Some carcinomas show that one or more metastatic sites appear with unknown origins. The identification of primary or metastatic tumor tissues is crucial for physicians to develop precise treatment plans for patients. With unknown primary origin sites, it is challenging to design specific plans for patients. Usually, those patients receive broad-spectrum chemotherapy, while still having poor prognosis though. Machine learning has been widely used and already achieved significant advantages in clinical practices.

Intelligence, in simpler words, can be explained as the mental ability of reasoning, problem-solving, and learning. Intelligence comes with perception, attention, and planning. Humans are the only resource of intelligence on this planet and this is what makes us stand out from all the natural god-gifted resources on this planet. The human brain has the capability of making decisions, remembering things of the past, and calculating for the future. Artificial intelligence as the name itself suggests it is a man-made intelligent machine.

Every possible organization that one can think of relies on data to achieve the set objectives. On that note, having access to data that isn't smart enough to get goals accomplished poses a hurdle. It is thus important to have data that is transformed in a manner that can cater to the needs and objectives of the organization. With most organizations relying on Artificial Intelligence (AI) and machine learning, the necessity of dealing with the right data is all the more important for the sole reason that the models employed aim at obtaining meaningful insights. No wonder data is vast and one shouldn't ideally fall short of it while aiming at the objectives.

AI Researcher, Cognitive Technologist Inventor - AI Thinking, Think Chain Innovator - AIOT, XAI, Autonomous Cars, IIOT Founder Fisheyebox Spatial Computing Savant, Transformative Leader, Industry X.0 Practitioner Why #MLOps is the key for productionized ML system? ML model code is only a small part ( 5–10%) of a successful ML system, and the objective should be to create value by placing ML models into production. F1 score) while stakeholders focus on business metrics (e.g. Improving labelling consistency is an iterative process, so consider repeating the process until disagreements are resolved as far as possible. For instance, partial automation with a human in the loop can be an ideal design for AI-based interpretation of medical scans, with human judgement coming in for cases where prediction confidence is low.

Consider you have a prediction system h1 (example a photo tagger) whose output is consumed in real world (example tagging your photos on phone). Now, you train a system h2 whose aggregate metrics suggest that it is better than h1. Let's consider an unlabeled dataset D of examples (a pool of all user photos). Prediction update refers to the process where h2 is used to score examples in dataset D and update the predictions provided by h1. The problem here is that even though h2 is better than h1 globally, we haven't determined if h2 is significantly worse for some users or some specific pattern of examples.

A patent from Apple suggests the company is considering how machine learning can make augmented reality (AR) more useful. Most current AR applications are somewhat gimmicky, with barely a handful that have achieved any form of mass adoption. Apple's decision to introduce LiDAR in its recent devices has given AR a boost but it's clear that more needs to be done to make applications more useful. A newly filed patent suggests that Apple is exploring how machine learning can be used to automatically (or "automagically," the company would probably say) detect objects in AR. The first proposed use of the technology would be for Apple's own Measure app. Measure's previously dubious accuracy improved greatly after Apple introduced LiDAR but most people probably just grabbed an actual tape measure unless they were truly stuck without one available.

Protein-ligand binding prediction has extensive biological significance. Binding affinity helps in understanding the degree of protein-ligand interactions and is a useful measure in drug design. Protein-ligand docking using virtual screening and molecular dynamic simulations are required to predict the binding affinity of a ligand to its cognate receptor. Performing such analyses to cover the entire chemical space of small molecules requires intense computational power. Recent developments using deep learning have enabled us to make sense of massive amounts of complex data sets where the ability of the model to "learn" intrinsic patterns in a complex plane of data is the strength of the approach.

In the nine years since AlexNet spawned the age of deep learning, artificial intelligence (AI) has made significant technological progress in medical imaging, with more than 80 deep-learning algorithms approved by the U.S. FDA since 2012 for clinical applications in image detection and measurement. A 2020 survey found that more than 82% of imaging providers believe AI will improve diagnostic imaging over the next 10 years and the market for AI in medical imaging is expected to grow 10-fold in the same period. Despite this optimistic outlook, AI still falls short of widespread clinical adoption in radiology. A 2020 survey by the American College of Radiology (ACR) revealed that only about a third of radiologists use AI, mostly to enhance image detection and interpretation; of the two thirds who did not use AI, the majority said they saw no benefit to it. In fact, most radiologists would say that AI has not transformed image reading or improved their practices.

In May 2020, with technical support from the UN FAO, China Agricultural University and Chinese e-commerce platform Pinduoduo hosted a "smart agriculture competition". Three teams of top strawberry growers – the Traditional teams – and four teams of scientific AI experts – the Technology teams – took part in a strawberry-growing competition in the province of Yunnan, China, billed as an agricultural version of the historical match between a human Go player and Google's DeepMind AI. At the beginning, the Traditional teams were expected to draw best practices from their collective planting and agricultural experience. And they did – for a while. They led in efficient production for a few months before the Technology teams gradually caught up, employing internet-enabled devices (such as intelligent sensors), data analysis and fully digital greenhouse automation.

Researchers Zhi Wang, Chaoge Liu, and Xiang Cui published a paper last Monday demonstrating a new technique for slipping malware past automated detection tools--in this case, by hiding it inside a neural network. The three embedded 36.9MiB of malware into a 178MiB AlexNet model without significantly altering the function of the model itself. The malware-embedded model classified images with near-identical accuracy, within 1% of the malware-free model. Just as importantly, squirreling the malware away into the model broke it up in ways that prevented detection by standard antivirus engines. VirusTotal, a service that "inspects items with over 70 antivirus scanners and URL/domain blocklisting services, in addition to a myriad of tools to extract signals from the studied content," did not raise any suspicions about the malware-embedded model.