Top 5 AI Trends to Watch in 2022
By Susan Hu
According to a 2021 IDC report, global spending on AI systems will jump from $85.3 billion in 2021 to over $204 billion in 2025 with a compound annual growth rate (CAGR) of 24.5%. McKinsey's Nov 2020 survey also suggested that over half of the organizations surveyed already adopted AI in at least one function.
As businesses across industries start to see the value in incorporating AI to improve their productivity, efficiency and customer satisfaction, more organizations are increasing their investments in AI. So how are you planning your AI strategy for 2022?
Here are some of the AI trends to watch.
No code AI
No code AI is one of the most significant trends we are expecting to see in 2022. It allows any organization, regardless of its size or knowledge of advanced AI to reap the benefits of the technology without the cost of hiring an expert team of developers and data scientists.
No code AI refers to a system that empowers companies to use AI to perform various functions without coding. It is usually built upon advanced AI platforms with complex AI models and an easy-to-use interface. It can often be integrated with an organization's existing system and does not require its end-user to have in-depth data science or coding knowledge to operate. For example, Graphen's Ardi Learning module is supported by Graphen's most advanced transfer learning and hyperparameter search technology, it allows users to easily develop high-quality custom machine learning models without writing training code.
With no code AI, organizations across industries will be able to solve their specific problems effortlessly. It will, without a doubt, speed up the adoption of AI in 2022.
Conversational AI is AI technology that recognizes and responds to speech and text, transforming conventional chatbots which only reply with scripted responses with a human-like element. It is often built upon cognitive technologies including Machine Learning, Machine Understanding and Machine Reasoning to help businesses to meet the ever-increasing demands of customer interactions, improve customer experiences, increase business efficiency and employee satisfaction.
Effective Conversational AI builds upon advanced Natural Language Processing (NPL), deep video understanding, supervised and unsupervised learning, and situation recognition to mimic and deliver human-like responses and interactions. In a post-great resignation world where customer services are in demand of quality work force, Conversational AI is an innovative solution to the future of customer interaction. Particularly, Conversational AI with extensive industry knowledge can bring a company's customer success to the next level. The applications of Conversational AI can transform customer-facing businesses across industries.
AI in Robotics
Robots have been around for quite some time. In the past, due to the limitations of computing power and knowledge of neural networks, robots were designed to perform only specific, simple tasks.
With the advancement of AI, we are now utilizing AI to train robots to perform their functions more efficiently and act more intelligently under different circumstances. The development in computer vision, emotion recognition and NPL are making robots more and more human-like.
AI-empowered robots can easily absorb manual repetitive tasks to lower operational costs and free human resources. They can significantly increase customer satisfaction with excellent in-person interactions, digitize customer experiences and collect real-time data for analysis with a programmatic approach.The use of AI robotics can also help protect humans from dangerous working environments.
Risk Detection and Prevention
As AI technology develops over the last few years, especially with the development of Graph Computing, the way we process and analyze large amounts of data has become much more effective. With such a strong foundation, we expect AI to be applied across industries to help with risk detection and prevention.
For example, the ability to detect unknown-unknown suspicious activity while combing with traditional rule-based analysis, AI can be used for real-time fraud detection systems to integrate customer data and transaction information, effectively identifying the risk of fraud in customer transactions through aspect management. It can help banks and financial institutions to manage their anti-money laundering systems by importing behavior models to detect money laundering patterns and utilize machine learning management modules for model optimization. AI can also perform graph analysis to discover hidden relationships and use machine learning to create behavioral models through known suspicious patterns - optimize results and detect insurance fraud.
With the adaption of AI, the future of risk detection can be a lot more effective with much lower false positives and higher accuracy.
Responsible AI (Ethics + Explainability)
The ethics of AI has been a heated topic over the last few years. Due to the depths of AI and its potential, vendors' responsibility to keep a code of ethics and compliance when building their technology.
Companies across industries have been exploring ways to make AI more responsible. We see two main areas that can help regulate the technology: AI Ethics as a guideline and Explainability.
With Explainable AI, humans can have the ability to explain how a model and algorithm work inside of and AI and provide a better understanding of the model with important insights. It holds AI companies accountable for their transparency.
Above are the top 5 AI trends we are expecting to see in 2022. We hope it helps you to prepare your AI strategy.
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