Article being discussed in this thread isn't intended to be a luddite rejection of AI. It's just a mistake I see people keep making (and have made myself) and some thoughts on how to avoid it with the tools we have today.
DataSine | Software Engineer | London | ONSITE | Full Time
DataSine (Techstars 2016) is a VC-backed tech startup bringing together machine learning and psychology to enable companies to personalise how they talk to their customers at scale. We are a small team that is growing fast, and are looking for creative engineers across the full stack to join our team in London.
You will be building a fast-growing product suite of intelligent content authoring tools, analytics and visualisation software, cloud-hosted, scalable and using a bleeding-edge machine learning stack. You will working closely with the rest of the technology team, our data science and psychology R&D team, and the rest of the company up to and including the CEO.
We are a diverse and friendly team and welcome applications from all backgrounds. This is an exciting opportunity to join a successful startup as it reaches an inflection point - if this sounds appealing then please get in touch.
DataSine | Software Engineer | London | ONSITE | Full Time
DataSine (Techstars 2016) is a VC-backed tech startup bringing together machine learning and psychology to enable companies to personalise how they talk to their customers at scale. We are a small team that is growing fast, and are looking for creative engineers across the full stack to join our team in London.
You will be building a fast-growing product suite of intelligent content authoring tools, analytics and visualisation software, cloud-hosted, scalable and using a bleeding-edge machine learning stack. You will working closely with the rest of the technology team, our data science and psychology R&D team, and the rest of the company up to and including the CEO.
We are a diverse and friendly team and particularly welcome applications from groups often underrepresented in the tech industry. This is an exciting opportunity to join a successful startup as it reaches an inflection point - if this sounds appealing then please get in touch.
DataSine | Software Engineer | London | ONSITE | Full Time
DataSine (Techstars 2016) is a VC-backed tech startup bringing together machine learning and psychology to enable companies to personalise how they talk to their customers at scale. We are a small team that is growing fast, and are looking for creative engineers across the full stack to join our team in London.
You will be building a fast-growing product suite of intelligent content authoring tools, analytics and visualisation software, cloud-hosted, scalable and using a bleeding-edge machine learning stack. You will working closely with the rest of the technology team, our data science and psychology R&D team, and the rest of the company up to and including the CEO.
We are a diverse and friendly team and particularly welcome applications from groups often underrepresented in the tech industry. This is an exciting opportunity to join a successful startup as it reaches an inflection point - if this sounds appealing then please get in touch.
Author here - completely agree with you. I think the presence of these things can be a nice shortcut to establishing expertise, but lack of them does not imply lack of expertise. These are just suggestions of things to look for, and it certainly isn't my intention that people be ruled out because of an empty GitHub profile. I hope that isn't what it sounds like.
Wow, I thought this one had just disappeared into the void. Glad to see I got people's attention and provoked debate, even if it seems much of it disagrees with my underlying assumptions.
For what it's worth, one thing I don't cover in the article is whether this is a good idea in the first place. It obviously isn't ideal, and as I say it is a difficult situation to be in. From my perspective, I think a non-technical founder is obviously better looking for a trusted former colleague or friend to join as CTO. Proven mutual history is the best thing you can lean on here.
But if this isn't possible, I would definitely espouse an interview process - however formal you prefer that to be. My post is therefore really intended as a how-to guide for someone in this awkward spot, looking to identify the skills and experience for technical leadership without the background to do so.
Of course other soft skills and fit are just as important. But any decent founder will need to identify those in all people joining the founding team. This is just intended as a step-by-step guide for those specific skills needed for a technical co-founder or CTO. I hope it is helpful for someone!
Front-End Engineer | DataSine | datasine.com | London, UK | Full time | ONSITE
DataSine (TechStars London '16, VC-backed) brings together machine learning and psychology to change how businesses interact with their customers. Our technology combines deep customer analytics with automated content customisation to improve customer experience, through the prism of personality and psychometrics.
We're looking for a motivated engineer to join our front-end team that is building our green-field web-based platform for content personalisation and customer analytics.
=> Skills needed: JavaScript, HTML5, CSS3, React, graphic design
=> What we look for: passionate about quality software, scientific in approach, collaborative in a small team, self-starter
If you're interested, send us your CV, your portfolio, your GitHub profile, or anything else that might impress to [email protected]
Ah, I had missed that. I guess this will mitigate the risk a lot, although I would still like to have seen results against a test set of images from a different context (social media for example).
If you read the paper, the photos and labels were sourced from a dating website. In my opinion, there is a good chance that the model may be overfitting to how people wish to present themselves in that context. - e.g. framing of the photo, facial expression etc. Things with a heavy amount of cultural conditioning.
Some of the press around this seems a bit alarmist - I doubt you would see anywhere near this accuracy out in the real world.
Software Engineer | DataSine | datasine.com | London, UK | Full time | ONSITE
DataSine (TechStars 2016) brings together machine learning, psychology and finance to change how high street banks interact with their customers.
We have a small and highly skilled technical team, building our cutting edge data science platform. As demand for our products grow, we are looking for a gifted and highly motivated software engineer to join our team.
=> Skills needed: Experience in Python plus one other language a must, experience with Docker and/or real-time systems a plus, an interest in machine learning very preferable.
=> What we look for: passionate about quality software, scientific in approach, collaborative in a small team, keen to take on new responsibilities and learn new skills.
If you're interested, send us your CV, a link to your GitHub profile, or anything else that might impress to [email protected]
I think what you're trying to do at Grasswire is interesting, but I feel like here you're arguing against the very point that Swartz was making. I think of all people he is the last one that you could accuse of not caring, and yet he claims not to have read the news for his entire adult life. The difference is that he voraciously read books [1] and took what I guess was a long view of issues of interest, which I think is what he is promoting here.
That said I still like the idea of being able to "touch base" with the news as it were - something that I can scan quickly and, if I'm disciplined enough, filter down to the bits that are actually relevant to me. Most days, hopefully, that's nothing at all.
Thanks Martin, unfortunately it's past the point where I can edit my original comment or I'd go back and fix it. As I recall you said it off-mike in Bill Venners' talk, otherwise I would have fact-checked it!
Anyway, details aside I thought it was a great illustration of the impact on compiler performance that code complexity can have.
I've written a lot of Scala and I don't think I've seen anything as slow as even 1 line/second. Odersky was talking about Shapeless, which is an edge case in that it is a home for all the type-heavy calculations that scalac can compile but isn't currently optimised towards.
Compilation speeds are largely dependent on the type of code you're writing. At London's Scala Exchange last year, Odersky claimed he can get 10k lines/second (10x slower than javac, but still not bad) out of scalac by writing simple imperative code, but libraries that make heavy use of the type system and implicit scope can be as slow as 1 line/minute.
I consider it fair to expect the compiler to be slower when it's doing more heavy lifting, but it would definitely be nice to see the exponential blow-up on the tail curbed in future.
edit: To clarify, the numbers above were what Odersky claimed; I haven't verified them. The 1 line/minute was claimed of trying to build Shapeless - I've never seen anything approaching that in the wild.