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The 21st Century is Information Age. It is about volumetric data; it is about the time needed to process the data; it is about hardware size and memory; it is about space and time complexity. Competitive advantage means managing this complexity through smart algorithms by knowing, controlling, and reacting fast based on data-driven decisions, predictions, learning, and modeling.
The scalability and the so-called intelligence depend on designing powerful and meaningful algorithms.
An algorithm is a mathematical solution to a real-world problem.
Designing an algorithm means coming up with a finite set of unambiguous instructions that can be performed to achieve a specific goal, given a set of initial conditions.
Three design requirements for intelligent algorithms:
The efficiency and the performance of an algorithm depend on the time& space complexity of the problem. They are strongly related to the data dimension (the 3Vs), volume, velocity, variety, and data structure.
Smart Artificial Intelligence and Learning Techniques with fewer operations and energy consumption.
“Machines are intelligent to the extent that their actions can be expected to achieve their objectives. “- Stuard Russell.
The machine “objectives” are objectives introduced by humans to solve a particular challenge. Human Intelligence is strongly required to maximize efficiency and create meaningful models for machines to achieve the goal.
1. Data = Facts. It is the asset and the fuel for any Intelligent Application. Data comes from your business activity, sensors, other devices, physical processes, and systems.
2. Information = Organization and interpretation of the data. This is achieved by Artificial Intelligence (AI). AI models and systems = development of computer systems to perform and automate tasks usually done by humans.
“Machines are intelligent to the extent that their actions can be expected to achieve their objectives. “- Stuard Russell.
The machine “objectives” are objectives introduced by humans to solve a particular challenge. Human Intelligence is strongly required to maximize efficiency and create meaningful models for machines to achieve the goal.
1. Data = Facts. It is the asset and the fuel for any Intelligent Application. Data comes from your business activity, sensors, other devices, physical processes, and systems.
2. Information = Organization and interpretation of the data. This is achieved by Artificial Intelligence (AI). AI models and systems = development of computer systems to perform and automate tasks usually done by humans.
3. Knowledge=Learning, Perceiving, or Discovering. It is the state of knowing something with cognizance through understanding concepts, study, and experience. This is achieved through different learning technologies, Machine Learning ( ML), Deep Learning ( DL), and Transferable Learning (TL).
Our goal is to develop smart technologies towards fewer data and sustainable AI.
We use different tools from Mathematics and Computer Science
We integrate different learning techniques and models.
Imitate real-world processes or systems using mathematical and computational models.
Simulation represents how the mathematical & computational model of the system evolves under different conditions over time and space and even with random inputs.
Simulation is used to predict the behavior or the outcome of a real-world or physical system as a complementary technology for systems or processes for which analytic solutions are impossible.
Using simulation to model complex and changeable dynamic systems can offer insights that are difficult to gain using other methods.
Depending on different attributes and processes, simulations can be broken down into models that can be stochastic or deterministic as well as local or distributed.
Digital Twins expends on simulations by using real-time feedback and flow of information to create a virtual simulated real-time system or process.
These programs can integrate technologies, big data, and smart algorithms to drive innovation and improve performance, predictions, control, and engineering tasks.
Different sources provide the external data requirements:
The complex interplay between inputs and assumptions for achieving Optimal Decisions among many choices to meet challenge requirements.
Optimization means minimizing or maximizing an objective function ( your business to be mapped to this mathematical function) to find “ the best available “ values for the outcome, given a set of input data, the complex constraints of your business, and your desired outcomes. The requirements vary from precision or accuracy to speed, depending on your problem.
The optimization techniques vary for static or real-time requirements.
The optimization can be classified into two main classes, Linear Optimization, and Non-linear Optimization.
We will provide the following optimization models:
Optimization means minimizing or maximizing an objective function ( your business to be mapped to this mathematical function) to find “ the best available “ values for the outcome, given a set of input data, the complex constraints of your business, and your desired outcomes. The requirements vary from precision or accuracy to speed, depending on your problem.
The optimization techniques vary for static or real-time requirements.
The optimization can be classified into two main classes, Linear Optimization, and Non-linear Optimization.
We will provide the following optimization models:
ARTIFICIAL INTELLIGENCE AND LEARNING ALGORITHMS, SIMULATIONS& DIGITAL TWINS, AND OPTIMIZATION TO MAXIMIZE THE INFORMATION FROM YOUR DATA DIMENSION, MEETING THE BUSINESS REQUIREMENTS AND OBJECTIVES.
Your challenges depend on your data dimension and performance requirements; the algorithmic solutions are specific to your challenge.
We work together with the Industrial R&D department to strengthen their innovative capabilities, access and identify challenges and valuable problems, guarantee cross-technological innovative solutions, and create a competitive advantage while bringing business value.
Modeling and Development of the Cross-Technological Innovative Solutions.
Innovations meeting Marketability Criteria.
