The field of artificial intelligence (AI) has been steadily progressing. Traditional AI with static learning capabilities and slow development times is becoming outdated. Adaptive AI is on the rise. This emerging technology, when compared to traditional AI, proves to have greater benefits and improved learning capabilities.
A traditional AI is able to process new data but it is unable to learn from interacting with this data; adaptive AI learns from new data and is able to revise itself[4]. Essentially, adaptive AI can change its own code to incorporate what it has learned from its experiences with new data. Through this process of learning, adaptive AI is then able “to adjust operation conditions dependent upon current needs”[2].
Adaptive AI can change its own code to incorporate what it has learned from its experiences with new data.
For a traditional AI to change its operating conditions an interaction with a human developer is required. This development cycle of updating an existing AI can take months, meanwhile, an adaptive AI is able to perform these same updates in a fraction of the time. The reduced development time and cost is one benefit of using adaptive AI.
Another benefit of employing adaptive AI is that it is customizable to any situation or field because of its ability to learn from its own experiences. For example, in the medical field, and adaptive AI can pull information from hospitals across its network to provide proper drug-dosing information or other such measurements, while also taking into consideration local data to “fine-tune performance to match the needs of each institution”[4]. Adaptive AI’s ability to view an entire network in the context of local data provides clinicians with feedback tailored to their patients’ needs.
In the field of education, adaptive AI can provide “instructor-like” learning through “real-time adaptations based on learner performance and behavior” and by adjusting “the context and difficulty of the content” based on the learner[5]. Adaptative AI will learn through interacting with the learner how to best support their learning style and help them learn at their own pace.
How is an AI able to learn from data and its environment? AI learns through a process called machine learning (ML). At the simplest level, ML uses “statistics to find patterns in … data”[3]. It is through pattern recognition that AI learns. The different forms of ML include supervised learning, unsupervised learning, and reinforcement learning. For traditional AI these approaches train AI “with massive amounts of data collected in stationary environments” which means if there is a shift in the environment the AI will need to be retrained to continue finding the correct patterns[6].
For an adaptive AI, the process of retraining is built into the system, thus eliminating the need for intervention.
For an adaptive AI the process of retraining is built into the system, thus eliminating the need for intervention. Adaptive AI can adjust its operating conditions by using the “learning architectures and targets” built into its system; this is usually based on reinforcement learning in which an AI learns through interacting with its environment through trial and error[1,3,6].
Thought AI’s approach to adaptive AI breaks the mold of traditional ML by utilizing complex adaptive systems to handle all ML functionality. Through this approach, the AI can seek out its goals while simultaneously learning the path of best performance and adjusting its learning method to fit the situation. Thought AI’s approach is unique in that the AI does not function singularly, instead, like a flock of birds, the AI contains smaller parts, called agents, that work in tandem to find the best paths to their goal. Essentially, Thought AI’s adaptive AI is not a monolithic entity it is a group of agents that work together. These smaller agents can process and exchange data in parallel. This proves to be faster than a single AI attempting to process the same data on its own.
In conclusion, adaptive AI is outpacing traditional AI with its flexibility and improved learning capabilities. Adaptive AI’s possible utility across multiple fields allows it to fit into any business application. Thought AI’s unique approach to adaptive AI improves upon adaptive AI’s impressive learning capabilities by further increases its learning speed and functionality.
If you are curious to learn more about Thought AI’s approach to adaptive AI and how we are helping organizations world-wide develop next-generation solutions contact us.
References:
1. Shweta Bhatt. 2019. Reinforcement Learning 101. Medium. Retrieved March 9, 2021 from https://towardsdatascience.com/reinforcement-learning-101-e24b50e1d292
2. Priya Dialani. 2020. Understanding Benefits of Adaptive Artificial Intelligence. Analytics Insight. Retrieved March 9, 2021 from https://www.analyticsinsight.net/benefits-adaptive-artificial-intelligence/
3. Karen Hao. What is machine learning? MIT Technology Review. Retrieved March 10, 2021 from https://www.technologyreview.com/2018/11/17/103781/what-is-machine-learning-we-drew-you-another-flowchart/
4. Sam Surette. 2020. How should the FDA go about regulating adaptive AI? STAT. Retrieved March 9, 2021 from https://www.statnews.com/2020/10/02/how-should-fda-regulate-adaptive-ai/
5. Patrick Weir. Adaptive Learning 3.0. Training Industry. Retrieved March 9, 2021 from https://trainingindustry.com/magazine/mar-apr-2019/adaptive-learning-3-0/
6. Adaptive machine learning for changing environments. The Alan Turing Institute. Retrieved March 9, 2021 from https://www.turing.ac.uk/research/research-projects/adaptive-machine-learning-changing-environments
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