Wednesday, December 31, 2014
This is the time of the year when pundits make their 2015 predictions. But to make predictions about Data Science, shouldn't one use data? Here are four charts from Google Trends that show trending performance of various data science technologies. Apache Spark really is overtaking Apache Hadoop.
In this R vs. IPython Notebook chart, we should just gather the trends rather than the absolute magnitudes. "R" is notoriously difficult to Google for, and "R Cran" is just one of the many tricks R users employ to Google for information about R. And, sadly, Google Trends has no way to additively combine search trends together (e.g. "R Cran" OR "R Project"). But, we can still see that IPython Notebook is skyrocketing upward while R is sagging.
This is a little hard to read and requires some explaining. The former name for "Apache Storm" was "Twitter Storm" when Twitter first open-sourced Storm onto GitHub in 2011. But "Twitter Storm" has another common usage, which is a "storm of tweets" such as about a celebrity. I'm guessing about half the searches for "Twitter Storm" are for this latter usage.
The takeaway is that Storm got a two-year head start on Spark Streaming and has been chugging away ever since. Part of the reason is that Spark Streaming, despite the surge in popularity of base Spark, had a lot of catching up to do to Storm in terms of graceful handling of errors and graceful shutdown/restart. A lot of that is addressed in the new HA Spark Streaming features introduced in Spark 1.2.0, released a week ago.
But the other interesting trend is that the academic term "complex event processing" is falling away in favor of the more industry-oriented terms "Storm" and "Spark Streaming".
People forget that "Machine Learning" was quite popular back in the dot-com era. And then it started to fade. That is, until Geoffrey Hinton's invention of deep learning in 2006. That seems to have lifted the popularity of machine learning in general. Well, at least we can say there's a correlation.
The other interesting thing is the very recent (within the past month) uptick in interest in DeepMind. Of course there was a barrage of interest in October when the over-hyped headlines blared "mimics human". But I think people only this past month started getting past the hype and started looking at the actual DeepMind paper which is interesting because it shows how they added state to a neural network, and that that is how they achieved "short term memory".
Saturday, December 13, 2014
|Brain||Artificial Neural Network|
|Asynchronous||Global synchronous clock|
|Shaped waves||Scalar values|
|Storage and compute synonymous||Storage and compute separate|
|Training is a Mystery||Backpropagation|
|Adaptive network topology||Fixed network|
|Cycles in topology||Cycle-free topology|
The table above lists the differences between a regular artificial neural network (feed-forward non-spiking, to be specific) and a biological brain. An artificial neural network (ANN) is so far in architecture and function from a biological brain that attempts to simulate a brain in silicon go by a different term altogether: neuromorphic
In the table above, if the last row is modified to allow a neural network to have cycles in its network topology, then it becomes known as a recurrent neural network -- still not quite neuromorphic. But by also modifying the first row of the table to remove the global synchronous clock from neural networks, IBM's TrueNorth chip announced August 2014 claims the neuromorphic moniker. (Asynchronous neural networks are also called spiking neural networks (SNN), but TrueNorth combines the properties of both RNNs and SNNs.)
It has only 78 CABs (Computational Analog Block), contrasted to the largest FPGAs which have over one million logic elements. Researchers in 2013 were able to simulate 18 neuromorphic neurons with this RASP 2.9 analog FPAA chip.
The human brain has 100 billion neurons, so it would hypothetically take 100,000 TrueNorth chips to approach equivalence, based on number of neurons alone. Of course, the other factors, in particular the variable wave shape of biological neurons, would like put any TrueNorth simulation of a brain at a great disadvantage. A lot more information can be carried in a wave shape than in a single scalar value.In the diagram at the top of this blog post, the different wave shapes resulted from showing an animal lights spots of different diameters. An artificial neural network, in contrast, would require N number of output neurons to represent N different distinct diameters.
But with an analog FPAA, perhaps neurons that support wave shapes could be simulated, even if for now one may be limited to a dozen or so neurons. But then there is the real mystery: how a biological brain learns, and by extension how to train a neuromorphic system.