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) …
This article was published as a part of the Data Science Blogathon. The variance is the difference between the model and the ground truth value, whereas the error is the outcome of sensitivity to tiny perturbations in the training set. Excessive bias might cause an algorithm to miss unique relationships between the intended outputs and the features (underfitting). There is a high variance in the algorithm that models random noise in the training data (overfitting). The bias-variance tradeoff is a characteristic of a model that states to lower the bias in estimated parameters, the variance of the parameter estimated across samples has increased.
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 Suppose that P is a set of events and that is a causal relation on P. Then is a partial causal order if it is reflexive, antisymmetric, and transitive, that is, if for all a, b and c in P, we have that: a a (reflexivity) if a b and b a then a b (antisymmetry) if a b is true then b a is also true (symmetry) if a b and b c then a c (transitivity). There are always the converse, dual relation, or transpose, of a causal binary relation, as the opposite or dual of the original relation, or the inverse of the original relation, or the reciprocal of the original causal relation. There are special types of causal order, as partial orders, linear orders, total orders, or chains. While many familiar orders are linear, real orders are nonlinear, as a cyclic causal order, a way to arrange a set of things/objects/events in a circle. A cyclic causal order on a set of changes X with n variables or elements is like an arrangement of X on a clock face, for an n-hour clock.
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This Chemicals Stocks forecast is designed for investors and analysts who need predictions of the best chemical stocks to buy for the whole Chemistry Industry (see Chemicals Stocks Package). Package Name: Chemicals Stocks Recommended Positions: Long Forecast Length: 1 Year (5/23/21 – 5/23/22) I Know First Average: 41.59% For this 1 Year forecast, the algorithm had successfully predicted 9 out of 10 movements. OXY was the top-performing prediction with a return of 160.43%. Additional high returns came from VHI and MOS, at 79.37% and 72.02% respectively.
Most of us have heard the phrase, "Image is everything." But when it comes to taking AI to the next level, it's context that is everything. Contextual awareness embodies all the subtle nuances of human learning. It is the'who', 'why', 'when', and'why' that inform human decisions and behavior. Without context, the current foundation models are destined to spin their wheels and ultimately interrupt the trajectory of expectation for AI to improve our lives.
Tom is also a co-inventor of an advanced 3D flight navigation algorithm for drones which is being utilised in new software applications for Aerologix. Tom guest lecturers at one of Australia's top universities – The University of New South Wales, teaching subject matter on Unmanned flight, he also holds a position on a government subcommittee dedicated to developing rules and regulations for unmanned aerial vehicles. Tom's passion for disruptive technology is infections, he is always looking for new challenges, especially drone tech and IoT. Tom has a very successful track record of establishing, executing and delivering large complex technical projects, Tom recently set up the largest drone network in Australia to monitor 1700 km of coastline to enhance swimmer safety. Tom enjoys complex problem solving and welcomes the challenge of empowering team members and creating new innovative ways to solve real-world problems. He has a high passion for life and enjoys a healthy lifestyle, and loves adventure sports such as kitesurfing, mountain biking when time permits.
Abstract: Graph contrastive learning (GCL) has attracted a surge of attention due to its superior performance for learning node/graph representations without labels. However, in practice, unlabeled nodes for the given graph usually follow an implicit imbalanced class distribution, where the majority of nodes belong to a small fraction of classes (a.k.a., head class) and the rest classes occupy only a few samples (a.k.a., tail classes). This highly imbalanced class distribution inevitably deteriorates the quality of learned node representations in GCL. Indeed, we empirically find that most state-of-the-art GCL methods exhibit poor performance on imbalanced node classification. Motivated by this observation, we propose a principled GCL framework on Imbalanced node classification (ImGCL), which automatically and adaptively balances the representation learned from GCL without knowing the labels.
Which innovations will have the greatest impact in radiotherapy by 2030? That was the question posed in the closing session of last week's ESTRO 2022 congress; and five experts stepped up to respond. As often seen in debate-style ESTRO sessions, competition was intense and gimmicks were plentiful, with all talk titles based on movies and a definite sci-fi twist. Before battle commenced, the audience voted for their preferred innovation based on the presentation titles. This opening vote put personalized inter-fraction adaptation as the winner.