Skip to main content

Understanding Neocortex to Create Intelligence

There are two approaches to create intelligence in machines. One is to understand how human brain creates intelligence and replicate the methods used by it to create AI. The other is to take a fresh engineering approach to create intelligence.

There is an ongoing debate as to which approach is feasible or better. Few companies like Numenta and Vicarious have invested in understanding the neocortex. They have launched successful applications that work on the principles of neocortex. Other big companies including Google, Microsoft and Facebook use deep learning to create applications that can do major classification tasks. But deep learning does not exploit the concepts of neocortex, and hence is not up to the mark to do continuous learning. The deep learning models first go through a training phase and then the models are used to do classification.

The idea that makes neocortex truly intelligent is that it is able to do pattern matching and prediction. This has given humans the ability to survive and dominate the animal kingdom. Neocortex, a thin layer formed on top of the brain, started gaining prominence in mammals. A rat with a small sheet of neocortex is able to travel in a maze. It uses the trained patterns to predict the route in a maze. Researchers like Jeff Hawkins, founder of Numenta, believes that this ability of neocortex if replicated in machines will make them truly intelligent. He has created an architecture that exploits the hierarchy of neocortex. He calls it Hierarchical Temporal Memory (HTM). HTM is able to perform online learning like humans, where it doesn't need to use stored models, but the models get updated continuously.

Other approaches that model the neocortex have come into prominence and have received funding. The Blue Brain Project conducted by IBM has received a funding of $4.9 million. The idea is to artificially link the neurons in the computer by placing thirty million synapses in their proper three dimensional position. The Human Brain Project funded by European Union is attempting to provide a collaborative informatics infrastructure and first draft rodent and human whole brain models. Henry Markram is the director of both Blue Brain Project and Human Brain Project. He believes that the best way to figure out something is to try to build it from scratch.

Researchers at CereLabs believe that both approaches are essential for the success of a true AI company. We are keeping a close eye on deep learning as well as architectures like HTM. Although deep learning is giving us ready made solutions to create intelligence on your data, architectures like HTM will provide general intelligence solutions. We are betting on General Artificial Intelligence based on the concepts of neocortex. Are you?

Labels:

Comments

Post a Comment

Popular posts from this blog

GPU - The brain of Artificial Intelligence

Machine Learning algorithms require tens and thousands of CPU based servers to train a model, which turns out to be an expensive activity. Machine Learning researchers and engineers are often faced with the problem of running their algorithms fast. Although initially invented for processing graphics in computer games, GPUs today are used in machine learning to perform feature detection from vast amount of unlabeled data. Compared to CPUs, GPUs take far less time to train models that perform classification and prediction. Characteristics of GPUs that make them ideal for machine learning Handle large datasets Needs far less data centre infrastructure Can be specialized for specific machine learning needs Perform vector computations faster than any known processor Designed to perform data parallel computation NVIDIA CUDA GPUs today are used to build deep learning image processing tools for  Adobe Creative Cloud. According to NVIDIA blog future Adobe applicati
7 comments
Read more

Understanding Projection Pursuit Regression

The following article gives an overview of the paper "Projection Pursuit Regression” published by Friedman J. H and Stuetzle W. You will need basic background of Machine Learning and Regression before understanding this article. The algorithms and images are taken from the paper. ( http://www.stat.washington.edu/courses/stat527/s13/readings/FriedmanStuetzle_JASA_1981.pdf )  What is Regression? Regression is a machine learning technology used to predict a response variable given multiple predictor variables or features. The main distinction is that the response to be predicted is any real value and not just any class or cluster name. Hence though similar to Classification in terms of making a prediction, it is largely different given what it’s predicting.  A simple to understand real world problem of regression would be predicting the sale price of a particular house based on it’s square footage, given that we have data of similar houses sold in that area in the past. T
1 comment
Read more

Anomaly Detection based on Prediction - A Step Closer to General Artificial Intelligence

Anomaly detection refers to the problem of finding patterns that do not conform to expected behavior [1]. In the last article "Understanding Neocortex to Create Intelligence" , we explored how applications based on the workings of neocortex create intelligence. Pattern recognition along with prediction makes human brains the ultimate intelligent machines. Prediction help humans to detect anomalies in the environment. Before every action is taken, neocortex predicts the outcome. If there is a deviation from the expected outcome, neocortex detects anomalies, and will take necessary steps to handle them. A system which claims to be intelligent, should have anomaly detection in place. Recent findings using research on neocortex have made it possible to create applications that does anomaly detection. Numenta’s NuPIC using Hierarchical Temporal Memory (HTM) framework is able to do inference and prediction, and hence anomaly detection. HTM accurately predicts anomalies in real
8 comments
Read more