Artificial intelligence or AI learning is the science of getting computers to act without being explicitly programmed. This involves using data to learn patterns and then inferring conclusions from those patterns, which can forecast future events based on observations in the past. AI learning powers Apple’s Siri, Amazon’s Alexa, Google’s search engine, and many other technologies that we interact with every day. In essence, it is a form of machine learning that enables machines to learn autonomously through pattern recognition and problem-solving techniques. Some examples of applications include:
Face recognition systems are used in some smartphones to allow users to unlock devices with facial recognition software rather than remember a user numeric passcode. Credit card security systems use AI learning algorithms to analyze users’ thumbprints on credit cards and determine if they have been compromised by fraudulent activity. Some cars come equipped with “dashcams” that can capture driving conditions and provide evidence in an accident or insurance claim dispute. AI learning is also used in computer games that involve performing specific tasks based on player performance history. For example, some chess-playing AIs are taught which moves are more likely to win. Still, others learn through playing thousands of different possible board positions hundreds of times each, recording the next best move played by its opponent, and using that information to make future decisions.
What Are Some of the Challenges in AI Learning?
There are numerous challenges with applying machine learning and artificial intelligence systems. A common concern is a fear of losing control over how such systems operate since they can autonomously access large amounts of data and learn from it without human intervention.
What Are Some Examples of How AI Learning Is Being Used?
AI learning can be applied to many fields, including medical diagnosis and self-driving cars. For example, Google is training its driverless car AI system to drive more safely by feeding it driving data collected from its large fleet of test vehicles. Similarly, Google Translate learns new languages through machine learning algorithms fed with existing translations between already known languages. It also applies deep neural networks, which process information in a “neuronal-style” architecture that contains layers upon layers of neuron networks that progressively improve accuracy over time. Researchers have published research papers on how machine learning can be used in innovative ways concerning cancer screening. For example, one study showed that artificial intelligence models could be created to identify the most accurate radiologists’ interpretations of mammogram images concerning cancer presence or absence.
