Artificial Intelligence
Artificial Intelligence/ Machine Learning with Hemicube
With Artificial Intelligence and Machine Learning (AI/ML), organizations can leverage their data to make automated recommendations, take pre-emptive action and streamline decision-making. Hemicube provides the robust in-house technical capabilities required to create and implement custom algorithms to meet your specific objectives.
In today’s world, the vast amount of data generated by both humans and machines far outpaces a business’s ability to absorb and interpret that data in order to make complex business decisions. With constantly evolving preferences and the complexity of customer behaviors, businesses can no longer rely on traditional methods to drive growth. AI has opened up a new realm of possibilities to drive business growth through actionable insights generated from internal and external data sources. The custom AI algorithms developed at Hemicube allow organizations to analyze combinations of new and recommended data sets. In order to arrive at the best conclusion possible based on their specific objectives.
Hemicube’s Artificial Intelligence and Machine Learning (AI/ML) solutions allow companies to leverage data that resides within existing systems to make automated recommendations, streamline processes, take pre-emptive actions, and simplify decision-making processes. We create fully custom AI algorithms that aim to generate tailored predictions and classifications based on input data specific to our client’s needs, no matter the organization’s size
What is Artificial Intelligence?
At its simplest form, Artificial Intelligence is the technological field that combines computer science and robust datasets to enable problem-solving. It also encompasses sub-fields of machine learning and deep learning, which are frequently associated with AI.
AI essentially works by ingesting large amounts of labeled training data, analyzing the data for correlations and patterns, and using these patterns to make predictions about future states. For example, a chatbot fed samples of text chats can learn to produce lifelike exchanges with people, or an image recognition tool can learn to identify and describe objects in images by reviewing millions of examples.
An increase in efficiency and productivity are two of the most often cited benefits of implementing AI within enterprises. The technology handles tasks at a pace and scale that humans can’t match. Implementing AI to take care of tedious tasks allows employees to focus on higher-value assignments. This enables organizations to minimize the costs associated with performing mundane, repeatable tasks that technology can perform while maximizing the talent of their human capital.
Key Benefits of AI:
- An increase in efficiency and productivity.
- Improved speed of business.
- New capabilities and business model expansion.
- Better customer service.
- Improved monitoring.
- Better quality due to a reduction in human error.
- Better talent management.
What is Machine learning?
Machine learning is considered one of the most common types of artificial Intelligence used to process large amounts of data quickly and effectively. A key factor to machine learning algorithms is that they learn over time, getting better at mining the data they receive the more often it’s done. Therefore, organizations investing in machine learning are investing in a tool that will grow with them perpetually, and the value of this tool will last them a lifetime. Hemicube creates scalable, custom machine learning algorithms to meet any business objective.
What is Deep learning?
Deep Learning is a separate branch of machine learning that uses neural networks with many more layers. A deep neural network analyzes data with learned representations similar to how a human would look at a problem. In traditional machine learning, the algorithm is given a set of relevant features to analyze. However, in deep learning, the algorithm is given raw data and decides which features are relevant by itself. Like machine learning, deep learning networks improve as you increase the amount of data being used to train them. Deep learning allows machines to solve complex problems even when using a data sets that are very diverse, unstructured, and inter-connected unlike machine learning which will need to be fed structured data.
