NC State, Cambridge, Stanford grad. Materials Scientist, Computer Engineer. Founder, CEO of Citrine Informatics (citrine.io). There is nothing better in this world that working daily with smart, motivated, honest people, and I am lucky that I get to do that on a daily basis. @gregmulholland on Twitter.
Submissions
Navigating Uncertainty and Parenting in 2020
citrine.io
1 points·by gjmulhol··0 comments
Can machine learning identify the next high-temperature superconductor?
pubs.rsc.org
1 points·by gjmulhol··1 comments
A huge team of Apple veterans is now making smart accessories for your car
theverge.com
4 points·by gjmulhol··0 comments
Introducing Jelly, a New Search Engine
medium.com
1 points·by gjmulhol··0 comments
The Biggest Transformation of the Auto Industry – Mark Fields, CEO Ford
medium.com
2 points·by gjmulhol··0 comments
Discovery of Pi in Quantum Mechanics a 'Cunning Piece of Magic'
science20.com
2 points·by gjmulhol··0 comments
President Obama’s Clean Power Plan Demands Materials Innovation
medium.com
1 points·by gjmulhol··0 comments
Shaping Tomorrow's Breakthrough Materials
gsb.stanford.edu
1 points·by gjmulhol··0 comments
Data Processing with Ruby, RabbitMQ, and Sneakers
citrine.io
6 points·by gjmulhol··0 comments
Getting started with the Jackson JSON processor
citrine.io
1 points·by gjmulhol··0 comments
Google: Security questions aren't secure
research.google.com
3 points·by gjmulhol··2 comments
Machine Learning for the Materials Scientist, Part 1: Data
citrine.io
5 points·by gjmulhol··0 comments
Installing Custom Plugins in Elasticsearch
citrine.io
4 points·by gjmulhol··0 comments
Building a custom analyzer in Lucene for Elastisearch
citrine.io
5 points·by gjmulhol··0 comments
What is going to happen in materials in 2015
medium.com
1 points·by gjmulhol··0 comments
First Materials Science Hackathon Happening at Materials Research Society
mrs.org
3 points·by gjmulhol··0 comments
The Hustle Quotient: At a Startup HQ IQ + EQ
medium.com
1 points·by gjmulhol··0 comments
My Dream Design Curriculum
medium.com
2 points·by gjmulhol··0 comments
Homestar Runner is Back
techcrunch.com
2 points·by gjmulhol··0 comments
Job: Full Stack Developers at Responsive Home Startup
I have very mixed feelings about this. It is an incredible amount of trust in Apple (both security and actual execution, esp given their history in AI), but it would be such a massive upgrade to individual user security that it is hard to comprehend.
As a personal anecdote, I had a very well-known investor tell me that my business would never make more than $5m per year (not venture scale). And this was even knowing that my business is a pretty standard enterprise software business model. We are well past that at this point, so I am proud to have proved him wrong.
One of the big questions is whether we can extract these minerals from existing devices that are out of use/have been disposed of. Ideally, that means that we are designing new devices with disassembly in mind. Everyone from Apple to Huawei is talking about doing this, but the proof will be in the pudding.
Some of the comments are slightly inaccurate. We actually don't know where all the deposits are, and there was just a large discovery of rare earth elements off the coast of Japan. Much rare earth extraction has been happening in China because of relatively lax environmental standards that have reached back decades, so the US, Australia, and many other countries with deposits can't compete due to the costs of labor and complying with environmental standards and regulations. Mountain pass is one of a few deposits in the US, and happens to be one of the larger ones that is sitting idle. There are lots of mines globally and could be more, but economically they need to be attractive.
Copper is another interesting element. It is exceedingly hard to extract from existing devices because it is buried inside the chips, boards, etc. You can recover about 25% of the copper in a device, or about 3% of the total mineral content of a device when you extract copper. Widespread copper mining has destroyed parts of the Atacama desert in Chile and is a really nasty process.
Finally, work has been going on for a long time to replace rare earth elements or dramatically reduce how much is used. In some cases, you need small amounts because you need those f-orbital electrons but you can get away with using creative coatings instead of large volumes of materials. In other cases, you can replace them by multi-layering other materials to approximate their performance. There are huge opportunities here, but we are a couple decades away from seeing any real sea change in what our devices are made of.
Building physics and chemistry domain specific machine learning systems at Citrine Informatics. It's the best of both the physical science world and the data science world, has really hard problems, works with big companies and has big contracts, and is a team where more than half the people have technical graduate degrees.
This article is spot on. Using AI and ML in an enterprise setting is about changing behavior and helping people to understand why the AI/ML system is making the recommendations (or whatever the output is) that it is. I have seen this in dozens of companies now: even great AI requires cultural change to succeed. And good cultural change can make only mediocre AI models into game changers.
I have had good luck using SE Asia (in our case Singpore) for serving the region. The undersea cable map (https://i.redd.it/eo6248sth0pz.png) shows that it is probably your best bet, but as magicbuzz says, it is likely too far for anything where latency makes to pay a major price, which is most things these days.
Focusing on quarterly goals or the like just glosses over this as an issue. It is important that men be willing to say "that isn't how we talk around here" or "man, that is a pretty crude thing to say to someone in a professional environment." If you have the power, comments like that should be met with "you're fired" because honestly, if someone in my workplace can't maintain some sense of decorum around women, how should I expect him (in this case, always a him) to behave around clients, investors, etc.
This is not something around which to dance lightly. It is far too common, and it only takes a few people willing to stand up and say that this isn't acceptable to break the norms of a bunch of guys just laughing along while a few say crude things.
Is this actually a setback? While AI will undoubtedly have an impact in medicine, Watson is an NLP system-cum-amalgamated marketing machine that from what I have seen has done little more than to undermine the promise of properly implemented AI technologies with a lot of marketing gibberish and half-delivered results.
"These are the new leads. These are the Glengarry leads. And to you they're gold, and you don't get them. Why? Because to give them to you would be throwing them away. They're for closers."
Yes, this happens to everyone, but this happens to women much much more. Even well intentioned managers at good companies can find themselves victims to cognitive bias, unintentionally. This article raises specific things to watch out for.
Note: I am a male, a manager, and occasionally guilty of such things.
I agree -- there was also a very "product v. engineering" vibe to it also. Maybe my company is different from most they see, but we don't have product doing code reviews for developers. The place tension can arise seems to be more around the fact that product always wants to move faster and engineering expresses that they can't always do that. I don't think either side is right all the time, but the way the article starts with "Winning battle = product giving into engineering", "losing battle = product being pushed into a better decision by engineering" screams to me of a lack of understanding that in a good organization each party has the best possible information for making decisions that represent the interests of the company from different perspectives, and these meetings are to figure out how those interests align or conflict and sort them out.
