If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
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.
The more general point is that computer algorithms will have a devil of a time predicting which jobs are most at risk for being replaced by computers, since they have no comprehension of the skills required to do a particular job successfully. In one study that was widely covered (including by The Washington Post, The Economist, Ars Technica, and The Verge), Oxford University researchers used the U.S. Department of Labor's O NET database, which assesses the importance of various skill competencies for hundreds of occupations. For example, using a scale of 0 to 100, O*NET gauges finger dexterity to be more important for dentists (81) than for locksmiths (72) or barbers (60). The Oxford researchers then coded each of 70 occupations as either automatable or not and correlated these yes/no assessments with O*NET's scores for nine skill categories. Using these statistical correlations, the researchers then estimated the probability of computerization for 702 occupations.
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.
Artificial Intelligence (AI) has proven its ability to re-invent key business processes, dis-intermediate customer relationships, and transform industry value chains. We only need to check out the market capitalization of the world's leading data monetization companies in Figure 1 – and their accelerating growth of intangible intelligence assets – to understand that this AI Revolution is truly a game-changer! Unfortunately, this AI revolution has only occurred for the high priesthood of Innovator and Early Adopter organizations that can afford to invest in expensive AI and Big Data Engineers who can "roll their own" AI-infused business solutions. Technology vendors have a unique opportunity to transform how they serve their customers. They can leverage AI / ML to transition from product-centric vendor relationships, to value-based relationships where they own more and more of their customers' business and operational success… and can participate in (and profit from) those successes.