Publications like The Wall Street Journal, Forbes and Fortune have all called 2017 "The Year of AI." AI outperformed professional gamers and poker players in new realms. Access to deep learning education expanded through various online programs. The speech recognition accuracy record was broken multiple times, most recently by Microsoft. And research universities and organizations like Oxford, Massachusetts General Hospital and GE's Avitas Systems invested in deep learning supercomputers. These are a few of many milestones in 2017.

While the use of artificial intelligence to predict deaths may sound ludicrous, researchers are trying to establish the technology's potential in alerting physicians and medical professionals of patients that are at greater risks of dying in the near future. This way, doctors can administer the right end-of-life approach in dealing with the patients and their loved ones. A team at Stanford University has examined the use of artificial intelligence in palliative care in their paper "Improving Palliative Care with Deep Learning" published on the arXiv preprint server. Researchers used the machine learning technique called deep learning, which utilizes neural networks to filter and learn from massive data, in the study. What they did is come up with a model and fed its deep learning algorithm with data from the Electronic Health Records of 2 million adult and child patients admitted to either Stanford Hospital or Lucile Packard Children's hospital.

In October 2017, researchers at Google DeepMind published a paper on an artificial intelligence (AI) program called AlphaGo Zero. Unlike previous incarnations of AlphaGo, this updated version mastered the game of Go through self-play alone. Talking about the achievement, lead researcher David Silver explained that AlphaGo Zero had invented "its own variants which humans don't even know about or play at the moment." And it's here that a new and exciting use for AI comes to light. Could it be that AI might teach humans about the world around us?

We've talked about, speculated and often seen different applications for Artificial Intelligence - But what about one piece of technology that will not only gather relevant information, better customer service and could even differentiate your business from the crowd? ChatBots are here, and they came change and shape-shift how we've been conducting online business. Fortunately technology has advanced enough to make this a valuable tool something accessible that almost anybody can learn how to implement. If you want to learn one of the most attractive, customizable and cutting edge pieces of technology available, then this course is just for you!

In the last year, the hype around AI has been deafening. Despite a long hiatus in the AI research community without any major wins, we've made some amazing progress lately. From headlines about AI protecting our digital identities to driving our cars to even diagnosing our maladies, it seems like AI has been everywhere. Unless you have been living under a rock, chances are your feed has been littered with references to deep learning, convolutional neural networks (CNNs), recurrent neural nets (RNNs), or TensorFlow, each accompanied by a bold proclamation technology is about to solve everything from world hunger to health care. Ok, if you're not breaking out the champagne, I understand.

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US-based Workday, which is best known for its Software-as-a-Service (SaaS)-based applications, is acquiring SkipFlag, a start-up that uses deep learning to help enterprises make sense of the large amounts of data they collect each day, reports Enterprise Cloud News (FinTech Futures' sister publication). Joe Korngiebel, Workday CTO, notes that the deal is allowing the company to expand beyond SaaS in the human resources and financial planning fields and into more cutting-edge technologies. "SkipFlag joining Workday is yet another step in our journey to continually invest in areas such as machine learning, advanced search, and natural language processing – further powering our products to be faster and more intelligent so our customers can access even better insights when and where they need," Korngiebel says. Financial details of the acquisition were not released. Although a start-up, SkipFlag has roots in enterprise software and deep learning.

TextCNN, the convolutional neural network for text, is a useful deep learning algorithm for sentence classification tasks such as sentiment analysis and question classification. However, neural networks have long been known as black boxes because interpreting them is a challenging task. Researchers have developed several tools to understand a CNN for image classification by deep visualization, but research about deep TextCNNs is still insufficient. In this paper, we are trying to understand what a TextCNN learns on two classical NLP datasets. Our work focuses on functions of different convolutional kernels and correlations between convolutional kernels.

In this paper, we consider a general stochastic optimization problem which is often at the core of supervised learning, such as deep learning and linear classification. We consider a standard stochastic gradient descent (SGD) method with a fixed, large step size and propose a novel assumption on the objective function, under which this method has the improved convergence rates (to a neighborhood of the optimal solutions). We then empirically demonstrate that these assumptions hold for logistic regression and standard deep neural networks on classical data sets. Thus our analysis helps to explain when efficient behavior can be expected from the SGD method in training classification models and deep neural networks.

Transfer learning has revolutionized computer vision, but existing approaches in NLP still require task-specific modifications and training from scratch. We propose Fine-tuned Language Models (FitLaM), an effective transfer learning method that can be applied to any task in NLP, and introduce techniques that are key for fine-tuning a state-of-the-art language model. Our method significantly outperforms the state-of-the-art on five text classification tasks, reducing the error by 18-24% on the majority of datasets. We open-source our pretrained models and code to enable adoption by the community.