Numenta is very much alive. Grok is available in the form of an AMI on the AWS EC2 marketplace, and if you're curious about how it all works under the hood, NuPIC, the core implementation is free software, available at https://github.com/numenta/nupic
FWIW, there is no learning on-chip. Machine learning is not the goal of this project, nor is its success dependent on it's learning capabilities (at least not at this phase). Where it does succeed, however, is in low-power computation in an architecture that is scalable and fault tolerant. LeCunn is criticizing an orange for not tasting like an apple.
Learning may not happen on-chip, but the network is still learned, and the performance of the chip is dependent on the learning. The spiking architecture of the chip means that the best learning algorithms can't be used. An ASIC implementing a convolutional neural net could also be low-power, scalable, and fault-tolerant, while taking advantage of the best currently known learning algorithms and ultimately performing a lot better on real tasks.
In one of my previous roles as a software engineer at a local, independent office supply company, this would have been a compelling idea. They ran a small fleet of leased delivery trucks, and employed drivers hourly, so as a company there is an incentive to reduce delivery costs, and the benefits. At $29 per vehicle per month, it seems like optimized routes would pay for itself relatively quickly. You'd need only to shave a few hours and miles off the routes per month to cover the cost, and then anything else is pure cost saving.
However, I think there are some caveats. Drivers were given all items at the beginning of the day with a set of destinations and a deadline. As long as everything was delivered by the end of the day, everyone was happy. Sometimes there were exceptions, and multiple trips needed to be made, or certain customers needed to be given priority at the discretion of the sales rep. Obviously, the drivers aren't interested in optimized routes, as that would cut their pay, and other factors would override the initial route decisions.
Best case scenario, the company is at a point where they are having to turn away customers, and they are able to increase their revenue by adding capacity through optimized routes.
Assuming your workers can periodically report on progress, Socket.IO may fit the bill, has multiple implementations and provides graceful degradation for older browsers.
NuPIC was released as free software, in part, because there has been a strong interest in the academic research community. I encourage you to check out the mailing list (http://lists.numenta.org/mailman/listinfo/nupic_lists.nument...) for some recent examples.
Generally, the most relevant academic community would be the NIPS community and I have not noticed any Numenta papers at NIPS, but please point me towards any I have missed if you are aware of any. I expect a lot of people have an opinion along these lines: http://developers.slashdot.org/comments.pl?sid=225476&cid=18...
Don't get me wrong, I would love to see numenta produce something of value for the ML community, but it doesn't look good so far.
On a serious note, it's not likely more/better reflective clothing would have helped. It sounds like the driver was deliberately and abruptly deviating from the normal lane to bypass traffic. It was not a decision made with the safety of others in mind.
I don't see how that comic is relevant to Manta, and it seems to be a bit of an unfair comparison. The Manta web site emphasizes the ability to use common and existing tools to work with the data. In addition to standard Unix pipes, they have bindings for pretty much every language I could think I want to use.
I.e. Manta seems to emphasize the ability to use existing tools in a distributed fashion, rather than forcing you to learn a new language with unfamiliar syntax.
