Attention (machine learning)Machine learning-based attention is a mechanism mimicking cognitive attention. It calculates "soft" weights for each word, more precisely for its embedding, in the context window. It can do it either in parallel (such as in transformers) or sequentially (such as recursive neural networks). "Soft" weights can change during each runtime, in contrast to "hard" weights, which are (pre-)trained and fine-tuned and remain frozen afterwards. Multiple attention heads are used in transformer-based large language models.
Intelligent lightingIntelligent lighting refers to lighting that has automated or mechanical abilities beyond those of traditional, stationary illumination. Although the most advanced intelligent lights can produce extraordinarily complex effects, the intelligence lies with the human lighting designer, control system programmer(For example, Chamsys and Avolites), or the lighting operator, rather than the fixture itself. For this reason, intelligent lighting (ILS) is also known as automated lighting, moving lights, moving heads, or simply movers.
Performance appraisalA performance appraisal, also referred to as a performance review, performance evaluation, (career) development discussion, or employee appraisal, sometimes shortened to "PA", is a periodic and systematic process whereby the job performance of an employee is documented and evaluated. This is done after employees are trained about work and settle into their jobs. Performance appraisals are a part of career development and consist of regular reviews of employee performance within organizations.
Pattern recognitionPattern recognition is the automated recognition of patterns and regularities in data. While similar, pattern recognition (PR) is not to be confused with pattern machines (PM) which may possess (PR) capabilities but their primary function is to distinguish and create emergent pattern. PR has applications in statistical data analysis, signal processing, , information retrieval, bioinformatics, data compression, computer graphics and machine learning.
Lighting control consoleA lighting control console (also called a lightboard, lighting board, or lighting desk) is an electronic device used in theatrical lighting design to control multiple stage lights at once. They are used throughout the entertainment industry and are normally placed at the front of house (FOH) position or in a control booth. All lighting control consoles can control dimmers which control the intensity of the lights. Many modern consoles can control Intelligent lighting (lights that can move, change colors and gobo patterns), fog machines and hazers, and other special effects devices.
Free tradeFree trade is a trade policy that does not restrict imports or exports. In government, free trade is predominantly advocated by political parties that hold economically liberal positions, while economic nationalist and left-wing political parties generally support protectionism, the opposite of free trade. Most nations are today members of the World Trade Organization multilateral trade agreements. Free trade was best exemplified by the unilateral stance of Great Britain who reduced regulations and duties on imports and exports from the mid-nineteenth century to the 1920s.
Knowledge-based systemsA knowledge-based system (KBS) is a computer program that reasons and uses a knowledge base to solve complex problems. The term is broad and refers to many different kinds of systems. The one common theme that unites all knowledge based systems is an attempt to represent knowledge explicitly and a reasoning system that allows it to derive new knowledge. Thus, a knowledge-based system has two distinguishing features: a knowledge base and an inference engine.
Instrumental variables estimationIn statistics, econometrics, epidemiology and related disciplines, the method of instrumental variables (IV) is used to estimate causal relationships when controlled experiments are not feasible or when a treatment is not successfully delivered to every unit in a randomized experiment. Intuitively, IVs are used when an explanatory variable of interest is correlated with the error term, in which case ordinary least squares and ANOVA give biased results.
Diffusion modelIn machine learning, diffusion models, also known as diffusion probabilistic models or score-based generative models, are a class of latent variable models. They are Markov chains trained using variational inference. The goal of diffusion models is to learn the latent structure of a dataset by modeling the way in which data points diffuse through the latent space. In computer vision, this means that a neural network is trained to denoise images blurred with Gaussian noise by learning to reverse the diffusion process.
Rule-based systemIn computer science, a rule-based system is used to store and manipulate knowledge to interpret information in a useful way. It is often used in artificial intelligence applications and research. Normally, the term rule-based system is applied to systems involving human-crafted or curated rule sets. Rule-based systems constructed using automatic rule inference, such as rule-based machine learning, are normally excluded from this system type. A classic example of a rule-based system is the domain-specific expert system that uses rules to make deductions or choices.
