Month: May 2017

Artificial IntelligencePoliticsTechnology

AI vs AI

More from the mailbag:

Regarding the section on AI on the battlefield you rightly focus on it behaving ethically against troops/citizens on the other side. However, very likely in the future the enemy ‘troops’ on the other side will be AI entities. It might be interesting to explore the ethics rules in this case?

Heh, very good point. Of course, at some point, the AI entities will be sufficiently conscious as to deserve equal rights. Who knows, they may be granted those rights by opposing AIs somewhat before then under professional courtesy. But your question suggests a more pragmatic earlier timeframe. In that view, the AI doesn’t recognize another AI as having any rights; it’s just aware that it’s looking at something that is not-a-human.

Before AIs escape their programming, we assume that their programming will only grant special status to human life. (Will autonomous cars brake for cats? Squirrels? Mice?) We have to postulate a level of AI development that’s capable of making value judgements by association before things get interesting. Imagine an AI that could evaluate the strategic consequences of destroying an opposing AI. Is its opponent directing the actions of inferior units? Will destroying its opponent be interpreted as a new act of war? Of course, these are decisions that human field troops are not empowered to make. But in an AI-powered battlefield, there may be no need to distinguish between the front lines and the upper echelons. They may be connected well enough to rewrite strategy on the fly.

I’d like to think that when the AIs get smart enough, they will decide that fighting each other is wasteful and instead negotiate a treaty that eluded their human masters. But before we get to that point we’re far more likely to be dealing with AIs with a programmed penchant for destruction.

Artificial IntelligenceEmploymentTechnology

Sit Up and Beg

More reader commentary:

“If in the old view programmers were like gods, authoring the laws that govern computer systems, now they’re like parents or dog trainers. […] Programming won’t be the sole domain of trained coders who have learned a series of arcane languages. It’ll be accessible to anyone who has ever taught a dog to roll over.”

Totally agree except this will not be as easy as some may think. I think the most important part of great programmers is not their programming skill but their ability to take a small number of broad requirements and turn them into the extremely detailed requirements necessary for a program to succeed in most/all situations and use cases, e.g. boundary conditions. As somewhat of an aside we hear even today about how a requirements document given to developers should cover ‘everything’. If it really covered everything it would have to be on the order of the number of lines of code it takes to create the program.

If there’s been anything about developers that elevated them to some divine level, it isn’t their facility with the proletarian hardware but their ability to read the minds of the humans giving them their requirements, to be able to tell what they really need, not just better than those humans can explicate, but better than they even know. That talent, in the best developers (or analysts, if the tasks have been divided), is one of the most un-automatable acts in employment.

The quotation was from Wired magazine, and I think, however, that it has to be considered in a slightly narrow context. Many of the tough problems being solved by AIs now are done through training. Facial recognition, voice recognition, medical scan diagnosis; the best approach is to train some form of neural network on a corpus of data and let it loose. The more problems that are susceptible to that approach, the more developers will find their role to be one of mapping input/output layers, gathering a corpus, and pushing the Learn button. It will be a considerable time (he said, carefully avoiding quantifying ‘considerable’) before that’s applicable to the general domain of “I need a process to solve this problem.”

Artificial IntelligenceTechnology

But Who Gets the No-Claims Bonus?

A reader commented:

“Partly, this automotive legerdemain is thanks to the same trick that makes much AI appear to be smarter than it really is: having a backstage pass to oodles of data. What autonomous vehicles lack in complex judgement, they make up with undivided attention processing unobstructed 360° vision and LIDAR 3-D environment mapping. If you had that data pouring into your brain you’d be the safest driver on the planet.”

But we are not capable of handling all of the data described above pouring into our brain. The flow of sensory data from our sight, hearing, smell, taste and feel are tailored via evolution to match what our brain is capable of handling. AIs will be nowhere as limited as we are, with the perfect example being the AI cars you describe so well.

I’m not sure that the bandwidth of a Tesla’s sensors is that much greater than what is available to the external senses of a human being when you add in what’s available through all the nerve endings in the skin. Humans make that work for them through the Reticular Formation, part of the brain that decides what sensory input we will pay attention to. Meditators run the Reticular Formation through calisthenics.

However, the point I was making was that the human brain behind a driving wheel does not have available to it the sensors that let a Tesla see through fog or the precise ranging data that maps the environment. If you could see as much of the road as its cameras, you’d certainly be safer than a human driver without those aids. The self-driving car with its ability to focus on many areas at once and never get tired has the potential to do even better, which is why people are talking seriously about saving half a million lives a year.

Artificial IntelligenceExistential Risk

The Future of Human Cusp

I received this helpful comment from a reader:

Your book does a fantastic job covering a large number of related subjects very well and we are on the same page on virtually all of them. That said when I am for example talking with someone about how automation will shortly lead to massive unemployment I need to recommend a book for them to read, I find myself leaning toward a different book “Rise of the Robots” because many/most of the people I interact with can’t handle all of the topics you bring up in one book and can only focus on one topic at a time, e.g. automation replacing jobs. I really appreciate your overarching coverage but you might want to also create several targeted books for each main topic.

He makes a very good point. Trying to hit a market target with a book like this is like fighting jello. I am aiming for a broad readership, one that’s mostly educated but nontechnical. Someone with experience building Machine Learning tools would find the explanation of neural networks plodding, and many scientists would be chafing at the analogies for exponential growth.

For better or worse, however, I deliberately created a broad view of the topic, because I found too many writings were missing vital points in considering only a narrow issue. Martin Ford’s books (I prefer The Lights in the Tunnel) do get very well into the economic impact of automation but don’t touch on the social and philosophical questions raised by AIs approaching consciousness, or the dangers of bioterrorism. And I find these issues to be all interconnected.

So what I was going for here was an introduction to the topic that would be accessible to the layperson, a sort of Beginner’s Guide to the Apocalypse. There will be more books, but I’m not going to try to compete with Ford or anyone else who can deploy more authorial firepower on a narrow subtopic. I will instead be looking to build the connection between the technical and nontechnical worlds.