This post contains my answers from a Quora session I did on machine learning and artificial intelligence. Each section contains a link to the original Quora question, the overall session can be found here. Think carefully about what you actually want to achieve with it. Most fall into the latter camp, but it seems everyone fancies themselves as containing a bit of the former (particularly if they think they're going to solve AI). To do the former well, in the international community, requires really good foundations (particularly in mathematics) followed by a PhD with a supervisor who has experience of how that community works. Doing the second well is much easier from the perspective of learning machine learning. A data generator would often be a scientist or company that is working in a particular application and wants answers. They need access to machine learning researchers or statisticians to give advice on how to answer those questions. They should try and collaborate with experts in data analytics and data science, but they should be careful, there is a lot of hype around the term'big data' at the moment. It's a difficult area to navigate. Data generators typically need an interface to consume machine learning (or statistics) effectively, if this interface is poorly chosen a lot of wasted resource can result (things get very expensive very quickly for a lot of data generators!). A data consumer is where the largest demand is right at the moment, and should probably be the starting point for someone who wants to move in the right direction. An MSc in Data Science would be a good starting point. You can also use this experience to see if you want to transit into a machine learning generator (that's basically what happened to me). What are you passionate about? That is the route in to any subject. Is it a particular approach to learning or a particular application?

Is creativity a uniquely human trait? Defining the line between human and machine is becoming blurrier by the day as startups, big companies, and research institutions all compete to build the next generation of advanced AI. This arms race is bringing a new era of AI that won't prove its power by mastering human games, but by independently exhibiting ingenuity and creativity. Sophisticated AI is undertaking increasingly complex tasks like stock market predictions, research synthesis, political speech writing--don't worry, this article was still written by a human--and companies are beginning to pair deep learning with new robotics and digital manufacturing tools to create "smart manufacturing." Hod Lipson, professor of engineering at Columbia University and the director of Columbia's Creative Machines Labs, is pushing the next frontier of AI. It's an era that will be defined by biology-inspired machines that can evolve, self-model, and self-reflect--where machines will generate new ideas, and then build them. Fueling Lipson's work is the holy grail of AI--the pursuit of self-aware robots.

Despite the hype, there is a lot of work to be done before we can build conversational software. These are some notes about what interesting conversational software would look like, and what techniques we'll need to build it. It may be obvious, but I feel we have to point out that the giddy excitement around bots stems from being happy that there is something new to build/invest in/write medium posts about, and not from exciting new technology. For VCs, new platforms mean new opportunities to bundle and unbundle services, and new battlegrounds for the big players (likely leading to acquisitions). So even without real technological breakthroughs, there is at least some money to be made investing in bot startups.

This paper investigates the problem of cross-lingual transfer parsing, aiming at inducing dependency parsers for low-resource languages while using only training data from a resource-rich language (e.g., English). Existing model transfer approaches typically don't include lexical features, which are not transferable across languages. In this paper, we bridge the lexical feature gap by using distributed feature representations and their composition. We provide two algorithms for inducing cross-lingual distributed representations of words, which map vocabularies from two different languages into a common vector space. Consequently, both lexical features and non-lexical features can be used in our model for cross-lingual transfer. Furthermore, our framework is flexible enough to incorporate additional useful features such as cross-lingual word clusters. Our combined contributions achieve an average relative error reduction of 10.9% in labeled attachment score as compared with the delexicalized parser, trained on English universal treebank and transferred to three other languages. It also significantly outperforms state-of-the-art delexicalized models augmented with projected cluster features on identical data. Finally, we demonstrate that our models can be further boosted with minimal supervision (e.g., 100 annotated sentences) from target languages, which is of great significance for practical usage.

What a wonderful treasure trove this paper is! Schmidhuber provides all the background you need to gain an overview of deep learning (as of 2014) and how we got there through the preceding decades. Starting from recent DL results, I tried to trace back the origins of relevant ideas through the past half century and beyond. The main part of the paper runs to 35 pages, and then there are 53 pages of references. Now, I know that many of you think I read a lot of papers – just over 200 a year on this blog – but if I did nothing but review these key works in the development of deep learning it would take me about 4.5 years to get through them at that rate! And when I'd finished I'd still be about 6 years behind the then current state of the art!

The idea that humans are on the verge of developing an artificial intelligence whose abilities far outstrip our own is ridiculous, said Chris Bishop, Microsoft's director of research at Cambridge, highlighting the many limitations of AI systems today. "This is a good moment for a little reality check," he told a public discussion hosted by The Royal Society in London this week. While recent breakthroughs in machine learning have allowed computers to become as adept as the average person at recognising faces and objects and to make huge strides in areas such as voice recognition, Bishop cautioned against assuming that machines are outstripping human performance across the board. "Yes, deep learning has achieved human-level performance in object recognition but what does that mean? It means the machine makes about the same number of errors as the human. "The reason the machine is as good as the human at this is because it can distinguish between 157 varieties of mushroom, whereas it makes all kinds of stupid mistakes that humans wouldn't make." Even some of the most celebrated examples of machine intelligence, such as a Google DeepMind system beating a world champion in the notoriously complex game of Go, need to be understood in context of the time and effort that went into building the system, he said. The world's smartest cities: What IoT and smart governments will mean for you Intelligent cities are at the forefront of the next wave of the Internet of Things. The goals are to streamline communication and improve the lives of citizens. And save a little money along the way. "[Take] the Go example, where the machine has just about crept ahead of the best human.

We all know LSTM's are super powerful; So, we should know how they work and how to use them. Above is the element-wise product or Hadamard product. I'm using a sequence length of two here to demonstrate the unrolling over time of RNNs From here, we can pass forward our state and output and begin the next time-step. And since we're done our sequence we have everything we need to begin backpropogating. First we'll need to compute the difference in output from the expected (label).

I was downvoted to hell on my main post, and the replies ("but Kaggle and Gaussian noise!") strongly suggest we have a lot of enthusiasts/amateurs in the subreddit. Working professionals attracting top dollar know that focusing on non-NN techniques is currently a poor idea at best. I can't convey that better than Google/Facebook/everyone else in the world already have, through their hiring and their solutions to pretty much everything in the past few years. I feel for the people new to the field, and they should have exposure (through books and basic problems) to other techniques. They should then take "big" data sets, run NNs and the other techniques on them, and see what the performance differences are.

Elon Musk, founder of SpaceX, Tesla and PayPal, is worried about killer robots. "You know those stories where there's the guy with the pentagram and the holy water, and he's sure he can control the demon?" he has warned. That "unfriendly AI", as it is known in tech circles, would not be a boon for humanity is an easy cause to get behind. But unlike Musk – a tech entrepreneur who stands to make huge financial gains from AI in the short term – most of us don't have the luxury of taking the long view. The defeat, last week, of one of the world's strongest Go players, Lee Sedol, demonstrates the qualitative leap in AI that has already taken place.

Deep Learning Robot is built for research in deep learning and mobile robotics. It comes with pre-installed Ubuntu, Caffe, Torch, Theano, cuDNN v2, and CUDA 7.0. With researchers creating new deep learning algorithms and mobile robots collecting unprecedented amounts of data, computational capability is the key to unlocking insights from data in real time.