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Agent-Based Computing

This is the least mature of the established AI techniques, but it is quickly gaining in popularity. Software agents are persistent, autonomous, goal-oriented programs that act on behalf of users or other programs. Chatbots, for example, are increasingly popular agents. Two main classes of agent applications are commonly used with existing solutions today:

• Task automation agents can be generic (e.g., meeting scheduling assistants in email systems) or more specific (e.g., contract validation softbots for sales automation applications).

• Autonomous object programs can serve functions such as automatic temperature-setting (e.g., found in car diagnostic systems or home thermostats).

Adaptive AI

This allows for model behavior change postdeployment by learning behavioral patterns from past human and machine experience and within runtime environments to adapt more quickly to changing real-world circumstances.

Advanced Virtual Assistants (AVAs)

Sometimes called conversational AI agents, process human inputs to execute tasks, deliver predictions and offer decisions. AVAs are powered by a combination of more advanced user interfaces, natural language processing and deep learning techniques enabling decision support and personalization, as well as contextual and domain-specific knowledge.

Artificial general intelligence (AGI)

Artificial general intelligence (AGI), also known as strong AI, is the (currently hypothetical) intelligence of a machine that can accomplish any intellectual task that a human can perform. AGI is a trait attributed to future autonomous AI systems that can achieve goals in a wide range of real or virtual environments at least as effectively as humans can.

Artificial Intelligence (AI)

Artificial intelligence (AI) applies advanced analysis and logic-based techniques, including machine learning, to interpret events, support and automate decisions, and take actions. AI as applies advanced analysis and logic-based techniques, including machine learning (ML), to interpret events, support and automate decisions, and take actions.

Augmented Artificial Intelligence

This trend that is also referred to as “intelligent X” and points to systems where AI techniques provide additional and untapped functionality.

ChatGPT

ChatGPT is an OpenAI service that incorporates a conversational chatbot with LLM to create content. It was trained on a foundational model of billions of words from multiple sources and then fine-tuned by reinforcement learning from human feedback.

Composite AI

This refers to the combined application of different AI techniques to improve learning efficiency. It allows organizations to broaden the level of knowledge representations and, ultimately, to solve a wider range of business problems in a more efficient manner. 

Computer vision (CV)

CV is a process that can capture, process and analyze real-world images to allow machines to extract meaningful, contextual information from the physical world. CV techniques have technology and infrastructure requirements that differ from traditional ML approaches. 

Deep learning (DL)

This is a variant of machine learning algorithms, uses multiple layers to solve problems by extracting knowledge from raw data and transforming it at every level. These layers incrementally obtain higher-level features from the raw data, allowing the solution of more complex problems with higher accuracy and less manual tuning.

Established AI techniques

The majority of use cases in AI today rely on robust and mature techniques that fall into three main categories:

Probabilistic reasoning. These techniques (often generalized as machine learning) extract value from the large amount of data gathered by enterprises. This category includes techniques aimed at unveiling unknown knowledge held within a large amount of data (or dimensions). 

Computational logic. Often referred to as rule-based systems, these techniques use and extend the implicit and explicit know-how of the organization. These techniques are aimed at capturing known knowledge in a structured manner, often in the form of rules. 

Optimization techniques. Traditionally used by operations research groups, optimization techniques maximize benefits while managing business trade-offs. They do this by finding optimal combinations of resources, given a number of constraints in a specified amount of time. 

Edge AI 

This refers to the use of AI techniques embedded in Internet of Things (IoT) endpoints, gateways and edge servers, in applications ranging from autonomous vehicles to streaming analytics. It offers the potential to deliver differentiated use cases for digital business. 

Generative AI (GenAI)

GenAI learns about artifacts from data and generates innovative new creations that are similar to but don’t repeat the original. Generative AI has the potential to create new forms of creative content, such as video, and accelerate R&D cycles in fields ranging from medicine to product development. GenAI is taking off as a general-purpose technology that has the potential to drastically alter society through its impact on existing economic and social structures.

Generative AI refers to AI techniques that learn a representation of artifacts from data, and use it to generate brand-new, unique artifacts that resemble but don’t repeat the original data. These artifacts can serve benign or nefarious purposes. Generative AI can produce totally novel content (including text, images, video, audio, structures), computer code, synthetic data, workflows and models of physical objects. Generative AI also can be used in art, drug discovery or material design.

Knowledge representation

Capabilities such as knowledge graphs or semantic networks aim to facilitate and accelerate access to and analysis of data networks and graphs. Through their representations of knowledge, these mechanisms tend to be more intuitive for specific types of problems. Adoption of knowledge graph techniques has accelerated quickly over the last three years.

Large Language Models?

Large language models (LLMs) are text-oriented generative artificial intelligences, and they have been in mainstream headlines since OpenAI’s ChatGPT hit the market in November 2022.   LLMs are trained on large volumes of text, typically billions of words, that are simulated or taken from public or private data collections. This enables them to interpret textual inputs and generate human-like textual outputs. LLMs already help search engines understand a question and formulate an answer. Breakthroughs in the LLM field have the potential to drastically change the way organizations conduct business, including enabling the automation of tasks previously done by humans, from generating code to answering questions.