AI as a Service

AIaaS is growing in popularity due to AI-based solutions that can be economically used as a service by enterprises for different purposes. Enterprises that deliver AI-based solutions target needs and understand vertical industries that build sophisticated models to find actionable information with remarkable efficiency. Owing to the cloud, providers can deliver various AI solutions as a service that is accessible and can be refined along with expansion in different ways that were absent in the past.

AI is a process by studying how the human brain thinks, learns, decides, and works to solve a problem, and then further use the finding of the study to develop intelligent software and systems.

Areas that contribute to AI

  • Mathematics
  • Sociology
  • Philosophy
  • Computer Science
  • Psychology
  • NeuroScience
  • Biology

Core Areas of Artificial Intelligence

Artificial Intelligence is a term that most of us are well-acquainted with due to its capability of solving real-world problems. It works in a similar way as human intelligence, make decisions, detect objects, and solve complex problems.

The core of Artificial Intelligence consists of various components. IoT being an integral part as it is currently re-engineering businesses and modifying optimal ways of running them and will influence various industries like manufacturing, healthcare, and transportation. The need for connected devices, human augmentation, Blockchain technologies, advanced technologies like AI will continue to drive the IoT trends.

Physical AI like Industrial automation is handling different processes and machinery in industries with the help of computers, robots, and IT solutions. Industrial automation solutions are growing in implementation owing to the benefits of Higher productivity, quality, flexibility, accuracy, and safety. Though the initial implementing cost is higher, businesses are understanding the long-term benefits.

Similarly, cognitive AI is bridging the gap between the growing latest technology and the skillsets of the workforce. Explainable AI is helping in the development of interpretable and inclusive learning models while deploying with a set of various tools and frameworks for the business. Explainable AI will develop machine learning techniques that will align to predictions and at the same time encourage wider adoption of machine learning techniques.

AI in Telecom

AI is revolutionizing business across all industry domains and sizes. AI is being used across the Telecom industry for designing products and services, life-cycle management, remote and onsite operations, and manage large amounts of data being handled. Below are some implementations of AI in the telecom industry

  • AI in the telecom industry encompasses a myriad of focus areas the main areas are customer service and network optimization. The implementation of AI in these areas would be the main focus causing telecom companies investing more in AI systems over the months.
  • Predictive analytics is one of the most promising capabilities of AI technology, especially for the telecom community. With the help data from analytics generated as a conclusion from AI solutions, machine learning software predicts future industry trends and potential snags. AI helps in assessing services for bugs and identify potential pitfalls.
  • Also, AI-based chatbots and virtual assistants will re-invent the way businesses work with customer service. By automation of conversations and replicate human speech, while drawing data and insights to provide personalized and positive experiences.

Trends of AI

Here are some leading industry trends that are leading the world of technology.

Less Hype More action

As machine learning and neural network technology will perform routine tasks, there would be progress towards augmenting human productivity and driving value from time- consuming tasks.

Human-free Interactions

The customer service will increasingly have reduced human interactions. With the implementation of AI chatbots and virtual assistant, routine tasks will be managed.

Prescriptive analytics

For optimizing various business processes, companies will incorporate prescriptive analytical tools.

AI in Medicine

AI is being increasingly adopted at various stages by healthcare systems. From identifying solutions for population health to operations of hospitals it is being adapted for a range of clinical specialties.

Machine Learning, its types, and deep learning

Machine learning is an application of artificial intelligence (AI) it provides various systems the power to learn and improve from experience without being programmed. The focus of Machine learning is on the development of various computer programs that can access data and learn for themselves. Machine learning is classified as Supervised learning, unsupervised learning, and reinforcement learning.

Deep Learning

Deep learning is an offset of Machine Learning and uses neural networks that analyze various factors. It is inspired by the working of the human brain. It has the ability to learn unsupervised from all forms of data be it structured or unstructured.

The network learns at the initial level in the hierarchy and then transfers information to the next level. where it combines and creates bit more complex information, and passes it on the third level. This process continues as each level in the hierarchy and builds something more complex from the input it received from the previous level.

Artificial intelligence has various applications that are reinventing the world of technology. The idea of having a AI system that is as intelligent as humans is still a dream, however, ML is already allowing the computers to outperform various human tasks like computations, anomaly detection, and pattern recognition.