9. Towards abundant intelligence

This page contains the opening portion of Chapter 9 from
Sustainable Superabundance: A universal transhumanist invitation

tam graphic 9

9. Towards abundant intelligence

The sustainable superabundance that potentially lies ahead involves more than just a better environment – clean energy, nutritious food, and ample material goods (the subjects of earlier chapters). It involves more than just the radical enhancement of our bodily health (the subject of the previous chapter). Crucially, it also involves the radical enhancement of our brains, mind, and spirit. In other words, with the right choices and the right actions, we can look forward to a blossoming of all-round intelligence.

This intelligence will reside in three locations. First, our individual brains can be improved, and will work much better than before. Second, artificial intelligence, resident in all kinds of computing hardware, can jump upwards in capability. Third, the aggregate intelligence of whole societies of people can be upgraded, allowing groups to draw on collective insight to solve problems that would previously have defeated them.

None of this will take place automatically. Nor will increases in intelligence necessarily lead to beneficial scenarios, rather than to deadly scenarios. As with all the other spheres of abundance discussed in this Invitation, the actual outcome will depend critically on choices taken by humanity over the next few years.

A key complication is that merely becoming more intelligent is no guarantee that someone will become wiser. Far from it. Some of the world’s nastiest politicians are evidently highly intelligent; likewise some of the world’s most ruthless criminals. A given quantity of intelligence can be applied in service of any number of different goals – including destructive goals as well as constructive goals. Intelligence can be deployed to confuse and mislead – to cajole and bamboozle. Greater intelligence gives people greater ability to accomplish whatever objectives they have already decided to pursue – and greater ability to find clever justifications to promote those objectives.

Although we humans like to think of ourselves as rational beings, a better description in many cases is that we are rationalising beings. We are expert in finding reasons that support our preexisting choices. As modern online searches place more ever information at our disposal, the easier it becomes for us to discover special cases that appear to back up our own favourite worldviews. As we connect into ever wider online communities, the more we can come across people who seem to share our viewpoints, reassuring us that we are on the right lines. As for evidence that appears to contradict our views, and critics who disagree with us, the online world can provide us with ingenious reasons to disregard that evidence and critics.

True all-round intelligence will rise above such narrow intensity and blinkered reasoning. But reaching this level of intelligence will require a lot more than merely turbo-charging our existing modes of reasoning.

The rise of Artificial Intelligence

Intelligence can be defined as the ability to figure out how to accomplish goals. In simple environments with simple goals – for example, to win in a game of chess, or to find the quickest route between two locations on a map – the intelligence required is “narrow”. For more complex environments and more complex goals, intelligence needs more general capabilities.

Human intelligence involves being able to understand and predict the motion of both animate and inanimate objects. It involves the development of a “theory of mind” – an understanding of the factors that can motivate creatures with minds to change their own beliefs and behaviours. It involves the skill of breaking down a complex task into a series of subtasks. It involves being able to select and accumulate resources that could be of use at later stages. It involves the ability to collect more information, for example by designing and carrying out experiments, in order to make better decisions. It involves being able to learn from setbacks and surprises, rather than merely repeating the same actions over and over.

From the 1940s onward, various aspects of human intelligence have been duplicated in electronic computers – starting from code-breaking and the calculation of missile trajectories. Over the decades, so-called “expert systems” emerged, that could assist humans to carry out all kinds of different decisions.

In more recent times, a disruptive new wave of computer programs called “machine learning” has achieved surprising success, often dramatically surpassing the performance of expert systems. Machine learning software can in effect infer by itself the relationships between various sorts of input and output data. For example, to tell the difference between pictures of cats and pictures of dogs, an expert system would include large numbers of specific rules, entered individually by human programmers, along with information about exceptions to each rule. A machine learning system, in contrast, would be shown lots of pictures of cats and dogs, and would, via a process known as “training”, figure out a set of factors to distinguish the two cases.

Since the operation of successful machine learning involves numerous layers of simple binary decisions that have some elements in common with the on-off firing of neurons in the brain, the names “deep learning” and “neural networks” are commonly used.

Transhumanists anticipate that, just as expert systems have recently been overtaken in capability by the new wave of deep learning, so will deep learning be in turn overtaken in capability by yet new waves of artificial intelligence.

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A reliability index for politicians?

Reliability calcuator

Imagine there’s a reliability index (R) for what a politician says.

An R value of 100 would mean that a politician has an excellent track record: there is no evidence of them having said anything false.

An R value of 0 would mean that nothing they said can be trusted.

Imagine that R values are updated regularly, and are published in real-time by a process that is transparent, pulling together diverse sets of data from multiple spheres of discourse, using criteria agreed by people from all sides of politics.

Then, next time we hear a politician passing on some claim – some statistic about past spending, about economic performance, about homelessness, about their voting record, or about what they have previously said – we could use their current R value as a guide to whether to take the claim seriously.

Ideally, R values would also be calculated for political commentators too.

My view is that truth matters. A world where lies win, and where politicians are expected to bend the truth on regular occasions, is a world in which we are all worse off. Much worse off.

Far better is a world where politicians no longer manufacture or pass on claims, just because these claims cause consternation to their opponents, sow confusion, and distract attention. Far better if any time a politician did such a thing, their R value would visibly drop. Far better if politicians cared much more than at present about always telling the truth.

Some comparisons

R values would play roles broadly similar to what already happens with credit scores. If someone is known to be a bad credit risk, there should be more barriers for them to receive financial loans.

Another comparison is with the “page rank” idea at the heart of online searches. The pages that have incoming links from other pages that are already believed to be important, grow in importance in turn.

Consider also the Klout score, which is (sometimes) used as the measure of influence of social media users or brands.

Some questions

Evidently, many questions arise. Would a reliability index be possible? Is the reliability of a politician’s statements a single quantity, or should it vary from subject to subject? How should the influence of older statements decline over time? How could the index avoid being gamed? How should satire be accommodated?

Then there are questions not just over practicality but also over desirability. Will the reliability index result in better politics, or a worse politics? Would it impede honest conversation, or usher in new types of implicit censorship? Would the “cure” be worse than the “disease”?

Next steps

My view is that a good reliability index will be hard to achieve, but it’s by no means impossible. It will require clarity of thinking, an amalgamation of insights from multiple perspectives, and a great deal of focus and diligence. It will presumably need to evolve over time, from simpler beginnings into a more rounded calculation. That’s a project we should all be willing to get behind.

The reliability index will need to be created outside of any commercial framework. It deserves to be funded by public funds in a non-political way, akin to the operation of judges and juries. It will need to be resistant to howls of outrage from those politicians (and journalists) whose R values plummet on account of exposure of their untruths and distortions.

If done well, I believe the reliability index would soon have a positive impact upon political discourse. It will help ensure discussions are objective and open-minded, rather than being dominated by loud, powerful voices. It’s part of what I see as the better politics that is possible in the not-so-distant future.

There’s a lot more to say about the topic, but for now, I’ll finish with just one more question. Has such a proposal been pursued before?

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