When most people think about artificial intelligence (AI), they envision high-tech robots and automated processes, or, in a more romantic sense, the promise of technology finally being realized. Hardly anyone thinks about the actual people behind the technology. However, for every game-changing technology and innovative product, there is a team of humans conceiving new ideas, powering them from behind the scenes, and collaborating alongside it. AI technologies have existed in some form for a few decades, but until recently, the possibilities were more science fiction than reality. It's only been in the past few years that the potential of AI has started to come to life, and it is growing at an incredibly fast rate.

What if instead of searching through menus within programs like Microsoft Excel, our computers could understand the problem we're trying to solve and write the software to solve it? It's a hyper-futuristic idea, but one that has recently seen progress from Microsoft Research and the University of Cambridge. In a November 2016 paper, which gained notoriety after being accepted into one of the year's largest artificial intelligence conferences, Microsoft and Cambridge built an algorithm capable of writing code that would solve simple math problems. The algorithm, named DeepCoder, would be able to augment its own ability by also looking at potential combinations of code for how a problem could be solved. However, this doesn't mean it steals code, or copy and pastes it from existing software, or searches the internet for solutions, as some reports have claimed. "We're targeting the people who can't or don't want to code, but can specify what their problem is," says Marc Brockschmidt of Microsoft Research, a co-author of the paper, likening the work to Excel formulas that could take simple commands to solve for answers without being given the mathematical equation.

Dr. Stephen Parker, VP of professional graphics, took the stage to give an overview of the use of artificial intelligence in graphics applications. While the first working algorithm using deep feedforward perceptrons was published around 52 years ago in 1965 by Alexey Ivakhnenko and Valentin Lapa, deep learning in graphics applications has reached a combinatorial explosion thanks to some great work that has been recently accomplished by a group of researchers at the University of Toronto. In 2012, professor Geoffrey E Hinton and two students, Alex Krizhevsky and Ilya Sutskever, entered an image recognition content to build computer vision algorithms that learned to identify millions of objects in millions of pictures. Using the most efficient algorithms at the time, the team was able to take the error rate of an average human and cut it in half. They later created a company called DNNresearch, which Google bought the following year.

Citizens often do not make the connection between AI and the amazing new products and services broadly available on the market. But the impact of AI goes far beyond innovative consumer products. Historically, humans exclusively created and delivered knowledge. Now, in the era of cognitive computing, machines have the potential to help people unlock knowledge and insights from massive volumes of data. This combination of human and machine makes AI both powerful and transformative.

Tensorflow is a powerful open-source software library for machine learning developed by researchers at Google Brain. It has many pre-built functions to ease the task of building different neural networks. Tensorflow allows distribution of computation across different computers, as well as multiple CPUs and GPUs within a single machine. TensorFlow provides a Python API, as well as a less documented C API. For this course, we will be using Python.

Naver and LINE Corp. unveiled their artificial intelligence (AI)-based assistant platform Clova at the Mobile World Congress (MWC) 2017 in Barcelona, the companies reported Thursday. According to Naver and its mobile service subsidiary, Clova, which stands for "cloud virtual assistant," aims at an AI platform that works based on the five human senses. "Clova is a platform that incorporates various AI technologies including vocal, visual and conversational engines," LINE Corp. CEO Idezawa Takeshi said in his keynote at the MWC 2017, Wednesday. The two companies are jointly developing Clova by improving Naver Labs' voice-recognizing assistant service AMICA. They said it will expand the system to have more cognitive capabilities to make it more humanlike.

Here's a quick story you've probably heard before, followed by one you probably haven't. In 1979 a young Steve Jobs paid a visit to Xerox PARC, the legendary R&D lab in Palo Alto, California, and witnessed a demonstration of something now called the graphical user interface. An engineer from PARC used a prototype mouse to navigate a computer screen studded with icons, drop-down menus, and "windows" that overlapped each other like sheets of paper on a desktop. It was unlike anything Jobs had seen before, and he was beside himself. "Within 10 minutes," he would later say, "it was so obvious that every computer would work this way someday." As legend has it, Jobs raced back to Apple and commanded a team to set about replicating and improving on what he had just seen at PARC. And with that, personal computing sprinted off in the direction it has been traveling for the past 40 years, from the first Macintosh all the way up to the iPhone.

Yield curve forecasting is an important problem in finance. In this work we explore the use of Gaussian Processes in conjunction with a dynamic modeling strategy, much like the Kalman Filter, to model the yield curve. Gaussian Processes have been successfully applied to model functional data in a variety of applications. A Gaussian Process is used to model the yield curve. The hyper-parameters of the Gaussian Process model are updated as the algorithm receives yield curve data. Yield curve data is typically available as a time series with a frequency of one day. We compare existing methods to forecast the yield curve with the proposed method. The results of this study showed that while a competing method (a multivariate time series method) performed well in forecasting the yields at the short term structure region of the yield curve, Gaussian Processes perform well in the medium and long term structure regions of the yield curve. Accuracy in the long term structure region of the yield curve has important practical implications. The Gaussian Process framework yields uncertainty and probability estimates directly in contrast to other competing methods. Analysts are frequently interested in this information. In this study the proposed method has been applied to yield curve forecasting, however it can be applied to model high frequency time series data or data streams in other domains.

Early and accurate identification of parkinsonian syndromes (PS) involving presynaptic degeneration from non-degenerative variants such as Scans Without Evidence of Dopaminergic Deficit (SWEDD) and tremor disorders, is important for effective patient management as the course, therapy and prognosis differ substantially between the two groups. In this study, we use Single Photon Emission Computed Tomography (SPECT) images from healthy normal, early PD and SWEDD subjects, as obtained from the Parkinson's Progression Markers Initiative (PPMI) database, and process them to compute shape- and surface fitting-based features for the three groups. We use these features to develop and compare various classification models that can discriminate between scans showing dopaminergic deficit, as in PD, from scans without the deficit, as in healthy normal or SWEDD. Along with it, we also compare these features with Striatal Binding Ratio (SBR)-based features, which are well-established and clinically used, by computing a feature importance score using Random forests technique. We observe that the Support Vector Machine (SVM) classifier gave the best performance with an accuracy of 97.29%. These features also showed higher importance than the SBR-based features. We infer from the study that shape analysis and surface fitting are useful and promising methods for extracting discriminatory features that can be used to develop diagnostic models that might have the potential to help clinicians in the diagnostic process.

Topic models are one of the most popular methods for learning representations of text, but a major challenge is that any change to the topic model requires mathematically deriving a new inference algorithm. A promising approach to address this problem is autoencoding variational Bayes (AEVB), but it has proven diffi- cult to apply to topic models in practice. We present what is to our knowledge the first effective AEVB based inference method for latent Dirichlet allocation (LDA), which we call Autoencoded Variational Inference For Topic Model (AVITM). This model tackles the problems caused for AEVB by the Dirichlet prior and by component collapsing. We find that AVITM matches traditional methods in accuracy with much better inference time. Indeed, because of the inference network, we find that it is unnecessary to pay the computational cost of running variational optimization on test data. Because AVITM is black box, it is readily applied to new topic models. As a dramatic illustration of this, we present a new topic model called ProdLDA, that replaces the mixture model in LDA with a product of experts. By changing only one line of code from LDA, we find that ProdLDA yields much more interpretable topics, even if LDA is trained via collapsed Gibbs sampling.