Figure 1 AI Tributaries
Logical
Machine LearningDefinition
Machine learning is the kind of AI that teaches computers to learn from experiences represented by data and information that does not rely on a predetermined equation or sets of rules. Machine algorithms adaptively improve their performance as the number of data samples increases, thus increasing the learning process.
When to Use
Use machine learning when you can't code rules, such as human tasks involving recognition where there are many variables with frequent change.
When Not to Use
When the data is problematic, including too much noise, too dirty, or grossly incomplete.
Deep Learning
Definition
Deep learning is a distinct/specialized form of machine learning that attempts to learn like humans by identifying objects and linking them to each other using a neural network, which is layered with interconnected nodes called neurons that work together to process and learn from data. It's a form of patterned learning.
When to Use
Use learning is used where there is a large amount of data available and there is a requirement for higher accuracy. Typically, deep learning learns from its mistakes and includes the lessons learned.
When Not to Use
Deep learning has a high computational cost that must be factored into solutions. There is, of course, a high dependence on the data quality. The scope of the data it is trained on may limit its ability to deal with unforeseen consequences.
Pattern Recognition/Perception
Definition
Pattern recognition is the automated recognition and regularities in data of various sorts. These patterns can be classified and leveraged to make decisions or predictions. New and emergent patterns can be detected for further analysis.
When to Use
Pattern recognition is critical in improving comprehension of the intricacies of complex problems. It is beneficial for recognizing objects in images, scanning, and photo-related interpretations.
When Not to Use
Again, the state of the data is critical, but dealing with significant variations in the data may disqualify pattern recognition as a solution.
Natural Language Processing (NLP)
Definition
NLP is a form of AI that allows computers to understand human language in any form and leverage it in a more seamless human-computer experience.
When to Use
NLP is a significant bridging mechanism between humans in their own language and computers. It is often used for computers to read text or hear speech to interpret and measure sentiment, helping to identify important words/phrases.
When Not to Use
NLP is not as helpful when a particular language is inconsistent or ambiguous, particularly regarding sarcasm and culture.
Real-time Universal Translation
Definition
Real-time Translation helps people translate one language to another instantly. People speaking differently can have a conversation or meeting in different languages with minimal delays or issues with accuracy.
When to Use
Universal translation is an essential tool for breaking down language barriers and facilitating cross-cultural communication.
When Not to Use
UT cannot correctly translate expressions, idioms, slang, abbreviations, or acronyms. Additionally, it cannot provide an accurate yet creative translation. Therefore, it should be used with caution.
Chatbots
Definition
A chatbot is a software application or web interface that aims to mimic human conversation through text or voice interactions. Chatbots that represent real-world interactions and incremental learning are the most effective.
When to Use
Chatbots are used in timely, always-on assistance for customers or employees. Often, they are helpful in social media, messaging, and phone calls.
When Not to Use
Chatbots are not helpful when addressing customer grievances as every individual is unique, and the problem could be complex over a more extended period than any one business event or transaction.
Real-time Emotion Analytics (EA)
Definition
Emotion analytics collects data and analyzes how a person communicates verbally and nonverbally to understand a person’s mood or attitude in the context of an interaction. EA provides insights into how a customer perceives a product or service.
When to Use
EA can help you improve the usability, engagement, and satisfaction of your users, as well as identify and address any pain points or frustrations.
When Not to Use
Like other forms of technology, emotional AI can display biases and inaccuracies. Consumers have to consent to being analyzed by emotional AI, which may present some privacy concerns.
Virtual Companions
Definition
A virtual companion is an embodied AI character that advances multiple forms of companionship. It includes not only the experience of togetherness with an AI character but can also augment the nurturing of companionship between people or animals.
When to Use
These interactive programs are accessible through the web or mobile, that serves as a companion or partner for therapy and mentorship. Early uses are about a boyfriend or girlfriend relationship, fulfilling some of the functions usually associated with these relationships, but also used for elderly care—emerging benefits around mentorship and collaboration in business.
When Not to Use
Be careful, as they can cause harm, such as hurting users emotionally or giving dangerous advice. Sometimes, perpetuating biases and problematic dynamics are a result of their use.
Expert Systems
Definition
Expert systems leverage AI to simulate the judgment and behavior of a human or an organization with expertise or experience in a particular field.
When to Use
Expert systems can be used standalone or to assist non-experts. It's helpful when skills are scarce locally, expensive, error-prone, and people are too slow.
When Not to Use
Expert systems do not leverage common sense and often lack creative or sensitive responses that humans can deliver. Often, expert systems lack explainability.
Generative AI
Definition
Generative AI refers to models or algorithms that create brand-new output, such as text, photos, videos, code, data, or 3D renderings, from the vast amounts of data they are trained on. The models 'generate' new content by referring to the data they have been trained on, making new predictions and output.
When to Use
Generative AI creates new and often original content, responses, designs, and synthetic data. It’s valuable in creative fields and novel problem-solving while generating new types of outputs.
When Not to Use
Generative AI can provide helpful outputs based on users' queries, but sometimes, the material generated can be offensive, inappropriate, or inaccurate. Human guidance can correct the result and put it into context.
Physical
Edge AIDefinition
Edge AI is all about putting intelligence closest to any device or edge computing environment. Edge AI allows computations to be done close to where the data is collected rather than at a centralized cloud computing facility or offsite data center.
When to Use
When speedy, always-on, and decisions are necessary, close to where data is sensed and collected.
When Not to Use
Edge AI devices may not all have the same level of encryption, authentication, and protection, therefore making them more vulnerable to cyberattacks. Scalability is also a challenge.
Sensing AI
Definition
Sensing AI is an AI awareness that is driven by one or many human-replicated sensing capabilities such as voice, vision, touch, taste, or smell. Sensing AI gives a presence in one or more physical contexts to present data to the logical side of AI.
When to Use
Any time in context computing will assist; any or all of these senses will give immediate and vital feedback to computing systems and humans. These are often used in dangerous environments.
When Not to Use
When Physical senses do not contribute to desired outcomes or where immediate feedback is unnecessary.
Autonomous Robotics (AR)
Definition
ARs are autonomous intelligent machines that can perform tasks and operate in environments independently without human intervention.
When to Use
Ars are great at automating manual or repetitive activities in corporate or industrial settings, but they also are great at working in unpredictable or hazardous environments.
When Not to Use
Robots only do what they are programmed to do and can't do more than expected unless some kind of learning AI powers them.
Next-Gen Cloud Robotics
Definition
Cloud robotics is the use of cloud computing, cloud storage, and other internet technologies in the field of robotics. One of the main advantages of cloud robotics is its ability to provide vast amounts of data to robotic devices without incorporating it directly via onboard memory.
When to Use
Cloud-based robot systems are capable of collaborative tasks. For example, a series of industrial robotic devices can process a custom order, manufacture the order, and deliver it all on its own—without human operators.
When Not to Use
Tasks that involve real-time execution require on-board processing. Cloud-based applications can get slow or unavailable due to high-latency responses or network hitch.
Robotic Personal Assistants
Definition
A robot personal assistant is an artificial intelligence that assists you with routine domestic chores and improves your quality of life.
When to Use
Today, these robots are used in specialized services such as cleaning.
When Not to Use
For tasks that require empathy or dynamic adaptability,
Management & Control
Artificial General Intelligence (AGI)Definition
AGI represents generalized human cognitive abilities on software that can solve an unfamiliar task.
When to Use
If realized, an AGI could learn to accomplish any intellectual task humans or animals can perform. Alternatively, AGI has been defined as an autonomous system that surpasses human capabilities in most economically valuable tasks.
When Not to Use
It is not here yet.
Digital Twin
Definition
A digital twin is the digital representation of a physical object, person, or process contextualized in a digital version of its environment. Digital twin links the logical side of AI and the physical side of AI in an artificial environment to visualize, simulate, and try actions without real consequences, ultimately promoting better decisions by humans or machines.
When to Use
Digital twin technology enables you to create higher-quality products, buildings, or even entire cities. By creating a simulation of a system or a physical object, designers can test different design scenarios, identify potential design flaws, and make improvements before construction begins.
When Not to Use
It is challenging to maintain a digital asset. Many digital twin efforts fail because the digital assets don't receive the same maintenance effort as the physical ones. The digital twin requires consistent upkeep, significant observation, and time to document all real-time changes.
Smart Self-Generating/Adaptive Applications, Processes and Journeys
Definition
Self-adaptive software systems can adjust their behavior in response to their perception of the environment and the system itself. Applications, processes, and journeys coordinate competent and not-so-smart resources and must constantly be tweaked to stay current with needs.
When to Use
When a system or process supports emerging conditions and desired outcomes.
When Not to Use
When the system or process exhibits long-term stability
Goal-Driven & Constraint Behavior
Definition
When Management goals change to reflect the latest thinking or emerging governance constraints, systems and processes seek these goals within governance boundaries.
When to Use
When volatility is a crucial consideration, or there is a robust environment of emergence
When Not to Use
When stability creates a Constance.
Cognitive Cybersecurity
Definition
Cognitive security is the interception between cognitive science and artificial intelligence techniques used to protect institutions against cyberattacks.
When to Use
When bad actors generate intelligent attacks
When Not to Use
It is not optional today and is part of the intelligent infrastructure
Net; Net:
It is essential to understand all the flavors of AI so that solutions can leverage AI where it makes sense in the current and future business environments. The AI tributaries will combine into solutions that will be more business or consumer-ready. Leading organizations will not wait long to take advantage of these tributaries and emerging combinations. Even the following organizations need to understand these tributaries to ask the right questions to vendors or internal developers. AI is shape-shifting, so let's stay on top of this emerging movement.
Additional Reading:
Definition of AI
