Many CPUs have been used in space craft, space stations and other such probes. Such CPUs must be highly reliable, and very durable. The temperatures in space, even with heaters, can vary widely. The radiation that a system is exposed too can be immense. So when designing one of these systems designers don't always use the latest and greatest microprocessor. They use a chip that has been tried and tested. That they KNOW will work.
A CPU for use in space must first be MIL-STD-883 (usually Class M or S, ground based is B). This means it has met the over 100 tests that the Department of Defense has developed to insure reliable operation. These tests include: thermal, mechanical, AC electrical and DC electrical tests as well as sampling requirements for individual wafer inspections.
Most CPUs that pass come from the center of a wafer. This eliminates edge defects and generally makes for a more radiation resistant device.
Also note that MOST spacecraft use many CPUs. Either for redundancy or to split tasks. Being able to separately control EACH component of a spacecraft is very important. This would be impossible if one CPU controlled them all. With each sub-system powered by its own CPU the sub-systems can be better controlled for power management and fault tolerance. (for example if one CPU dies it only would disable one instrument, not the whole spacecraft)
Spectrum: If we could turn now to the subject of big data, a theme that runs through your remarks is that there is a certain fool’s gold element to our current obsession with it. For example, you’ve predicted that society is about to experience an epidemic of false positives coming out of big-data projects.
Michael Jordan: When you have large amounts of data, your appetite for hypotheses tends to get even larger. And if it’s growing faster than the statistical strength of the data, then many of your inferences are likely to be false. They are likely to be white noise.
Spectrum: How so?
Michael Jordan: In a classical database, you have maybe a few thousand people in them. You can think of those as the rows of the database. And the columns would be the features of those people: their age, height, weight, income, et cetera.
Now, the number of combinations of these columns grows exponentially with the number of columns. So if you have many, many columns—and we do in modern databases—you’ll get up into millions and millions of attributes for each person.
Now, if I start allowing myself to look at all of the combinations of these features—if you live in Beijing, and you ride bike to work, and you work in a certain job, and are a certain age—what’s the probability you will have a certain disease or you will like my advertisement? Now I’m getting combinations of millions of attributes, and the number of such combinations is exponential; it gets to be the size of the number of atoms in the universe.
Those are the hypotheses that I’m willing to consider. And for any particular database, I will find some combination of columns that will predict perfectly any outcome, just by chance alone. If I just look at all the people who have a heart attack and compare them to all the people that don’t have a heart attack, and I’m looking for combinations of the columns that predict heart attacks, I will find all kinds of spurious combinations of columns, because there are huge numbers of them.
So it’s like having billions of monkeys typing. One of them will write Shakespeare.
Spectrum:Do you think this aspect of big data is currently underappreciated?
Michael Jordan: Definitely.
Spectrum: What are some of the things that people are promising for big data that you don’t think they will be able to deliver?
Michael Jordan: I think data analysis can deliver inferences at certain levels of quality. But we have to be clear about what levels of quality. We have to have error bars around all our predictions. That is something that’s missing in much of the current machine learning literature.
Apple announced a 5k display a few days ago solving the problem of driving 14.7 million pixels by eliminating conventional connections that aren't up to the task and building a computer inside the display to drive the screen directly with some custom silicon. Marco Armenti goes into some detail on what is, for most Apple desktop users, the most desirable Mac (assuming they can afford it). I'm going to avoid looking at one. Early reports comment on how beautiful the display is.