Advantages of Adaptive AI Over Traditional Machine Learning Models – insideBIGDATA

Advantages of Adaptive AI Over Traditional Machine Learning Models – insideBIGDATA

With the ever-evolving technological landscape, business needs and outcomes no longer exist as a default. Organizations across industries are adopting artificial intelligence (AI) systems to solve complex business problems, design intelligent and self-sustaining solutions and, essentially, stay competitive at all times. To this end, continued efforts are being made to reinvent AI systems so that more can be achieved with less.

Adaptive AI is a key step in that direction. The reason why it could outpace traditional machine learning (ML) models in the near future is for its potential to empower businesses in achieving better outcomes while investing less time, effort and resources.

Why is the traditional machine learning model not up to the task anymore?

A traditional ML model has two pipelines – training and prediction. The training pipeline collects and ingests data through the various stages of data cleaning, grouping, transformation, etc. The prediction pipeline analyzes the data to yield accurate insights and predictions for effective decision making. 

However, having two pipelines to cover the miles between ingestion and insight comes with its share of downsides. In addition to the obvious, surface-level challenges, like putting up an elaborate infrastructure for the two pipelines and the associated cost overheads, is the fact that the turnaround time is almost always long.

Now, let’s just say we have an organization with the most ideal conditions, i.e. an organization that reserves a generous budget for AI and is willing to invest enough time to let the two pipelines wrestle and wrangle all the data.  Does that solve problems across the board? Largely not, because the very nature of traditional AI poses a major challenge that any organization has to deal with on an ongoing basis.

In traditional AI systems, the learning methodologies deployed in production are challenged when:

  • the system’s operational environment changes; or
  • the underlining input to the system is altered; or
  • the outcome desired by the organization changes.

Each of these conditions or events can significantly affect the functional accuracy and efficiency of an AI system.

So, where did the traditional ML model fall behind?

Consider the following example.  You run a news website which has its revenue tied to the number of users that click on the news items posted throughout the day. Now, a user’s browser history and cookies help you in user profiling and thus serving them interest-focused news content. But then a large-scale event concerning the national security takes place — let’s say tensions over the border with your neighboring country escalate and there’s a growing fear of a war breaking out. In this matter, the government communicates that they’ll be holding a press conference some time in the near future. As expected, everyone is interested in reading about national affairs, including those who restrict their dose of news to sports or finance.

Herein lies the challenge for you.  Even if you had batch-trained your model every single day, it would still be sharing items based on the content consumed the day before since the model is not quick enough to adapt to the dramatic change in user preferences on the same day.  Now, when on the following day the data pertaining to the heightened interest in national affairs is fed to the new training cycle, the users start to receive the related news recommendations. However, since the data is from the day before, the users may no longer be as interested in national affairs as they were on the day of the press conference.

While the model is doing its job of refreshing the type of content delivered on a daily basis, what you would have wanted it to do was to take the latest developments happening in the country and update the content-type by the minute or second. This holds true for businesses of all stripes. In the highly competitive and unpredictable business environment of today, your business can’t afford to wait an entire day for your AI to adapt and deliver.

How Adaptive AI is Different

The Adaptive Learning method employs a single pipeline. With this method, you can use a continuously enriched learning approach that keeps the system updated and helps it achieve high performance levels.  The Adaptive Learning process monitors and learns the new changes made to the input and output values and their associated characteristics. In addition, it learns from the events that may alter the market behavior in real time and, hence, maintains its accuracy at all times. Adaptive AI accepts the feedback received from the operating environment and acts on it to make data-informed predictions.

In our work with communications service providers (CSPs) globally, we’ve evaluated the results generated through Adaptive Learning in a qualitative and quantitative manner. The results obtained are consistently accurate, have excellent coverage, and lead to a significant impact on the performance of the learning system.

The process eliminates the hassle of creating a training pipeline for ML/AI systems. The system is flexibly designed to learn from the new observations while working on older predictions, keeping the processes updated in real time. This flexibility removes the risk of learning systems becoming obsolete or working on outdated training samples that have made the conventional methods inefficient.

Adaptive Learning tries to solve these problems while building ML models at scale. Because the model is trained via a streaming approach, its efficient for domains with highly sparse datasets where noise handling is important.  The pipeline is designed to handle billions of features across vast datasets while each record can have hundreds of features, leading to sparse data records. This system works on a single pipeline as opposed to the conventional ML pipelines that are divided into two parts, as discussed earlier. This provides quick solutions to proof-of-concepts and easy deployment in production. The initial performance of the Adaptive Learning system is comparable to batch-model systems but goes on to surpass them by acting and learning from the feedback received by the system, making it far more robust and sustainable in long term.

Some Best Practices

Since CSPs are major beneficiaries of such an approach, here are a few things they should keep in mind while running an Adaptive Learning pipeline:

  • Data processing steps should be kept similar for new data sources so that all the  observations that the AI system learns remain consistent.
  • The methodology where  the AI system transforms and stores individual observation should be the same over the entire duration of the pipeline.
  • Feedback to the Adaptive Learning method should be readily available so that the system remains current.

The Adaptive AI  method can replace traditional supervised (classification/regression) ML methods in all possible use cases with streaming data use cases showing the maximum improvement. The characteristics of Adaptive AI make it highly reliable in the dynamic software environments of CSPs where inputs/outputs change with every system upgrade.  It can play a key role in their digital transformation across network operations, customer care, marketing, security and IoT – and help transform their customer experience.

About the Authors

Vishal Nigam is Senior Manager of Analytics (AI and ML) at Guavus, an industry-recognized expert in CSP AI, computational learning, and analytics solutions. Vishal leads Guavus’ Research and Development team in Gurgaon, India, where he and his team are responsible for transforming innovative concepts and customer-stated business needs into a precise technical problem and designing powerful customer solutions using ML and AI. Prior to Guavus, he was at Goldman Sachs and Ola Cabs.

Mudit Jain is an Analytics Manager at Guavus, where he is responsible for developing AI-based solutions for the CSP domain. He has more than 7 years of experience in machine learning and artificial intelligence. Previously, he worked as a machine learning analyst at Capital One and Opera Solutions.

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