Two years ago when I was living in New York City, my friend Sam came through town and was looking for a place to crash. We met at my apartment, took in the night skyline, and toasted to the opportunity to catch up. I had just spent the past few days deep in spreadsheets modeling the intricacies of my company's finances, and he was in the midst of modeling the impact of whether he should take a new job in a new city -- with all the different fixed costs, variable costs, cost of living, and other options. We ended up having an impassioned conversation deep into the night about the shortfalls of the financial services and tools available to us. We both had steady jobs, and might actually be making progress towards paying off our debt.

In building intelligent transportation systems such as taxi or rideshare services, accurate prediction of travel time and distance is crucial for customer experience and resource management. Using the NYC taxi dataset, which contains taxi trips data collected from GPS-enabled taxis [23], this paper investigates the use of deep neural networks to jointly predict taxi trip time and distance. We propose a model, called ST-NN (Spatio-Temporal Neural Network), which first predicts the travel distance between an origin and a destination GPS coordinate, then combines this prediction with the time of day to predict the travel time. The beauty of ST-NN is that it uses only the raw trips data without requiring further feature engineering and provides a joint estimate of travel time and distance. We compare the performance of ST-NN to that of state-of-the-art travel time estimation methods, and we observe that the proposed approach generalizes better than state-of-the-art methods. We show that ST-NN approach significantly reduces the mean absolute error for both predicted travel time and distance, about 17% for travel time prediction. We also observe that the proposed approach is more robust to outliers present in the dataset by testing the performance of ST-NN on the datasets with and without outliers.

Molecular machine learning has been maturing rapidly over the last few years. Improved methods and the presence of larger datasets have enabled machine learning algorithms to make increasingly accurate predictions about molecular properties. However, algorithmic progress has been limited due to the lack of a standard benchmark to compare the efficacy of proposed methods; most new algorithms are benchmarked on different datasets making it challenging to gauge the quality of proposed methods. This work introduces MoleculeNet, a large scale benchmark for molecular machine learning. MoleculeNet curates multiple public datasets, establishes metrics for evaluation, and offers high quality open-source implementations of multiple previously proposed molecular featurization and learning algorithms (released as part of the DeepChem open source library). MoleculeNet benchmarks demonstrate that learnable representations are powerful tools for molecular machine learning and broadly offer the best performance. However, this result comes with caveats. Learnable representations still struggle to deal with complex tasks under data scarcity and highly imbalanced classification. For quantum mechanical and biophysical datasets, the use of physics-aware featurizations can be more important than choice of particular learning algorithm.

Recent advancements in machine learning are reaching a level of sophistication that are exceeding the expectations of industry analysts and executives alike. We're familiar with Google DeepMind's AlphaGo that bested the greatest masters of the ancient Chinese game "Go" 10 years earlier than expected. More recently, a new exhibition at the New York Gallery Metro Pictures depicts machine-made images to people using algorithms. Retailers are redefining customer experiences with real-time personalization and convenience. Even most stock trades are governed by automated analysis of market outcomes and determination of future trends faster and more accurately than humans alone.

New AI technologies like machine learning and deep learning are fitting ever more snugly into the shifting enterprise landscape. Deep learning in particular is being adopted by an increasing number of enterprises for expanded insights and with the aim to better serving their clients. Thanks to more powerful systems and graphics processing units (GPUs), we are able to train complex AI models that enable these insights. IBM has long been one of the leaders in analytics and over the last year or two introduced two key new products, Data Science Experience and IBM PowerAI, designed to enable enterprises to more easily start using advanced AI technologies. Today we're unveiling that we are bringing these two key software tools for data scientists together.

Radiant is a robust tool for business analytics and running sophisticated models without any need for code development. It leverages the functions and tools in R and at the same time provides a user-friendly interface. With Radiant, you can manipulate and visualize your data, run different models from simple OLS to decision trees (CART) and neural networks, and evaluate your results. The application is based on the Shiny package and can be run locally or on a server. Radiant was developed by Vicent Nijs.

Even as machines known as "deep neural networks" have learned to converse, drive cars, beat video games and Go champions, dream, paint pictures and help make scientific discoveries, they have also confounded their human creators, who never expected so-called "deep-learning" algorithms to work so well. No underlying principle has guided the design of these learning systems, other than vague inspiration drawn from the architecture of the brain (and no one really understands how that operates either). Like a brain, a deep neural network has layers of neurons -- artificial ones that are figments of computer memory. When a neuron fires, it sends signals to connected neurons in the layer above. During deep learning, connections in the network are strengthened or weakened as needed to make the system better at sending signals from input data -- the pixels of a photo of a dog, for instance -- up through the layers to neurons associated with the right high-level concepts, such as "dog."

Open source has revolutionized and democratized the software in a big way. Hardware world, though, has remained resolutely insulated from opening up. Nvidia, world's leading graphics chip maker, is set to change that status quo. It recently made a revolutionary move that's sure to shake things up help shape the hardware chips industry. Nvidia has gone ahead with open sourcing the design of one of its AI chips designed to power deep learning.

It's a key question for many data scientists -- especially those that are new to the field: is Python or R better for data science? For those first venturing into the world of data science, it's important to master one language first, rather than looking to be a Jack of all trades from the offset. This is because your processes and techniques are what really matter most, and mastering these in one language before branching out into learning more is what is going to get you a strong footing in the data science world. Once you have a strong set of skills and techniques under your belt, moving into other languages is a great way of skilling up and ensuring that you stay competitive in your field, but your first programming language should allow you to learn as much as you can. And there's no shortage of languages that you can pick as your weapon of choice for doing so -- when it comes to data science, there's plenty on offer, including (but not limited to): Java, C, C, Scala, Perl, Clojure, Julia, and more.

Is there anyone working on either a decentralized deep learning algorithm, or a consumer facing app that uses AI to help people diagnose themselves? My wife was just diagnosed with CVID a couple of weeks ago, it's like AIDS except it's not Aquired, it's part genetic and part environmental - but it's a rare primary immunodeficiency disease. She's had this her entire life. She was misdiagnosed 3 or 4 times, most recently she was eating gluten free for the last 8 years because she was diagnosed as celiac disease. She's lost most of her hair over the last 6 months and has been in the hospital 3-4 times this year.