Biomedical applications of machine learning have great potential, but they suffer from a lack of data problem. It's great to see Little championing for a more open database, hopefully it has a positive effect.<p>For more traditional machine learning research, there are common sets of data (i.e. MNIST for handwriting recognition) which serve to benchmark new algorithms.<p>The main problem with biomedical data is the difficulty of acquisition, and the fact that many researchers are afraid of discovering findings that they may have missed.
I really think this is cool, machine learning has a lot to offer the world and this can improve quality of life for lots of people. However, two things come to mind:<p>1. <a href="http://archive.ics.uci.edu/ml/datasets/Parkinsons" rel="nofollow">http://archive.ics.uci.edu/ml/datasets/Parkinsons</a>
Does it really take 5 years for research to go mainstream? Max Little's original research on this was published in 2007. I think if I were able to better diagnose Parkinson's I would want to get it out to the public as soon as possible.<p>2. Why the need for clinical testing? It's not like it's a drug. Last time I checked a voice recording wasn't something that had too many side effects.