Skip to main content

Cognition and Bayesian

There is a growing consensus that the brain uses Bayesian to perform cognition. Our brain is capable of learning using only positive examples, unlike the approach taken in machine learning where there is a need to provide both positive and negative examples. Consider an example where a parent says to a child “Look at that dog!” A child is capable of categorizing all future dogs it looks at from only one or two examples. The brain of that child is generalizing using some form of Bayesian inference. Welcome to the world of One Shot Learning.

The discovery that Bayes himself abandoned for unknown reasons, today stands at the forefront of making Artificial Intelligence a reality. Learning from few examples is what we are good at, and any intelligent machine is expected to do. Thanks to Pierre Simon Laplace who rediscovered it and gave Bayes' theorem a mathematical form, cognitive AI research uses Bayesian to make machines learn.

                                                               Fig: Bayes Theorem


The above formula calculates the probability of a hypothesis given new data. Any Artificial Intelligent System should use this fact to update its beliefs when new evidence arrives.

Eric G. Miller, Nicholos E. Matsakis and Paul A. Viola suggested that using probability density over the set of transforms may be shared by many classes, and demonstrated how using this density as “prior knowledge” , a classifier based on a single training example for each class can be developed. [1]

Li Fei-Fei, Rob Fergus and Pietro Perona presented a method for learning object categories from just a few images. In their Bayesian framework: Object categories are represented by probabilistic models, “prior” knowledge is represented as a probability density function on the parameters of these models. The “posterior” model for an object category is obtained by updating the prior in the light of one or more examples. They demonstrated this method on four diverse categories (human faces, airplanes, motorcycles, spotted cats). Three categories are first learnt from hundreds of training examples, and a “prior” is estimated from these. Then the model of the fourth category is learnt from 1 to 5 training examples, and is used to detect new exemplars a set of test images. [2]

Bayesian is used in many fields including animal learning, language processing and acquisition, visual scene perception and many more[3]. Leading universities have dedicated teams that are applying Bayesian techniques in cognition. Josh Tenenbaum and his Computational Cognitive Science group at MIT explores computational basis of many aspects of human cognition including learning concepts, judging similarity, learning word meanings and syntactic principles in natural language. They are betting on Bayesian techniques and their results closely matches with human subjects. The results of their research will have a huge impact in the world of Information Extraction, Virtual Assistants and robotics.

Generative models can be programmed in Church, a probabilistic programming language. Church makes it easy to develop generative models as it includes a novel language construct, the stochastic memoizer, which enables simple description of many complex non-parametric models. [4]

Although there is a debate as to whether the brain uses Bayesian, or whether Bayesian techniques closely mimics the cognitive learning features of the brain, applications of AI will use Bayesian, no matter which side wins.

References

[1] Miller, E.G.; Matsakis, N.E.; Viola, P.A., "Learning from one example through shared densities on transforms," in Computer Vision and Pattern Recognition, 2000. Proceedings. IEEE Conference on , vol.1, no., pp.464-471 vol.1, 2000

[2] L. Fei-Fei, R. Fergus, and P. Perona, “A Bayesian Approach to Unsupervised One-Shot Learning of Object Categories,” Proc. Ninth Int’l Conf. Computer Vision, pp. 1134-1141, Oct. 2003.

[3] Griffiths, T. L., Kemp, C., and Tenenbaum, J. B, "Bayesian models of cognition." In Ron Sun (ed.), Cambridge Handbook of Computational Cognitive Modeling. Cambridge University Press, (2008)

[4] Noah D. Goodman, Vikash K. Mansinghka, Daniel M. Roy, Keith Bonawitz, and Joshua B. Tenenbaum, "Church: a language for generative models," Proc. Uncertainty in Artificial Intelligence (UAI), 2008.
Labels:

Comments

Post a Comment

Popular posts from this blog

How is AI Saving the Future

Meanwhile the talk of AI being the number one risk of human extinction is going on, there are lot many ways it is helping humanity. Recent developments in Machine Learning are helping scientists to solve difficult problems ranging from climate change to finding the cure for cancer. It will be a daunting task for humans to understand enormous amount of data that is generated all over the world. Machine Learning is helping scientists to use algorithms that learn from data and find patterns. Below is a list of few of the problems AI is working on to help find solutions which otherwise would not have been possible: Cancer Diagnostics : Recently, scientists at University of California (UCLA) applied Deep Learning to extract features for achieving high accuracy in label-free cell classification. This technique will help in faster cancer diagnostics, and thus will save a lot of lives. Low Cost Renewable Energy : Artificial-intelligence is helping wind power forecasts of u...
6 comments
Read more

In the World of Document Similarity

How does a human infer whether two documents are similar? This question has dazzled cognitive scientists, and is one area under which a lot of research is taking place. As of  now there is no product that is able to match or surpass human capability in finding the similarity in documents. But things are improving in this domain, and companies such as IBM and Microsoft are investing a lot in this area. We at Cere Labs, an Artificial Intelligence startup based in Mumbai, also are working in this area, and have applied LDA and Word2Vec techniques, both giving us promising results: Latent Dirichlet Allocation (LDA) : LDA is a technique used mainly for topic modeling. You c an leverage on this topic modeling to find the similarity between documents. It is assumed that more the topics two documents overlap, more are the chances that those documents carry semantic similarity. You can study LDA in the following paper: https://www.cs.princeton.edu/~blei/papers/BleiNgJordan20...
Post a Comment
Read more

Understanding Generative Adversarial Networks - Part II

In "Understanding Generative Adversarial Networks - Part I" you gained a conceptual understanding of how GAN works. In this post let us get a mathematical understanding of GANs. The loss functions can be designed most easily using the idea of zero-sum games.  The sum of the costs of all players is 0.         This is the Minimax algorithm for GANs Let’s break it down. Some terminology: V(D, G) : The value function for a minimax game E(X) : Expectation of a random variable X, also equal to its average value D(x) : The discriminator output for an input x from real data, represents probability G(z): The generator's output when its given z from the noise distribution D(G(z)) : Combining the above, this represents the output of the discriminator when  given a generated image G(z) as input Now, as explained above, the discriminator is the maximizer and hence it tries to  maximize V(D, G) . The discriminator wa...
Post a Comment
Read more