Narrow AI
Definition
Narrow AI, also known as Weak Artificial Intelligence, is a type of artificial intelligence that is designed to perform a specific task, such as voice recognition or driving a car. It is contrasted with Strong Artificial Intelligence, which is designed to perform any intellectual task that a human being can do[^1^].
Characteristics
Narrow AI systems are characterized by their ability to perform specific tasks without possessing any genuine understanding or consciousness[^2^]. These systems are capable of learning from their experiences and improving their performance over time, but they are limited to their specific domain of expertise[^3^].
Examples
Examples of Narrow AI include Google's search algorithms, Amazon's recommendation algorithms, and Apple's voice assistant, Siri. These systems are designed to perform specific tasks, such as searching the internet, recommending products, or answering questions, and they do so with a high degree of accuracy[^4^].
Development
The development of Narrow AI systems involves training the system to perform its specific task. This is typically done using a combination of machine learning techniques and large amounts of data[^5^]. The system is trained to recognize patterns in the data and make predictions or decisions based on these patterns[^6^].
Limitations
Despite their impressive capabilities, Narrow AI systems have several limitations. They are only capable of performing the specific tasks they were designed for, and they lack the ability to understand or learn anything outside of their domain of expertise[^7^]. Furthermore, these systems are dependent on the quality and quantity of the data they are trained on, and they can make mistakes or produce biased results if the data is flawed[^8^].
Future Prospects
The future of Narrow AI is likely to involve improvements in the accuracy and efficiency of these systems, as well as the development of new applications in fields such as healthcare, finance, and transportation[^9^]. However, the development of Narrow AI also raises a number of ethical and societal questions, such as the impact on jobs and privacy[^10^].
See Also
References
[^1^]: Russell, S. and Norvig, P. (2016). Artificial Intelligence: A Modern Approach. 3rd Edition. Prentice Hall. ISBN 978-0136042594. [^2^]: Poole, D. and Mackworth, A. (2017). Artificial Intelligence: Foundations of Computational Agents. 2nd Edition. Cambridge University Press. ISBN 978-1107195394. [^3^]: Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning. MIT Press. ISBN 978-0262035613. [^4^]: LeCun, Y., Bengio, Y., and Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444. DOI: 10.1038/nature14539. [^5^]: Bishop, C. (2006). Pattern Recognition and Machine Learning. Springer. ISBN 978-0387310732. [^6^]: Hastie, T., Tibshirani, R., and Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd Edition. Springer. ISBN 978-0387848570. [^7^]: Bostrom, N. (2014). Superintelligence: Paths, Dangers, Strategies. Oxford University Press. ISBN 978-0198739838. [^8^]: O'Neil, C. (2016). Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Crown. ISBN 978-0553418811. [^9^]: Susskind, R. and Susskind, D. (2015). The Future of the Professions: How Technology Will Transform the Work of Human Experts. Oxford University Press. ISBN 978-0198713395. [^10^]: Bostrom, N. and Yudkowsky, E. (2014). The Ethics of Artificial Intelligence. In: Ramsey, W. and Frankish, K. (eds.) Cambridge Handbook of Artificial Intelligence. Cambridge University Press. ISBN 978-0521691918.