We are an Artificial Intelligence development company offering a range of AI development services to help businesses automate their day-to-day operations and overcome complex business challenges.
Artificial intelligence (AI) plays a central role in current processes of technological change in AI for financial services. Its prominent place on innovation agendas speaks to the significant benefits that AI for Financial Services technologies can enable for firms, consumers, and markets. At the same time, AI systems have the potential to cause significant harm.
There are real opportunities for AI for healthcare, not only to automate some of the problem-solving carried out by doctors and other medical professionals, but also to make quicker and better decisions and apply problem-solving techniques that humans alone could not.
AI for manufacturing is the intelligence of machines to perform humanlike tasks, responding to events internally and externally, even anticipating events autonomously. The machines can detect a tool wearing out or something unexpected, and they can react and work around the problem.
Strategic Applications for your Organisation. Makarov offers specialist AI services for specific business scenarios. Our Artificial intelligence services can help you improve your workflows, technology and entire organization, implementing a data-first strategy, to generate positive business growth.
Artificial intelligence leverages computers and machines to mimic the problem-solving and decision-making capabilities of the human mind. Artificial intelligence is intelligence demonstrated by machines, as opposed to natural intelligence displayed by animals including humans.
Knowledge engineering refers to all technical, scientific and social aspects involved in building, maintaining and using knowledge-based systems. Knowledge engineering is a field of study where we do the engineering of all such thought processes for specific domains.
In artificial intelligence, an expert system is a computer system emulating the decision-making ability of a human expert. Expert systems are designed to solve complex problems by reasoning through bodies of knowledge, represented mainly as if–then rules rather than procedural code.
For manufacturing firms, the prospect of transforming business models, initiating new operating paradigms to support those models and monetizing information for new levels of productivity has made machine learning a top technology priority.
We have an innovative way of working, removing unnecessary costs, offering lower prices than other similar web design agencies. Please explore the services and packages that we offer. De Morgan also offers Cyber Security services through its Cyber and Risk partner company.
As cyberattacks grow in volume and complexity, artificial intelligence is helping under-resourced security operations analysts stay ahead of threats. Curating threat intelligence from millions of research papers, blogs and news stories, AI technologies like machine learning and natural language processing provide rapid insights to cut through the noise of daily alerts, drastically reducing response times.
For the Fourth Industrial Revolution marking the beginning of the Imagination Age. The traits and capabilities that researchers expect an intelligent system to display.
"Automated reasoning (AR) was investigated by early researchers who developed algorithms that imitated step-by-step reasoning that humans use when they solve puzzles or make logical deductions. By the late 1980s and 1990s, AI research had developed methods for dealing with uncertain or incomplete information, employing concepts from probability and economics. Many of these algorithms proved"
Automated Reasoning
"Knowledge representation and knowledge engineering allow AI programs to answer questions intelligently and make deductions about real world facts. Knowledge representation is a very important concept in expert systems and artificial intelligence (AI) in general. It involves the consideration of intelligent (expert) systems and how it presents knowledge. It is best understood in term of"
Knowledge Representation
"An intelligent agent that can plan makes a representation of the state of the world, makes predictions about how their actions will change it and makes choices that maximize the utility (or “value”) of the available choices. In classical planning problems, the agent can assume that it is the only system acting in the world,"
Automated Planning and Scheduling
"Machine learning (ML), a fundamental concept of AI research since the field’s inception, is the study of computer algorithms that improve automatically through experience. Unsupervised learning finds patterns in a stream of input. Supervised learning requires a human to label the input data first, and comes in two main varieties: classification and numerical regression. Classification"
Machine Learning
"Natural language processing (NLP) allows machines to read and understand human language. A sufficiently powerful natural language processing system would enable natural-language user interfaces and the acquisition of knowledge directly from human-written sources, such as newswire texts. Some straightforward applications of NLP include information retrieval, question answering and machine translation. Symbolic AI used formal syntax"
Natural Language Processing
"Machine perception is the ability to use input from sensors (such as cameras, microphones, wireless signals, and active lidar, sonar, radar, and tactile sensors) to deduce aspects of the world. Applications include speech recognition, facial recognition, and object recognition. Computer vision is the ability to analyse visual input. Machine perception is the capability of a"
Machine PerceptionFor Realising Decision Intelligence and AI driven solutions across the enterprise
Many problems in AI can be solved theoretically by intelligently using search optimisation, going through many possible solutions: Reasoning can be reduced to performing a search. For example, logical proof can be viewed as searching for a path that leads from premises to conclusions, where each step is the application of an inference rule. Planning
Logic or the Science of Reasoning (SoR) is used for knowledge representation and problem solving, but it can be applied to other problems as well. For example, the satplan algorithm uses logic for planning and inductive logic programming is a method for learning. Several different forms of logic are used in AI research. Propositional logic
Many problems in AI (in reasoning, planning, learning, perception, and robotics) require the agent to use probabilistic methods, to operate with incomplete or uncertain information. AI researchers have devised a number of powerful tools to solve these problems using methods from probability theory and economics. Bayesian networks are a very general tool that can be
Statistical learning methods are focused on supervised and unsupervised modeling and prediction. Statistical Learning Methods are used to estimate functions that connect inputs to outputs. Most statistical learning methods can be characterised as either parametric or non-parametric. Restrictive models are much more intepretable than flexible ones. Flexible approaches can be so complicated that it is hard to
Artificial neural networks were inspired by the architecture of neurons in the human brain. A simple “neuron” N accepts input from other neurons, each of which, when activated (or “fired”), casts a weighted “vote” for or against whether neuron N should itself activate. Learning requires an algorithm to adjust these weights based on the training
Deep learning uses several layers of neurons between the network’s inputs and outputs. The multiple layers can progressively extract higher-level features from the raw input. For example, in image processing, lower layers may identify edges, while higher layers may identify the concepts relevant to a human such as digits or letters or faces. Deep learning
Group & Artificial Intelligence News. Providing news, analysis and opinion on the AI ecosystem
Deepfake technologies: What they are, what they do, and how they’re made! A growing unease has settled around evolving deepfake technologies that make it possible to create evidence of scenes that never happened. Celebrities have found themselves the unwitting stars of pornography, and politicians have turned up in videos appearing to speak words they never really
Dark Web Hunter monitors the dark web for compromised credentials and sensitive data. When problems arise the service sends a notification. This signal is a critical early warning to enable protecting yourself and your identity. Dark Web Hunter (Personal Search) Do you have an email address? Do you use the Internet? Do you have a
BC67 dark web monitor detects threats by thinking like an adversary. There are thousands of threats lurking on the dark web, knowing when your business is at risk of attack is a significant challenge. The dark web is an attractive destination for cybercriminals, due to its privacy and anonymity, whether they are looking to trade in stolen
Expert systems have proven to be a viable technology for organisations. Due to the expense involved in developing and maintaining such systems, Makarov has made substantial research efforts focusing on identifying factors that contribute to the success and usage of such systems. Our research allows system managers, designers, and users to make informed design decisions early in the process of the systems construction.
We recommend that expert systems must be applied to core business problems to gain high payoff. Although technical and operational feasibility are important factors in gaining managerial support for systems development, such a venture must undergo rigorous economic assessment in order to gain managerial support. Obtaining managerial commitment early in the design process is important in terms of harnessing organisational resources and funds, motivating users about the effectiveness of the project, and ensuring user involvement in the design of the expert systems. Effective management of user motivations and involvement is crucial to system acceptance and usage in the postimplementation phases.
Expert system shells must be selected judiciously by giving careful consideration to features such as methods of knowledge representation, costs, and support. Developers must select knowledge representation schemes that are suited to the task and are supported by the shell. Multiple sources of knowledge are recommended for populating the knowledge base. In another variation to this, multiple experts should be used to support knowledge elicitation.
Verification and validation must be an ongoing process and multiple approaches to validation must be used. Adequate resources and efforts must be put into retaining key development personnel in order to provide long-term support for the system.
Expert systems software is made by developers for many reasons, but these programs commonly are made to look at data and then do something with, or react to, the information. Diagnosis and repair systems software looks at problems, recommends a plan of action, and may create a schedule to help fix the problem. Instructional systems use tests or other methods to gauge the abilities of the user and then present material in the best order for the learner.
Interpretation and prediction systems are similar, except one compares data to find an answer and the other uses data to predict an outcome. Monitoring systems are automatic systems that watch over an environment, such as for manufacturing, and respond to functions and needs.
Diagnosis and repair systems software are similar programs, but how they respond to information is different. Both look at information to determine a problem, and both recommend the best way to fix the problem. The difference is that a diagnosis expert system just tells the user the best remedy or trouble-shooting steps for the problem. A repair system will detail a schedule and all the steps needed to correct the problem.
Instruction systems software is used to train new employees or to provide individual instruction to students. This system first administers tests to collect information about the user, to understand his or her strengths and weaknesses. After collecting the data, the instructional system will then present material that best complements the student’s learning profile, so he or she will learn at maximum efficiency.
Interpretation and prediction expert systems software are both made to look at data and create an analysis of the information. An interpretation system is often used in mineral and gas drilling to looks at images and other factors to determine the best way to mine the material, and helps workers understand what material has been found. A prediction system looks at information and predicts an outcome, such as with weather forecasting services.
Monitoring expert systems software is used mostly in manufacturing and energy plants, and it automates all the processes. Rules are built into the system that tell the system what the best operating temperatures are, what should be done with faulty equipment, and other factors that commonly occur in the plant. The expert system will then constantly analyse the environment and will respond to any changes to ensure everything is working optimally, correcting problems as needed.
This website uses cookies. By continuing to use this site, you accept our use of cookies.
