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Top 10 Artificial Intelligence Technologies

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by Madhavi Gundavajyala
Last modified: March 22nd 2021

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 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.

Artificial intelligence adoption has grown from 4% to 15% during 2018-2019, according to the Gartner report. 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.

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Latest Artificial Intelligence Technologies 

1. Natural language generation

natural-language-generationMachines process and communicate in a different way than the human brain. Natural language generation is a trendy technology that converts structured data into the native language. The machines are programmed with algorithms to convert the data into a desirable format for the user. Natural language is a subset of artificial intelligence that helps content developers to automate content and deliver in the desired format. The content developers can use the automated content to promote on various social media platforms, and other media platforms to reach the targeted audience. Human intervention will significantly reduce as data will be converted into desired formats. The data can be visualized in the form of charts, graphs, etc.

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2. Speech recognition

speech-recognitionSpeech recognition is another important subset of artificial intelligence that converts human speech into a useful and understandable format by computers. Speech recognition is a bridge between human and computer interactions. The technology recognizes and converts human speech in several languages. Siri of iPhone is a classic example of speech recognition.

3. Virtual agents

growlerVirtual agents have become valuable tools for instructional designers. A virtual agent is a computer application that interacts with humans. Web and mobile applications provide chatbots 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.

4. Decision management

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 the automation of the process. The decision management system is widely implemented in the financial sector, the health care sector, trading, insurance sector, e-commerce, etc.

5. Biometrics

bio-metricsDeep learning 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, helps in improving worker safety by identifying risk incidents when a worker gets close to a machine, helps to detect cancer cells, etc.

6. Machine learning

machine-learningMachine learning is a division of artificial intelligence which empowers machine to make sense from 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, consumer behavior, by analyzing customer data.

For More Info: Machine learning vs Artificial Intelligence

7. Robotic process automation

robotic-process-automationRobotic 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.

8. Peer-to-peer network

machine-learningThe peer-to-peer network helps to connect between different systems and computers for data sharing without the data transmitting via server. Peer-to-peer networks have the ability to solve the most complex problems. This technology is used in cryptocurrencies. The implementation is cost-effective as individual workstations are connected and servers are not installed.

9. Deep learning platforms

deep-learningDeep learning 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, helps in improving worker safety by identifying risk incidents when a worker gets close to a machine, helps to detect cancer cells, etc.

10. AL optimized hardware

ai-optimized-hardwareArtificial intelligence software has a high demand in the business world. As the attention for the software increased, a need for the hardware that supports the software also arise. A conventional chip cannot support artificial intelligence models. A new generation of artificial intelligence chips is being developed for neural networks, deep learning, and computer vision. The AL hardware includes CPUs to handle scalable workloads, special purpose built-in silicon for neural networks, neuromorphic chips, etc. Organizations like Nvidia, Qualcomm. AMD is creating chips that can perform complex AI calculations. Healthcare and automobile may be the industries that will benefit from these chips.

Conclusion

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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About Author

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NameMadhavi Gundavajyala
Author Bio

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.