Top 4 Technologies Fueling the AI Craze

Future advances in artificial intelligence (AI) are being driven by four factors. The operationalization of AI platforms, small and wide data approaches, responsible AI, and effective use of data, model, and computation resources are some of these developments.

According to Shubhangi Vashisth, senior principal research analyst at Gartner, “AI innovation is developing at a rapid rate, with an above-average proportion of innovations on the Hype Cycle attaining mainstream acceptance within two to five years.” Edge AI, computer vision, decision intelligence, and machine learning are among the innovations anticipated to have a transformative effect on the industry in the upcoming years.

With a large proportion of AI breakthroughs showing up on the upward-sloping Innovation Trigger, the AI market is still in an evolutionary stage. This demonstrates a market trend where end users are increasingly looking for specialized technological capabilities frequently outside the scope of available AI solutions.

These are the four factors promoting AI innovation:

Responsible AI

Svetlana Sicular, Research VP at Gartner, stated that “increased trust, transparency, fairness and auditability of AI technology continues to be of rising relevance to a wide variety of stakeholders.” According to the study, “Responsible AI helps accomplish justice, even while biases are encoded into the data, acquire trust, even though transparency and explain ability approaches are growing, and secure regulatory compliance, while juggling AI’s probabilistic nature”.  In fact, all employees employed for AI development and training activities will need to show proficiency in responsible AI by 2023.

 Wide and Small Data

The basis of effective AI endeavors is data. In addition to reducing the reliance on big data, small and broad data techniques also give richer, more comprehensive situational awareness and more sophisticated analytics.  By 2025, 70% of enterprises will be forced to switch their attention from large to small and wide data, giving analytics more context and making AI less data hungry.

According to Sicular, “Broad data permits the analysis and synergy of a number of data sources, whereas little data focuses on the use of analytical methodologies that require less data but nevertheless provide meaningful insights. These techniques offer more powerful analytics and aid in achieving a better 360-degree picture of company challenges together”.

AI Platform Operationalization 

The demand for the operationalization of AI platforms is being driven by the urgency and importance of harnessing AI for business transformation. This entails putting AI initiatives into production so that they may be trusted to handle enterprise-wide issues.

Only half of AI initiatives, on average, take nine months to get from pilot to production, according to Sicular. “Innovations like model operationalization (ModelOps) and AI orchestration and automation platforms (AIOAPs) are allowing reuse, scalability, and governance, speeding AI adoption and growth.”

Effective Resource Management

AI innovation necessitates the optimum use of these resources due to the complexity and volume of the data, models, and computational resources utilized in AI deployments. Due to their capacity to more effectively address a variety of business challenges, multi experience, composite, generative, and transformer AI are becoming increasingly visible in the AI industry.