jonnycowboy

Not in this case. With SpaceX they also need high (de-entry, Mach heating) and low temperature (cryo fuel & oxygen) resistance but in the case of Boom the surface won’t heat enough to need high temp materials.

That said, I’m not sure their choice of materials is the good one for a startup.

In interaction with anything rigid (or even deformable), position control does not mean anything. Force (or torque) control defines the actual physical interaction.

Also interested in answers to these two questions, as well as OpenCL performance in vanilla linux (iMX6 and above).

Did you go to Sherbrooke?

Sad to hear, I demoed a system with a Tobii sensor and it performed very well (even with my glasses). We were wondering about the effect of the constant IR blasts (are they continuous or pulsing?), thanks for the input.

The thing is that there was a lot about those old systems that was slow, so you were very, very careful how you programmed. You tended not to use vast library stacks, you went close to the metal and you coded in languages like Assembler, COBOL or FORTRAN. I/O was often run through specialised co-processors (such as IBM's channel processors) and the terminals could sometimes help too.

I have friends who have been looking after legacy applications for an airline running on Unisys. The core apps for reservation, Cargo booking and weight/balance were written in FORTRAN. In recent times, the front end was written in Java to give web access. They tried to rewrite the core apps but it was impossible to do so and get the performance.

the real question of course would be how to train the AI to have varying levels of "expertise" (ie: penalize if score is too high)

Shorting insurance companies.

Very interesting, thanks for the share.

One point I noticed not mentioned yet is that the images used for training are only 64x64! In the original google "grasping" research, the images were 472 × 472, 54 times bigger! I think they are looking for "minimum visual information" required to trigger the required learning. This will help in mobile applications (ie: robotics, smartphones, etc) where processing power is severely limited.

I think this is mainly a "data" challege, how does Udacity plan on gaining an upper hand on this, especially with competitors like Uber and Tesla which gather data exponentially with each new ride/car sold?

Not anymore, at least with CRISPR.

Do you know how long it took to train using that dataset and with what hardware configuration?

I would say: automation & AI:

- automated cities & robotic workers - HARC working on the interfaces for the "workers" - basic income to replace minimum wage earners' incomes

Would love to work on this path!