Biases in AI
Reporting bias occurs when the frequency of events, properties, and/or outcomes captured in a data set does not accurately reflect their real-world frequency. This bias can arise because people tend to focus on documenting circumstances that are unusual or especially memorable, assuming that the ordinary can “go without saying.” @Google
Several studies have shown to improve the management of stress due to exposures to natural surroundings. This includes natural environments, semi-natural and urban built environments.
Among 35 random people who took part in the study, to visit these sites:
5 Case Studies were Asked in a Survey among Peers at an educational programme
Artificial Intelligence has pitfalls related to its ethics and bias, its implementation enterprise-wide, and making people used to its evolution in the market. The Pitfalls happen due to their maturity in any of its stages.
The format shown here below refers to a question and answer session with a question as a descriptive text and a single line question seeking for a term or phrase that the answer refers to.
Watson for Oncology
IBM’s Watson for Oncology is an AI machine for dealing with cancer treatments and solve healthcare problems using Machine Learning. Watson for Oncology was quoted by PaulvanderLaken.com …
Significant for shallow and deep foundations that act on wedged shaped soils. Given is a Python implementation on a rough figurative stress distribution experienced by the wedge soil.
COCO Dataset consists of annotated images with face keypoints and object detection keypoints and also contains an evaluator to perform bounding box measurements.
COCO Dataset can be used to train the dataset for an image segmentation problem into a deep learning model. In Computer Vision, Image Segmentation is a technique that addresses segmentation of pixels of images in either a bounding box format or keypoints format.
Let us consider a scenario where we train the Pascal VOC dataset for bounding box detection and finally mAP calculation for extracting the benchmarking results of the deep learning model. …
Early Intervention is a term used in identification, characterisation and propagation of mental health issues that touch on peoples lives. Would a representation help people identify its benefits and side-effects. Side-effects, I mean to say, the advanced use of methods and sophistication used to solve a mental health problem. At times its true that obsessive use of sophistication may lead to harmful and good side-effects.
If such a frame of mind or frame of reference towards policing exists, there exists a health aspect adjacent to the three elements referring to policing in order to balance mental health framework which is a sub-legal framework and other legal frameworks. …
The very process by which an idea transforms to a product is defined by Unified Process in Project Methodology. The events described by Inception range from stakeholder meetings to project blast-off stage from brainstorming and validation of ideas.
Here, is a demonstration of a Unity project simulation produced for buildings or rooms, please watch it below.
The above presentation of dropping the building structure starts when you plan the building layout for incident solar light. That is when the designer considers the layout of the building with restrictions to access to each bounded region via doors and windows. …
Includes Code Samples from TFCO — TensorFlow Constrained Optimization
The above article models business functions which is equivalent to modelling the conceptual structure of the system. It is always good to model the business process (BPMN) because that is the standardised way of modeling the system. Business Functions model the category of operations of the system routine.
In order to work with Deep Learning Libraries, I have created an article that showcases about TensorFlow Constrained Optimization (TFCO) which works similar to Boxing and Unboxing technique as explained above in the article.
In this example, I have provided a class which assigns the responsibilities to the TensorFlow operations defined in the example. The example here uses Recall constraints which recalls the data objects based on a Hinge Loss. A Recall is a metric that is equivalent to TPR (True Positive Rate). Recalling a data object implies assessing the correctness measure of the object’s existence. The constraint optimization problem is defined within a Class using an Object Oriented Programming fashion. Each constraint of the class is defined in a method as a tensor totally relying on Object Constraint Language (OCL) like syntax. Implying, each method returns a tensor of unit variable for single constraint. The TFCO process takes in one input data point similar to the two data points structure taken by a DEA model. The Data Management Units (DMUs) are similar to weights accepted the TFCO in this model but there is a Characteristic Loss Function as explained below. …
The topic discusses about geoengineering tasks in sections that are useful to control the earth’s climate
Our responsibilities for managing the environment is growing at chaotically regularised pace which makes us a better human than decades before.
It is with great dedication that the environment is cleaned, recycled, intervened, transformed and reduced by architecting and monitoring the atmosphere of earth. Back in the 1990s, the European Union came up with a plan to introduce carbon tax in countries to regulate pollution from motor-vehicles and industries. During the same time, the European Commission was faced with issues from industry lobbying and the carbon tax reform was cancelled. …
Inspired from Modern Control Systems Theory by Katsuhiko Ogata
State Vector: If n state variables are required to completely describe the behaviour of a given system, then these n state variables can be considered n components of a vector x. Such a vector is called a state vector. A state vector is thus a vector that determines uniquely the system state x(t) for any time t >= t0, once the state at t = t0 is given and the input u(t) for t >= t0 is specified.
State Space: The n-dimensional space whose coordinate axes consist of x1 axis, x2 axis, …, xn axis is called a state space. …