Understanding AI and Next Generation AI

As someone that has emersed themself into the world of AI as part of our next venture 360Pro.ai , which is an AI-powered Virtual Marketing Agency that is underpinned in every facet with the very latest AI capability, enabling small businesses, with limited resources and budgets, to learn, build and execute a scalable world-class digital marketing and lead-generation strategies, across multiple media channels.

Subsequently, I am often asked by investors and strategic partners, to provide explanations around various aspects of the AI ecosystem.

It's all moving and evolving at lightning speed and its difficult to stay on top of and comprehend.

So I thought I would put together a reference document, that anyone can read and get up to speed.

An Explanation of What AI Is and what AI Does

Artificial Intelligence (AI) simulates human intelligence in machines, enabling them to perform tasks like visual perception, speech recognition, decision-making, and language translation. AI systems use algorithms and models to process data, recognize patterns, and make decisions.

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Why AI Is Different from Machine Learning and Predictive Analytics

AI is a broad field that includes various technologies, while Machine Learning (ML) and Predictive Analytics are specific subsets:

• Machine Learning (ML): Involves training algorithms on large datasets to recognize patterns and make predictions without explicit programming for each task.
• Predictive Analytics: Uses statistical techniques and ML algorithms to analyze historical data and predict future events, focusing on forecasting outcomes based on data trends.

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Short Summaries on Key AI Topics

• Generative AI: Creates new content like text, images, or music by learning from existing data. Examples include GPT-3 for text generation and DALL-E for image creation.
• LLMs (Large Language Models): AI models trained on vast amounts of text data to understand and generate human language, performing tasks from translation to summarization.
• Predictive AI: Focuses on making predictions about future events based on historical data, used in fields like finance, healthcare, and marketing.
• Conversational AI: Enables machines to engage in human-like conversations, such as chatbots and virtual assistants like Siri and Alexa.
• Self-Optimizing Machine-Automation and AI: Systems that automatically improve their performance over time without human intervention, using feedback loops to learn and optimize processes.
• Agentic AI: AI systems that act autonomously to achieve specific goals, making decisions and taking actions independently, often used in complex enterprise software, robotics and autonomous vehicles.

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The Next Generation of AI

Next-generation AI systems are designed to surpass the limitations of current AI models. They feature enhanced learning algorithms, improved language processing, personalized applications, data-driven decision-making, and privacy-preserving techniques.

Unlike current Generative AI and LLMs, next-generation AI will focus on creating more autonomous, adaptive, and intelligent systems that understand context, learn from minimal data, and interact seamlessly with humans.

These advancements will lead to AI that not only generates content but also anticipates human needs, integrating more deeply into daily life and various industries..

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Short list of primary Terms and Acronyms

• AI (Artificial Intelligence): Simulation of human intelligence in machines.
• Agentic AI: AI systems that act autonomously to achieve goals.
• Conversational AI: AI that enables human-like conversations.
• Generative AI: AI that creates new content from existing data.
• LLM (Large Language Model): AI models trained on large text datasets for language tasks.
• Machine Learning (ML): Algorithms that learn from data to make predictions.
• Predictive AI: AI focused on forecasting future events based on historical data.
• Self-Optimizing Machine-Automation and AI: Systems that improve performance autonomously.

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I hope this summary helps to provide a clear and engaging overview of AI and its next generation!

If you have any questions or need further details, feel free to ask.

And if you would like to find out more about how we are embracing the next generation of AI to help internal marketing teams operate more efficiently, and execute more effectively, you can visit our website at 360pro.ai

Register your interest on the site, as an early adopter and gain access to an exclusive community and get early access to the technology and tools, with huge concessions for joining our BETA program.

A to Z of associated AI terms, acronyms and explanations

AI?- Short form of?Artificial Intelligence.

Algorithm?- An algorithm is a set of instructions given to a piece of computer software. Algorithms use data to make a decision and perform an action. At a basic level this might use simple logic tests; if the correct password is entered, then log the user in. More complex algorithms use many more data, rules and calculations to make more complex decisions.

Artificial General Intelligence (AGI)?-?Artificial General Intelligence (AGI), also known as General AI, should not be confused with Generative AI. AGI?refers to an advanced form of artificial intelligence that possesses the ability to understand, learn, and apply knowledge across a wide range of tasks and domains in a manner comparable to or exceeding that of human intelligence. Unlike narrow AI, which is designed for specific tasks, AGI aims to exhibit general cognitive capabilities, including reasoning, problem-solving, perception, learning, and adaptability, across diverse contexts and challenges. Achieving AGI represents a significant milestone in AI research and could have profound implications for various fields, including technology, society, and the economy. AGI does not yet exist. See also Narrow AI.

Artificial Intelligence (AI)?- The construction of computer programs that engage in tasks that are currently more satisfactorily performed by human beings because they require high-level mental processes such as: perceptual learning, memory organization and critical reasoning.” (Minsky et al., 2006)?

Bias?- Bias in?GenAI ?is where output produced can be unfair, prejudiced or perpetuates stereotypes. Bias is often a result of?Training Data ?that contains limited or prejudiced data, for example if an image generator was trained using a photo library where most of the photographs of doctors were white men, it might later assume that when you ask for an image of a doctor you expect to see an image of a white man.??

Bot?- A?computer program that is designed to perform an automated, repetitive task. It is programmed to look at the data given and then perform certain tasks accordingly.??

Chatbot?- A Chatbot is a computer program that is designed to simulate a conversation between a person and a computer, usually with the human asking questions and the computer program attempting to answer. Chatbots can range in how advanced they work; simpler programs pick out keywords or phrases from a question and answer with links whereas more complex AI-driven chatbots can handle more complicated conversations and answer more naturally.

ControlNet.?- ControlNet is defined as a group of neural networks refined using Stable Diffusion, which empowers precise artistic and structural control in generating images. This means you can use an existing image to influence the output of an AI generated image along with the prompt. For example,?it can be used to specify a particular pose, the colour palette or the artistic style of your output image.

Data Poisoning?- When?AI generated data pollutes the training set of?subsequent AI models, leading to a degradation in outputs. Also see Model Collapse and Recursion.

Generative AI?- Gen?AI is a type of artificial intelligence model that generates new data or content that is similar to, but not exactly the same as, the data it was trained on. GenAI learns?the underlying patterns and structures of the training data and then uses that knowledge to create new instances that resemble the original data. GenAI models can be used for image generation, text generation, music generation and video generation.

Hallucination?- GenAI can produce outputs that are surreal, bizarre, nonsensical or unexpected. These are known as hallucinations. Hallucinations can occur in AI-generated content for various reasons, including biases in the training data, the quality of the training data, errors in the model's understanding of context, or simply the probabilistic nature of the model's generation process. Fake references could be considered a form of hallucination in the context of GenAI models.?

Large Language Model?- A Large Language Model is a type of GenAI that uses huge amounts of text-based Training Data. Large Language Models work by looking at how often words appear together and using this to predict what word should come next, much like a highly complex predictive text.?

Machine Learning -?A type of AI that allows a system to learn and improve from examples without all its instructions being explicitly programmed. Machine learning systems learn by finding patterns in training datasets. They then create a model (with algorithms) encompassing their findings. This model is then typically applied to new data to make predictions or provide other useful outputs, such as translating text. Training machine learning systems for specific applications can involve different forms of learning, such as supervised, unsupervised, semi-supervised and reinforcement learning.?

Model Collapse?- Model collapse occurs when AI systems use training data that has been created by existing AI models, rather than real life data. When trained on model-generated content, new models exhibit defects, with degradation in the quality and reliability of outputs, and with becoming more homogenous or increasingly “wrong”. With more text being created using AI tools, the reuse of this text to train AI tools, could lead to data pollution on a large scale.? Also see Data Poisoning and Recursion.

Narrow AI?- Narrow AI is designed and trained for specific tasks or domains, unlike Artificial General Intelligence (AGI), which would possess human-like cognitive abilities across a wide range of tasks and domains. Generative AI tools, such as ChatGPT, Midjourney and Stable Diffusion and Gemini, are all examples of narrow AI. See also Artificial General Intelligence (AGI.

Natural Language/ Natural Language Processing?- Natural language describes the way people talk to each other or describe their needs using words and sentences. With many computer programs you have to convert your commands into a machine-readable format such as computer code, software that allows Natural Language Processing can interpret your commands in human language.?

Prompt -?A prompt?allows you to enter a question or phrase in the language you normally use to describe the task that an AI should perform. A prompt for a text-to-text language model can be a query, a command such as "write a poem about leaves falling", or a longer statement including context, instructions, and conversation history.?

Prompt Engineering?- Prompt engineering is a technique used to develop or refine the output from GenAI software. Prompt engineering techniques can include providing extra information or context in your prompt or giving the software further commands to refine the output.?

Recursion?- Recursion can occur when a GenAI model relies too heavily on its own outputs as inputs without introducing fresh or diverse information. This can lead to the degradation of output quality over successive iterations. This degradation can occur due to the accumulation of errors or biases present in the model's initial outputs, which may be magnified or compounded with each iteration. Without the introduction of new, diverse, or high-quality inputs, the model's outputs may become increasingly distorted, repetitive, or nonsensical. This phenomenon is often referred to as "hallucination," where the model generates outputs that diverge from reality or lack coherence due to the limited scope of information it is operating on.? Also see Data Poisoning and Model Collapse.

Responsible AI?- The practice of designing, developing, and deploying AI with certain values, such as being trustworthy, ethical, transparent, explainable, fair, robust and upholding privacy rights.

Stable Diffusion?- Stable diffusion is one of the deep-learning generative AI models for text-to-image generation. It was released in 2022 by Stability AI. ?The stable diffusion technology is integrated into other Image Generation tools.??

Token?- A token is the smallest amount of information that the AI software can process. In GenAI programs, tokens are usually individual words or phrases.??

Training Data?- Training data is the information that a GenAI program uses to perform a task. In Large Language Models training data consists of millions of webpages, documents and text that it has scanned to make predictions. Training data is important as the accuracy, currency and size of a program’s training data can affect how well it produces outputs. Bias in training data (such as only using data in the English language) can also mean that outputs are biased.??

Turing Test?-?The Turing Test, created by computer scientist Alan Turing, was to evaluate a machine’s ability to exhibit intelligence equal to humans, especially in language and behaviour. When facilitating the test, a human evaluator judges conversations between a human and machine. If the evaluator cannot tell the difference, then the machine is said to have passed the Turing Test. The term is often used to express the human-like qualities of AI.?

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Understanding AI terminology and acronyms

Adversarial data

A machine-learning training technique in which scientists intentionally expose algorithms to corrupted data to trick them into making?faulty predictions ?or reach incorrect conclusions. The technique allows developers to uncover security vulnerabilities that could be exploited by hackers or to examine the results for hidden bias that could lead to flawed results.

AI architect

A data scientist who takes a direct role in applying artificial intelligence to improve business processes. AI architects look for applications of AI for the company as a whole, such as automating recruiting and hiring, as well as for ways to put AI to work automating routine work (like developing chatbots for customer service).

Learn more about how companies are leveraging AI today.

AIOps

Processes that automate IT operations using Big Data analytics in real time. AIOps uses advanced data analysis and pattern recognition to enable IT teams to streamline many of their traditional management functions, maximizing systems performance. By 2022, Gartner predicts that?40% of large companies ?will replace current human-led IT services with automated AIOps systems.

Algorithmic auditing

Also known as?shadow auditing. The process is used to identify AI “blind spots .” As concerns grow about the hidden bias in AI systems, algorithmic auditing is a way to seek out flaws in structural design, coding and training data sets and to assess the system for consistency, transparency, accuracy and fairness. Such auditing is being used to detect bias in AI tools used to make decisions in financial services, the criminal justice system and hiring.

Ambient intelligence

The integration of intelligent systems with physical objects that can interact with people and adapt to their needs. Its best known applications are in the various devices that use Amazon’s Alexa and Apple’s voice-control technology, and experts expect ambient intelligence to be a key feature in all sorts of smart, networked devices that make up the Internet of Things. Imagine a?“smart home” for seniors ?that not only adjusts the temperature for maximum comfort, but reminds them to take medications and monitors for medical emergencies.?

Chief data officer (CDO)

The executive responsible for a company’s data assets has grown in importance with the adoption of AI. Once primarily charged with ensuring data quality, the CDO is taking an?increasingly strategic role , guiding the use of data for solving pressing business problems and for creating long-term business value.

Data curator

This is a new role that bridges the gap between data scientists and those in the business that consume data-driven insights. Data curators combine an understanding of business objectives with knowledge of data collection, processing and analytics, enabling them to streamline the use of data to solve business problems. As AI becomes more central to the enterprise, data curators are increasingly necessary to make its findings understandable.

Edge AI

The application of?edge computing , which processes data on devices at the nodes of a network, to artificial intelligence. With edge AI, data on devices like sensors or smartphones can be used to train machine learning algorithms, enabling faster decision making and real-time responsiveness. By removing the need to connect to cloud-based systems, edge AI eliminates latency delays and decreases data vulnerability and storage costs. Emerging applications include self-driving cars, robots and AI-powered industrial equipment.

Emotion AI

Also known as?affective computing. These AI systems can recognize, interpret and simulate?human emotions . To achieve this, deep-learning algorithms and biometric technologies are trained to respond to emotional states based on pattern recognition in text, voice and facial patterns. The insurance industry today uses the technology in chatbots to detect stress or other “lying cues” in tone of voice or facial expressions. In healthcare, a virtual nurse armed with emotion AI could monitor patients, detect distress and respond in a soothing manner.

Human-in-the-loop (HITL) testing

A technique for training a machine-learning algorithm to check and refine its results. For example, an image-recognition system trained on pictures that have been manually labeled requires humans to check and score the accuracy of results. The technique can also be used to improve the accuracy of mapping technology, speech recognition and product categories. Research suggests that a mixed approach using 80% machine testing, 19% human input and 1% random input reduces faulty recommendations and yields higher quality results.

Inference

The process that neural networks use to make decisions about new, sometimes incomplete data. In cognitive psychology, inference refers to the ability of humans to make educated guesses. In artificial intelligence, computers use?inference ?to emulate human decision making. The ability is essential to voice recognition, natural-language processing and other advanced uses of machine learning.

Long short-term memory (LSTM)

A type of recurrent neural network that can recall sequences of data, such as speech and video, for longer periods of time than standard neural networks. It’s a more complex way of processing data that makes it possible to pick out a moment in a video with the ease of searching for a static image. The technology makes possible natural-language generation,?musical composition ?and analysis, and handwriting recognition, and can be used to predict?disease outbreaks .

Natural language generation (NLG)

Like natural language processing, which allows machines to understand human text and voice, NLG technology makes it possible to translate structured data into reports or summaries in conversational language. NLG uses deep-learning algorithms to go?beyond basic comprehension ?and understand the context that makes the data relevant, much as a human analyst would. While current applications rely on structured data, a machine-learning algorithm recently compiled a textbook using the unstructured sentences in academic papers.

“No-code” machine learning

Also known as?AutoML.?The process allows developers to build customized algorithms simply by using a?drag-and-drop ?visual interface on open source platforms such as Microsoft’s Azure ML, Baidu’s EZDL and Google’s AutoML Vision. The technique eliminates the need for specialized coding knowledge and experience and holds the potential to democratize artificial intelligence programming within companies.

XAI (explainable AI)

Artificial intelligence that is programmed to describe its purpose, rationale and decision making to the average person. Ethics advocates are urging greater use of XAI to promote greater?transparency and fairness ?and to move away from “black box algorithms.”

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