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Artificial Intelligence
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Artificial Narrow Intelligence (ANI)
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Artificial Neural Network (ANN)
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Backpropagation
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Big Data
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Business Analyst (BA)
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Business Analytics (BA)
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Business Intelligence (BI)
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Categorical Variable
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Clustering
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Command Line
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Computer Vision
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Data Analysis
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Imputation
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Operand
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Quantile
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Random Forest
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Recall
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Scalar
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Target
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Tensor
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TensorFlow
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Time Series
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Time Series Data
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Tokenization
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Training Set
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Transfer Learning
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Underfitting
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Univariate Analysis
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Unstructured Data
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Unsupervised Learning
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Validation Loss
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Vanishing Gradient Problem
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Validation Set
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Variable (Python)
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Variable Importances
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Variational Autoencoder (VAE)
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Word Embedding
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X Variable
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Artificial Narrow Intelligence (ANI)
Artificial narrow intelligence (ANI) is a term used to describe artificial intelligence (AI) systems that are designed and programmed to perform a specific task or set of tasks. ANI is sometimes referred to as "weak AI" because it is limited to performing specific tasks, rather than possessing general intelligence and the ability to learn and adapt to new situations.
Managing Repetetive Tasks
ANI systems are designed to take the place of humans to perform overly-complex or time-consuming tasks. For example, the healthcare industry is beginning to use artificial narrow intelligence to predict early signs of disease. Using machine learning, a computer can become highly adept at looking at images and making a “diagnosis” of lung cancer or stroke based on a CT scan. Allowing computers to do some of this kind of work takes the strain off the limited supply of doctors who would otherwise be responsible for the task.
Similar technologies are becoming more common across other industries. Your car likely uses ANI if it warns you when it’s time to change your oil or check your tire pressure. Your mechanic could pay close attention to his records and make a guess about when it is time to call you to come in to get your oil changed, but it’s far more practical to let a computer make that assessment and send you an automated email.
What is Artificial Narrow Intelligence used for?
Typically ANI is used to automate repetitive tasks like data entry, image analysis, and quality control. By using ANI to automate these tasks, businesses can save time and money while improving accuracy and efficiency.
ANI systems are programmed to perform a specific task, and they are not capable of performing tasks outside of their specific domain. One of the key features of ANI is that it is designed to be highly specialized. This means that while ANI systems can be highly effective at performing their intended task, they are not capable of generalizing and adapting to new situations in the way that humans can. The ANI system watching your driving habits when you get behind the wheel may be able to assess the movements of your hands on the steering wheel and warn you to pull over before you fall asleep, but it cannot take your pulse and warn you that you may have high blood pressure. Those are two separate tasks. A human could observe you and do both. A computer ANI system is designed to do one or the other.
How is ANI changing industry?
While ANI is not as advanced as other types of AI, such as artificial general intelligence (AGI) or artificial superintelligence (ASI), it is still a powerful tool that has the potential to revolutionize many industries. ANI systems can be trained using large amounts of data, allowing them to identify patterns and make predictions based on that data. This makes them highly effective at performing tasks like image recognition, speech recognition, and natural language processing.
Risks of Artificial Narrow Intelligence
Despite its many benefits, there are also concerns about the impact of ANI on jobs and society as a whole. As ANI continues to advance, there is a risk that it could replace human workers in certain industries. This could lead to job losses and economic disruption, especially in industries that rely heavily on repetitive human tasks.
There are also concerns about the ethical implications of ANI. As ANI becomes more advanced, there is a risk that it could be used to automate decision-making processes in areas like criminal justice, healthcare, and finance. This raises important questions about accountability and transparency, as well as the potential for bias and discrimination in automated decision-making.