Online Web Agency does research and development on Artificial intelligence and decision making systems: “machine learning, data analytics, optimization techniques, complex systems, and Bayesian methods”.
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Online Web Agency has technical product skills and experience in business process knowledge to create custom software that is modular upon Artificial intelligence and decision making systems: “machine learning, data analytics, optimization techniques, complex systems, and Bayesian methods”.
Online Web Agency has product technical expertise on Artificial intelligence and decision making systems: “machine learning, data analytics, optimization techniques, complex systems, and Bayesian methods” and transfers corporate culture and above all transfers experience.
The history of 'Artificial intelligence (AI) and Decision Systems is long and intertwined. Here is an overview of the key moments and their relationships:
The First Steps (50s-60s)
- Birth of AI: The idea of intelligent machines dates back much further, but the term “Artificial intelligence” is coined at the Dartmouth Summer Research Project on Artificial Intelligence in 1956.
- First symbolic approaches: The first systems based on fixed rules and symbolic processing were born, aimed at replicating human reasoning processes.
70s and early 80s: Periods of enthusiasm and stagnation “AI Winters”
- Expert Systems: Expert systems are developed for particular limited fields (e.g. medical diagnostics), demonstrating that machines can make inferences on a knowledge base.
- Limits of symbolic techniques: The scalability and adaptation limits of logical and rule-based approaches slow down enthusiasm for certain AI research.
Rebirth thanks to machine learning and systems based on data collected (80s – early 2000s)
- Machine Learning Algorithms: Research on artificial neural networks, support vector machines, decision trees introduce methods based on data collected and on training models using examples.
- Increase in power calculation: New hardware makes it easier to run complex algorithms.
- Hybrid Systems: AI and Decision Systems are starting to integrate, with algorithms for learning decision rules and optimization on business scenarios.
The explosion of modern AI (2010s – present)
- deep learning: Deep (multilevel) neural networks become disruptive for fields such as computer vision, natural language understanding and many others.
- Huge quantities of data collected: Availability of sets of data collected of unparalleled size, enabling you to train example-hungry algorithms.
- AI-driven Decision Systems: AI becomes key to automating decision-making processes with unprecedented precision and scalability, for example in credit risk assessment, medical diagnostics, and advice for personalization in the commercial sector.
Trends and challenges
- Interpretability: A central issue concerns making decisions made by AI systems explainable and impartial, decreasing their use as “black boxes.”
- Ethics and responsibility: Ensure ethical use of AI-based systems, minimizing bias and potentially discriminatory or unfair impact.
- Human-Machine Collaboration: Decision-making models that exploit the different capabilities of AI and human experts offer enormous potential, especially in fields where the sensitivity of human interpretation is crucial (e.g. legal field, certain aspects of health care).
Conclusion
The history of AI and Decision Systems is a continuous co-evolution. From the first attempts to modern deep learning, the results of AI research provide new tools to automate and support decision-making processes in extremely diverse fields. However, this progress must be evaluated in tandem with considerations on ethics, explainability of decisions and the right balance in the collaboration between machine and human experts based on the individual domain of use.
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