Rapid development of AI applications has stimulated demand for, and has given rise to, the rapidly growing number and diversity of AI MSc degrees. AI and Robotics research communities, industries and students are becoming increasingly aware of the problems caused by unsafe or insecure AI applications. Among them, perhaps the most famous example is vulnerability of deep neural networks to ``adversarial attacks''. Owing to wide-spread use of neural networks in all areas of AI, this problem is seen as particularly acute and pervasive. Despite of the growing number of research papers about safety and security vulnerabilities of AI applications, there is a noticeable shortage of accessible tools, methods and teaching materials for incorporating verification into AI programs. LAIV -- the Lab for AI and Verification -- is a newly opened research lab at Heriot-Watt university that engages AI and Robotics MSc students in verification projects, as part of their MSc dissertation work. In this paper, we will report on successes and unexpected difficulties LAIV faces, many of which arise from limitations of existing programming languages used for verification. We will discuss future directions for incorporating verification into AI degrees.

Uber is one of those organizations that rely heavily on data. Each day, millions of trips take place in 700 cities across the world, generating information on traffic, preferred routes, estimated times of arrival/delivery, drop-off locations, and more that enables Uber to deliver a smooth riding experience to its customers. With access to the rich dataset coming from the cabs, drivers, and users, Uber has been investing in machine learning and artificial intelligence to enhance its business. Uber AI Labs consists of ML researchers and practitioners that translate the benefits of the state of the art machine learning techniques and advancements to Uber's core business. From computer vision to conversational AI to sensing and perception, Uber has successfully infused ML and AI into its ride-sharing platform.

Perhaps "artificial" is too artificial of a word for the AI equation. Augmented intelligence describes the essence of the technology in a more elegant and accurate way. AI has been around for some time, and Dr. David Bray, executive director of the People-Centered Internet, sees the current "third wave" of AI as a convergence of neural networks, deep learning, pattern matching, Internet of Things, and scaling tasks beyond human limitations. AI's power and potential arises from pairing humans and machines, so that "the human is learning from the machine and, at the same time, the machine is learning from the human. Bray explored the possibilities of augmented intelligence in with a recent CXOTalk interview, where he was joined by Fred Laluyaux, CEO and President of Aera Technology. The discussion was hosted by Michael Krigsman, founder of CXOTalk and well-known industry commentator. Are organizations ready to embrace the power of augmented intelligence? Business leaders or C-level executives "intuitively know that the way they organize and the way decisions are being made in their organization is not sufficient anymore," he explains. "The way decisions are made has not really fundamentally evolved.

AI is transforming the health industry. As a society we are becoming more data hungry than ever before, and this is also evident at an individual level though our growing fascination with wearable technology and e-health. Previously, we've summed up AI and deep learning from a beginner's perspective, and discussed some of their use cases in healthcare, specifically medical diagnoses. Below, we continue in healthcare, providing a brief overview of deep learning in drug discovery, e-health and electronic health records. Drug discovery from idea conception to a marketable product, can take over decade, and on average costs US$2.6 billion.

The same technology that powers Siri and face recognition on your iPhone has also found success in medicine. By automatically analyzing microscopic images of breast tumor biopsies, artificial intelligence may one day help guide cancer treatments. This particular type of AI is called deep learning, and over the last few years has become a part of our everyday lives. Its applications continue to expand to areas like language translation and self-driving cars, enabled by massive repositories of data. While deep learning was first applied to recognizing people, cars, and other everyday objects in photographs, it has more recently been adapted to study cancer.

The use of machine learning (ML) has been increasing rapidly in the medical imaging field, including computer-aided diagnosis (CAD), radiomics, and medical image analysis. Recently, an ML area called deep learning emerged in the computer vision field and became very popular in many fields.

Reproducibility is an essential requirement for a lot of fields related to research. It also includes areas that are based on machine learning techniques. But it's also true that most of the ML-based research publications are either not reproducible or are too difficult to reproduce. The PyTorch Team announced the release of PyTorch Hub yesterday. If you don't know, PyTorch is basically a machine learning library for Python.

Object detection is a very popular application for deep learning, used from simple home automation apps to safety critical autonomous driving. GPUs have proven to be very powerful at executing deep learning training and inference. Many libraries and tools exist today to perform these tasks. In this piece, we'll show you how to run a high-performance object detection pipeline for inference on GPUs in 10 mins. Our Python application takes frames from a live video stream and performs object detection on GPUs.

Do you sometimes feel that machine learning is too broad and vast to keep up? I certainly feel that way. It can become overwhelming as a data scientist to simply keep track of all that's happening in machine learning. My aim of running this GitHub series since January 2018 has been to take that pain away for our community. We trawl through every open source machine learning release each month and pick out the top developments we feel you should absolutely know.

Handwriting is a one of the challenge in NLP task. It is because it can be various among different people. Sometimes, "O" can be written as "0" while human begin has the capability to distinguish whether it is "O" or "0" from contextualize information. For example, "0" will be used in phone number while "O" will be used as part of English word. Another skill is lexicon searching.