Traffic prediction is a fundamental and vital task in Intelligence Transportation System (ITS), but it is very challenging to get high accuracy while containing low computational complexity due to the spatiotemporal characteristics of traffic flow, especially under the metropolitan circumstances. In this work, a new topological framework, called Linkage Network, is proposed to model the road networks and present the propagation patterns of traffic flow. Based on the Linkage Network model, a novel online predictor, named Graph Recurrent Neural Network (GRNN), is designed to learn the propagation patterns in the graph. It could simultaneously predict traffic flow for all road segments based on the information gathered from the whole graph, which thus reduces the computational complexity significantly from O(nm) to O(n m), while keeping the high accuracy. Moreover, it can also predict the variations of traffic trends. Experiments based on real-world data demonstrate that the proposed method outperforms the existing prediction methods.

Deep Neural Networks (DNNs) require very large amounts of computation both for training and for inference when deployed in the field. Many different algorithms have been proposed to implement the most computationally expensive layers of DNNs. Further, each of these algorithms has a large number of variants, which offer different trade-offs of parallelism, data locality, memory footprint, and execution time. In addition, specific algorithms operate much more efficiently on specialized data layouts and formats. We state the problem of optimal primitive selection in the presence of data format transformations, and show that it is NP-hard by demonstrating an embedding in the Partitioned Boolean Quadratic Assignment problem (PBQP). We propose an analytic solution via a PBQP solver, and evaluate our approach experimentally by optimizing several popular DNNs using a library of more than 70 DNN primitives, on an embedded platform and a general purpose platform. We show experimentally that significant gains are possible versus the state of the art vendor libraries by using a principled analytic solution to the problem of layout selection in the presence of data format transformations.

Glaucoma is the second leading cause of blindness in the world, impacting approximately 2.7 million people in the U.S alone [1]. It is a complex set of diseases and, if left untreated, can lead to blindness. It's a particularly large issue in Australia, where only 50% of all people who have it are actually diagnosed and receive the treatment they need [2]. As part of a team of scientists from IBM and New York University, my colleagues and I are looking at new ways AI could be used to help ophthalmologists and optometrists further utilize eye images, and potentially help to speed the process for detecting glaucoma in images. In a recent paper, we detail a new deep learning framework that detects glaucoma directly from raw optical coherence tomographic (OCT) imaging, a method which uses light waves to take cross-section pictures of the retina.

Researchers from two different CIFAR programs collaborate to show fruit flies can do more than previously thought possible. Despite the simplicity of their visual system, fruit flies are able to reliably distinguish between individuals based on sight alone. This is a task that even humans who spend their whole lives studying Drosophila melanogaster struggle with. Researchers have now built a neural network that mimics the fruit fly's visual system and can distinguish and re-identify flies. This may allow the thousands of labs worldwide that use fruit flies as a model organism to do more longitudinal work, looking at how individual flies change over time.

This spring I presented a talk entitled "Effective Transfer Learning for NLP" at ODSC East. The talk was intended to demonstrate how surprisingly effective pre-trained word and document embeddings are at low training data volumes, and to lay out a set of practical recommendations for applying these techniques to your own tasks. Thanks to some excellent research by Alec Radford and the team at OpenAI, our recommendations are beginning to change. To explain why the tides are shifting, let's first walk through the rubric we use at Indico to evaluate whether or not a novel machine learning method is viable for industry use. Let's see how well pre-trained word document embeddings satisfy these requirements: In short, using pre-trained embeddings is computationally cheap and performs well at the lower extremes of training data availability, but using static representations imposes an unfortunate cap on the benefit gained from additional training data.

An Indian student in the US has created a space-detecting algorithm that can help tackle the problem of finding a parking spot by using big data analytics and save a person's time and money. Sai Nikhil Reddy Mettupally, who is studying at The University of Alabama in Huntsville (UAH), has also won second prize at the 2018 Science and Technology Open House competition for his creation. According to a university press release, Sai's creation relies on big data analytics and deep-learning techniques to lead drivers directly to an empty parking spot. Big data analytics is a complex process of examining large and varied data sets to uncover information including hidden patterns, unknown correlations, market trends and customer preferences. Sai conceived the idea shortly after the university transitioned to zone parking last fall. "The data show that, on a typical day, there is a high chance that students or faculty members will have difficulty getting a parking spot between 11 am and 1 pm, leading to the wastage of time and fuel, and adding to the pollution," he says.

Now, the eager execution enables you to interact with the framework as a Python programmer. So, you get all the associated advantages of writing the code line-by-line rather than waiting around while you create huge graphs. No wonder more and more developers are slowly gravitating towards TensorFlow eager execution now. Let's get something straight right off the beat – when you combine TensorFlow with Keras API, you are left with one outcome – simple and efficient neural network construction. The thing is, Keras is more or less concerned with simple prototyping and user-friendliness. This was something that was sorely missing in the old versions of TensorFlow. But over time, these issues decreased, culminating in tf.keras. This combination has proven a boon for developers who prefer to build the neural networks layer by layer and adopt an object-oriented approach. For example, you now possess the power to create an advanced yet sequential neural network with all the basic bells and bells such as dropout using only a handful of code lines.

Now, the eager execution enables you to interact with the framework as a Python programmer. So, you get all the associated advantages of writing the code line-by-line rather than waiting around while you create huge graphs. No wonder more and more developers are slowly gravitating towards TensorFlow eager execution now. Let's get something straight right off the beat – when you combine TensorFlow with Keras API, you are left with one outcome – simple and efficient neural network construction. The thing is, Keras is more or less concerned with simple prototyping and user-friendliness. This was something that was sorely missing in the old versions of TensorFlow. But over time, these issues decreased, culminating in tf.keras. This combination has proven a boon for developers who prefer to build the neural networks layer by layer and adopt an object-oriented approach. For example, you now possess the power to create an advanced yet sequential neural network with all the basic bells and bells such as dropout using only a handful of code lines.

According to IDC, global spending on Artificial Intelligence (AI) and cognitive systems will reach $19 billion by 2018. This is an increase by approximately 54% over the total amount consumed in 2017. Mergers and acquisitions are constantly taking place. We all know that AI is creating new opportunities in every sector be it healthcare or travel. So, companies all around the world are enchasing on such opportunities to offer much-improved products or services to consumers through mergers and acquisitions.

The industry is witnessing too much of technological advances especially in the field of Artificial Intelligence. The disruption has been so overwhelming that there is no dearth of professionals who aren't awestruck by the awesomeness and rise of the exponential technologies. Enterprises with ever-growing business demands are beginning to dig deeper into the findings and services of technology leaders like Amazon, Google and Microsoft. Therefore, the various emerging technologies belonging to the AI portfolio are gaining a lot of importance and being discussed as well as tried out by stalwarts belonging to different fields. As the amount of data business houses generate continues to grow to massive mind-boggling levels, the AI maturity and the potential problems AI can help solve grow right along.