As artificial intelligence (AI) continues to reshape industries, understanding the benefits of AI technology, and the distinctions between technologies, becomes increasingly crucial for making informed business decisions and navigating the rapidly evolving digital landscape. From the foundations of machine learning and deep learning to the complexities of natural language processing, the AI technology landscape encompasses a spectrum of cutting-edge tools that are re-defining how organizations engage with intelligent systems.
Demystifying the AI technology landscape and having a familiarity with these technologies enables organizations to identify the most suitable solution for their specific needs and ensures effective implementation and integration into existing workflows.
AI technologies can be best understood as a kind of nesting of layered technologies that all fall under the overarching category of IPA.
IPA is a grouping of technologies used to manage and automate digital processes and is designed to assist human workers by augmenting human labor and performing tasks that are typically repetitive processes. IPA includes the following technologies:
Moving into the AI realm, IPA begins to make decisions based on the provided data and identified trends to augment human decisions or even make decisions independently. IPA utilizes AI to drive innovation, optimize and streamline workforce efficiency.
AI is an overarching term that encompasses the field of developing computer systems to perform tasks that require human intelligence, such as reasoning, decision-making and pattern learning and includes any technology that falls within the scope of ML and automation.
Additional subfields of AI include:
With the continued advancements in autonomous vehicles and the ability to tackle complex, nuanced challenges, artificial intelligence continues to evolve, inching closer to human-like cognitive abilities.
Machine Learning (ML) enables AI systems to learn from data by recognizing patterns and making predictions or recommendations based on statistical analysis. It can adapt over time with new data, but its learning is generally guided by specific features and rules defined by developers.
There are four types of ML:
ML is widely used in various applications, such as speech recognition, image recognition, spam filtering, fraud detection and self-driving cars.
Deep learning is a subset of ML that uses artificial neural networks to enable digital systems to learn and make decisions based on unstructured, unlabeled data. A neural network is a series of algorithms that can learn from input data to discern features, such as distinguishing characteristics among various images, to make independent decisions without explicit programming.
ML enables AI systems to learn from data by recognizing patterns and making predictions or recommendations based on statistical analysis. It can adapt over time with new data, but its learning is generally guided by specific features and rules defined by developers.
On the other hand, deep learning employs neural networks with multiple layers (hence why it's often referred to as deep) to analyze various factors of data. Unlike traditional ML, deep learning autonomously extracts features from raw data, eliminating the need for manual feature extraction. It learns from data in a way that is somewhat analogous to human learning, through a hierarchy of concepts where each layer of the network extracts and refines features from the input data.
Generative models, like those used in generative adversarial networks (GANs), are a notable application of deep learning. They learn to generate new data that resembles the training data and can be used to improve decision-making capabilities over time as they are exposed to more data. However, it's not just generative AI that benefits from deep learning; other forms of AI, like those used in image and speech recognition, natural language processing and autonomous systems also leverage deep learning to enhance their performance and capabilities over time.
Generative AI refers to a category of algorithms that are capable of generating new data that resembles a given set of training data. While it's not exclusively about language models, it indeed encompasses them. These algorithms can create a variety of content types including images, text, computer code or audio based on the patterns they learn from the input data, aiding in accelerating the creative process.
Large language models like ChatGPT are instances of Generative AI applied to text generation. They are trained on vast datasets to produce human-like text based on the input prompts they receive. These models can be fine-tuned to perform a myriad of tasks, enhancing their versatility and utility across different domains.
It's important to note that generative AI also includes models like GANs and variational autoencoders (VAEs) which are used for generating images, audio and other types of data beyond text. Hence, while large language models are a subset of generative AI, the term generative AI encompasses a broader range of models and capabilities.
Generative AI has evolved into an advanced search and content generation tool integrated across various industries to augment daily tasks and produce new content. With 55% of organizations piloting or in production mode with generative AI , the growth of AI in the market highlights the need for risk management as the creation of new AI-driven content accompanies inherent risks.
As organizations continue to embrace AI-driven solutions, understanding the differences in capabilities across available technologies is critical for organization’s looking to harness its transformative power to drive innovation, increase efficiency, unlock new possibilities and stay ahead of competitors in the rapidly evolving digital landscape.
Baker Tilly Digital is here to help your organization securely reap the benefits of these technologies, wherever you are on your AI journey. Interested in learning more?