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As Physical AI moves intelligence into cameras, sensors, robotics and Edge infrastructure, enterprises are beginning to build operations that can observe, interpret and act in real time
Read the articleEnterprise AI is increasingly being driven by business urgency, forcing organizations to align speed, governance and cross-functional execution more effectively
Read the articleScaling AI across aerospace and defense
Watch nowAs AI adoption accelerates, travel and hospitality companies need to focus investment on the use cases that solve strategic problems, deliver measurable value and justify the cost of scaling
Read the articleAI, changing demand, capacity constraints and geopolitical disruption are reshaping how travel, transportation, logistics and hospitality organizations operate, compete and create value
Read the articleThe industry’s AI story is comforting. That is the problem with it.
Read the articleA slow or poorly functioning clinical service desk does more than frustrate users. It disrupts care, strains compliance and drains clinician capacity, making Agentic AI support a strategic priority.
Read the articleAs telecom operators move from AI experimentation to enterprise-wide execution, AI-native operating models are key to network automation, customer operations, service assurance and new revenue growth
Read the articleFrom fragmented operations to connected decisions in transport, travel, logistics and hospitality
Why digital twins, Edge AI and cloud intelligence matter now across asset-intensive, service-critical industries
Read the articleEnterprises cannot scale AI on fragmented data, technical debt and aging core systems, making legacy modernization a strategic requirement for turning AI ambition into operational impact
Read the articleAI can help organizations accelerate product development, improve productivity and strengthen decision-making, but only when it is connected across the full product lifecycle
Read the articleTrending questions
The term "artificial intelligence" was coined by John McCarthy in 1956. Alan Mathison Turing, followed by Newell, Simon, McCarthy and Minsky, are key figures in AI development. Newell and Simon's 1956 "Logic Theorist" program marked a milestone. These pioneers, known as the founding fathers of AI, significantly advanced the field.
- Increased productivity
- Increased automation
- Smart decision-making
- Solve complex problems
- Managing repetitive tasks
- Strengthens economy
- Personalization
- Disaster management
- Enhances lifestyle
- Global defense
Machine learning is the brain of AI that emulates logical decision-making based on the data fed to it and an AI model is the creation, training and deployment of the ML algorithms. With advancements in intelligence methodologies, AI models support in tandem with real-time analytics, predictive analytics and augmented analytics using natural language processing (NLP), ML, statistical analysis and algorithmic execution.
Loaded with human capabilities and beyond, the importance of artificial intelligence (AI) is rising and gradually spreading to various industries, making way for new possibilities and better efficiencies. Data in today’s world is an asset and valued across industries. With AI, humans are now able to absorb, interpret and make complex decisions.
While artificial intelligence is a system that mimics or imitates human intelligence, machine learning is the brain that helps it work.
Generative AI refers to a subset of artificial intelligence algorithms and models designed to generate new data that resembles existing data. These sophisticated algorithms can create content in various forms, such as text, images, music and even complex structures like designs and models. Generative AI is primarily powered by advances in neural networks, particularly Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs).
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