Most of us are wondering about "what's next for AI in 2019 leading up to 2020?" Some of us might be wondering about "Singularity". Others are still thinking, "AI is all hype and no action" (Although this subset is on the decline). All valid questions to ponder upon. Artificial Intelligence has made substantial progress in 2018.

Despite all its benefits and the ease that technology has brought in, the fear that new-age technologies like artificial intelligence (AI), machine learning and robotics would displace human jobs still looms. However, some researchers don't agree with the idea that technology would take away jobs from humans anytime soon. Some researchers at University of California, Los Angeles (UCLA) in the US conducted various experiments, which show the severe limitations of'deep learning' machines. "How smart is the form of AI known as deep learning computer networks, and how closely do these machines mimic the human brain? They have improved greatly in recent years, but still have a long way to go," reports a team of UCLA cognitive psychologists in the journal PLOS Computational Biology.

In this post I will show how to build a deep learning network to identify 102 different types of flowers. In this post, I will walk through how I used PyTorch to complete this project. As I was new to creating deep learning models with PyTorch, I hope this post can help others who are using this deep learning library for the first time. Before we get too far along, let's take a look at the data we are working with, as well as how the folder structure was set up for this project. The folder structure used in this situation is how you should set up your folders for essentially every PyTorch model you create.

TensorFlow is a very powerful and open source library for implementing and deploying large-scale machine learning models. This makes it perfect for research and production. Over the years it has become one of the most popular libraries for deep learning. The goal of this post is to build an intuition and understanding for how deep learning libraries work under the hood, specifically TensorFlow. To achieve this goal, we will mimic its API and implement its core building blocks from scratch. This has the neat little side effect that, by the end of this post, you will be able to use TensorFlow with confidence, because you'll have a deep conceptual understanding of the inner workings. You will also gain further understanding of things like variables, tensors, sessions or operations. So let's get started, shall we? TensorFlow is a framework composed of two core building blocks -- a library for defining computational graphs and a runtime for executing such graphs on a variety of different hardware. A computational graph has many advantages but more on that in just a moment.

Machine learning is an incredibly broad and diverse field, with a non-stop increase on research, along a multitude of applications. Thus writing a list enlisting the best machine learning researchers on the field proves challenging for a number of reasons. Please mind that this list encompasses researchers who are currently working on the field. Also, please mind that this list is by no means ranked. Everyone listed below has done extraordinary work to advance humanity's state of AI further.

In a case study, Alexandre Hubert -- lead data scientist of software company Dataiku's U.K. operations -- worked on a team of three to automate mail processing with deep learning.

Many biosensing applications rely on characterization of specific analytes such as proteins, viruses and bacteria, among many other targets, which can be accomplished by using micro- or nano-scale particles. In such biosensors, these particles are coated with a surface chemistry that makes them stick to the target analyte forming clusters in response. The higher the target analyte concentration is, the larger the number of clusters gets. Therefore, monitoring and characterizing these particle clusters can tell us if the target analyte is present in a sample and in what concentration. Current methods to perform such an analysis are limited in that they are either capable of only a coarse readout or rely on expensive and bulky microscopes, which limit their applicability to address different biosensing needs, especially in resource limited environments.

If a doctor told that you needed surgery, you would want to know why -- and you'd expect the explanation to make sense to you, even if you'd never gone to medical school. Been Kim, a research scientist at Google Brain, believes that we should expect nothing less from artificial intelligence. As a specialist in "interpretable" machine learning, she wants to build AI software that can explain itself to anyone. Since its ascendance roughly a decade ago, the neural-network technology behind artificial intelligence has transformed everything from email to drug discovery with its increasingly powerful ability to learn from and identify patterns in data. But that power has come with an uncanny caveat: The very complexity that lets modern deep-learning networks successfully teach themselves how to drive cars and spot insurance fraud also makes their inner workings nearly impossible to make sense of, even by AI experts.

Artificial intelligence (AI) is not a new concept, with its origins dating back to the 1950s. Yet AI has only recently emerged to the forefront of investment interest from industry, government, and venture capital after decades of relative dormancy. What contributed to the thawing of AI's winter and current boom? AI is booming largely due to advancements in pattern recognition capabilities resulting from deep learning--a subset of machine learning where an artificial neural network consists of over two layers of processing. Machine learning is a subset of AI that involves algorithms that are able to learn from data, versus a programmer having to explicitly code instructions.

We report FPGA implementation results of low precision CNN convolution layers optimized for sparse and constant parameters. We describe techniques that amortizes the cost of common factor multiplication and automatically leverage dense hand tuned LUT structures. We apply this method to corner case residual blocks of Resnet on a sparse Resnet50 model to assess achievable utilization and frequency and demonstrate an effective performance of 131 and 23 TOP/chip for the corner case blocks. The projected performance on a multichip persistent implementation of all Resnet50 convolution layers is 10k im/s/chip at batch size 2. This is 1.37x higher than V100 GPU upper bound at the same batch size after normalizing for sparsity.