Why More Data Is Not Enough For AI?

As estimated by Gartner Reports in 2022, almost 85% artificial intelligence or machine learning projects have failed and only half of total projects make their way to prototype phase.

Sep 30th, 2022 — We are all aware that artificial intelligence (AI) is revolutionizing all facets of life, and many rapidly expanding tech companies and organizations are using it to fuel their products. However, these initiatives' success rates have fallen short of what experts and engineers had predicted.

This article is everything you need to know about why artificial intelligence cannot get more intelligent by simply adding additional data.

Dependence of Artificial Intelligence:

Artificial intelligence makes decisions from pattern recognition and dataset analysis. It’s “intelligence” completely relies on large datasets. Without datasets, it is unaware of the world outside of this dataset, which poses a great risk to many fields of life.

Due to its sole reliance on data sets, the AI algorithm is extremely vulnerable to spoofing by nefarious entities that can alter the data in almost imperceptible ways. As a result of which, an AI system will likely make inaccurate predictions.

Because of actual business and technological limitations, adding more data does not necessarily solve these issues. Additionally, processing enormous datasets necessitates ever-larger AI models, which exceed the capabilities of hardware and unacceptably increase the carbon footprint of this technology.

How To Improve Effectiveness of Artificial Intelligence?

Connecting data-driven AI with additional scientific or human inputs regarding the application’s subject has been suggested as a potential alternate treatment. It is built on our two decades of expertise implementing AI for several applications while collaborating with academics and business executives. There are four options for doing it.

1. Integrate AI with scientific principles: To take advantage of each discipline of science and outperform its shortcomings, we can integrate the basic laws at hand with pertinent physical, chemical, and biological laws.
2. Add Human Understanding to Data for Better Results: The intelligence of AI can be boosted by human intuition when data is scarce. Humans and AI can effectively complement and support one another.
3. Using Device to Show AI’s decision-making process: All too often, AI is treated as a mysterious black box that dispenses assured advice without offering an explanation. AI is useless if its decision-making process cannot be understood by humans. No human should ever trust an AI recommendation they can’t explain intuitively, whether it’s a medical diagnosis or the shutoff of a vital service.
4. Better Predictions Using Other Models: Data-driven AI analyses behaviour between real observations, or estimation, within the dataset’s limits. To expand, or forecast behaviour outside the existing data, we need domain expertise. Any public dataset is incomplete, and processing ever-larger datasets isn’t possible or sustainable. Adding domain knowledge can make data-driven AI safer, more efficient, and better suited to handle difficulties.

Summary: When an AI-based system’s interpretation of the physical world falls short of expectations, adding additional data isn’t always an option. Fortunately, this shortcoming can often be rectified by combining AI with scientific rules, adding data with expert human observations, using the devices to explain how AI makes judgments and using other forecasting models to predict behaviour.