AI is a rapidly evolving field with new advancements regularly. Companies that embrace AI tools and technologies are gaining a competitive advantage in the market. By learning AI technologies, you can stay ahead of the curve and become a valuable asset to any organization. This blog lists some popular AI technologies that are widely used and highly regarded. Take a look!
Artificial intelligence has changed the way we live with innovative technologies. AI has taken a storm in every industry and has a profound impact on every sector of society. The term Artificial intelligence terms were first coined in 1956 at a conference. The discussion of the conference led to interdisciplinary information tech natural language generationnology. The advent of the internet helped technology to progress exponentially. Artificial intelligence technology was a stand-alone technology for thirty years, but now the applications are widespread in every sphere of life. Artificial intelligence is known by the AL acronym and is the process of recreating human intelligence in machines.
Many new and emerging technologies are embedded in artificial intelligence. Start-ups to gigantic organizations are in a rat race to implement artificial intelligence for operational excellence, data mining, etc. Let us discuss the Ten Latest Artificial Intelligence Technologies.
|Table of contents: Top 10 Artificial Intelligence Technologies|
Related Topic - Top 10 OSINT Tools You Should Knowchatbots as their customer service agents to interact with humans to answer their queries. Google Assistant helps to organize meetings, and Alexia from Amazon helps to make your shopping easy. A virtual assistant also acts like a language assistant, which picks cues from your choice and preference. The IBM Watson understands the typical customer service queries which are asked in several ways. Virtual agents act as software-as-a-service too.
Modern organizations are implementing decision management systems for data conversion and interpretation into predictive models. Enterprise-level applications implement decision management systems to receive up-to-date information to perform business data analysis to aid in organizational decision-making. Decision management helps in making quick decisions, avoidance of risks, and in automation the process. The decision management system is widely implemented in the financial sector, the healthcare sector, trading, the insurance sector, e-commerce, etc.Deep learning is another branch of artificial intelligence that functions based on artificial neural networks. This technique teaches computers and machines to learn by example just the way humans do. The term “deep” is coined because it has hidden layers in neural networks. Typically, a neural network has 2-3 hidden layers and can have a maximum of 150 hidden layers. Deep learning is effective on huge data to train a model and a graphic processing unit. The algorithms work in a hierarchy to automate predictive analytics. Deep learning has spread its wings in many domains like aerospace and military to detect objects from satellites, help in improving worker safety by identifying risk incidents when a worker gets close to a machine, help to detect cancer cells, etc. Machine learning is a division of artificial intelligence that empowers machines to make sense of data sets without being actually programmed. Machine learning technique helps businesses to make informed decisions with data analytics performed using algorithms and statistical models. Enterprises are investing heavily in machine learning to reap the benefits of its application in diverse domains. Healthcare and the medical profession need machine learning techniques to analyze patient data for the prediction of diseases and effective treatment. The banking and financial sector needs machine learning for customer data analysis to identify and suggest investment options to customers and for risk and fraud prevention. Retailers utilize machine learning for predicting changing customer preferences, and consumer behavior, by analyzing customer data.
For More Info: Machine learning vs Artificial IntelligenceRobotic process automation is an application of artificial intelligence that configures a robot (software application) to interpret, communicate and analyze data. This discipline of artificial intelligence helps to automate partially or fully manual operations that are repetitive and rule-based.
To conclude, Artificial Intelligence represents computational models of intelligence. Intelligence can be described as structures, models, and operational functions that can be programmed for problem-solving, inferences, language processing, etc. The benefits of using artificial intelligence are already reaped in many sectors. Organizations adopting artificial intelligence should run prerelease trials to eliminate biases and errors. The design, models, should be robust. After releasing artificial systems, enterprises should monitor continuously in different scenarios. Organizations should create and maintain standards and hire experts from various disciplines for better decision-making. The objective and future goals of artificial intelligence are to automate all complex human activities and eliminate errors and biases.
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Madhavi Gundavajyala is a Content contributor at Mindmajix.com. She is passionate about writing articles and blogs on trending technologies, project management topics. She is well-versed on AI & Machine Learning, Big data, IoT, Blockchain, STLC, Java, Python, Apache technologies, Databases.
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