Applications of AI useful for humans
There are many applications of AI that are beneficial to society, helping to protect us from disease, from crime, from hunger, and from ourselves.
In the health field, AI systems are making impacts in controlling the spread of infectious diseases like Dengue fever, yellow fever, and Zika, by predicting outbreaks. The Artificial Intelligence in Medical Epidemiology (Aime) system uses over 270 variables to predict the next Dengue fever outbreak, and has an 88% accuracy rate up to three months in advance. (Aime)
Early detection is crucial in the successful treatment of many cancers, sight loss, and other health problems. AI is having an impact here too. IBM Watson systems are being trained to identify tumors and help diagnose breast, lung, prostrate, and other cancers. (IBM)
Google DeepMind is working with the National Health Service (NHS) in the UK to train AI systems to interpret eye scans. (DeepMind, Vox, Forbes)
Violent crime is a seemingly insoluble issue, but again, AI is having an impact in two major areas: gun violence and knife crime. In the US, the Shotspotter system is being used to detect the sound of gunshots and alert authorities quickly. (Shotspotter)
In the UK, violent knife crime is a rapidly growing problem. Police Forces across the UK are exploring the use of an AI system called National Data Analytics Solution (NDAS). This system focuses on identifying people already known to the police who may be more likely to commit knife crime. The intention is to prioritize getting appropriate help and support for those people, but some people are interpreting this as predicting a crime before it happens, making the plan very contentious. (PublicTechnology.net)
In agriculture, keeping crops healthy and free from disease is a never-ending challenge. In areas at risk of famine, growers must be able to accurately identify multiple crop diseases with similar appearances and different treatments. In Uganda, the Mcrops project combines the use of photographs taken on cheap smartphones and computer vision to help farmers keep their crops healthy. (Mcrops)
Maximizing our efficient use of energy is critical to reducing the cost and impact of generating power. AI systems are being used here too, for managing increasingly complex electricity grids, locating damaged cables, and even helping to reduce the demand that devices make. (The conversation)
How different is Natural Language Processing from AI
NLP is one of the most important subfields of machine learning for a variety of reasons. Natural language is the most natural interface between a user and a machine. In the ideal case, this involves speech recognition and voice generation. Even Alan Turing recognized this in his “intelligence” article, in which he defined the “Turing test” as a way to test a machine’s ability to exhibit intelligent behavior through a natural language conversation.
NLP isn’t a singular entity but a spectrum of areas of research. Figure 1 illustrates a voice assistant, which is a common product of NLP today. The NLP areas of study are shown in the context of the fundamental blocks of the voice assistant application. …
Algorithms used in machine learning fall roughly into three categories: supervised, unsupervised, and reinforcement learning. Supervised learning involves feedback to indicate when a prediction is right or wrong, whereas unsupervised learning involves no response: The algorithm simply tries to categorize data based on its hidden structure. Reinforcement learning is similar to supervised learning in that it receives feedback, but it’s not necessarily for each input or state. This tutorial explores the ideas behind these learning models and some key algorithms used for each.
Machine-learning algorithms continue to grow and evolve. In most cases, however, algorithms tend to settle into one of three models for learning. …
- Machine Learning, a subset of AI, uses computer algorithms to analyze data and make intelligent decisions based on what it has learned. The three main categories of machine learning algorithms include Supervised Learning, Unsupervised Learning, and Reinforcement learning.
- Deep Learning, a specialized subset of Machine Learning, layers algorithms to create a neural network enabling AI systems to learn from unstructured data and continue learning on the job.
- Neural Networks, a collection of computing units modeled on biological neurons, take incoming data and learn to make decisions over time. …