Artificial Intelligence (AI) impacts the world and plays an essential role in our lives and economy. By 2035, AI could double annual global economic growth rates as per the Accenture research publication. This growth will be driven in three critical ways: by increasing labor productivity (up to 40%), by creating a virtual workforce, and by creating new revenue streams in different sectors. Innovative technologies, intelligent automation, and economic benefits will be majorly responsible for this growth. As per PricewaterhouseCoopers (PwC), by 2030, the global GDP is estimated to increase by up to 14%.
AI technology has gone deep in the market beyond traditional systems and software platforms that were conventionally dependent on humans. Though there are many such advantages AI provides, it is equally challenging to build AI companies. AI businesses face challenges such as lower gross margins, dynamic defensive moats, and scalability issues. Thus, it is necessary to optimize AI economics using various methods in model development, data engineering, organizational design, product management, cloud operations, and so forth. AI companies should build efficient systems to deal with data distributed in long-tail, for example, about 70% of the internet search data lies in the long tail, 18.5% in the fathead, and 11% in the chunky middle, but existing techniques are not well equipped to handle the data in a long tail.
Experimentation is the base for AI development, and statistical models need to fit in real world complex data. Though long-tailed data is a major economic challenge of building AI business the same as machine learning (AI), AI models should be able to handle unpredictable, messy, highly entropic, and heavy-tailed data. AI systems are built to predict interactions among complex underlying systems that cause long-tailed data distributions; thus, building a series of experiments is the best solution in the current scenario. Accurate understanding and analysis of the problem can lead AI developers in the right direction. When a problem can have a bounded solution, skipping deep ML is the best choice, but using scalable, interpretable, and cost-effective models should be preferred if data complexity increases.
While dealing with long-tail data, it is advised to optimize the model, narrow the problem, and convert it. Optimization can be achieved up to some extent by increasing input data, hyperparameters adjustments, or model architecture tweaking. If a problem has a fat head or is susceptible to human errors, it is always suggested to control the data that users can enter. Conversion of a problem into a single-turn interface can cover exceptional cases of human failover. Many problems can be solved using these techniques, but real-world problems are much harder and diverse that need to be solved using ML solutions.
Processing massive data sets, collecting inputs from various sources can not be treated as a single entity and need clustering techniques and categorization. Each category can be treated as a unique supervised learning model. Another problem with massive data sets is they can contain overlap of the data with local data. This overlap can be segregated for regulatory or commercial reasons and thus nontrivial. But, long-tailed data ML problems should be addressed for customization of local data. Metamodel pattern trains a single model and reduces maintenance of the number of models in AI and thus reduces cost. Transfer learning is another popular solution in ML used by many companies today. It contains pre-trained models that are fine-tuned as per customer that requires very small amounts of data.
AI models can be developed and trained for specific tasks. Over time, models having similar functionality can be clubbed together in a common ‘trunk’ that reduces complexity. Accuracy is the prime concern while using trunk models by thickening the model but keeping the task-specific branch as thin as possible. The improvisation of AI economics depends on building an edge case engine, consolidating data pipelines, compressing, compiling, optimizing, and owning the infrastructure. Testing and improvising models are the best ways to optimize AI economics.