2,681 karma · joined March 5, 2009
Before: Founder @ Steamship. Head of ML @ Instabase, YC Alum (S15), PhD @ MIT CSAIL, Google Research
Contact: - @edwardbenson - edward.benson@gmail.com
Websites: - https://edwardbenson.com - https://robotbookclub.com - https://kaya-lang.org
We know and understand your hesitation. It's coming this week. We'll have a free tier that we're aiming to make pretty broad in capability.
- Ted from Cloudstitch [ted@cloudstitch.io]
For a great example look at all the papers Andrew McCallum's group has been able to publish by building on top of FACTORIE: they get to focus their time on the problem at hand rather than all the math required to solve it. Basically they can write code that generates a model dynamically but the framework handles all the inference. Compare that to how these things are built without such a framework: you spend most of your time painstakingly hand-deriving update rules and then implementing them as code.
IMHO, the exciting thing is that ML is getting closer and closer to being an everyday tool for engineers rather than something that requires you to be a full-time math person to use effectively.
It may just be the PIs have a good track record..
E.g., boot up a bitcoin mining cluster (though this particular idea would obviously yield low return since it would be running on off the shelf hardware)
A telling moment that happened two nights ago for me. A friend from ~8 years ago came to visit and we wanted to send a joking message out to a few people from the old group of friends we used to hang out with.
The conversation was literally:
A: "Post it to Facebook and tag everyone." B: "Ehhh. It would be funnier to find an old email thread and reply to it."
We went with B. It seemed like a better idea, plus we weren't sure if folks in the group checked Facebook. But everyone reads email -- and sure enough, we got life updates from the whole gang within 12 hours.
People hate Comcast, and yet we all use it because (for many of us) there are no other options.
Sentiment is an important measure because it indicates the activation energy required to leave if a sufficient replacement emerges.
And for a "middle class" of blocked sites, while not technically blocked, the packet delay is so great most people (except a dedicated few) will just shrug and figure that cross-continent internet connections must be too slow to be practical. Accessible, but only if you're willing to wait a minute. A brilliant political play, because it's hard to pin a charge oppression on anyone, since the information is technically there, but so slow nobody will access it.
[1] edit: Because I'm not sure if I just made that phrase up or if it came from one of your papers, the idea that ML libraries that take declarative model descriptions are great, but what's even better is if we also have an imperative API that can dynamically generate those declarative specs for us, even based on train-time inputs, so we can essentially "program" the structure of a model but still benefit from keeping everything generalized and declarative at the base.
One question: can I deploy to my own server (for free) or only through your hosted service? It wasn't clear from the documentation I read online.
I'm not an audiophile by any means, but I also get the sense that many around me seem to have disdain for Bose. I also stumbled into possession of QC headphones and some Bose speakers for my house and think they're great. So I've always wondered at what the precisely stated and justified beef is.
As a tenure track professor, you don't really answer to many people. You have to beg for money (grants), constantly prove yourself (publications), and always be recruiting (students). Of course you also have to teach classes, do community service, and participate in department politics and initiatives. I'm not counting these as "answering to people," however, because none of them involve someone telling you to do something. They're all activities you must decide to do yourself, and then it's completely up to you how to do them. Is this what you meant by answering to lots of people, or something else?
The relevant academic joke is: "It's the best job ever! You can do whatever you want with your 80 hour weeks!"
[1] In CS, from my experience, postdocing is usually a 1-year "reset your brain" time before taking an academic job. I know in other fields it's often a mandatory many-year grunt-work experience.
If you're a graduating PhD headed into academia, you will be the boss of your group, with almost nothing above you in the org chart telling you what to do. So it is natural for a PhD headed into industry to start from a place of expecting the same---from their perspective.
But from the perspective of industry, the PhD's value isn't judged the same way. They've just spent six years doing research, not shipping code. Their management experience is managing students and classrooms, not employees.
So this mismatch results in a perception of "I'm being undervalued!" from the perspective of the PhD and "He's overvaluing himself" from the perspective of the company.
If the student and company don't have high social IQ to navigate this situation, I can see how it would result in no offers being made.
Source: I've made hiring decisions about PhDs when I worked in industry, and now I'm a graduating PhD student going back into industry, so I can see it from both sides.
var htmlFragment = "" + (<r><![CDATA[
l(a
le
af
fa
ll
s)
one
l
iness
- e.e. cummings
;
]]></r>);
And then `htmlFragment` would contain your multi-line string. (The addition with the empty string coerces the <r> element to output its `toString()` value, which happens to be the innerHTML).This is largely a historical factoid, though. I don't think browsers beyond Mozilla ended up supporting E4X.
Really clever hack with the function's toString method!