjoyed centuries of the high inventiveness that made them the rich nations they are today, the West in particular would be set on a path to decline unless it brings in inventive people from elsewhere. To compensate for decreasing inventiveness, even in 3rd generation immigrants (1st and 2nd are largely immune), they would need to attract ongoing immigration to survive in a competitive global environment. So one consequence of this pollution is that it requires increasing immigration to maintain a prosperous economy. As AI increases its effect on making up deficiencies, this effect would drop in importance, but will still have an impact until AI exceeds the applicable intelligence levels of the top male scientists and engineers. By ‘applicable’, I’m recognizing that different aspects of intelligence might be appropriate in inventiveness and insight levels, and a simple IQ measurement might not be sufficient indicator.

Another interesting aspect of AI/gender interaction is that AI is currently being criticised from some directions for having bias, because it uses massive existing datasets for its training. These datasets contain actual data rather than ideological spin, so ‘insights’ are therefore not always politically correct. Nevertheless, they but could be genuinely affected by actual biases in data collection. While there may well be actual biases in such training datasets, it is not easy to determine what they are without having access to a correct dataset to compare with. That introduces a great deal of subjectivity, because ‘correct’ is a very politically sensitive term. There would be no agreement on what the correct rules would be for dataset collection or processing. Pressure groups will always demand favour for their favorite groups and any results that suggest that any group is better or worse than any other will always meet with objections from activists, who will demand changes in the rules until their own notion of ‘equality’ results. If AI is to be trained to be politically correct rather than to reflect the ‘real world’, that will inevitably reduce any correlation between AI’s world models and actual reality, and reduce its effective general intelligence. I’d be very much against sabotaging AI by brainwashing it to conform to current politically correct fashions, but then I don’t control AI companies. PC distortion of AI may result from any pressure group or prejudice – race, gender, sexuality, age, religion, political leaning and so on. Now that the IT industry seems to have already caved in to PC demands, the future for AI will be inevitably sub-optimal.

A combination of feminization, decreasing heterosexuality and fast-reducing sperm counts would result in reducing reproductive rate among xenoestrogen exposed communities, again with 1st and 2nd generation immigrants immune. That correlates well with observations, albeit there are other possible explanations. With increasing immigration, relatively higher reproductive rates among recent immigrants, and reducing reproduction rates among native (3rd generation or more) populations, high ethnic replacement of native populations will occur. Racial mix will become very different very quickly, with groups resident longest being displaced most. Allowing xenoestrogens to remain is therefore a sort of racial suicide, reverse ethnic cleansing. I make no value judgement here on changing racial mix, I’m just predicting it.

With less testosterone and more men resisting military activities, exposed communities will also become more militarily vulnerable and consequently less influential.

Now increasingly acknowledged, this pollution is starting to be tackled. A few of these chemicals have been banned and more are likely to follow. If successful, effects will start to disappear, and new babies will no longer be affected. But even that will  create another problem, with two generations of people with significantly different characteristics from those before and after them. These two generations will have substantially more transgender people, more feminine men, and fewer macho men than those following. Their descendants may have all the usual inter-generational conflicts but with a few others added.

LGBTQ issues are topical and ubiquitous. Certainly we must aim for a society that treats everyone with equality and dignity as far as possible, but we should also aim for one where people’s very nature isn’t dictated by pollution.