Our Technology
Machine Learning and Data Analytics
Machine learning and data analytics can be leveraged across a variety of industries and aspects of education. Data will be transformed through machine learning technologies into structured information, also called Data Models. These models help businesses predict, classify or decide factors for how to improve their business needs. Finance, healthcare, retail and education industries are some of the key industries utilizing these technologies for their needs.
Computer Vision
How do computers see the world? Computers use different artificial intelligence techniques by extracting high-dimensional data from the world through methods such as image acquisition, data processing, digital image context, analysis and transformation. This enables computers to experience the world around them for making informed decisions or observations.
Object tracking and detection systems are typical computer vision systems used in construction and education industries.
Natural Language Processing
Natural Language Processing (NLP) involves human-computer interaction. It is an interdisciplinary technique involving computer science, artificial intelligence, deep learning models, and linguistics. These technologies utilize computers to understand human language in the form of speech or text, and providing feedback to humans accordingly through speech or other forms of action.Recently, NLP is playing an increasingly important role in businesses, helping to increase employee productivity and streamline mission-critical business processes.
Voice and Speech Processing
Voice and speech processing have permeated every aspect of our lives. Technologies include Automatic speech recognition (ASR), computerized speech recognition or speech-to-text (STT) implemented in digital assistants, speech-to-text dictation software, chatbots, and GPS. These systems require speech and voice processing to collect sonic data for analysis to provide feedback accordingly. Feedback can be in the form of voice commands such as navigation or text to provide suggestions for chatbots.
With the help of just a few voice commands, we can boost productivity within our daily lives. Use cases such as customer support take advantage of combining voice and speech recognition with artificial intelligence, providing efficient and effective solutions for clients.
Optimization
Each entity has its own in-house solutions that are built specifically for their own needs. However, as a business or solution grows, so does the potential of bottlenecking, cluttering, and redundancies. Optimization plays a key role in understanding the critical components of any system architecture, benchmarking key areas and providing ways to help improve the flow of information. Through the use of AI technologies, these weaknesses can be identified independently for better decision making or perhaps for complete autonomous use.
Companies often need to optimize when scaling or the introduction of new solutions are involved.
Uncertainty Management
As with the nature of certain businesses, uncertainty can present itself in many forms from known variables to unknown variables. Companies leverage standard statistical techniques to form predictions as to how to best address these uncertainties, needing to constantly monitor and analyze the pool of information for changes. Newer AI technologies are able to autonomously monitor the state of information and adapt quickly to any addition form of uncertainty or new variables introduced. These AI techniques are more dynamic and versetile, making them more suitable for any set or subset of diverse industries.
Applications related to healthcare, organization, and interpersonal relationships benefit from this for more personalized uncertainty managemet.
Knowledge Engineering
Before the third AI Spring, our predecessors carefully and cleverly implemented systems that could address the needs of many industries through means of data extraction and analysis. Such systems were called Expert Systems, a fitting homage to our parent company who stood at the forefront of these technologies during such a crucial time. Knowledge Engineering encapsulates the entire process, design, and implementation of Expert Systems (or Knowledge-based Systems).
The importance of legacy and traditional systems offer benefits of security through older languages, keeping them safe from more modern-facing cyber attacks. Banks utilize this and keep legacy systems as they currently work and do not need additional modifications.