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Top-5 AI Trends of 2023 You Should Know About

COVID-19 pandemic has given rise to the adoption of artificial intelligence and machine learning technologies in 2022. These technologies will play a leading role in transforming business decision-making and operational efficiency in 2023 also. According to Gartner, in 2023, the worldwide market of AI software would reach $62 billion. In a poll conducted by Toolbox, 42% of technology experts believe that artificial intelligence is going to be the biggest tech trend in 2023. 

AI Trends

In 2019, IDC predicted that the value of AI technologies will hit $97.9 billion by 2023. And because of COVID-19, the potential value of AI has skyrocketed. It shows that enterprises automation combined with AI hardware and software advancements is transforming applied AI into a reality. Also, companies would be looking for AI in software testing solutions to improve the quality of their AI products. Let us take a look at the top-5 artificial intelligence trends of 2023 that you can expect to improve the stakeholder's experience:

1. Adoption of AIOps Culture

With the advancement of technology, IT systems have become more complex. An official from Forrester recently said in their statement that vendors would want to implement platform solutions that work with multiple monitoring disciplines such as networking, application, and infrastructure. With AIOps solutions, operations, development, and other teams can improve their decision-making process and tasks. Further, Forrester added that business leaders should partner with AIOps providers that facilitate cross-team collaboration through end-to-end IT operations management toolchain integration, data correlation, and digital experiences.

2. Use of AI-driven Technology in the Healthcare Industry

The president and chief operating officer of LeanTaaS, Sanjeev Agrawal said that the healthcare industry will continue to leverage AI-driven technology and advanced analytics in 2023 and beyond to optimize their capacity. Healthcare systems should invest in the right AI-driven technology that addresses their problems while making it easy for users and IT staff to adapt to new changes. They must recognize the value of AI and that investing in new technology would be beneficial in improving healthcare facilities. For example, artificial intelligence and machine learning can optimize the efficiency of operating rooms, infusion chairs, and inpatient beds. 

3. Streamlined and Simplified MLOps Approach

The process of implementing machine learning into industrial production is complex. It takes numerous frameworks and tools to implement machine learning operations. MLOps is similar to the DevOps process. The MLOps ecosystem includes:

● Configuring the feature store

● Installing and configuring inference and training infrastructure

● Model registry configuring

● Detecting model drift

● Monitoring models for decay

MLOps have been incorporated into machine learning platforms based on clouds such as Azure ML, Google vertex AI, Amazon web services, and Amazon SageMaker. Now, with the growing popularity of open-source projects such as MLflow and Kubeflow, the MLOps is now easily accessible. In the upcoming years, you will witness a simplified and streamlined approach to MLOps, spanning edge computing and cloud environments.

4. Integrating Cloud and AI Applications

The director of client innovation at legal services provider Exigent, Rico Burnett said that AI will play a significant role in the adoption of cloud solutions. With the deployment of AI, it will become much easier to manage and monitor cloud resources and huge data sets.

5. Large Language Models Defining Next Conversational AI Wave

Language models are natural processing algorithms and techniques used for determining the probability of the sequence of the words in a given sentence. These models have the capability to predict the next word in a sentence, create visual charts from plain text, and summarize information. OpenAI GPT-2 and GPT-3 and Google BERT are some examples of large language models (LLM). AI startups such as Cohere, OpenAI, AI21 Labs, and Hugging Face are investing a lot in LLM by training models with various parameters. Naver, a South Korean company has built a comprehensive AI-based language model, HyperCLOVA, which is a GPT-3 Korean language model. It is clear that in 2023, LLM will lay a foundation for next-generation conversational AI tools. 

Wrapping Up

Do you think that artificial intelligence will reshape business decision-making in 2023? Well, we all can agree on the fact that for better AI software deployment, businesses need to implement reliable AI testing strategies. No doubt that the above-mentioned trends will lead the business industry in 2023 and in upcoming years. But they need a solution to ensure the quality and reliability of their end-products. Quality assurance companies such as QASource offer customized and latest AI and ML testing solutions for businesses of all sizes. To know more about software quality assurance and testing, contact QASource now.

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