skymind
understand why skymind is a good enterprise choice for those embarking on ai initiatives .
skymind has partnered with the largest software and hardware vendors in the enterprise it ecosystem .
copyright © 2017. skymind. nd4j is distributed under an apache 2. 0 license .
cisco integrates with skymind to bring ai / ml workloads to hyperconverged infrastructure. read more →
orange tests skymind' s deep learning software to identify fraud in telecommunications. read more →
chris nicholson is a co - founder at skymind, an enterprise - facing deep learning company .
softbank integrates skymind' s skil somatic for building household robots with sensory data. read more →
in september, skymind raised $ 3 million from y combinator and the chinese internet giant tencent .
the skymind intelligence layer is the open - source enterprise distribution which is a pre - built and pre - configured collection of software packages focused on machine learning. skymind' s products include :
2015 presentation on deep learning for the enterprise by skymind, the commercial support arm of deeplearning4j .
create a new experiment in the skymind intelligence layer (skil) by importing a zeppelin notebook .
the latest step down this path is tuesday’s announcement of cloudian’s collaboration with skymind, an ai company .
as a member of several global consortiums, skymind is part of an ecosystem of complementary products and services .
$ 42 billion french telecom orange sa is already using skymind and its skymind intelligence layer (skil) to build and deploy production code for deep - learning projects. skil facilitates the digital interactions between skymind and existing tools like hadoop. nicholson also notes interest from medium - size companies in traditional data - rich sectors like financial services and e - commerce .
skymind is the company behind the deeplearning4j framework. deeplearning4j makes efficient use of gpus and integrates with distributed systems such as hadoop and spark to scale to large data sets. skymind sells an enterprise editions of its software together with training and support .
with nvidia’s powerful dgx - 1 ai supercomputer and skymind’s open - source deep learning distribution, skil (skymind intelligence layer), large organizations can leverage a single, unified platform that works natively with their existing software to build deep learning solutions .
the libraries are completely open - source, apache 2. 0, and maintained by the developer community and skymind team .
skymind academy enables your team to build deep learning solutions. we offer private corporate seminars and public workshops. learn more at urltoken
500, 000 docker pulls for the skymind intelligence layer ce. check out the rest api for our free machine learning model server :
less than 48 hours after skymind’s funding round, two other emerging analytics providers called arcompany and realsociable inc. merged into new company bearing the name
skymind sells software and services based on deeplearning4j, a popular tool that enterprise programmers are using to build systems to detect fraud and recognize images for business .
20. urltoken help @ urltoken skymind inc. u. s. a. 1328 mission street suite 9 san francisco, ca 94103 help @ urltoken urltoken
we had a chance to talk with chris nicholson, founder and ceo of the deep learning company skymind, to discuss where the future of ai is heading .
( for most of that time, i’ve been working on a deep learning startup called skymind, and raised more than $ 3 million dollars from angels, seed funds, and larger investors on the idea that deep learning will transform most industries. skymind built deeplearning4j, an open - source framework for java and scala. )
without the engagement of skymind customers and community, and the generous backing of skymind’s investors – tencent ’s investment team, led by david wallerstein; liquid 2 ventures; mandra capital; sv angel; ullas naik; hemi ventures; wei guo; ray lanes’s greatpoint ventures and others – deeplearning4j would not exist in its present form .
new startup skymind offers support for open source deep learning - it offers support and services for deeplearning4j, an open source deep learning project it has created. [ 5 ]
skymind is an early - stage company, but it' s quickly establishing itself as one of silicon valley' s premier sources of expertise on artificial intelligence in the enterprise .
skymind is its commercial support arm, bundling deeplearning4j and other libraries such as tensorflow and keras in the skymind intelligence layer (community edition), a deep learning environment that gives developers an easy, fast way to train and deploy ai models. skil ce is free and downloadable here. skil acts as a bridge between python data science environments and the jvm .
open - source deep - learning allows retailers and e - commerce companies to empower their internal teams to create, customize and adapt recommender solutions with the help of deep - learning experts at skymind. online retail has wholeheartedly embraced open - source, starting with linux as an operating system, continuing through the big data ecosystem of hadoop and spark, and ending with the analytics necessary to take actions. skymind’s deep learning offers record - breaking analytics tools to the open - source enterprise. with them, skymind clients are actively modernizing how they sell .
this screencast shows how to use skymind' s datavec to build an image pipeline to prepare data for processing in a neural network using deeplearning4j. topics covered include, parentpathlabelgenerat ...
deeplearning4j is part of a set of open source libraries for building deep learning applications on the java virtual machine. it is one of several open - source libraries maintained by skymind engineers .
skymind, a company developing an open - source deep - learning library for java, along with tools for implementation, today closed $ 3 million in financing from tencent, sv angel, greatpoint ventures, mandra capital, hemi ventures and y combinator. skymind was previously part of y combinator’s winter 2016 batch and has taken money from joe montana’s liquid 2 ventures and a number of other prominent angels .
for instance, skymind cofounder adam gibson and top engineer josh patterson were tapped by famed tech publisher o' reilly to write its guide to
deep learning ,
a core technology in building ai .
cloudian will combine its scalable, on - premises storage systems for hyperscale technology in the data center with skymind’s deeplearning4j ability to recognize patterns in massive amounts of data. this will give users actionable intelligence .
the skymind and dl4j team’s mascot is a mathematical object, the oloid, which is composed of two linked congruent circles in perpendicular planes. to us, it stands for non - dualism and interdependence .
we’d like to thank chris for taking the time to answer some of our questions. skymind was founded in 2014 and is headquartered in san francisco. to learn more about the company, check them out here .
this screencast shows how to use skymind' s datavec to ingest comma separated values from a text file, convert the fields to numeric using a datavec transform process in spark, and save the modified ...
i can spend months organizing data before i' m even to the point of being able to analyze it and that' s not even speaking to the confidence level of data accuracy. with skymind' s deep learning solutions, analysts like me can present solutions derived from organized data much, much quicker. skymind is pushing towards a future where every data point is accounted for, delivering the what so analysts can deliver the why .
the goal of skymind is become an open - source (that' s right, it' s all open - source) artificial intelligence layer for enterprise data systems, similar to how red hat supports the open - source operating system layer. in order to achieve that goal, skymind is not stopping with deeplearning4j. rather, it has recently introduced another deep learning layer called skil (skymind intelligence layer). thanks to a $ 3 million seed round from tencent, sv angels, great point ventures, mandra capital and y combinator, the company that was started in bunk beds in a hacker house is moving forward with bringing more deep learning solutions to enterprise data warehouses .
object detection skymind' s image recognition technology can be trained to recognize specific objects or patterns such as scenes in images. for example, it can detect corporate logos and therefore perform copyright infringement functions. alternatively, it can detect weapons, both carried openly and hidden under clothing. open - source deep - learning allows those institutions to empower their internal teams to create, customize and adapt image recogntion solutions with the help of deep - learning experts at skymind .
skymind launches with open - source, plug - and - play deep learning features for your app - deep learning essentially drives computers to make inferences like human brains do, based on the consideration of lots of information. [ 7 ]
this screencast shows how to use skymind' s datavec to build an image pipeline to prepare data for processing in a neural network using deeplearning4j. topics covered include, parentpathlabelgenerator and image scaling. browse all our public training courses at urltoken .
14. deep learning for enterprise 14 skil® the skymind intelligence layer - deep learning in production skil is skymind’s proprietary enterprise distribution. it contains all of the necessary components and dependencies to deploy to production deeplearning4j as well as the proprietary vendor integrations and open source components. r core components core components are composed of our full suite of open - source libraries such as deeplearning4j, nd4j, datavec, javacpp and libnd4j. this is everything you need to build a deep learning application. vendor integrations vendor integrations are separate proprietary pieces of software that we bundle with our enterprise distribution. this includes hadoop distributions, connectors and chip / blas integrations. reference architecture skymind intelligence layer (skil) works natively with the jvm stack .
skymind employs 15 people, but they are not all centralized in a single opulent palo alto or soma headquarters like most startups these days. instead, members of the team operate out of japan, south korea and australia. many of these engineers came directly from the skymind open - source community, where they were working on their own challenges with the library. of course, the company does have a small soma headquarters, but it’s not nearly as integral to the core business .
to help enterprises keep pace in the new phase of digital transformation with artificial intelligence (ai), skymind offers an end - to - end ai service with a leading deep - learning framework written in enterprise - friendly java, called deeplearning4j (dl4j) .
these are just a few examples of how businesses are using ai to add value. the combination of massive gpu computing resources with a deep learning framework adapted to java and the enterprise production stack makes the nvidia and skymind offering a powerful tool for enterprise teams .
skymind is the company behind deeplearning4j, the only commercial - grade, open - source, distributed deep - learning library written for java and scala. integrated with hadoop and spark, dl4j is specifically designed to run in business environments on distributed gpus and cpus .
skymind is the company behind deeplearning4j, the only commercial - grade, open - source, distributed deep - learning library written in java and scala. used by fortune 500 corporations, dl4j is specifically designed to run in business environments on distributed gpus and cpus .
17. urltoken help @ urltoken 17 skymind university machine learning is one of the fastest - growing and most exciting fields in technology, and deep learning represents the state of the art. through skymind university, you can master our deep learning technologies with our lab - intensive, real - world training. get certified our certification program helps professionals demonstrate their skills and credentials and build their careers. it gives employers a meaningful way to develop qualified professionals and also allows entrepreneur to build amazing products using deep learning technology. skymind university starts with the practical, teaching you what you need to knowtostartsolvingrealworldproblems. our program covers everything from data pipelines and deep learning model development to deploying deep learning to production. whether you’re updating your expertise or building brand new skills, this is where it all begins .
the mission to bring google’s ai to the rest of the world - skymind will serve not only as a steward for this open source project but as a consultant that will help others use the code to build their own ai - powered online services. [ 6 ]
nd4j is an open - source, distributed deep - learning project in java spearheaded by the people at skymind, a san francisco - based business intelligence and enterprise software firm. we’re a team of data scientists, deep - learning specialists, java programmers and semi - sentient robots .
eclipse deeplearning4j is an open - source, distributed deep - learning project in java and scala spearheaded by the people at skymind, a san francisco - based business intelligence and enterprise software firm. we’re a team of data scientists, deep - learning specialists, java systems engineers and semi - sentient robots .
but while it certainly lends itself to a wide range of use cases, hadoop can’t support every single data - crunching workload in the enterprise. applications that use deep learning algorithms to process data, for instance, often need to be run on specialized frameworks like the deeplearning4j engine from skymind inc. , which
a startup called skymind launches, pushing open source deep learning - the existence of open source libraries backed by professional services could certainly help spur adoption — especially for a field of data analysis previously relegated to top universities and research labs at companies such as google, microsoft, facebook and baidu. [ 9 ]
if you have any questions, please join us on gitter; for premium support, contact us at skymind. nd4j is the java - based scientific computing engine powering our matrix operations. on large matrices, our benchmarks show it runs roughly twice as fast as numpy. dl4j' s programming guide is available here .
skymind libraries were downloaded 22 thousand times last month, and that number is growing at 17 percent month over month. nicholson wants the company to continue to develop tools for data scientists. these tools could eventually include packages to better democratize access to reinforcement learning, a categorization of reward - based machine learning, for existing businesses .
skymind, the creator of the deeplearning4j open source deep - learning library, and cloudian are set to create data - management solutions for the hyper - scalable data sets necessary for ai and ml use cases, that target vertical industries such as medical, fraud detection for financial services, security and robotics, to name a handful .
3. adam gibson | skymind who is deep learning for? ● chief information officers looking for the highest performance in machine learning ● businesses looking to maximize the value of small data science teams, creating apps and cutting costs. ● data scientists and programmers who seek to process huge amounts of unstructured data without laborious feature extraction. users
one sector that will not be leveraging skymind is the top layer of large tech companies that have poured billions of dollars into developing their own open - source technologies. google’s tensorflow, amazon’s dsstne and baidu’s paddle have all found their way into a number of external applications. importantly, however, none of these tools are built for java .
“cloudian and skymind help organizations extract actionable information from vast pools of unstructured data, ” said tso. “training data is the foundation of ai and ml. more training data and better metadata tagging result in quicker, more effective learning, making data the dynamic asset that is the key to unlocking ai and ml’s business impact, ” he added .
there are a lot of knobs to turn when you’re training a distributed deep - learning network. we’ve done our best to explain them, so that deeplearning4j can serve as a diy tool for java, scala and clojure programmers working on hadoop and other file systems. if you require support or customization beyond these tutorials, please contact us at skymind .
our open - core product, the skymind intelligence layer, bundles the deeplearning4j suite of libraries alongside a conda environment with tensorflow, keras, scikit - learn and other popular machine learning tools. skil makes it easy to perform distributed training, testing against production environments, and deployment of trained models to production, automating redundant, everyday workflow tasks faced by data scientists .
6. deep learning for enterprise 6 what is skymind? we make deep learning accessible to enterprises about us skymind is tackling some of the most advanced problems in data analysis and machine intelligence. we offer state - of - the - art, flexible, scalable deep learning for enterprise. deep learning is becoming an important tool for natural - language processing (nlp), computer vision, database predictions, pattern recognition, speech recognition, predictive analytics and fraud detection. we support urltoken and nd4j. org, the only commercial - grade, open - source, distributed deep - learning library written for java and scala. integrated with hadoop and spark, dl4j is specifically designed to run in business environments on distributed gpus and cpus. r
to be fair, skymind' s target market is a bit above my pay grade. it is aimed squarely at cios who are staring at thousands of data tables, sipping their coffee and wondering what do with all that information. i' ve worked for these cios. they need insight, they need to be able to make forecasts and analyze historical transactions or customer behavior .
deeplearning4j aims to be cutting - edge plug and play, more convention than configuration, which allows for fast prototyping for data scientists, machine - learning practitioners and software engineers. dl4j is customizable at scale. released under the apache 2. 0 license, all derivatives of dl4j belong to their authors. dl4j can import neural net models from most major frameworks via keras, including tensorflow, caffe and theano, bridging the gap between the python ecosystem and the jvm with a cross - team toolkit for data scientists, data engineers and devops. keras is deeplearning4j' s python api. skymind is the second - largest contributor to keras after google, and offers commercial support for keras. machine learning models are served in production with skymind' s machine learning server .
read: urltoken at skymind, we’re tackling some of the most advanced problems in data analysis and machine intelligence. we offer state - of - the - art, flexible, scalable deep learning for industry. deep learning is becoming an important tool set for natural - language processing (nlp), computer vision, database predictions, pattern recognition, image / video processing and fraud detection .
the skymind intelligence layer (skil) is an open - source enterprise distribution that contains all necessary components and proprietary vendor integrations to build production - grade deep learning solutions. core components are composed of the full suite of open - source libraries such as deeplearning4j, nd4j, datavec, javacpp and libnd4j. along with vendor integrations, you get everything you need to build a deep learning application .
yes! deeplearning4j' s python api employs keras, a high - level, intuitive abstraction layer that also takes tensorflow and theano as a backend. teams that have trained models on other python frameworks can import them to the jvm and deeplearning4j using keras model import. and developers just starting to train configure and train a model can do so via our python interface. skymind is the second - largest contributor to keras and offers commercial support for the framework .
image classification image classification categorizes objects that appear in images. for example, skymind' s image recognition technology can be trained to recognize vehicle categories such as cars, trucks and motorcycles. this allows users to sort images in real time and flag certain objects as necessary. skymind' s image recognition technology also provides pre - trained classifiers that can label images with existing categories, so that it becomes easier to automatically tag large collections of images. in the vanguard of the ai revolution, a few companies are moving now to apply the most accurate techniques to their costliest and most challenging problems. they are the companies embracing deep learning to cut risk and modernize compliance. optical character recognition ocr is the process of extracting text from images. ocr can be applied to scanned or photographed documents, pictures, or photos to them them to computer - readable formats. that makes it easier to sort, store and search those documents .
the enterprise demands performance, fault tolerance, and scalability. skymind helps you take any tensorflow, keras, deeplearning4j, or pmml model and deploy it as an enterprise - ready microservice. we give you all of the tooling you need to satisfy your systems engineers such as monitoring and integration support. our flagship software skil helps you bridge r & d; to deployment and improve your models for any use case: fraud detection, computer vision, product recommendation, and more .
to be sure, the algorithms championed by gibson are still an awfully long way from cloning the human brain–which means even the artificial intelligence moniker is a big of a stretch–and skymind is still very much in its infancy. but google and microsoft have shown that deep learning can advance the state of the art, and with his startup, gibson has at least identified the next logical step for this fledgling technology. if he doesn' t bring deep learning to the rest of world, someone else will .
skil will bundle up deeplearning4j and the other skymind open - source libraries and integrate with numerous big - data tools like hadoop and spark, as well as message queue kafka and cloud controlling openstack. skil will allow users to build production grade deep learning deployments, adding that artificial intelligence layer to the dirty piles of unorganized (or even slightly organized) data. we collect a lot of data, sometimes we need help not only cleaning up that data for presentation - - but knowing what it is .
7. • “a breakthrough in machine learning would be worth 10 microsofts. ” - bill gates, microsoft • “machine learning is the next internet. ” - tony tether, darpa director • “web rankings today are mostly a matter of machine learning. ” - prabhakar raghavan, yahoo director of research • “machine learning is going to result in a real revolution. ” - greg papadopoulos, sun cto • “machine learning is todayʼs discontinuity. ” - jerry yang, yahoo deep learning is that breakthrough, and skymind is bringing it to industry .
9. urltoken help @ urltoken 9 skymind’s suite of tools takes advantage of the latest distributed computing frameworks including hadoop and apache spark to improve model training. first commercial - grade, open - source, distributed deep learning library deeplearning4j is the most widely used open - source deep learning tool for the jvm. its aim is to bring deep learning to the production stack, integrating tightly with popular big data frameworks like hadoop and spark. deep learning excels at identifying patterns in unstructured data. this includes images, sound, time series and text. sound text time series image video
team. deepmind raised and finally was acquired on the strength of its team. starting with the founders, deepmind gathered some of the best minds in deep learning, cleverly working their connections in academia to find the top graduate students until they almost cornered a very small market. smart people gravitate to smart people. for skymind, open - source gave us a way to send out a beacon and gather talent together in one team. a lot of the world’s smartest engineers want to work on open - source ai, and they consider it vastly preferable to polishing another mobile app .
people gather data without knowing what it is. most data storage has a bunch of stuff that no algorithm has tagged ,
skymind' s co - founder / ceo chris nicholson tells me via email .
you can have a million photos and not know what' s in the photos. but deep learning can tell you what' s in them. and deep learning can tell you which transactions, of the billion your bank processed today, are probably fraudulent. deep learning takes the raw data of business, and interprets it so people can know what they' re dealing with .
alongside another engineer named josh patterson, who previously worked for big data startup cloudera, gibson has built a new library of deep learning software tools that' s freely available to anyone, and skymind will serve not only as a steward for this open source project but as a consultant that will help others use the code to build their own ai - powered online services. based on academic papers published by some of the deep learning engineers now working for google and facebook, the software could help power everything from voice recognition to language translation to the kind of automatic product recommendations you see when you visit amazon. com .
i have built a career on the back of disorganized data. while one might assume i am but a humble satirical tech columnist, in another life i' m a data analyst. my specialty has been working with small to medium sized companies and getting their data reporting on track to actually help the business. many companies experience growth without the tools in place to keep the junk drawer clean. this isn' t a digital resume, i have a point. back in my day we didn' t have deep learning artificial intelligence to help organize data. thanks to companies like skymind, that' s exactly what we have now at the enterprise level .
whether it' s google' s headline - grabbing deepmind alphago victory, or apple' s weaving of
using deep neural network technology
into ios 10, deep learning and artificial intelligence are all the rage these days, promising to take applications to new heights in how they interact with us mere mortals. to go deeper (yes, i went there) on the subject, i reached out to the team at the deep learning - focused company skymind, creators of deep learning for java (dl4j), and authors of the recently released o' reilly book deep learning: a practitioner' s approach, josh patterson and adam gibson ...
the technology has so much promise, it has sparked a kind of arms race among the giants of tech. google and facebook recently hired the two academics who originally laid out the concepts behind deep learning, and earlier this month, chinese search giant baidu followed suit when it snapped up another academic at the heart of the movement. but adam gibson, an independent software engineer based in san francisco, doesn' t want this new technology locked inside the biggest names on the net. he believes deep learning techniques should be available to any website, company, or developer interested in using them. and that' s why he' s launching a new startup called skymind .
with regards to deep learning, we realized last year that javacpp had the characteristics of something that was in demand especially in that field, that is to say an easy way to access native libraries from an efficient platform like java: java meets caffe, deep learning in perspective. since then, samuel has switched jobs and now works for skymind, integrating javacpp, the javacpp presets, and javacv into nd4j and deeplearning4j, as well as pursuing other avenues, such as maintaining not only bindings for caffe and cudnn, but also for mxnet and tensorflow, among others. many thanks to adam gibson and chris nicholson for their trust! i am sure we will achieve great things together .
19. urltoken help @ urltoken 19 case study: orange sv orange is working with skymind to prevent subscriber identity module box (simbox) fraud on its mobile network. using an artificial neural network (ann) called an autoencoder, orange silicon valley analyzes call detail records (cdrs) to find patterns that identify fraud. the ann also predicts the likelihood that an instance is fraudulent. where a static rule system flags cases only as likely fraud or not, the ann enables orange’s analysts to prioritize high probability cases of sim box fraud. “the problem is that fraud moves so quickly that it has been difficult for orange’s static algorithms to detect it, ” told by georges nahon, ceo of orange silicon valley
skymind is the company behind deeplearning4j, if you' ve been keeping up on your enterprise - level software. deeplearning4j is a distributed deep learning library written for java and scala. this library integrates with existing hadoop and spark frameworks. basically, deeplearning4j is slapped into existing large databases and then it does my job for me. at least, part of it. it searches for patterns in unstructured data (which a lot of data tends to be these days, especially as we move forward with data collection) in order to make connections for streamlined analysis. i know, sexy right? from an analytical perspective and having seen databases with over 16, 000 un - linked tables in my career, it' s enough to make my analytical pants sweat .
deeplearning on apachespark with deeplearning4j - java user group basel 10. 1. 17
chris nicholson, the company’s co - founder and ceo, decided to start the company after he noticed the steady stream of deep - learning researchers leaving the halls of academia for the six - and seven - figure salaries of large tech companies. with human capital becoming a finite resource, the challenge quickly became about helping companies leverage existing resources to play in the world of deep learning .
eighty percent of the world’s programmers are versed in java programming. nicholson, and his co - founder adam gibson, began to question whether it would be possible to build a library that would let those developers put their skills to work on ai challenges. this idea became deeplearning4j, and it remains the core of
nicholson describes his company as the red hat of deep - learning. red hat, a company that produces open - source software for the enterprise community, has grown to become an over $ 14 billion company .
for startups that produce software for the fortune 100, the pressure to create in open - source has never been higher. not only does open - source allow companies to stay competitive, it allows the startups producing the technologies to hire the best engineers .
other investors in the party round include muse’s matthew bellamy, hemi ventures, jpush’s chris lo, wei guo, rising tide fund, ullas naik’s streamlined ventures, soma capital, lookout’s kevin mahaffey, gmo venture partners, fundersclub, s2 capital, former head of google search amit singhal and google news creator krishna bharat .
tl; dr: deep learning startups are in vogue, but that doesn’t mean fundraising is easy. here are the pitfalls startups should avoid to get funded and build a business .
this how - to is for founders and technologists who know something about ai and are currently seeking out their very first investment .
i’m going to generalize a little bit about vcs and the deep learning landscape to keep it readable .
advances in deep learning have made ai hot again, and that heat has launched a lot of startups, investments, and headlines in the last couple years .
but a quick warning to machine - learning grad students: you’re not going to raise on an algorithm. the marginal improvement you made in the basement lab, and which you’re planning to hide from the world, is simply not enough .
vicarious is probably the only ai startup that’s really raised a lot of money on their secret sauce. but the real advances in deep learning are happening now in the public domain, accelerated by a slew of papers shared among major universities and corporate research labs .
it’s going to be hard for you to compete with a community of thousands of grad students and researchers backed by massive corporate gpu clusters, testing their ideas and iterating faster than you can imagine .
the algorithm fallacy is a special case of a more widespread mistake made in silicon valley. technologists admire machines that run faster, do more, and break some record to become superlative. a billion row join! in under 3 nanoseconds! they build solutions in search of a problem .
but a lot of the time, those superlative solutions leave potential customers cold. so forget about the bright new algorithm and remember you just need to be good at two things :
in other words, you have to make the algorithm useful in a new way, and only users can tell you if it is .
the world hasn’t caught up to last year’s ai yet, let alone the research we don’t even know about yet. the harder nut to crack is not better ai, but how you will sell the ai that works now. how will you change the world’s behavior so that it drops whatever it’s doing and adopts what you’ve made? this is not a technological problem; it’s a sales problem. and when you talk about sales, you talk about human relationships: listening, learning, and persuading .
good investors know this, and they are going to ask you: why should the world care? how will you sell this thing you’ve made? what’s your go - to - market? and many more pesky questions like that .
the first 10, 000 pitches are the hardest. convincing other people to shower you with their money gets easier after that. that is, you’re going to pitch a lot of investors, and almost all of them are going to say no. please don’t feel bitter. don’t conclude that life is unfair. saying no is their job. vcs, like hiring managers, mostly reject. they need to learn about a lot of companies to find a couple they like .
they also have much more time than you. vc time is measured in years and tied to the lifecycle of their funds; early - stage startup time is months or weeks. that means they won’t feel your urgency, and they may say silly things like “let’s circle back early next year” when you’re staring down the barrel of next month’s rent .
but you only need one good investor to say yes. after that, others will follow because they trust the signal from the first. so your job is to get through the no to arrive at the yes. don’t tell yourself that you’re wasting precious time. when actors rehearse before their debut, they’re not wasting time. they’re getting better. pitches are just rehearsals. their purpose is to make you better for when your big day comes .
one of the reasons we managed to raise a large seed at the end of y combinator is because we had pitching down. not every startup is as lucky. our secret weapon was a desperate year knocking on doors .
investors come in all stripes and with all sorts of experience. some investors will understand ai and the industry you’re tackling, and others won’t. the two things that all investors understand, regardless of expertise, are traction and social proof. traction is how many users or customers you have, and social proof is how many important players are backing you .
so how do you get traction and social proof? to get users, you need a product, to get a product, you need funding, and to get funding, you need users. i’m sure you can appreciate the catch - 22 implied in that reasoning. the typical workaround is a bootstrapping marathon of many nights and weekends alongside co - founders who are smart enough to build the tech and dumb enough to accept the risk for very little pay .
just because an investor isn’t an expert on ai doesn’t mean you can get hand - wavy about it. non - experts will have you vetted by people who are smarter than you, me, or them, and the person vetting you will feel a righteous anger if you can’t back up your claims, and your potential investor will hear about it .
so be honest and know your shit. if you have a vision to add ai to your product down the road, be very clear about who you will need to hire, what they will do, and when that moment of adding ai will actually happen .
if you are ready to implement ai now, be ready to name your algorithm and compare it to others to explain your choice. there are usually tradeoffs. for instance, you might accept lower accuracy for more interpretability .
platforms. they’re powerful but rare. if you can build the platform, you control the api, and you have the potential to become the foundation of an enormous ecosystem. that’s what we did, and it worked. but the moment for new platforms in deep learning is passing quickly because network effects are in force. the leading platforms are established .
products. now is the time to build products that are infused invisibly with ai. to your customers, the invisible ai won’t matter, because all they want is the experience your technology you can give them. either you classify photos correctly or you don’t. but investors care a lot about these ai - infused products because they have an edge over the dumb objects we have wrestled with for millennia. this is the edge where new winners are established and old victors are felled .
data. investors will want to know about your data. how will you get the data? what kind will it be? is it proprietary? is it sufficient? whether your data gives you an edge or not, you should have a plan for how to gather, move, and store it, just like every business building an ai solution. if you can’t answer these questions, you’ll look unprepared .
some people raise on pedigree. they get funded half way through their stanford ph. d. , or immediately after leaving google’s machine learning team, and more power to them. if you don’t have pedigree, you need a product with traction. if you don’t have a product, you need a personality that makes your idea compelling. if you can’t even raise on personality, all you’ve got left is persistence. persistence is what we raised on, and it has come in useful in other ways since the round .
disclosure: when y combinator comes up, it’s worth noting that referly, which later became mattermark, was a y combinator company. as always, the editorial team at mattermark is independent .
adam gibson (right) teaches deep learning techniques at the zipfian academy in san francisco .
at google, it helps drive the voice recognition service that lets you search the web merely by talking into your android smartphone. at microsoft, it underpins the new skype translation tool that lets you instantly communicate with people who speak another language. and at facebook, a newly assembled team of engineers is exploring how it might be used to recognize faces in online photos. it' s called deep learning, and it seeks to remake computing by more closely mimicking the way the human brain processes information, giving machines more power to
learn
as time goes on .
>' we want to give people machine learning without them having to hire a data scientist.'
we want to give people machine learning without them having to hire a data scientist ,
says gibson, 24, a college dropout who has taught himself the vagaries of deep learning from public academic papers and has served as a kind of machine learning consultant for various companies while teaching courses on the subject through an outfit called the zipfian academy .
we' re trying to clone what google does ,
says patterson. and though the project is still in the early stages, gibson says the code is already capable of bringing deep learning techniques to live web services .
we' re handling production - level systems ,
he says, while declining to name which companies are using it .
at the very least, we' re able to reproduce the results that the academic papers are producing .
there are other ways of using deep learning. the academic community that founded the movement offers its own open source software tools written in the python programming language, and these serve as the basis for ersatz, a service that lets you tap deep learning algorithms via the internet. but with his open source project, known as deeplearning4j, gibson has bigger ambitions. unlike the academic tools that are already available, his software is built with the java programming language–thus the
4j
–and that means it can run atop hadoop, the massive number crunching system that has become a staple inside many of the world' s online operations .
based on software designed at google, hadoop is a way of storing and processing enormous amounts of data across hundreds of ordinary computer servers, and this sort of distributed computing power is what deep learning requires .
hadoop is becoming the system of record for all data ,
patterson says .
we need to move deep learning to the data that already lives in hadoop .
an existing open source project, known as mahout, already provides a way of running artificial intelligence algorithms atop hadoop. urltoken uses mahout to drive product recommendations on its popular retail site. but deep learning is something very different from this older breed of ai. according to those who have used it, deep learning comes closer to creating
neural networks
that mirror the way the brain operates. whereas older ai systems must be
taught
to preform tasks by human engineers in many cases, deep learning algorithms are better at learning and adapting on their own .
>' there are more java programmers out there, but there are probably more machine learning programmers who use python or other languages.'
david sullivan, who oversees ersatz, the online deep - learning service, calls gibson' s project
interesting ,
and he calls gibson
a very sharp dude .
but he questions whether the move to java is really that important .
there are more java programmers out there, but there are probably more machine learning programmers who use python or other languages ,
he says .
gibson and patterson also argue that java can eventually provide deep learning calculations at much faster speeds. but yoshua bengio, a university of montreal professor who sits at the heart of the deep learning academic community, says this isn' t necessarily the case .
there are other languages which seem better suited for statistical and numerical computation, not just because of the language itself but because of the community around and the set of tools that have been developed around it ,
he explains .
but bengio still welcomes gibson' s project–
i' m a big advocate of diversity ,
he says–and if deep learning is to reach a much wider audience, it must certainly find a place in the world of java. the language has become one of the primary ways of building big - time web services .
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github is home to over 28 million developers working together to host and review code, manage projects, and build software together .
if nothing happens, download the github extension for visual studio and try again .
* offer the apache license, version 2. 0, as a new choice of license, …
update version in the 'pom. xml 'file to 1. 4. 2 - snapshot
javacpp provides efficient access to native c + + inside java, not unlike the way some c / c + + compilers interact with assembly language. no need to invent new languages such as with swig, sip, c + + / cli, cython, or rpython as required by cppyy. instead, it exploits the syntactic and semantic similarities between java and c + +. under the hood, it uses jni, so it works with all implementations of java se, in addition to android, avian, and robovm (instructions) .
as a case in point, we have already used it to produce complete interfaces to opencv, ffmpeg, libdc1394, pgr flycapture, openkinect, videoinput, artoolkitplus, and others as part of the javacpp presets subproject, also demonstrating early parsing capabilities of c / c + + header files that show promising and useful results .
please feel free to ask questions on the mailing list if you encounter any problems with the software! i am sure it is far from perfect ...
to produce binary files for android 4. 0 or newer, you will also have to install :
finally, because we are dealing with native code, bugs can easily crash the virtual machine. luckily, the hotspot vm provides some tools to help us debug under those circumstances :
to implement native methods, javacpp generates appropriate code for jni, and passes it to the c + + compiler to build a native library. at no point do we need to get our hands dirty with jni, makefiles, or other native tools. the important thing to realize here is that, while we do all customization inside the java language using annotations, javacpp produces code that has zero overhead compared to manually coded jni functions (verify the generated. cpp files to convince yourself). moreover, at runtime, the loader. load () method automatically loads the native libraries from java resources, which were placed in the right directory by the building process. they can even be archived in a jar file, it changes nothing. users simply do not need to figure out how to make the system load the files. these characteristics make javacpp suitable for either
in addition to the few examples provided below, to learn more about how to use the features of this tool, please refer to the source code of the javacpp presets. for more information about the api itself, one may refer to the documentation generated by javadoc .
as a matter of course, this all works with the scala language as well, but to make the process even smoother, it should not be too hard to add support for
native properties
, such that declarations like @ native var could generate native getter and setter methods...
skymind is an artificial intelligence company supporting the open source deep learning framework deeplearning4j and the jvm-based scientific computing library nd4j . the company was founded in 2014 by adam gibson and chris nicholson , and is headquartered in san francisco , california . it is privately funded , and raised $ 3 million in seed financing in 2016 . 
skymind is an artificial intelligence company supporting the open source deep learning framework deeplearning4j and the jvm-based scientific computing library nd4j. the company was founded in 2014 by adam gibson and chris nicholson, and is headquartered in san francisco, california. it is privately funded, and raised $ 3 million in seed financing in 2016.
skymind is an artificial intelligence company supporting the open source deep learning framework deeplearning4j and the jvm-based scientific computing library nd4j. the company was founded in 2014 by adam gibson and chris nicholson, and is headquartered in san francisco, california. it is privately funded, and raised $ 3 million in seed financing in 2016.