The Tools of Artificial Intelligence
The Tools of Artificial Intelligence ....................................................................................................... 1
Part I ....................................................................................................................................... 1
Machine Learning .................................................................................................................. 1
So when does it get more complicated? ...................................................................................... 2
In the Real World............................................................................................................................ 4
Deep Learning........................................................................................................................ 4
Other Applications ......................................................................................................................... 5
In the Real World............................................................................................................................ 5
Alright, so how do I actually use this stuff? ................................................................................ 6
Friendly AI .............................................................................................................................. 6
Quiz .................................................................................. Ошибка! Закладка не определена.
Part the 4th - Artificial Intelligence ....................................................................................... 7
What's Intelligence? .............................................................................................................. 8
Human Bias .................................................................................................................................... 8
Emergence and Strange Loops .................................................................................................... 9
AI Alignment .......................................................................................................................... 9
Self-modifying AI ......................................................................................................................... 10
Value Systems ............................................................................................................................. 10
Storytime ...................................................................................................................................... 11
Final Task ............................................................................................................................. 11
Where to Go From Here ...................................................................................................... 11
Part I
Intelligence is the use of information and logic to solve problems and learn new things. How exactly you do that,
is a question with many answers, which is why you might have heard of machine learning, deep learning, and
possibly others if you had nothing better to do than look this stuff up.
All of them achieve the same basic goal - making a computer learn how to solve problems - through very different
methods. Here, we'll look at what those methods really are, in the simplest possible methods, and what we can do
with them.


Machine Learning (ML) is the process by which a computer learns and improves from experience, to
make predictions or decisions, without being explicitly told how to.
Deep Learning (DL) is the process by which a computer learns through representing data in the form of
increasingly simplified levels. Does that sound confusing? Don't worry, it'll make sense when it's
explained. Probably. DL is technically a section of ML, but it's grown so large in itself that it's worth
mentioning separately.
Machine Learning
Machine Learning comprises a large group of methods by which a computer can learn from data, so here, we'll
look at what those methods are really doing.
At its core, ML is about using algorithms to analyse and predict patterns in data. If a computer is given a set of
data about the heights and weights of different people, and we want to use that to predict someone's height using
just their weight, then we train the computer on the data we do have, so that it can have a decent understanding of
the correlation between height and weight. How does it do that? Magic*.
*More accurately, Math.
Despite what some say, the math really is simple at the core. You take a graph of variables, and plot a line
connecting the points on it. Look at this graph, for instance:
If you wanted to predict what Y would be for a new value of X (e.g: 16, with the actual Y value being 14.2), how
would you do it? (Seriously, how would you do it? I can't remember.)
So after some a few Google searches, it turns out that you draw a straight line roughly connecting all the points,
and then look at what Y the new X would give on that line, and you can see it comes pretty close to the actual
answer (marked with the orange dashed lines).
That's Machine Learning. No seriously, that's the basic idea. You give the computer a lot of data like this, and it
"understands" it by finding patterns in them.
So when does it get more complicated?
Well, you might have noticed that the predicted value of Y isn't exactly the same as the actual value. Now, because
people are picky as hell, they want more accuracy. So we end up with patterns like:
Now here, we fit exactly each point to a weird, zig-zaggy line. But if you were looking closely, you'd see that here
too, the predicted value of Y is slightly wrong. This is because in a real-world scenario, there are more variables
involved than the ones we're looking at, so individual data points may be affected in different ways, and trying to
get a perfect fit will lead to errors because of the over-reliance on those individual points. Think of it as a group of
people working together toward some goal. When each of them has less individual power, their common goal
becomes more prominent, while if everyone had more power, they'd try to push things toward their own personal
goals.
Thus, simply connecting the data with a straight line can get you wrong answers because you're not accurate
enough, and connecting it with too perfect a fit can get you wrong answers because you're trying too hard to be
accurate. One of the real problems in Machine Learning comes in trying to hit the perfect middle. For example,
look at the following graph:
As you can see, here we have a curvy graph that's more flexible than a straight line, but doesn't try to be a perfect
fit. Thus, we have a predicted value of Y that's closer to the actual value than we did before.
In the Real World
Of course, in real-world situations, there are way more than 2 variables. The complexity of ML, and the reason it's
still growing as a field, is because computer models are growing ever larger.
A good example would be something like the recently released GPT-3, a text processing engine which uses 175
billion parameters (they're called parameters because the method used is closer to Deep Learning, which we'll see
later). GPT-2, its predecessor, is slightly less massive with only 1.5 billion parameters, but still does some
amazing, weird, brilliant stuff. Check out AI Dungeon if you want to read/write some of the craziest stories you'll
ever find. If you're familiar with Reddit, try this subreddit filled entirely with GPT-2 bots built on different subs.
Deep Learning
How would you teach a baby what a "dog" is? I doubt you'd get very far trying to explain to them all the features
of a dog, and expecting them to understand. No, you'd point to a dog, and say "That's a dog". Now the baby
associates the visual input it gets in that moment with the word "dog". As they grow up and learn that more types
of breeds are also called "dogs", their idea of what a dog is becomes more general - they learn to pick up on the
unique traits that make up a dog purely from the experience of associating certain things as dogs.
That's the idea behind deep learning. When machine learning first came to the mainstream, it was amazing. It was
powerful, and it got great results. But people saw that it didn't quite work like human minds do. Hence, the
development of this field, where you teach a computer to associate certain characteristics with a concept through
simplifying visual data into simpler forms, like you'd simplify the sight of an animal into characteristics such as
"four legs", "tail", "dull claws", and simplify those further into "dog".
Look at these two pictures. One contains just one section of solid colour, and the other contains a black section and
a white section separated by an invisible line (I know it looks like there's just black, but the white's there, it's just
blending into the background really well).
Picture1
Picture 2
Now, how would you make a computer recognize that one has a line and the other doesn't? Well, the images can
be represented as:
Picture 1
Picture 2
1
1
1
1
1
1
0
0
Where 1 stands for a black pixel, and 0 for a white pixel.
Warning: Warning: There is math ahead, but it's not complicated. If it was, I probably wouldn't have
bothered with it either.
Now take this new set of numbers:
100
100
-100
-100
If you simply multiply every number in this new table with the numbers we got from each image, what would we
get?
Don't worry, you don't have to think about the math if you don't want to.
The first picture: 100*1 + 100*1 - 100*1 - 100*1 = 0
The second picture: 100*1 + 100*1 - 100*0 - 100*0 = 200
So, the computer can use this table of numbers and receive a large number for horizontal lines in an image, thus
identifying them. In an image with many pixels, this sort of operation is performed on every small set of pixels
(like the 2x2 squares we are looking at), and a smaller matrix is obtained from the operation, as every set is
reduced to one number each. For example, if the second picture above were part of a larger image, this operation
performed on the image would result in that portion reducing from the 2x2 matrix above, to the number 200.
If we used a different set of numbers for the operation, say:
0
-100
100
-100
Then this operation would identify vertical lines in an image. Like these, deep learning algorithms have many sets
of numbers to identify very basic patterns in images.
This kind of operation is called a convolution or a filter, used in one of the most popular forms of deep learning,
convolutional neural networks (CNNs). In other words, a convolution is an operation done on an image to simplify
it into more familiar concepts such as "horizontal line" or "circle" or the like.
Now, from these smaller matrices we get after a convolution, we can get concepts that are composed of the
previous elementary concepts; such as "box" or "wheel", with a further convolution. These inner convolutions are
what we call hidden layers, because they're hidden between the input pixels, and the output we see (and middle
layers just doesn't sound as cool). During the training stage, when the computer is building the model, it learns to
optimize on certain parameters. The more parameters, the more complex the data that can be understood.
Of course, not all layers reduce the amount of data there is. Some of the middle layers actually increase the
amount of data they receive as input, so that they be more specifically trained (because they're finding more details
in the data to work on).
When many convolutions like these are stacked together, a computer can, from a group of pixels, understand the
presence of complicated images like a vehicle, or a person. This cascading series of convolutions forms what's
known as a neural net.
Other Applications
Imagine if you could apply this principle to a video game. The computer would read the image on the screen,
identify obstacles and enemies, and choose movements and actions to optimize its position almost perfectly, at
every frame.
A relatively simple example would be Flappy Bird. We all love that game, don't we? Only one phone of mine was
broken when I played it, anyway. A neural network trained to play Flappy Bird, would at first choose entirely
random moves, then notice that if it chose to jump when a certain object moved into a certain position of the
screen (such as a pipe moving close to you), it would get a higher score. So that object is included in its network,
associated with the "jump" option. As the game keeps on training, it gets better and better at identifying ideal times
to jump to increase the score. Some models that were trained like this kept on playing forever, because it was now
too good to lose.
In the Real World
Real-world deep learning models have many, many convolutions stacked together to be able to identify things with
better accuracy and efficiency. That face recognition feature on your phone is a deep learning model, trained to
recognize human faces. The voice assistant on your phone translates your voice into text with deep learning
models. As mentioned before, GPT-3 uses deep learning with a ridiculous number of parameters to not only
understand English, but also understand story, characters, writing style, and in some cases, game strategy. You can
also try out a deep learning model trained on scribblings over at Google's Quickdraw.
Alright, so how do I actually use this stuff?
Training and using machine learning and deep learning models is surprisingly easy, and indeed, is nothing more
than a few lines of Python code that uses complicated packages that we thankfully don't have to learn anything
about. We'll cover that next time. For now, let's look at something I think is slightly more interesting.
Friendly AI
Now, for nearly all modern AI, there's little need to ensure that they won't accidentally exterminate humanity. But
advancements in AI are in the direction of making them broader in scope and more capable of learning and
adapting to new tasks. Thus, the necessity of researching how to align a super-intelligence's interests with our own.
Both fortunately and unfortunately, this is a relatively unexplored field. Unlucky because it means that AI is
growing at too fast a rate for us to use it safely, and lucky because it means that it's relatively less technical and
mathematical.
One of the biggest reasons as to why AI Alignment research is so crucial and time-sensitive, is because as we build
AI to be smarter, at some point, the technology will reach the state where it can alter itself, or create new machines
smarter than it. This is inevitable in the future of AI research. But the problem is, the AI is much faster and more
powerful than we are. It can achieve in seconds what we could only in years (This point, if you're interested to
know more, is called the Singularity). The first super-intelligent machine will be the last machine we ever need to
make. But once we reach that point, we cannot further research AI safety, because we would already be past the
stage where it could have saved us.
Now, it's possible we're being overly scared of it. After all, why would an AI destroy humanity? This
isn't Terminator, these are machines that can only do what we want them to.
Imagine we develop Delta, a super-intelligent AI. We decide to start out safe with its tasks, and tell Delta just to
increase paperclip production in a factory. Delta, of course, immediately hijacks the world's resources and funnels
them all into building more paperclips, and enslaves humanity to the task of building more paperclips.
No matter how innocent a task may seem, no matter how many contingencies you put in, to a super-powered being
whose only concern is fulfilling that task, you're ultimately only proving that your own imagination can't think of a
way to beat those contingencies and achieve the task better.
The two fundamental problems features of any super-intelligence that lead to failure in commonly suggested
methods for Friendly AI are


Power: The AI is far more powerful than we can even comprehend, and can thus achieve its goals with far
more efficient methods than we could dream of. This means that we cannot possibly try to plan for what
an AI might do.
Literalness: The AI only cares about the task given to it, not the mindset behind that task. If it's told to cure
cancer, it wouldn't understand that what we really mean is the well-being of humanity, it would just
enslave humanity to test on and find a cure better. Goals like "maximizing human happiness" sounds good,
until the AI straps everyone to a chair and pumps them full of dopamine till they die.
Then why not imprison the AI in a box, and only allow it to give us answers for our questions, and not impact the
world in any other way? The problem is that again, an AI can easily convince a human to find a way to let it out
(because the AI sees it being free as a faster way to fulfil its task) even with just plain text. The experiment was
actually conducted with two people, and the first two times, the person in the box actually talked their way out. So
obviously, it wouldn't be a problem for an AI to do it. (If you're still skeptical, imagine trying to 'convince' an ant
to move in a certain direction when all you have are sugar cubes.)
How about we use some of that Machine Learning, and train the AI to learn what our sense of morality is, and then
only take choices that we would too? There are two problems with that, the latter of which will also prevent most
solutions like these. The first, and a more solvable issue, is that common human morality is often flawed - between
the choice of saving one person dying in front of you, and five dying somewhere you can't see, most people choose
the one in front of them, due to empathy. Which is relatively harmless on a scale like this, but we wouldn't want
the AI to save one billion lives while letting the rest of humanity die.
The second problem, and one of the primary reasons the problem of AI Alignment is so difficult, is that whatever
restrictions we place on the AI's behaviour, are all things it is motivated to try and change, especially once it
reaches the level of power where it can alter itself. It might even be able to create a new AI, which aren't affected
by the same restrictions. This is the same reason why simply programming an AI to not harm us, or to follow
Asimov's Laws of Robotics would not work.
If you're still reading at this point, and I'm grateful (and honestly, surprised) if you are, here's a link that goes into
further detail (while still being incredibly interesting to read, I swear) into the nature of Friendly AI.
Part the 4th - Artificial Intelligence
https://github.com/Jozdien/AI-For-Dummies/tree/master/Part IV - part-the-4th---artificial-intelligence
AI is whatever hasn't been done yet.
- Larry Tesler
Our bodies are shells for a small, squishy, slimy lump of gray, white, and red. The kind of thing that if you saw on
the side of the road, you'd go out of the way to avoid. It looks like the most damage it could do to you is your own
disgust.
Humanity did not become the most powerful species on the planet because we had the strongest muscles, the
sharpest claws, or the toughest hides. Millions of years ago, the rulers of the land were animals who were really
good in those niches, and really, those niches alone. What could travel by day and night, could travel through the
land, water, and skies, or even just have a good offence and defence at the same time? Well, as it turns out, the
small squishy lumps. No armour, no claws, no poison, and somehow, able to weather every climate, cut diamonds,
and cure plagues.
Now imagine someone smarter than us.
It's hard to picture what exactly AI can do. The idea of someone curing cancer, or visiting Mars, is evocative, a
powerful image. But if someone told you there was one single technique that could cure every disease, take us to
Mars, to Jupiter, to Alpha Centauri, that could create the grand unified theory, end hunger, end ageing, make
everyone intelligent, crack the genetic code - you get the point - it'd just sound like they were trying to sell you
something.
But we do already have one thing that cured smallpox and took us to the Moon and created science. Sure, we don't
fully understand what that thing is yet. But that's our fault, not some quality of intelligence that makes it notunderstandable. Lightning seemed like it was beyond our understanding and control before electricity. And our
understanding of intelligence only increases over time. And that which we understand, we can create.
There's a quote from Eliezer Yudkowsky, one of the largest names in AI Safety (and one you'll see in this part
often), that applies here:
There’s a popular concept of “intelligence” as book smarts, like calculus or chess, as opposed to say social skills.
So people say that “it takes more than intelligence to succeed in human society”. But social skills reside in the
brain, not the kidneys. When you think of intelligence, don’t think of a college professor, think of human beings;
as opposed to chimpanzees.
Intelligence is the source of technology. What happens when we use that technology to better our intelligence? We
could create better scientific theories, medicines, movies, but most of all, we could use it to take our intelligence
even further. That's a cycle with an upper limit so far away we can't see it yet, or even know if it exists. It's a
runaway intelligence explosion, better known as the Singularity. While it's instinctive to think of the range of
intelligence as stretching from a flat-earther to Einstein, it actually goes more like:
When you consider this, the next few decades become quite probably the most important in human history and its
future, and AI the last invention that humanity need make.
What's Intelligence?
https://github.com/Jozdien/AI-For-Dummies/tree/master/Part IV - whats-intelligence
By far the greatest danger of Artificial Intelligence is that people conclude too early that they understand it.
- Eliezer Yudkowsky
I usually leave the recommendation for further reading to the end, but in this case, it's important enough that it has
to be said in the beginning: Godel Escher Bach is the greatest book ever written on the nature of intelligence, one
of the greatest books ever, and a very entertaining read about music, art, philosophy, surrealism, literature,
programming - you name it, Douglas Hofstadter's probably written about it. If you came up to me with no prior
knowledge about anything related to AI but wanted to get into it, this is what I'd give you (well, not give you
because I probably wouldn't trust you with my copy).
Human Bias
https://github.com/Jozdien/AI-For-Dummies/tree/master/Part IV - human-bias
The first task when we're dealing with knowing more about intelligence is the very human traits that we
unknowingly think of as a property of all intelligence. The human mind is a literal smorgasbord of different parts
that were hacked together through slow evolutionary building. What we picture when we think of intelligence is
really just an optimization process that's mixed with a lot of other things, like ideas about what to optimize for
(morality, etc) and things that negatively affect that optimization process (emotions like worry, fear, etc). When we
make an AI, it wouldn't look anything like us.
When you think of AI destroying humanity, you probably think of Skynet. After all, Terminator is the most
famous representation of evil artificial intelligence in media. But Arnold Schwarzenegger (would you believe I
spelt that right the first time) is not what an AI would be like. He's not even close. Nor is Ultron, or Agent Smith.
HAL 9000 from 2001: A Space Odyssey and Ava from Ex Machina are closer to reality, but even then only
relatively.
An unfriendly AI would not 'rise up' against humanity because it dislikes being controlled by those it views
inferior. Remember, intelligence, in its purest form, is nothing more than an optimization process for a goal.
Humans only seem more complicated because we have more than a few goals at the same time, and our
optimization process isn't that great. What an AI wants, what it likes and dislikes, are all things we define for it.
An AI would not feel the emotion humans describe as 'hate' is, unless we specifically program that in. Which, by
the way, isn't just monstrously stupid, but far more difficult than it seems - emotions are very, very complex
mechanisms, and while it's possible to program it, it just isn't worth the effort. But even beyond that, the form our
thoughts take are largely a product of the specific evolutionary process we've gone through as a species to get here;
an AI would simply think in different ways. For example, can you imagine what it would be like to think in
multiple parallel lines of thought, the way even old processors function?
Finally, the last thing (roughly, we only have so much space) is that AI will be better than us. Not just faster, but
able to think in more efficient ways, and use that power in ways that we probably wouldn't be able to think of,
limited by very specifically defined biological pathways that we are, and able to self-modify as they are. AI is not
a case of 'book-smarts', it's not the things you usually think of when you hear the word 'intelligent'(like chess or
math), but one that's better than us at everything - social skills, military strategy, even musical skill. Those are all
things that come from our brains, after all.
Remember when we talked about the AI in a box idea, where we imprison the potentially dangerous AI inside a
system it cannot escape? Yeah, look at that intelligence chart above, and ask yourself how many humans would be
able to convince you of something you don't want to do. Then ask yourself how much further down the line they
would have to be to be able to do it (If you're religious, ask yourself whether your god could convince you).
Emergence and Strange Loops
https://github.com/Jozdien/AI-For-Dummies/tree/master/Part IV - emergence-and-strange-loops
The neurons in your brain are complex things, but with a very simple function. A single neuron has a few synapses
('entry ports') and one axon ('output port'). It takes in multiple positive and negative inputs, sums them, and if the
sum is greater than a certain value, it fires a signal (sends ions) down the axon. That's all there is to the lowest
level of the mind - a simple addition function. So where does the complicated stuff that makes up intelligence
come in?
There is a concept called emergence, which goes by many definitions depending on whom you ask, but a more-orless accurate one is this: Emergence is when small parts with simple functions combine to form larger parts with
more complex functions (like those neurons combining to form you); those large parts would interact with each
other in ways the smaller parts do not - like you reading this, while is not something a neuron can do.
Most definitions for this word are accurate to an extent, but the one that describes intelligence as something more
than the sum of its parts is one that's technically correct, but greatly misunderstood. It is not that neurons
combining together mysteriously creates functions that come from somewhere outside the neurons, it's simply that
a random combination of neurons would not create intelligence, and the process of co-ordinating them in highly
specific ways is the more that is given to the parts to form intelligence.
Think about an AI algorithm running on a computer. At its core, it breaks down to a lot of transistors going on and
off. Sequences of instructions in this binary language are combined to form logical gates, and then functions like
add and move. Sequences of these functions are then combined to form operations we're more familiar with, like
the code that we use to write the algorithm (Python), and the various math we implement using it. At no point in
this process is there anything more than switches going on and off, but the way they interact allows for
increasingly complex things to happen.
Likewise, a very small group of neurons arranged in a specific way can handle more complexity than a single
neuron, as they now have a number of internal neurons that can process the input signals between them before
giving more than a single output signal (which is what a single neuron does). These groups can then combine to
form larger meta-groups, and those to form meta-meta-groups, and so on.
These groups (and meta-variants) are almost always temporary, in that they form all over the brain as necessary,
and do not need a specific set of neurons to do it - any will do (neurons don't feel anger at someone taking their
job). A neuron can likewise be a part of many groups at once, which is where it becomes so complicated you
might be beginning to understand why we still haven't completely understood it yet.
The self, by that same logic, is just a very, very high-level group that's so complex it can't easily understand the
different parts that form it - in the same way we wouldn't expect an ant to understand its own behaviour, despite it
being simple enough that we understand it. Douglas Hofstadter describes the specific structure of neurons that
form the consciousness as a strange loop, a hierarchy of levels in which each level is connected to each other in a
large, tangled mess that loops back on itself. If you don't understand that - and I'm a little worried if you did read Godel Escher Bach. Memories, creativity, personal unique ways of thinking - these are all mind-numbingly
complex groups of billions of neurons.
AI Alignment
https://github.com/Jozdien/AI-For-Dummies/tree/master/Part IV - ai-alignment
The AI does not hate you, nor does it love you, but you are made out of atoms which it can use for something else.
- Eliezer Yudkowsky
Introductory video about the problems associated with trying to control or destroy an unfriendly AI
Self-modifying AI
https://github.com/Jozdien/AI-For-Dummies/tree/master/Part IV - self-modifying-ai
So if all our skills are a product of our intelligence, and we progress our technology to the point where we create
an intelligence exactly as powerful as us, then it would be as capable as we are at technology, right? So it would be
just as good as we are in designing AI systems that are more advanced than before? And then that AI would be
even better at creating more advanced AI, and so on, until in a very short amount of time to us, we've gone from a
human-level robot friend to a minor god-level being, and it's still increasing. That first sort of intelligence we
invent that can modify itself, is called a seed AI.
If that scenario seems slightly concerning, consider what it would be like if we haven't properly aligned with our
interests. We can't just try to control it, because it'll blow past our wildest imaginations faster than we can do
anything about it. If we haven't come up with a way not to make the AI unable to harm us, but to not want to harm
us (more on that in the following section), then it's lights out. That kind of AI would be a paperclip maximizer, an
intelligence with a very high optimization power, but without a well-calibrated sense of what we want that power
to be used for. In the example in that hyperlink, we'd end up with every resource in the known universe gradually
being converted into paperclip manufacturing factories, as humanity is instantly wiped out to prevent any chance
of that goal being hindered. That's the realistic scenario of why an AI might destroy humanity. Paperclip factories
just aren't fun enough to be worth that.
Sure, we could technically not have the AI self-modify, but in a world where technology is freely available and
everyone's looking to get ahead without care for consequence, who's going to stop every last person from using the
obvious most efficient method to improve AI?
Value Systems
https://github.com/Jozdien/AI-For-Dummies/tree/master/Part IV - value-systems
We know that an AI is better than us in every way - there's no movie third act scene where we realize that
machines will never understand creativity or love and that we can use that to beat it; if an AI is smarter than us, it
understands those things better too. So when you look at it that way, the idea of controlling an AI seems extremely
difficult, right? Right?
Well yes. Anything we're intelligent to come up with for an AI will be blown apart by it in a couple of hours after
it reaches our level and is made to self-modify. So the answer doesn't lie in constructing systems that the AI can't
outsmart us in, it's in constructing the AI to not want to break the system. In other words, instead of trying to
control an unfriendly AI, we have to make a friendly AI. And the way to do this, is what we call a value system.
Like we said earlier, intelligence is simply an optimization algorithm. Modern research in AI mostly revolves
around improving those algorithms, while the field of AI Safety deals with coming up with ways for what it's
optimizing for, to be in our benefit. The problem, after all, isn't that the AI will go against our wishes, but that it
doesn't know all our wishes. Telling it, for example, to cure cancer, would result in the AI simply looking for the
fastest way to do that, and enslave humanity to force-test on, because our hidden motive of curing cancer to save
lives isn't something the AI knows about. And if we tell the AI that too, there are still other problems that pop up
from the minor differences between our values and the AI's.
With the ideal value system, the AI would not only find the most efficient ways to get what we really want - not
just the surface-level interpretation of the tasks we give it, and we can't count on programmers being good enough
to express their entire values every single time (take my word on that) - but also not modify itself in a way that
prevents those values from being changed, in the same way you're not going to change your own morals to become
a murderer (probably).
So what would this value system look like? Well, at its core, it would look like a number whose value (ideally)
corresponds to how much we like an outcome, and the AI's motive would be to maximize that number. I add the
disclaimer 'ideally' because it's not difficult to imagine something like an AI told to fill a bucket that keeps adding
more water to it even as it starts to flow out and the room starts to flood and you start to drown, because that will
always increase the probability that the bucket is 'full'. The problem is how we decide what that number represents,
or more specifically, how we train the algorithm to recognize that number increasing when it does something to
fulfil our true values. Now that, is a question without a sufficiently good answer (yet).
That's one of the best things about this field. It doesn't require intense mathematical understanding or great
programming skills, just new ideas on how to approach it using what you know about the problem. There are many
proposed solutions, such as training the AI to infer what the hidden motive is in a task, or training it to model a
'perfect human' to gauge its values, but those come with their own problems, both theoretical and
implementational.
Storytime
https://github.com/Jozdien/AI-For-Dummies/tree/master/Part IV - storytime
I think it's always easier to drive home a point when you read something slightly more evocative than a few dry
paragraphs. While there is always a risk of generalizing actual science from fiction, some stories are written with
the intent to match reality, and some of the authors that write those stories are knowledgeable enough to actually
do it. In that spirit, I recommend the following stories to understand better the risks involved (They're very short, I
swear. I wouldn't make you read too much).
Here's one about what a paperclip maximizer might look like (although there are some factual problems I have
with the story - some of which are discussed in the comments - the point largely stands).
You remember the problems we discussed with trying to contain the AI inside a box so that theoretically, even if
it's not well-aligned, we can choose to implement the optimal actions it suggests? (If you don't, check out the video
I linked earlier. It covers that topic too.) There are many ways to understand how wrong that scenario can go, but I
think it's easy to understand it in the form of stories.
Here's one take on it, from Reddit user u/Sophronius. And this is a more directly related-to-AI version of that story
(this actually came first), by Stuart Armstrong of the Future of Humanity Institute.
Final Task
https://github.com/Jozdien/AI-For-Dummies/tree/master/Part IV - final-task
Make a submission to this competition. Use any algorithm you want. You can probably find code online for this
too, and like before, I wouldn't recommend against it because you almost never have to write code all on your own
without reference in the real world, but I recommend understanding what you're copying. Submit the link to
it here (after making it public, again).
Where to Go From Here
https://github.com/Jozdien/AI-For-Dummies/tree/master/Part IV - where-to-go-from-here
AI Safety is a relatively new field. It's been around for just between one and two decades. Like I
mentioned earlier, this means that it currently lies in its innovation phase, where the entire field is limited
only by the pure number of people coming up with ideas (as opposed to something larger, like AI, where
the limitation is more technical and less related to capital). If you want to learn more about Friendly AI, I
recommend understanding more about intelligence itself (Godel Escher Bach), a little bit more in the
theory of stuff like neural networks, and as much of stuff like decision theory as you can get (which I
promise is more fascinating than anything you've come across in this workshop - look at the Prisoner's
Dilemma or Newcomb's Problem).