ChronoMID builds on the success of cross-modal convolutional neural networks (X-CNNs), making the novel application of the technique to medical imaging data. Specifically, this paper presents and compares alternative approaches - timestamps and difference images - to incorporate temporal information for the classification of bone disease in mice, applied to micro-CT scans of mouse tibiae. Whilst much previous work on diseases and disease classification has been based on mathematical models incorporating domain expertise and the explicit encoding of assumptions, the approaches given here utilise the growing availability of computing resources to analyse large datasets and uncover subtle patterns in both space and time. After training on a balanced set of over 75000 images, all models incorporating temporal features outperformed a state-of-the-art CNN baseline on an unseen, balanced validation set comprising over 20000 images. The top-performing model achieved 99.54% accuracy, compared to 73.02% for the CNN baseline.

We propose nnstreamer, a software system that handles neural networks as filters of stream pipelines, applying the stream processing paradigm to neural network applications. A new trend with the wide-spread of deep neural network applications is on-device AI; i.e., processing neural networks directly on mobile devices or edge/IoT devices instead of cloud servers. Emerging privacy issues, data transmission costs, and operational costs signifies the need for on-device AI especially when a huge number of devices with real-time data processing are deployed. Nnstreamer efficiently handles neural networks with complex data stream pipelines on devices, improving the overall performance significantly with minimal efforts. Besides, nnstreamer simplifies the neural network pipeline implementations and allows reusing off-shelf multimedia stream filters directly; thus it reduces the developmental costs significantly. Nnstreamer is already being deployed with a product releasing soon and is open source software applicable to a wide range of hardware architectures and software platforms.

Imagine if a few hundred microbots were to be introduced into your bloodstream -- their task being the continuous monitoring of your vital signs, or diseases, like a hairline fracture, a clot in an artery or even cancer cells. They will analyze and remove these problems on the go and signal you when you need to take medication or require surgery. With such round-the-clock monitoring, the human body will hardly ever suffer from serious ailments. According to the historian-turned-philosopher, Yuval Noah Harari, with such technology humans can turn a-mortal, if not immortal. At the core of this scientific utopia will be AI technology of various kinds, like deep learning and neural networks.

Any organization that has a large media archive struggles with the same challenge – how can we transform our media archives into business value? Media content management is hard, and so is content discovery at scale. Content categorization by topics is an intuitive approach that makes it easier for people to search for the content they need. However, content categorization is usually deductive and doesn't necessarily appear explicitly in the video. For example, content that is focused on the topic of'healthcare' may not actually have the word'healthcare' presented in it, which makes the categorization an even harder problem to solve.

The promise of AI in healthcare is finally starting to move beyond speculation. In recent years companies have been funneling funds into advancements, especially those that cut costs and promote patient health. Spending on healthcare AI technology is expected to surpass $34 billion by 2025, compared to $2.1 billion in 2018, according to market intelligence firm Tractica. Amazon, Siemens, IBM, Optum and GE Healthcare and health systems Mayo Clinic, Memorial Sloan Kettering and Intermountain are mining patient records for health data to train AI algorithms, allowing the machines to learn by recognizing patterns and make key predictions. In some cases, such deep learning systems are already outperforming doctors.

In this essay, we are going to address the limitations of one of the core fields of AI. In the process, we will encounter a fun allegory, a set of methods of incorporating prior knowledge and instruction into deep learning, and a radical conclusion.[1] The first part, which you're reading right now, will set up what RL is and why it (or at least a particular version of it we shall name'pure RL' and soon define) is fundamentally flawed. It will contain some explanation that can be skipped by AI practitioners -- but be sure to stick around for the discussion of recent non pure-RL work we shall argue represents the fix to pure RL's foundational flaw. But for now, let us start with a fun allegory.

Artificial intelligence (AI) is the body of science, algorithms and machines able to perform some version of learning and independent problem solving, based on advanced software and hardware components. The speed of development of AI has aided its adoption in several industries, and the physical security market is increasingly looking to adopt AI applications, particularly in the case of deep learning algorithms in the video surveillance market. AI is currently implemented on devices, in the cloud or in a hybrid combination of both approaches, each coming with its own unique set of advantages and disadvantages. It's likely that the physical security industry will develop a variety of deployment technologies, with dedicated servers deployed first followed by cloud and potentially on-camera analytics if the processing power requirements can be met. AI in Physical Security, a white paper produced for the Security Industry Association in partnership with IHS Markit, aims to provide an introduction to AI and how it could be applied to the physical security market.

Fueled by enterprises seeking greater insight from their analytics, deep learning is now seeing widespread adoption. While this artificial intelligence (AI) discipline was first conceived in the late 1950s, the recent jump into deep learning and other AI methods is fueled by the recent increase in hardware power, the explosion of big data and desire for greater insight in several key industries. Deep learning – and AI in general – have taken off because organizations of all sizes and industries are capturing a greater variety of data and can mine bigger data, including unstructured data such as text, speech and images. The global deep learning market is expected to grow 41 percent from 2017 to 2023, reaching $18 billion, according to a Market Research Future report. And it's not just large companies like Amazon, Facebook and Google that have big data.

While deep learning models are able to help tackle many different types of problems, image classification is the most prevalent example for courses and frameworks, often acting as the "hello, world" introduction. FastAI is a high-level library built on top of PyTorch that makes it extremely easy to get started classifying images, with an example showing how train an accurate model in only four lines of code. With the new v1 release of the library, an API called data_block allows users a flexible way to simplify the data loading process. After competing in the Freesound General-Purpose Audio Tagging Kaggle competition over the summer, I decided to repurpose some of my code to take advantage of fastai's benefits for audio classification as well. This article will give a quick introduction to working with audio files in Python, give some background around creating spectrogram images, and then show how to leverage pretrained image models without actually having to generate images beforehand.

People can remember easily what happened a week or a month ago, but as time passes by, it becomes harder to hold on to memories. Interestingly, we have a hard time remembering what happened precisely 20 months ago but at the same time we have early memories from our childhood that stay with us forever. Many times a small thing like a smell, a sound or an image can trigger a trip down on memory lane and suddenly we remember not just the situation, but the whole experience and the feelings we had at the time. For me the smell of a two-stroke engine evokes one of my best memories: the first time my dad and my uncle took me to the motorcycle races. I still remember it as if it was yesterday: the joy I felt, the smell of motorcycles and the heat of the day. It was a real event for my 10 year old self; I felt present and engaged and I'm sure the memory of it will stay with me for the rest of my life.