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In science fiction, making robots too smart
never seems to end well. We see this in movies like the Terminator series,
to Ex Machina, to Blade runner. They seem to become downright evil once they’re powered up.
The fear behind these stories is that, if we create truly thinking machines, they might
figure out they don’t really need us any more. This fear of destructive forces unleashed by
efforts to create artificial beings is an old one – It’s what the book Frankenstein,
by Mary Shelly, published way back in 1818 was all about. But does it bear any resemblance to what’s
actually going on in AI research today? AI is becoming so pervasive now – from
self-driving cars to smart assistants, to medical diagnostics – that it’s time to ask:
what sort of machine minds will we be creating? Could they become self aware and conscious?
How will we get along with them – and what will they make of us? That’s coming up right now…
When AI began in the 1950s, many experts thought we’d have machines
that were clevere than humans within a few decades. Famed AI scientist, Marvin Minsky
of MIT proclaimed that “within a generation, I am convinced, the problems of creating ‘artificial
intelligence’ will be substantially solved.” Well, it’s turned out to be a lot harder
than he thought. One reason is that those early pioneers didn’t have a very realistic
notion of what “clever” or “intelligent” actually means. It’s one thing to make an AI
that can beat a human at chess, because that’s a pretty well defined task. In theory the machine
can figure out the best next move by crunching through all the possible moves and counter moves.
This is a task way beyond the human mind, but not so difficult for a fast computer.
However, translating languages or understanding text is a tougher challenge, because you often
need knowledge about context. Imagine this: Bob says “I’m leaving you”, and Alice replies, “Who
is she?” We can deduce the human drama playing out behind these words, but a machine is likely to be
clueless – it won’t know what “leaving you” actually implies or what the question
“who is she?” has to do with what Bob said. Sustaining a convincing conversation
is even harder for an AI. AI systems made for chatting might fool us for a short time,
but sooner or later they’re likely to give themselves away because of their formulaic and
nonsensical replies. If you don’t believe this, just try having a conversation with Alexa or Siri.
British mathematician Alan Turing, a pioneer of computer science and early AI, suggested in 1950
that an ability to hold a convincing conversation could be a litmus test for whether machines can
truly think. His reasoning was that If we can’t tell whether we're talking to a human
or a machine, how can we then justify saying that one can think but the other can’t? This is now known
as the Turing Test, and a version of it was used in the opening sequence of the movie Blade Runner
to identify the android replicants. But most scientists working in AI today
don’t think the Turing Test is a very useful measure of anything about machine minds.
It’s more a test of how gullible WE are. And the answer is we’re pretty gullible, because we’re
predisposed to interpreting all kinds of events as the products of other minds like ours. This
predisposition is similar to the way we tend to see faces in random shapes and shadows.
The Turing Test doesn’t tell us anything about what’s actually going on in a machine mind,
or whether it’s really a mind at all? No one believes that today’s computer systems,
however smart they seem, have any awareness. Early on in AI research, people tried to create
smart machines by programming them with rules for how to act when given particular stimuli. But
it's not clear we could ever make an AI system with the general, versatile abilities of humans
this way, because we’d never be able to think of rules for every situation.
Turing himself, however, suggested that it would be better to create AI the way we cultivate
intelligence in children: by teaching them. And that, loosely speaking, is pretty much
how most of today’s AI systems are made. They use what is called machine learning.
They contain a network of processors that act a little like the neurons in our brains, which learn
to produce the right output signals by altering the strength of the connections between them.
In machine learning, a set of training data is fed into the artificial neural network, and
the connections between the nodes of the network are adjusted until they produce the output we’re
looking for – for example, identifying whether an image is a picture of a school bus or not.
Once the system has been trained on enough data, it can reliably classify pictures it has never
seen before as “school bus” or “not school bus”. At least, that’s the idea. These systems often
work pretty well, but sometimes they make mistakes that seem idiotic, for example seeing school buses
in images that even a human toddler can tell are nothing of the sort. And they are easy to fool,
if you know how: an image-classifying AI might confidently identify as a peacock in a picture
that looks to us just like random screen static, but which has been cleverly chosen to fool it.
What machine-learning AI lacks is what we call common sense. But calling it that makes it sound
easy, even trivial – and to us it often is. But it’s very hard to pin down the rules we’re using
when we apply common sense – we just take it for granted. Many AI researchers now think we need to
get a better understanding of how the human mind works, like how we develop unspoken intuitions
such as common sense, if we’re going to make AI more reliable and less prone to dumb errors.
One of the keys to how we make decisions in a social context is that we assume other people
have minds similar to ours, but distinct. That’s how we anticipate what they’re likely to do
and how they’ll react, by taking into account that they are beings with similar minds
but with motivations and knowledge personal to them. Psychologists call this a “theory of mind”.
It’s not something we’re born with. For example, if you allow children to discover that a familiar
candy box actually contains pencils, and then you ask what their friend will think is in the box,
before looking inside it, three-year-old kids assume that the friend will know it has pencils
inside, just as they now do. But 5-year-olds recognize that the friend will be tricked,
just as they were. Three year olds haven’t
developed the idea that the other person has a separate mind that doesn’t know what they know.
They haven’t acquired a theory of mind yet. It’s likely that, if we want AI and robots
to work well alongside humans, we’ll need to somehow give them a theory of mind. That might
be essential for developing empathy: we need to be able to imagine ourselves in someone else’s shoes.
Some animals seem to use a theory of mind for deception: guessing what other animals will think
enables them to fool them, for example by hiding food. So it’s possible that AI with a theory of
mind will also have the capacity for cunning and deceptive behavior. When roboticist Alan Winfield
and his colleagues included a rudimentary “theory of mind” algorithm in their robots, they found
the machines started behaving competitively, and using more aggressive strategies. Wow!
Machine learning can get us a long way towards some pretty human-like behavior
in AI. Programs like this are now used to compose music and write poetry – and sometimes people find
it hard to tell the difference between this and what real humans produce. Here are some examples.
Can you tell which was written by Shakespeare and Which was written by an AI?
The first is the AI!
Here are two five second snippets of music. See if you can guess which one is written by Chopin.
…Again the first one was the AI. AI trained on Chopin’s music can
write a pretty good imitation of it, and likewise for AI trained on Shakespeare’s
sonnets. But these are one-trick machines: it’s no good expecting the Chopin AI to write a love poem,
or even to write a pop song. That’s because all the machine is doing is finding patterns
in the training data and then applying them in new examples. The Shakespeare AI will deduce
that the words “I love” are more likely to be followed by “thee” than by “custard.”
What an AI like this certainly isn’t doing is expressing any kind of sentiment.
It’s not thinking “This will move my readers.” It’s a zombie with no internal world – just an
algorithm that “knows” how to shuffle particular words into configurations like those in the
training data. If it’s only trained on Shakespeare, it is never going to write
a sonnet about a lovelorn computer, because it won’t find out about computers from Shakespeare.
Still, you could argue that these AI systems are showing a genuine kind of creativity,
coming up with outputs that surprise and even delight humans. That’s what happened with the
Go-playing AI, AlphaGo that beat Korean world champion Lee Sedol in 2016. Some of its moves
were totally new and unexpected, and even struck some Go experts as beautiful. This kind of machine
creativity can sometimes seem disconcerting. When researchers at the Facebook AI Research lab
set two chatbots negotiating with each other, they found that they had to tweak the system
to stop the bots veering away from human language and developing their own language.
Now don’t worry, this has nothing to do with the bots creating a secret language so that we don’t
know what they’re plotting – it’s just what we’d expect for a mindless machine-learning
system. AI might be getting creative with language because it’s a more efficient way to
communicate. it’s not getting sneaky or trying to hide something from its human overlords.
The hard question is whether there will ever be a moment when the lights go on for an AI: when it
becomes conscious, self-aware, something like what happened with Skynet in the Terminator movies.
Now, the honest answer to that is: nobody knows! No one has so far made an AI that can plausibly be
considered to be conscious. We don’t appear to be close to that. And while it’s quite possible that
we might soon develop AI systems and robots that act pretty convincingly as if they are conscious,
this would really be a sort of deep fake, using the information technology we have today.
But what about the technology of the future? There are differing views on this. Some argue that it’s
already feasible to imagine making electronic circuits such as artificial neural networks
that have about as many connections and nodes as there are between real neurons in the human brain.
If that’s so, why should such an artificial system lack any of the capacities of our
brains – including consciousness? After all, most scientists and many philosophers are convinced
that, whatever consciousness is, it arises out of the laws of physics and chemistry
that govern how our neurons work. It doesn’t require any mystical special sauce.
And if that’s so, why should it remain forever absent in massive computer simulations
that capture all the relevant physics and chemistry of brains? A simulation like that
is way beyond the computers we have right now, but it’s not impossible in the future.
In this viewpoint, the exponential growth in computer power will take us, soon,
to the point where the complexity of the machine gets close enough to that of the
brain for it to wake up and be self-aware. But others say consciousness just doesn’t work
that way. All we’d get is a simulation that gives the appearance of awareness. The machine won’t be
genuinely aware, any more than a simulation of water will make the insides of a computer wet.
The real problem is that we don't know what consciousness is, and so we have no idea
if it’s the kind of thing that can be wired into silicon circuits. Some researchers think it
arises from the way information processing in our brains is structured. According to a view called
the global workspace theory, consciousness appears when information from various brain
modules – for example, from vision, memory and emotions – is woven into an integrated picture
in a module called the workspace, and then broadcast out to the rest of the
brain. This sounds like an architecture you could build from computer circuits.
But another view, called integrated information theory, is that consciousness
is only possible if the elements handling the information are wired together in a pattern
quite different from that of today’s silicon chips: one that lets information that the brain
is processing be densely looped back on itself. There are other neuroscientists and philosophers
of mind who think that consciousness is intimately bound up with being alive:
that there’s something about biological wetware of cells and tissues that makes it able to host
consciousness. But between the idea that machines will never be
conscious and that one day they’ll wake up to the world, there’s a third possibility.
The boundaries between organism and machine is, and will continue to become blurred. Maybe, then,
we won’t be replaced by conscious machines, but will become them. Futurists such as Ray
Kurzweil think it will become possible to not only enhance our brains with non-organic prosthetics,
but also upload our minds into machines much as we upload files today.
Neurons have already been grown on silicon chips that can send and receive electrical
impulses to and from the electronics. And devices placed on the scalp to pick up the
electrical signals of the brain can allow machines to be controlled by thought alone.
In this way, some people can learn to operate artificial prosthetic limbs using their thoughts.
You might ask, what about future quantum computers? Will they give us some new ways to
link electronic components that better simulates the human brain? There is no reason to think
so because quantum computers provide advantages in only certain types of computational
challenges. I have a couple of videos on Quantum computing if you want to see the details.
There is no direct link that we know of between quantum physics and consciousness. Ideas like
this from scientists like Roger Penrose are interesting but fringe ideas. I also have a
video his ideas (ORCH OR theory), if you want to check it out. Many researchers suspect that the eventual
blending of our gooey brain-minds and inorganic computer hardware is almost inevitable.
If they’re right, then we being made of meat-only, could be just a passing phase. The philosopher
Susan Schneider thinks that the most advanced, “superintelligent” alien civilizations will
be not biological but “post-biological”: descended from biological organisms but now either blended,
like a cyborg with AI, or fully machine-like. What kinds of minds will these be? Schneider
argues that they will be based on the organic minds that originally made them. They’ll inherit
the same evolutionary impulses, like survival, reproduction, competition and cooperation.
This may happen to us.
The difference is that with the help of machine integration, we may gain the ability to use sonar,
to see heat, to sense magnetic fields, to communicate telepathically using wifi.
Our minds will be different. But since evolution has shaped our
minds to a large degree by the sensory inputs of our environment, perhaps this could lead
to a different kind of consciousness. We don’t know if the kind of consciousness we experience
currently is the only kind that can possibly exist. If global workplace theory is true,
and consciousness is really an integration of sensory inputs broadcast to the rest of the brain,
then perhaps these new sensory inputs to the brain could result in an enhanced consciousness,
even a kind of hive mind consciousness. Maybe we will become super-human. Or maybe we’ll
just be a more enhanced and effective version of ourselves.
We may become those machines that we fear in our movies and books. Or maybe the minds that are
partly or wholly machines will have a different kind of morality altogether. But ultimately,
they will be our creation. Let’s just hope that we use our new found powers for good rather than evil.
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