Exploring the four critical capabilities propelling today’s AI technology


In a recent post by Saifr, the company outlines four of the key capabilities involved in today’s world of AI.

Delving into the world of AI, there exist four crucial abilities that power its functionality. Saifr outlined these as foundation models, transformer architecture, emergent capabilities, and generative AI.

Foundation models serve as the backbone of contemporary AI, the firm outlined. Assembled from an extensive array of data collected from the internet, these models offer a broad and comprehensive understanding of the world, acting as a beneficial launching pad for future models. Foundation models enable us to avoid reinventing the wheel, particularly considering the immense computing power required to recreate such models.

A myriad of challenges can be effectively addressed with the aid of foundation models, however, for more complex conundrums, a process called “fine-tuning” is required, exclaimed Saifr. This involves building on the base model to cater to specific requirements. For instance, a model developed to identify cars can be fine-tuned to recognise trucks, or a language model could be customised to navigate particular regulations.

Meanwhile, Saifr underlined that the evolution of AI witnessed a pivotal shift in 2017 when Google developed a new form of neural network architecture, dubbed the transformer. Inspired by human brain functionality, neural networks utilise millions of interconnected processing nodes to analyse data. The transformer’s introduction revolutionised the AI landscape, becoming the common architecture for image, audio, and video, besides natural language.

As AI models, known as large language models (LLMs), acquire a vast number of weights, they exhibit emergent capabilities not present in their smaller counterparts. These capabilities, including in-context learning and self-reflection, enable more intricate problems to be solved, expanding the realm of possibilities.

Generative AI, as its name implies, can generate new content based on a user’s intent. For instance, LLMs can create articles so convincingly written that one might assume a human authored them. The potential of generative AI to transform the AI domain is significant, as it can augment human processes and enhance efficiency and productivity, Saifr said.

The AI landscape is in a constant state of flux, evolving at a lightning pace. As these techniques are implemented, their limitations will spur the development of the next wave of technologies.

Read the full post here.

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