You’re watching ColdFusion TV.
Hi, welcome to another ColdFusion video.
At the end of last year,
I made a video about five new technologies
which most people didn’t expect to exist.
The episode included an AI that could look at a static image
and dreamed up a video of what it thought would happen in the next few seconds.
Yes, it created a video from a still image.
I went on to describe another AI that could
guess what’s happening in the scene
just by listening to the audio.
It was consistently better than humans at doing this.
And in the same video I talked about yet another AI
that could predict human behavior
with training only from watching sit-coms.
If you haven’t seen this video yet, you’re missing out.
The link to the video will be the first one in the description.
Today, we will be looking at another interesting AI,
imagine typing a descriptive sentence of the scene
and having an artificial intelligence
generating a convincing photo-realistic image
just from your text input.
This is just being created and that’s what we will take a look at today.
After this, I’ll share some of my wild thoughts
on artificial intelligence.
Let’s get straight into it.
Before we dive straight into the AI,
we need to build up a bit of context.
The type of AI that we are about to look at is called the neural-network.
That’s basically a computing system that’s modeled after the human brain.
There’re processing nodes that act as neurons and
the neuron layers behave as segments of the brain.
This concept of an idea is nothing new and has been around since the 1980s.
But it’s only become feasible in the last five years
due to GPUs with hundreds of cores being cheap and accessible.
In addition to this,
the available open source tools from usual networks
are making it easier to create,
pushing progress parabolically faster.
So some examples of neural networks include Google’s AlphaGo
and also things like this,
an AI that’s capable of describing images in text form.
Another interesting example of neural networks is WaveNet
and AI that can generate raw sound such as speech and music
Here are some examples,
this first example is speech generated by WaveNet with a twist
WaveNet wasn’t specified what to say,
so it had to make up its own language,
notice the breathing and natural lip-sounds.
This next clip is the music that WaveNet generated
同样 没有任何指令告诉它放什么音乐 也没有乐谱
again, it wasn’t told what to play, there was no musical score,
it just played whatever it wanted.
For this piece, WaveNet was trained
by listening to classical music pieces.
So this is great, but what if you want to take things further?
What if we combine two neural networks together and make them compete against each other
so that they can train and improve themselves without human intervention.
That’s what our featured AI called StackGAN is doing.
It uses one neural network to generate images
and another neural network within the same system
to decide if the images generated are real or fake.
What ends up happening is that the generative neural network improves itself generating images
based on the feedback given by the deciding worknet
and in the same stride, the deciding network
gets better at distinguishing what’s real and fake.
This creates a feedback loop of continuous improvement without human intervention.
The end process of this is the creation of a low resolution-synthesized image.
After this, a second stage takes place.
In the second stage,
the AI is told to clean up any defects in the original picture,
and the results are nothing short of stunning.
Yes, what you’re looking at are images created just by a written text description.
This type of dual network of AI is called the Generative Adversarial Network,
and it’s the same type of system that
allows the AI mentioned in my previous video
to dream up those videos when shown as a still image.
This form of artificial intelligence is actually brand new
and was only invented in 2014
and already I’m beginning to think that
this is one of the most powerful methods for machine learning.
The text image AI comes hot on the heels of an artificial intelligence
from Carnegie Mellon University
beating the world’s best players of Texas Hold’em poker.
I remember some people commenting on my deep mind video,
telling me that this exact event was something that
artificial intelligence has been struggling with for some time.
There’s also an AI that’s been released by Gamellon, a Boston company.
This one can rewrite its own code based on
experience and probabilities rather than hard variables.
It’s creators say that it could make the tedious part of coding of AI completely automatic.
To give you an idea how far some areas of AI progressing
Here is a clip from Jeff Dean, a senior fellow at Google’s research group,
giving a presentation at a TED Talk,
To take an example, the field of computer vision,
every year there is a contest where
teams compete to see who can give the right categories
out of a thousand different categories when giving an image.
And in 2011, before people were using neuron labs
the winning team got an error rate of 26％
which doesn’t sound too good when you think that humans are
5％ on this task.
但是 发展得很快 在短短五年时间
But, fastforward, just five years,
we are now at 3% errors,
using deep learning and much more computational power.
we are actually better than humans on this task.
So in a way, computers can now see and recognize objects
better than us for the first time ever,
this’s never happened before.
In the same talk, he goes on to give an example of
how unreliable human ophthalmologists were
for diagnosing certain eyes diseases.
In the experiments, any two human ophthalmologists
will only agree with each other’s diagnosis 60% of the time.
And what’s worse, if you give any single ophthalmologist
the exact same image that they diagnosed a few hours earlier,
they will only agree with themselves 65% of the time
2017年 在这个领域 已经证明
In 2017, artificial intelligence image recognition
has been proven to perform better than professional humans in this field.
As you’re seeing this video, things are progressing fast
and may have taken some of you by surprise.
即便是Alphabet公司的董事 Sergy Brin
Even the president of Alphabet, Sergey Brin,
is being taken by surprise by the entire phenomena of AI in general.
AI effort, but, I didn’t pay attention to it at all, to be perfectly honest,
你也知道 我自己是在90年代 经过训练后成为一名计算机科学家的
and you know, myself have been
trained as a computer scientist in the 90s,
everybody knew AI didn’t work.
并不是说 你知道 他们都试过了
It’s not like, you know, people tried it,
they tried neural nets, none of them worked out.
Yeah, this kind of revolution in deep nets
has been very profound and
definitely surprised me, even though I was like right in there.
It’s an incredible time and it’s very hard to forecast.
You know, what can these things do, we don’t really know the limits.
Everything we can imagine and more,
it’s a hard thing to think through and has really incredible possibilities.
It seems like playtime has over in regards to AI
especially with techniques like deep learning and neural networks
I think such things will cause social discomfort as they begin to encroach upon jobs.
There’s definitely both positives and negatives to this though,
but the question is, what does all of this mean
and what are we to do with all of these continual shifts and what we thought was possible.
In a general sense, artificial intelligence can be seen as a continuation
of the industrial revolution happening over the passed 200 years.
Even right now,
artificial intelligence is helping in the medical field
and making driving and other risky tasks safer.
[voiceover]…that is a question and I think…
[voiceover] That just saved my…
In the future, many mundane tasks can be handled by AI,
giving more free time for people to do creative things.
But, of course, and I’ve heard this argument before:
not everyone is creative.
Life needs to have meaning, and for some individuals,
that meaning comes in the form of their day-to-day, nine-to-five job.
I’ve been thinking about
making a video about possible financial solutions to AI disruption.
This includes concepts such as universal basic income
based on blockchain technology
and insights into how some researchers are starting to thinking about such things.
Now, of course, that some of you going to be thinking of a Skynet scenario
but right now, it’s still far too early to predict
Predicting the fully developed AI of the future
would be like trying to predict today’s computing industry
from the point of the view of the 1960s.
Personally I think what we all should actually be worried about
is who controls the AI.
If the strong artificial intelligence of the future
is open-source and available to everyone,
there’s a high probability that this will be a good thing.
Here is a small example of an open-source AI being put to good use:
A farmer in Japan used AI computer vision and robotics
to enable his cucumbers to be automatically selected and categorized,
saving him hundreds of hours.
The artificial intelligence driving his farm is called TensorFlow
and it’s open-source and was created by Google.
Maybe in the future, instead of working for a company,
there will be more common for people to run their own businesses
with their own personal AI acting as a proactivity aid.
So that’s an example of open-source AI being used in a good way,
if some of the best AI in the world is held in the hands of a few,
the probability of a good outcome overall drops significantly.
In the latter case, it might be a case of
whoever owns the AI makes the rules.
To stop AI getting into the wrong hands and to stop it becoming dangerous,
已经设立了Future of Life 这样的机构
institutions such as the Future of Life Institute have been set up.
It has tens of millions of dollars in funding,
and shows that people are already thinking about the next best steps for the future.
That’s pretty much the end of this video, and I’m gonna hand the ball to you guys.
What do you think of the progress being made by AI in the past few years?
Leave your thoughts in the comment section below.
总之 感谢收看 这里是GOGO 你正在收看的是ColdFusion
Anyway, thanks for watching guys, this’ve been GOGO, you’ve been watching ColdFusion.
Subscribe to this channel if you just stumble across it,
definitely get to check out my other video
in the Five New Technologies That You Wouldn’t Think to Exist Yet.
And of course, I’ll see you again soon for the next video.
Cheers guys, have a good one!