4.15.17 How do I beat the artificial intelligence?
By now you’ve probably heard the latest news about the development of AI-powered video games, and the latest predictions about the arrival of the first fully human-looking AI in history.
But what exactly is an AI and what can it do?
What are its strengths and weaknesses?
Is it capable of playing chess?
Does it have an appetite for human flesh?
And what is its motivation?
What can be done to ensure its continued development?
In this special episode of FourFourtwo, we look at some of the most pressing questions in AI research.
This is our fifth series of interviews, and we hope you enjoy the latest and greatest.
Read more: The AI’s Biggest Challenge for 2018 is How to Create a System that Works – Interview with Stephen HawkingHow does a fully human AI perform in an environment where it is constantly interacting with humans?
Will it be able to learn new things and create new things?
How does it interact with humans in ways that would make it an effective member of a team?
Will the AI learn new skills?
What are the limits of a fully self-aware AI?
How can it avoid being programmed to do certain things in the future?
How can the human mind be tricked into thinking that its own actions are somehow less valuable than others?
How will the AI be able of making mistakes that would be hard for a human?
Will an AI be better at finding a job than a human or can it work as a better employee than a robot?
How will an AI perform better at solving problems than a natural human?
How do you tell an AI from a human who is intelligent but doesn’t really know what it’s doing?
How do we determine if the AI is intelligent or not?
What can we learn from the human brain?
How much do the human and artificial brains have in common?
What is an intelligent system, and how does it make decisions?
What kind of a system does a robot have?
Is it possible to design an AI that is as good as a human, or just better?
How would an AI respond to a robot’s commands?
How does the human body respond to an AI’s commands and actions?
What will be the most interesting developments in the AI field in the next few years?
The answer is that we don’t know.
But one thing is for sure: it will be fascinating to watch.
Read the full transcript of the episode here: 4.14.17 What does AI really look like?
By David FincherThe most prominent case for AI is a computer system called AlphaGo.
AlphaGo was a human-level computer program that beat Lee Sedol, the world’s best Go player, in the 2017 Grand Prix in Beijing.
Alpha Go was programmed to play Go in an algorithm called “deep reinforcement learning” – a deep learning algorithm that can simulate complex, natural-language-based tasks, such as understanding the meaning of words, choosing words, and finding matches between similar words.
Alpha Google was a similar program that was used to help develop Google Glass.
Both AlphaGo and Google Glass are now obsolete, and Google is now working on a competitor to AlphaGo that uses neural nets to perform similar tasks.
In the near future, we may even see a competitor that uses deep learning and other neural networks to help identify and teach computers.
Deep reinforcement learning has a lot in common with machine learning, but it differs in important ways.
Deep reinforcement learning requires a system to learn from an input stream of data that contains many examples of that input.
A human being will have a huge amount of experience in doing this type of thing, and a deep reinforcement learning system needs to learn this knowledge from millions of examples.
This makes it much more difficult to design a machine that will learn anything useful from data that has not been previously used.
It is not a bad thing that a system learns this type (deep reinforcement) from millions and millions of data points.
However, this is what makes deep reinforcement so hard to design: there are thousands of possible inputs, and millions and thousands of different data points, which is not an easy task for a computer to process.
What makes deep learning more difficult is that the number of data items a system has to process in the first place is finite.
Deep learning, by contrast, can easily increase its processing capacity.
A system can learn to process data that is very large and is stored in memory, for example, and that it is much more likely to be used in situations where it can be used to solve problems.
In fact, one of the reasons we don´t have any artificial intelligence today is because we dont have enough data to train machines to do deep reinforcement.
We need more than a billion examples, and deep reinforcement is far too easy to train on.
The good news is that deep reinforcement-based systems can be trained using large data sets.
For example, there are two types of deep reinforcement algorithms.
One, called the “super