Finding and
Funding AI
Intersog can help you find the value prop for AI.
TRUSTED BY LEADING BRANDS
TRUSTED BY LEADING BRANDS
Finding and
Intersog can help you find the value prop for AI.
simple,
measurable
benefits, helps
organization and users,
readily adoptable
good data to start, risks assessed,
clear ripple effect
We can help if you are set with your direction and need particular skills or if you need AI strategy, discovery and/or execution services.
We can help if you are set with your direction and need particular skills or if you need AI strategy, discovery and/or execution services.
We can help if you are set with your direction and need particular skills or if you need AI strategy, discovery and/or execution services.
Intelligent Document Processing (IDP) AI solution services use a machine learning engine to automate the workflow and/or analysis of large volumes of documents and other data sets. IDP facilitates robotics process automation, task automation, marketing automation, fraud detection, predictive analytics and so much more. Think of the volume and pervasiveness of document and systems data that is available. AI combines almost any data set to create business insights and execute repetitive tasks. Quality, learning and work-life balance are all improved.
ChatGPT and similar alternatives are the drivers behind chatbots, conversational AI, sentiment analysis, buyer intent, empathetic responses, language translation and data set training. ChatGPT has great potential when combined with IDP and other neural network initiatives. Consider ChatGPT as another foundational building block for AI. Undeniably, AI considerations can be instrumental in helping to define strategic initiatives.
NLP is an AI and ML solution that understands, processes and generates human language responses. Large Language Models (LLM) have become the predominant NLP function. ChatGPT being the most popular. With NLP, computers can read text or hear speech, interpret it, measure the sentiment and determine actionable insights based on the content and the context. Indeed, NLP is the basis of chatbots, voice assistants, language translators and advanced data classification.
Computer Vision (CV) is object recognition based AI. It trains machines to perform human vision functions. With the capability of high end video and image technology, CV quickly surpasses the human eye in regards to capturing quality control issues on a high volume output production line. CV is the base of face recognition, self-driving cars and AI healthcare diagnostics. Basically, CV is here to stay and will continue to evolve to help operational and service output.
AI is based on expected inputs and outputs. Additionally, machine learning can take these expectations and predict outcomes. Automated actions can be taken and insights can be derived from setting up AI models focused on particular. Also, the healthier the onset data inputs and outputs are, the healthier the AI system will grow. This is a critical aspect to any AI initiatives. Basically, data readiness will be one of the early aspects of the transformation.
Machine Learning (ML) an AI system constantly collecting data and learning. This data could be documents, forms, machines, websites, system interactions, etc. The ML engine will learn from this data without being explicitly instructed to do so. Thus, ML is used for insights and actions. It is prepared with adjustable parameters and is inclusive of several other AI functions including supervised and unsupervised learning.
Data Science aims to extract meaningful insights from disparate data sources. As an AI augmentation discipline, data science combines data visualization, complex algorithms, statistical analysis, predictive modeling, trends forecasting, etc.; working with structured and unstructured data of almost any source; scada systems, databases, spreadsheets, text documents, images, videos, sensor data, etc. Also, Intersog specializes in custom AI Solution Services.
The basics of the neural network including the following steps: Define the problem, gather and prepare the data, design network architecture which includes deciding the number and types of layers, the number of neurons in each layer, the connections between layers, the activation functions, initialization methods, forward and back-propagation, defined loss function, editable parameters and the training process itself. Therefore, upon launch and evaluation, adjustments to the parameters can be made.
The AI Infrastructure requires several key components for success. These include high-performance servers (GPUs & TPUs), robust and scalable storage, a secure, fast and highly available network infrastructure, a data management strategy with strong execution, AI frameworks and tools, a model training and inference platform, and IDE for devs. In addition, secured and private AI environment, UI for AI management and an AI executive dashboard are typically preferred.
