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Understanding the risks associated with AI and the need for secure, ethical and compliant AI implementations
Read the articleFrancesca Curzi on how hyperautomation is revolutionizing business operations
Watch nowThe future of automation lies in the seamless integration of AI and natural language processing
Read the articleAs organizations continue to adopt AI, GenAI and the large datasets that power them, there are significant implications for governance practices
Read the articleHCLTech’s Ashu Uniyal discusses GenAI's impact on banking and customer experience
Watch nowThe emergence of generative AI has enabled organizations to accelerate complex transformations and streamline program delivery processes. Discover what this could mean for packaged applications
Read the articleTan Tiew Hin shares insights on transforming with AI and emerging technologies at SGX
Watch nowHow AI is transforming clinical trials through smarter site selection and faster patient recruitment
Read the articleInsights from Tan Tiew Hin, Managing Director, Head of Technology, Shared Services & Operations at Singapore Exchange (SGX) on charting a transformative journey with AI and emerging technologies
Read the articleWealth management institutions are leveraging AI to enhance client experience and improve efficiency in core operations and control functions, while maintaining data privacy, security and compliance
Read the articleIn this podcast, Nick Ismail and Anjana Singh discuss scaling generative AI from pilot projects to full scale production
Leaders from Blue Yonder and HCLTech discuss advanced tech's impact on supply chains
Watch nowPagination
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Trending 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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