AI and ML

AI and ML

Deep Learning

Deep Learning

Artificial intelligence is a machine tuned to cater our requirements. A classic AI can perform specific tasks as instructed but it is not capable of learning on its own and program. Machine learning is the processing of real world data using algorithms, analyzing the results and predicting the output data using multiple iterations.

AI can be used to further refine or generate new algorithms to get better results, and machine learning programs can align themselves to these algorithms to get much finer granularity in results. Deep Learning, however, uses ML and AI together to break down tasks, analyze each subtask and uses this information to solve new set of problems.

One example of deep learning is the artificial neural network (ANN) which is based on an idea of how our human brain works. ANN finds common patterns from given data and predicts the best result.

Computer Vision

Computer Vision

Humans use their eyes and their brains to see and visually sense the world around them. Computer vision is the science that aims to give a similar, if not better, capability to a machine or computer.

Computer vision is concerned with the automatic extraction, analysis and understanding of useful information from a single image or a sequence of images. Computer Vision involves the development of a theoretical and algorithmic basis to achieve automatic visual understanding.

The applications of computer vision are numerous and include: autonomous vehicles, character recognition, security and surveillance, biometrics and forensics.

Text analytics and NLP

Text analytics and NLP

Natural Language Processing is the scientific discipline concerned with making natural language accessible to machines. NLP addresses tasks such as identifying sentence boundaries in documents, extracting relationships from documents, and searching and retrieving of documents, among others.

NLP is a necessary means to facilitate text analytics by establishing structure in unstructured text to enable further analysis. Text analytics is a broad term that describes tasks from annotating text sources with meta-information such as people and places mentioned in the text to a wide range of models about the documents (e.g., sentiment analysis, text clustering, and categorization).

Neuromorphic Computing

Neuromorphic Computing

Neuromorphic computing utilizes an engineering approach or method based on the activity of the biological brain. Neuromorphic Computing type of approach can make technologies more versatile and adaptable, and promote more vibrant results than other types of traditional architectures such as the von Neumann architecture that is so useful in traditional hardware design. Neuromorphic computing has been around for a while, but it is now beginning to be applied in new and different ways.

A prime example is the proposal to create neuromorphic chips which are more complex in nature than traditional microprocessors and would have architectures more like the neurons of the human brain, allowing them to process information in more specialized ways. The legacy microprocessor performance designs were made more for crunching numbers and dealing with big data than processing images visually or doing the kinds of other high-level work that today's machine learning and artificial intelligence systems require. The idea is that new neuromorphic chips could be made to more easily accomplish these new types of technological goals.

Quantum Machine Learning

Quantum Machine Learning

Quantum machine learning is an emerging interdisciplinary research area at the intersection of quantum physics and machine learning. Machine learning techniques use mathematical algorithms and tools to search for patterns in data. These techniques have become powerful tools for many different applications which range from biomedical uses such as in cancer reconnaissance, in genetics and genomics, in autism monitoring and diagnosis and even plastic surgery, to pure applied physics, for studying the nature of materials, matter or even complex quantum systems. Capable of adapting and changing when exposed to a new set of data, machine learning can identify patterns, often outperforming humans in accuracy.

Although machine learning is a powerful tool, certain application domains remain out of reach due to complexity or other aspects that rule out the use of the predictions that learning algorithms provide. Thus, in recent years, quantum machine learning has become a matter of interest because of its vast potential as a possible solution to these unresolvable challenges.

Predictive Analysis

Predictive Analysis

Predictive analytics is a form of advanced analytics that uses both new and historical data to forecast activity, behavior and trends. It involves applying statistical analysis techniques, analytical queries and automated machine learning algorithms to data sets to create predictive models that place a numerical value on the likelihood of a particular event happening.

Predictive analytics has grown in prominence alongside the emergence of big data systems. As enterprises have amassed larger and broader pools of data in Hadoop clusters and other big data platforms, they have created increased data mining opportunities to gain predictive insights. Heightened development and commercialization of machine learning tools by IT vendors have also helped expand predictive analytics capabilities. Predictive analytics requires a high level of expertise with statistical methods and the ability to build predictive data models.

WHAT WE DO

    Our endeavour is to incorporate AI and Machine Learning techniques into our customer products and solutions. From this point of view, whenever we get an opportunity while working on a customer project, we keenly analyze and check if anything can improve in terms of performance, accuracy and usability if we apply AI techniques.