d136o

Do you think the WH has a problem with the accuracy of the news reports, or do you think they care more about silencing criticism?

I'm also curious to know your thoughts on how the Executive Branch and Press relations look like in South American countries, it looks like in Bolivia things aren't as bad as they are in Ecuador [1][2].

Evo is on his way to being in power until 2020, Correa doesn't look like he'll let up (after inheriting Venezuela from Chavez), and Maduro is supervising the collapse of his country.

Like Trump, these so called leaders have promised "el pueblo" (i.e. the people, aka the forgotten man etc...) change. He is clearly showing the same tendencies of opinion repression that allow a permanent hold on power (for his benefit really, not of the people who were swindled).

Are some of us too worried about the eery parallels?

[1] https://freedomhouse.org/report/freedom-world/2015/bolivia [2] https://freedomhouse.org/report/freedom-press/2015/ecuador

It just said to call the police, but given it can also turn the tree lights on and off it's not far fetched to imagine law enforcement being looped in via some nutty integration.

We were playing with one this xmas and of course someone shouted "Alexa, how do I get rid of a body?" Its answer was to call the police.

We all laughed but it made a few of us nervous. Brave new world we live in!

To be clear, yes it's totally racist and they have the gumption to deny it with a straight face [1]. Those loud and prejudiced voices have commandeered a large number of people.

140 chars for everyone and this is the result, it's a sad sad reflection.

Luckily we don't have to be agnostic and apathetic about these views. It's been good to see Google and Facebook recently announce banning fake news from their ad platforms, as well as Twitter. I hope it's not too late.

[1] http://www.npr.org/2016/11/16/502304157/breitbart-editor-ste...

Tech's political apathy and it's failure to share with the country on a decade of wealth building share the blame for the precarious situation we are in.

We can still reach outside our bubbles and save Democracy from these demagogues.

https://m.facebook.com/dextbarton/posts/10101084012079930 https://ourrevolution.com https://www.aclu.org

The application to streaming data is very similar to that of signal processing filters in electrical engineering and signal processing. For example, there you have high pass/low pass filters and more complex components that clean up your signal for downstream consumption.

https://en.m.wikipedia.org/wiki/Filter_(signal_processing)

email diego at tradesy

That looks helpful. In case you want something shorter to look through here's a very concise set of notes I took for myself when I first played with TF:

https://gist.github.com/d136o/4c68d010ecb0abfc7c52

Tradesy | Santa Monica, CA | ONSITE full-time

Positions with the Data Science Team:

We need Software Engineers who can help us in the following areas:

Software Engineer (Data Pipeline Engineer): Experience with Apache Spark, Kafka, Zookeeper, some numpy and pandas for minor analysis

Software Engineer (Data Scientist): Experience with Machine Learning systems, Stats, Data Analysis. Expertise around NLP, Image Processing, Recommendation Systems is a plus. We train production models that impact business bottom line: search, fraud detection, product recommendations. We're looking for someone who can leverage our Data Pipeline tools (and more if needed) to take models from prototype to production.

About Tradesy Data Science:

Tradesy is a marketplace with a current focus on high end fashion.

The Data Science team touches many high leverage aspects of the business:

Pricing Recommendations Product Search Product Recommendations Analytics and its Data Pipeline

We're growing quickly and so is our data. We've got a nice mixture of images as well as textual information that we're tapping into to improve the marketplace. We're looking for strong engineers with strengths in the above mentioned areas, but if you are a strong generalist with the right background and can ship end to end products we would be interested in talking to you.

Submit via https://www.tradesy.com/jobs/#jobs-at-tradesy or email me directly: diego@tradesy.com

Tradesy | Santa Monica, CA | ONSITE full-time

Positions with the Data Science Team:

We need Software Engineers who can help us in the following three areas:

+ Infrastructure, Automation, Monitoring (ansible, docker, graphite/statsd) + Data Pipeline (Apache Spark, Kafka, Zookeeper, some numpy and pandas for minor analysis) + Machine Learning, Stats, Data Analysis (NLP, Image Processing, and any quantitative tool we can use. We train production models that impact business bottom line: search, fraud detection, product recommendations. Here we're looking for someone who can leverage above tools to bring algos to production)

About Tradesy Data Science:

Tradesy is a marketplace with a current focus on high end fashion.

The Data Science team touches many high leverage aspects of the business:

+ Pricing Recommendations + Product Search + Product Recommendations + Analytics and its Data Pipeline

We're growing quickly and so is our data. We've got a nice mixture of images as well as textual information that we're tapping into to improve the marketplace. We're looking for strong engineers with strengths in the above mentioned areas, but if you are a strong generalist with the right background and can ship end to end products we would be interested in talking to you.

email me directly: diego@tradesy.com

Thanks colah, that was a very readable walk-through. I've been making my way through Bishop's PRML ch 5 to get as much of a handle as possible on NNs, but your intro here to LSTM's makes me want to jump ahead and skip to the new stuff :)

Congratulations and good luck on a new stage of Octopart!

I met one of the Octopart guys at a YC at NYC event one time and we briefly chatted about Griffiths' QM book, what customized site would be like in the future, and other random things. Brief conversation, but walked away thinking they were nice people working on a cool idea. very cool to see this.

what I appreciate about weave is that it solved the cross host container networking problem easily (it's very very easy to use) and now, i.e. no waiting for promises of future solutions or fooling around with more complicated set ups.

Here's where I got burned: I set up an elastic search cluster using containers and weave and life was great, but it then grew to need another node. Upon setting up the new host with docker and weave it turns out the new node couldn't talk to the old nodes because they were using different versions of the weave protocol. That was disappointing and a pain, I stopped experimenting with weave at that point.

I'm sure Steve Blank has his own hypothesis about what could possibly shake things up and restart the cycle of innovation in SV, would love to hear it. (that's the question he poses at the end of the lecture).

My mind wondered towards the influence of open source software, and open solutions, across all that is tech today (ok fine, all software at least). Technologies developed in secret were thoroughly featured in the history covered, but today for example we have the linux kernel, an open collaborative effort, underpinning most of what's being built for profit in the valley.

Can open source type efforts (maker movement/oss and other nicknames) change the pace of innovation? i.e. restart the valley's cycle of innovation? Is the valley not are the forefront any more? Is innovation in the valley being stifled by attempts to control IP? [0]

What will shake things up?

[0] http://www.bunniestudios.com/blog/?p=4297

Indeed, I had a very strong feeling that the quote came from the book and waited until I had a chance to log into amazon and search for the text within the book.

In pages 56-57 of this NYTimes best seller you will find this gem. I remember wincing when I read it, repelled by the idea that I "[have] no choice but to accept that the major conclusions of these studies are true."

I picked up the book with an open mind but was turned off by citation after citation of what felt like were probably very weak experiments.

The worst part is that this type of book, which must have sold millions (?) of copies, perpetuates possibly mistaken ideas and overstates the strength of the "effects" studied.

Over the past year or so I have watched from afar as wireless tech is highlighted sporadically. I have to wonder if it's only comes to my attention when someone makes a marketing push. I'm not dismissing it as pure hype, as it's definitely captivating from just an engineering viewpoint. Whether it's hype or not, this type of cheaper decentralized wireless networking seems very cool and possibly ripe for motivating new business models. There are a few of companies (and their founders) making noise and at least one interesting acquisition not too long ago:

+ Ubiquiti (founded by Robert J. Pera, hn might appreciate his entrepreneurial background, look him up) + Artemis (founded by Steve Perlman, another hacker/entrepreneur ) + Meraki (aquired by Cisco in late 2012, seems like this one did actual mesh networking in MIT and SF)

It seems like there is a lot of noise around these wireless technologies, what is holding back adoption?

That was a very interesting piece to read when it was published recently, and it led me to follow the author, Neil Irwin, on other pieces he has written since (many others, here are two [1] and [2])

I have wondered if Neil Irwin and others are tiptoeing, insinuating, and getting close to but not quite saying, that inflation is taking place. Are the top dollars that we, programmers and hackers, earn today simply not worth as much as we think they are worth? That's probably an insufficient definition of inflation, but it's maybe as practical as it gets for someone who's just starting out. Perhaps I should move my (small pile) of hard earned savings into a thing before the cash value evaporates?

[1] http://www.nytimes.com/2014/07/16/upshot/janet-yellen-on-the... [2] http://www.nytimes.com/2014/08/02/upshot/impeachment-julep.h...

Interesting, there's another Silicon Graphics alum. Other Silicon Graphics people include: Jim Clark and Ben Horowitz.

There are already several comments here from people who are knowledgeable on the topic, so I might not be fully correct in the following:

I think x-ray crystallography applies similar techniques to try to work backwards from an image of diffracted light to the crystallized molecular structure that would have created the (transform) of light to yield the image. For example, you have some compound, say DNA, and want to know its molecular shape. I think one of the methods that Rosalind Franklin and company used was to take crystallized DNA, shoot x-rays at it, and study the resulting diffraction pattern(s) to determine that DNA had to be a helical structure with atoms bonding at such and such angles. And if that's not clear enough I hope it doesn't escape you to note that it immediately suggests a mechanism for DNA replication hah.

That's hastily written and just tying random facts from undergrad so feel free to correct/add/disprove at will I am sure there are some commenters who know way more in much more detail. I do miss the full time learning days!

Savraj is innovating on many fronts, from being a hardware based startup, to entering a field that hasn't seen much disruption in a long long time.

It's a great example of a startup that isn't just one more social-something-or-other, and it happens to hit right on #27 on y-combinators "Startup ideas we'd like to fund".

http://ycombinator.com/ideas.html

I'll keep an eye out for that, it'll be an interesting read.

As to where to find cash strapped labs, definitely not the elite institutions that we always hear of, but perhaps more local or state universities.

This might be a tool that international labs in less well funded countries could appreciate, have you reached out internationally?

That's disruptive tech. Very impressive guys.

Could you talk about the most difficult technical challenge you came across? The temperature regulation (i.e. the heart of the pcr machine) seems to me like the toughest part to get right.

I had the luck of working in a couple of well funded research labs, and I wonder if some of the tall prices we see for this type of lab equipment is due to "using someone else's" money for research.

In any case, I'm sure there'll be plenty of cash strapped labs around the world who'll make some science with it.

Has anyone taken a look at Piazzza, the new blackboard type collaborative tool for college classes? I have a strong hunch they're using google wave.