In the first post of this series, I've written my thoughts about the rise of Artificial Intelligence, and how it will serve as an enabler for ever greater progress. In this one, I deal with its (hopefully) short-term implications. As we've seen, despite all current impressive technological advancements, jobs and professions that call for increased inventiveness and creativity, will remain under human control in the foreseeable future. Even more so for jobs where emotions and empathy play a great role. The same cannot be said for the kind of job, whether manual or mental, that requires little or no creativity.

ReplyBuy, a finalist in the 1st and Future competition, wants to use the text message to get you tickets for sporting events. The current version of ReplyBuy works like this -- the company sends a text message to all San Francisco 49ers fans; whoever replies "Buy Now" the fastest gets the tickets. Today, the company is making the platform immensely more useful with the launch of ReplyBuy.ai. Indeed, ReplyBuy is introducing artificial intelligence to the sports and entertainment vertical. Instead of just receiving text messages when tickets are available, users will now be able to send a text message with a request to buy tickets for whichever event they want; the chatbot will ask a few follow-up questions, like "how many tickets do you want?" and "what's your price range."

Four years ago an article was written for the Harvard Business Review by both Tom Davenport and DJ Patil entitled "Data Scientist: The Sexiest Job of the 21st century." The article predicted that the demand for skilled people in this area was only set to rise and as a recruitment/staffing specialist within this space, I can only confirm that this has come to pass. With this evolution, I now see the waters have become even harder to navigate, with companies now looking to hire people proficient in an even greater number of disciplines. That is why I wanted to do this post. As with any shortage, you need to get creative but from my perspective, I am often a little surprised at how little flexibility there is when looking to employ people for specialist niche roles.

Five years ago, music personalization at Spotify was a tiny team. The team read papers, developed models, wrote data pipelines and built services. Today personalization involves multiple teams in New York, Boston & Stockholm producing datasets, feature engineering and serving up products to users. Features like Discover Weekly and Release Radar are but the tip of a huge personalization iceberg. One thing we have noticed is the overhead of running services.

In the 2016 Google Founder's Letter, CEO Sundar Pichai cited Google's long-term investment in machine learning and AI. "It's what allows you to use your voice to search for information," he explained, "to translate the web from one language to another, to filter the spam from your inbox, to search for'hugs' in your photos and actually pull up pictures of people hugging ... to solve many of the problems we encounter in daily life. It's what has allowed us to build products that get better over time, making them increasingly useful and helpful." In addition to using machine learning for its own products, Google has released several applied machine learning services -- for vision, speech, natural language, and translation -- and has open-sourced its TensorFlow scalable machine learning package. An additional service based on TensorFlow, the Cloud Machine Learning Platform, is still in a closed alpha test phase.

Is there a program out there that's easy to set up that I could run on my unremarkable desktop PC to watch it try a simple game for a few hours and see how much it learns? I'd love to watch the evolution of a program as it learns something like Pacman on its own.

Bibliographic analysis considers the author's research areas, the citation network and the paper content among other things. In this paper, we combine these three in a topic model that produces a bibliographic model of authors, topics and documents, using a nonparametric extension of a combination of the Poisson mixed-topic link model and the author-topic model. This gives rise to the Citation Network Topic Model (CNTM). We propose a novel and efficient inference algorithm for the CNTM to explore subsets of research publications from CiteSeerX. The publication datasets are organised into three corpora, totalling to about 168k publications with about 62k authors. The queried datasets are made available online. In three publicly available corpora in addition to the queried datasets, our proposed model demonstrates an improved performance in both model fitting and document clustering, compared to several baselines. Moreover, our model allows extraction of additional useful knowledge from the corpora, such as the visualisation of the author-topics network. Additionally, we propose a simple method to incorporate supervision into topic modelling to achieve further improvement on the clustering task.

Several machine learning problems arising in natural language processing can be modeled as a sequence labeling problem. Gaussian processes (GPs) provide a Bayesian approach to learning such problems in a kernel based framework. We develop Gaussian process models based on pseudo-likelihood to solve sequence labeling problems. The pseudo-likelihood model enables one to capture multiple dependencies among the output components of the sequence without becoming computationally intractable. We use an efficient variational Gaussian approximation method to perform inference in the proposed model. We also provide an iterative algorithm which can effectively make use of the information from the neighboring labels to perform prediction. The ability to capture multiple dependencies makes the proposed approach useful for a wide range of sequence labeling problems. Numerical experiments on some sequence labeling problems in natural language processing demonstrate the usefulness of the proposed approach.

IBM Research have announced a multi-year collaboration with the Department of Brain & Cognitive Sciences at MIT to advance the scientific field of machine vision, a core aspect of artificial intelligence. The new IBM-MIT Laboratory for Brain-inspired Multimedia Machine Comprehension's (BM3C) goal will be to develop cognitive computing systems that emulate the human ability to understand and integrate inputs from multiple sources of audio and visual information into a detailed computer representation of the world that can be used in a variety of computer applications in industries such as healthcare, education, and entertainment. The BM3C will address technical challenges around both pattern recognition and prediction methods in the field of machine vision that are currently impossible for machines alone to accomplish. For instance, humans watching a short video of a real-world event can easily recognize and produce a verbal description of what happened in the clip as well as assess and predict the likelihood of a variety of subsequent events, but for a machine, this ability is currently impossible. Beginning in September 2016 in Cambridge, the BMC3 collaboration will bring together leading brain, cognitive, and computer scientists to conduct research in the field of unsupervised machine understanding of audio-visual streams of data, using insights from next-generation models of the brain to inform advances in machine vision.

Learn everything you need to design, train, and integrate neural network-powered machine learning into your applications with widely used open-source frameworks and the NVIDIA deep learning platform. As a perk, receive a certificate of attendance and free online training credits.