Friend or Foe? What is the future of artificial intelligence (AI)? Will it be possible for robots to be autonomous? If so, when will that happen and will it be a good thing? We asked four experts what they think. I would say that we are quite a long way off developing the computing power or the algorithms for fully autonomous AI, though I do think it will happen within the next thirty or forty years. We will probably remain in control of technology and it will help us solve many of the world’s problems. However, no one really knows what will happen if machines become more intelligent than humans. They may help us, ignore us or destroy us. I tend to believe AI will have a positive influence on our future lives, but whether that is true will be partly up to us. I have to admit that the potential consequences of creating something that can match or surpass human intelligence frighten me. Even now, scientists are teaching computers how to learn on their own. At some point in
Application of ML in Military Machine learning is now a critical component in modern warfare systems. Let’s explore 7 key military applications of artificial intelligence today. Machine learning has become a critical part of modern warfare, and a major point of interest for me, both as an Army veteran and data scientist. Compared with conventional systems, military systems equipped with ML/DL are capable of handling tremendously larger volumes of data more efficiently. Additionally, AI improves self-control, self-regulation, and self-actuation of combat systems due to its inherent computing and decision-making capabilities; a critical aspect to consider due to the nature of combat. AI/ML is being deployed in almost every military application, and increased funding for research and development from military research agencies promises to drive adoption of AI-driven systems in the military sector even further. For instance, the US Department of Defense’s (DoD) Defense Advanced Re
Supervised Learning In supervised learning, the computer is provided with example inputs that are labeled with their desired outputs. The purpose of this method is for the algorithm to be able to “learn” by comparing its actual output with the “taught” outputs to find errors, and modify the model accordingly. Supervised learning therefore uses patterns to predict label values on additional unlabeled data. For example, with supervised learning, an algorithm may be fed data with images of sharks labeled as fish and images of oceans labeled as water . By being trained on this data, the supervised learning algorithm should be able to later identify unlabeled shark images as fish and unlabeled ocean images as water . A common use case of supervised learning is to use historical data to predict statistically likely future events. It may use historical stock market information to anticipate upcoming fluctuations, or be employed to filter out spam emails. In supervised learning, tagged
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