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What should worry us most about artificial intelligence: losing our jobs to cheaper labor or losing our lives to killer robots? The real threat may lie in yet another danger: losing our minds.
AI research is making great strides toward its long-term goal of human-level or superhuman intelligent machines. If it succeeds in its current form, however, that could well be catastrophic for the human race. The reason is that the “standard model” of AI requires machines to pursue a fixed objective specified by humans.
We are unable to specify the objective completely and correctly, nor can we anticipate or prevent the harms that machines pursuing an incorrect objective will create when operating on a global scale with superhuman capabilities. Already, we see examples such as social-media algorithms that learn to optimize click-through by manipulating human preferences, with disastrous consequences for democratic systems.
A new release from OpenAI shows how complex behavior emerges.
This week, leading AI lab OpenAI released their latest project: an AI that can play hide-and-seek. It’s the latest example of how, with current machine learning techniques, a very simple setup can produce shockingly sophisticated results.
Fake Text uses AI to analyze text and then generate incredibly detailed and realistic written responses to it, giving the impression that an exchange between humans is taking place. The AI analyses text patterns to put together disturbingly lucid text, typified by this Reddit thread.
Launched by leading global AI research lab OpenAI, Fake Text is already recognized as so potentially dangerous that even its inventors have publicly warned about it.
A toy project started to see how well a simple LSTM model can autocomplete python code.
It gives quite decent results by saving above 30% key strokes in most files, and close to 50% in some. We calculated key strokes saved by making a single (best) prediction and selecting it with a single key.
We do a beam search to find predictions, upto ~10 characters ahead. So far it's too inefficient, if you are wondering about editor integration.
Your new best friend built with an artificial neural network - olivia-ai/olivia
Check out a cool project that leverages Stack Overflow Data and Google's Cloud AI to predict what tags would work best on Stack Overflow questions.
This article focuses on using a Deep LSTM Neural Network architecture to provide multidimensional time series forecasting using Keras and Tensorflow - specifically on stock market datasets to provide momentum indicators of stock price.
The following article sections will briefly touch on LSTM neuron cells, give a toy example of predicting a sine wave then walk through the application to a stochastic time series. The article assumes a basic working knowledge of simple deep neural networks.
Generates random text from Markov chains of tagged source text.
An example text is included which was derived from Plato's Ion:
Have you already forgotten what you were saying?
A rhapsode ought to interpret the mind of the poet.
For the rhapsode ought to interpret the mind of the poet.
For the poet is a light and winged and holy thing,
and there is Phanosthenes of Andros,
and Heraclides of Clazomenae,
whom they have also appointed
to the command of their armies and to other offices,
although aliens, after they had shown their merit.
And will they not choose Ion the Ephesian to be their general,
and honour him, if he prove himself worthy?I recently wrote a Markov chain package which included a random text generator. The generated text is not very good.
The rest of this post covers the evolution of the main algorithm.
Otter is a smart note-taking app that empowers you to remember, search, and share your voice conversations. Otter creates smart voice notes that combine audio, transcription, speaker identification, inline photos, and key phrases. It helps business people, journalists, and students to be more focused, collaborative, and efficient in meetings, interviews, lectures, and wherever important conversations happen.
Everyone loves neural networks. Until they start criticising your code, and your worth as a person…
“Don’t think of the overwhelming majority of the impossible.”
“Grew up your bliss and the world.”
“what we would end create, creates the ground and you are the one to warm it”
“look and give up in miracles”
All the quotes above have been generated by a computer, using a program that consists of less than 20 lines of python code.
I originally wrote this paper in 1981 for a course in writing research papers at Rose-Hulman Institute of Technology. It was written on a DEC PDP-11/70 computer using the RUNOFF text formatting program, and having it on line from the beginning made it easy to save an electronic copy for future use. The instructor, Dr. Peter Parshall (of "Peter Parshall picked apart my perfect paper" fame), awarded the grade of A- to my work.
Measure intelligence
A software platform for evaluating and training intelligent agents across the world’s supply of games, websites and other applications.
Part 1 of 2: "The Road to Superintelligence". Artificial Intelligence — the topic everyone in the world should be talking about.
In this paper, we consider multi-pursuer single-superior-evader pursuit-evasion differential games where the evader has a speed that is similar to or higher than the speed of each pursuer. A new fuzzy reinforcement learning algorithm is proposed in this work. The proposed algorithm uses the well-known Apollonius circle mechanism to define the capture region of the learning pursuer based on its location and the location of the superior evader. The proposed algorithm uses the Apollonius circle with a developed formation control approach in the tuning mechanism of the fuzzy logic controller (FLC) of the learning pursuer so that one or some of the learning pursuers can capture the superior evader. The formation control mechanism used by the proposed algorithm guarantees that the pursuers are distributed around the superior evader in order to avoid collision between pursuers. The formation control mechanism used by the proposed algorithm also makes the Apollonius circles of each two adjacent pursuers intersect or be at least tangent to each other so that the capture of the superior evader can occur. The proposed algorithm is a decentralized algorithm as no communication among the pursuers is required. The only information the proposed algorithm requires is the position and the speed of the superior evader. The proposed algorithm is used to learn different multi-pursuer single-superior-evader pursuit-evasion differential games. The simulation results show the effectiveness of the proposed algorithm.
A Learning Invader for the “Guarding a Territory” Game
A Reinforcement Learning Problem
This paper explores the use of a learning algorithm in the “guarding a territory” game. The game occurs in continuous time, where a single learning invader tries to get as close as possible to a territory before being captured by a guard. Previous research has approached the problem by letting only the guard learn. We will examine the other possibility of the game, in which only the invader is going to learn. Furthermore, in our case the guard is superior (faster) to the invader. We will also consider using models with non-holonomic constraints. A control system is designed and optimized for the invader to play the game and reach Nash Equilibrium. The paper shows how the learning system is able to adapt itself. The system’s performance is evaluated through different simulations and compared to the Nash Equilibrium. Experiments with real robots were conducted and verified our simulations in a real-life environment. Our results show that our learning invader behaved rationally in different circumstances.
Deep Learning has had a huge impact on computer science, making it possible to explore new frontiers of research and to develop amazingly useful products that millions of people use every day. Our internal deep learning infrastructure DistBelief, developed in 2011, has allowed Googlers to build ever larger neural networks and scale training to thousands of cores in our datacenters. We’ve used it to demonstrate that concepts like “cat” can be learned from unlabeled YouTube images, to improve speech recognition in the Google app by 25%, and to build image search in Google Photos. DistBelief also trained the Inception model that won Imagenet’s Large Scale Visual Recognition Challenge in 2014, and drove our experiments in automated image captioning as well as DeepDream.