We hear quite a few terms bandied about these days not all of them well understood, so we thought it’d be worth writing a blog to familiarise ourselves further with one of them; Artificial Intelligence (AI).
The term AI has become common, but that doesn’t mean it’s clearly grasped by all so here’s a primer.
What is AI? The term refers to the simulation of human intelligence in computers or machines. That’s achieved via algorithms and software which are developed to enable the machine in question to perform a task that historically would have required human intelligence. The sort of tasks AI can handle include problem-solving, learning, reasoning, understanding natural language, recognizing patterns, making decisions, and more.
AI-driven systems are scoped primarily to analyse data, adapt to changing situations, and self-improve their own performance over time. There are two main types:
- Narrow or Weak AI is s form of AI designed for a specific task or a narrow set of tasks. It’s not capable of performing outside its predefined scope. Examples of it include virtual personal assistants like Siri or Alexa and the image recognition software you’re probably familiar with. Weak AI is here now, and what you’ve probably – knowingly or not – experienced already.
- General or Strong AI is AI that possesses human-like intelligence and can perform a wide range of tasks at or even beyond a human level. It isn’t here yet, but it’s coming. Presently, strong AI is thus largely theoretical but ongoing research and development should soon change that.
One more word is in order. AI technologies rely on various subfields, among them another increasingly familiar term, machine learning or its acronym ML (other subfields are natural language processing, computer vision, and robotics). ML is a part of many AI applications. Its application requires training algorithms on large datasets to learn patterns and then make predictions or decisions without the need for additional, new explicit programming.
AI: some of the benefits
The application of AI in commercial environments should lead to innovations and improvements across vertical industry borders, differing from domain to domain. How? Here are some examples of how it may impact the telecoms industry:
- Network optimisation and management
AI can help predict issues and equipment failures, minimising service disruptions. Algorithms can also optimise allocation of network resources and help alleviate network congestion.
- Customer experience
Chatbots and virtual assistants can help customer support in a number of ways, and data analysed by AI can drive more personalised services.
- Fraud and security
AI can help identify unusual network behaviour and trigger immediate responses to potential threats. It can also provide protection from cyberattacks by identifying network vulnerabilities in real time.
- Network planning
AI can help select optimal locations for cell towers and other infrastructure based on factors like population density, traffic patterns, and coverage gaps.
- Quality of Service
AI can continually monitor network performance and automatically adjust configurations to meet QoS standard requirements. This helps telcos meet SLAs by proactively being able to address issues that may affect service quality.
AI has the potential to be transformative in multiple ways and as the technology continues to evolve, its impact is only likely to increase.