AI research is defined as the study of “intelligent agents”: any device that perceives its environment and takes actions that maximize its chance of success at some goal.
The traditional problems (or goals) of AI research include reasoning, knowledge, planning, learning, natural language processing, perception and the ability to move and manipulate objects. General intelligence is among the field’s long-term goals. Approaches include statistical methods, computational intelligence, and traditional symbolic AI. Many tools are used in AI, including versions of search and mathematical optimization, neural networks and methods based on statistics, probability and economics. The AI field draws upon computer science, mathematics, psychology, linguistics, philosophy, neuroscience, artificial psychology and many others.
Augmented reality (AR) is a live direct or indirect view of a physical, real-world environment whose elements are “augmented” by computer-generated or extracted real-world sensory input such as sound, video, graphics, haptics or GPS data. Augmented reality enhances one’s current perception of reality, whereas in contrast, virtual reality replaces the real world with a simulated one.
Augmented reality is used in order to enhance the experienced environments or situations and to offer enriched experiences. Augmented reality has a lot of potential in gathering and sharing tacit knowledge. Augmentation techniques are typically performed in real time and in semantic context with environmental elements, such as overlaying supplemental information like scores over a live video feed of a sporting event.
Virtual reality (VR) is a computer technology that uses virtual reality headsets or multi-projected environments, sometimes in combination with physical environments or props, to generate realistic images, sounds and other sensations that simulate a user’s physical presence in a virtual or imaginary environment. A person using virtual reality equipment is able to “look around” the artificial world, and with high quality VR move around in it and interact with virtual features or items. The effect is commonly created by VR headsets consisting of a head-mounted display with a small screen in front of the eyes, but can also be created through specially designed rooms with multiple large screens.
Mobile apps were originally offered for general productivity and information retrieval, including email, calendar, contacts, stock market and weather information. However, public demand and the availability of developer tools drove rapid expansion into other categories, such as those handled by desktop application software packages. As with other software, the explosion in number and variety of apps made discovery a challenge, which in turn led to the creation of a wide range of review, recommendation, and curation sources, including blogs, magazines, and dedicated online app-discovery services.
Usage of mobile apps has become increasingly prevalent across mobile phone users. Researchers found that usage of mobile apps strongly correlates with user context and depends on user’s location and time of the day. Mobile apps are playing an ever-increasing role within healthcare and when designed and integrated correctly can yield many benefits.
Digital image processing allows the use of much more complex algorithms, and hence, can offer both more sophisticated performance at simple tasks, and the implementation of methods which would be impossible by analog means.
In particular, digital image processing is the only practical technology for: Classification, Feature extraction, Multi-scale signal analysis, Pattern recognition, Projection
Some techniques which are used in digital image processing include: Anisotropic diffusion, Hidden Markov models, Image editing, Image restoration, Independent component analysis, Linear filtering, Neural networks, Partial differential equations, Pixelation, Principal components analysis, Self-organizing maps and Wavelets.
Computer vision deals with how computers can be made for gaining high-level understanding from digital images or videos. Computer vision tasks include methods for acquiring, processing, analyzing and understanding digital images, and extraction of high-dimensional data from the real world in order to produce numerical or symbolic information, e.g., in the forms of decisions. Understanding in this context means the transformation of visual images (the input of the retina) into descriptions of the world that can interface with other thought processes and elicit appropriate action. This image understanding can be seen as the disentangling of symbolic information from image data using models constructed with the aid of geometry, physics, statistics, and learning theory.
Sub-domains of computer vision include scene reconstruction, event detection, video tracking, object recognition, 3D pose estimation, learning, indexing, motion estimation, and image restoration.
Evolved from the study of pattern recognition and computational learning theory in artificial intelligence, machine learning explores the study and construction of algorithms that can learn from and make predictions on data – such algorithms overcome following strictly static program instructions by making data-driven predictions or decisions, through building a model from sample inputs.
Machine Learning Algorithms include Naïve Bayes Classifier, K Means Clustering, Support Vector Machine Algorithm, Apriori Algorithm, Linear Regression, Logistic Regression, Artificial Neural Networks, Random Forests Decision Trees and Nearest Neighbours.
Natural language processing (NLP) is a field of computer science, artificial intelligence and computational linguistics concerned with the interactions between computers and human (natural) languages, and, in particular, concerned with programming computers to fruitfully process large natural language corpora. Challenges in natural language processing frequently involve speech recognition, natural language understanding, natural language generation, connecting language and machine perception, dialog systems, or some combination thereof.
NLP techniques include Tokenization, Acronym normalization and tagging, Lemmatization/Stemming, Decompounding, Entity extraction, Regex extraction, Dictionary extraction, Complex pattern-based extraction, Statistical extraction, Phrase extraction, Part of speech tagging, Statistical phrase extraction, etc.
