Predictive AI: Its Use Cases and Benefits

What’s in Today’s Article?

• Why in News?
• What is Predictive AI?
• How does Predictive AI Work?
• Predictive AI vs. Generative AI
• Some Use Cases of Predictive AI

Why in News?

Predictive AI emerges as a transformative force, reshaping how businesses analyse data, make decisions, and stay ahead in their respective industries.

What is Predictive AI?

• Predictive artificial intelligence (AI) refers to the use of machine learning to identify patterns in past events and make predictions about future events.
• Unlike conventional AI, which predominantly focuses on analysing historical information, Predictive AI operates on a visionary principle: the ability to foresee and forecast future events.
• At its essence, this cutting-edge technology harnesses the power of advanced algorithms and machine learning models to scrutinise vast datasets, identifying intricate patterns, correlations and trends that might elude human perception.
• The key distinction lies in Predictive AI’s capacity to go beyond mere data analysis. It transforms data into a predictive asset, enabling organisations to -
  • Anticipate outcomes,
  • Anticipate market shifts, and
  • Make strategic decisions with unprecedented foresight.
• By learning from historical data and adapting to emerging patterns, Predictive AI becomes a strategic ally, guiding businesses through the complex terrain of uncertainty.

How does Predictive AI Work?

• Big data: In statistics more data generally results in more accurate analysis. Similarly, predictive AI requires access to vast quantities of data/ "big data".
• Machine learning (ML): ML is a subset of AI and a method for training a computer program to identify data without human intervention.
  • In predictive AI, ML is applied to the vast data collections described earlier.
  • A predictive AI model can process huge data sets without human supervision.
• Identifying patterns: Predictive AI learns to associate certain types of data or certain occurrences.
  • Predictive AI can look at hundreds or thousands of factors to identify patterns - which indicate events that can recur in the future.

Predictive AI vs. Generative AI

• Predictive and generative AI both use machine learning, combined with access to lots of data, in order to produce their outputs.
• However, predictive AI uses machine learning to extrapolate the future. Generative AI uses machine learning to create content.
• For example, a predictive-AI model tells fishermen when a storm is coming. The Generative AI model writes a novel that imagines various interactions between weather and fishing voyages.
• In a sense, generative AI is similar to predictive AI, as it uses statistical analysis to "predict" which words and concepts belong together.
• But the goals for generative and predictive AI are different, the machine learning models they use are different, and the use cases are different.

Some Use Cases of Predictive AI

• Analysing the impact of an extreme weather event:
  • A volcano in Iceland erupted (recently) for the 4th time since December, spewing smoke and molten lava into the air.
  • A 2010 eruption in Iceland had halted around 100,000 flights in Europe as ash clouds and haze enveloped the skies around the Arctic Circle.
  • Will it impact air travel this time? That’s where data analysis for pattern searches using predictive AI comes in.
  • Moscow-based Yandex has developed an interactive map that allows the real-time monitoring of ash clouds after eruptions.
• Oil and gas exploration:
  • For instance, an oil drilling company with wells around the world has the historical geological data on the regions where all oil drilling has led to successful finds.
  • A predictive AI system trained on this historical data could predict where a new oil well could be located.
  • Earlier this month, Saudi Aramco, the world’s largest oil producer, showcased its metabrain generative AI.
  • Metabrain is helping Aramco to analyse drilling plans and geological data as well as historical drilling times versus costs and provide precise forecasts.
• Medicine research:
  • The models of predictive AI could be used in drug discovery, which happens to be one of the most promising areas of research currently.
  • For this reason, the pharmaceutical industry is increasingly seeking to collaborate by pooling data.
  • A recent initiative to facilitate, the ‘MELLODDY Project’, involves the EU Innovative Medicines Initiative and around ten pharmaceutical companies.

Q1) What are the similarities between AI versus machine learning versus predictive analytics?

ML and predictive analytics are both sub-areas within the broader category of AI, and utilise it in their operations. Predictive analytics uses ML and AI as tools to parse data and predict possible outcomes.

Q2) What are the key differences between AI versus machine learning versus predictive analytics?

The goal of AI is to create computer systems that can imitate the human brain. ML is much more focused on training machines to perform certain tasks and learn while doing that. Predictive analytics often relies on human interaction to help query data.

Source: Analysing datasets: How predictive AI models are gaining traction | Gleematic | Cloudfare