Understanding Artificial Intelligence (AI) And The Need To Patent AI Inventions

 The world has transformed due to artificial intelligence in inconceivable ways. AI has undoubtedly changed the world as a whole, from lowering human work and time to making dangerous tasks look easy. There is probably no industry now where AI does not find some use. Whether it's Google's Siri or Amazon's Alexa, AI has changed how work is done. The IP system supports human-made advancements in the field of AI as applications of AI expand. Artificial intelligence has great potential for the future and will undoubtedly reach new heights. Therefore, one must seize every opportunity to patent AI ideas.

This page addresses the algorithms, many varieties, and underlying technologies of artificial intelligence. Additionally, it covers the trend in AI, the need to patent AI technology, and how defining an AI invention's technical impacts can result in the grant of an AI patent.

Types of AI

Two broad categories of artificial intelligence can be made: one based on capabilities, the other on functionality.

The first sort of artificial intelligence is based on capabilities.

AI can be further segmented based on capabilities into:

1. Narrow or weak AI: This category includes practically all of the current forms of artificial intelligence. It alludes to these autonomous AI devices that carry out particular tasks. These AI systems have constrained or specific competencies. There are several fields where narrow or weak AIs are used. A search tool, voice-based personal assistants, face recognition, and other features are common in smartphone applications. Other uses include visual recognition in self-driving cars, recommendation engines that provide product suggestions based on past purchases, and similar things.

2. General or strong AI refers to the idea of a machine with general intelligence that can learn and use its intelligence to solve any problem. It can replicate human intelligence and/or behaviour. The theory of mind AI framework that this powerful AI employs refers to the capacity to ascertain the wants, emotions, beliefs, and mental processes of other intelligent beings. The goal of mind-level AI is to teach machines to understand people in their entirety, not through simulation or duplication.

3. Artificial superintelligence: A hypothetical AI that does not merely imitate or comprehend human intelligence and behaviour is called artificial superintelligence. It is the point at which machines transcend human intelligence and ability and become self-aware. Although it would be able to mimic human abilities, artificial superintelligence would be far superior due to its vastly superior memory.

Artificial Intelligence type 2 – Based on Functionality

1. Reactive devices: These AI systems are mostly simple and fairly primitive, and they have few abilities. Since these machines lack memory storage, they attempt to respond to the current stimulus in the best way they can. They are also incapable of learning, according to this. They are unable to "learn" from their past mistakes and as a result, cannot apply them to their present behaviour to make it better. Only a small set of commands may be trusted to cause these machines to act.

2. Machines with limited memory can learn from their past mistakes and store data, but only for a relatively brief period of time. Consider them as advancements above reactive AI devices. It can work as a reactive mechanism, but it can also draw lessons from the past. It trains itself to solve issues in the future using vast amounts of data and its recollection of the past.

3. Theory of Mind: Despite the fact that researchers are currently working on new ideas, it is thought that this new kind of machine will have "human-like" emotions and beliefs and will be able to act just like people in social situations. Although the aforementioned machines are actual objects, this kind of AI system is still only an idea.

4. Self-Aware: Only in theory, such machines do exist. These incredibly clever machines, which are thought to represent the future of artificial intelligence, will have their own consciousness and be extremely "self-aware," just like Human Beings or even better.

Agents and environments

Any AI's agent and environment are two key components. It is important to have a concept of "an agent and an environment" before we can start to comprehend how an AI functions. An environment is what it perceives, but an agent is something that can perceive. An agent uses effectors to interact with the environment while using sensors to observe it. A robot, a chatbot, a programme, etc. are some examples of the agent. The sensory organs of a "human agent" serve as "sensors," while the effectors include the legs and hands. A "robotic agent" is similar in that it contains motors and actuators for effectors and cameras and infrared sensors for sensors.

Depending on the application domains, the agent and environment change. For instance, in the scenario of a robot in a room, the robot would be the agent and the environment would be the room. Similar to this, in the scenario of a moving vehicle, the former is the agent and the latter is the environment. Similar to this, a software programme acts as the agent while the environment consists of data and regulations.

As a result, the agent is essentially a solution to the environment's problems. The AI enters the picture because the agent needs intelligence to function in the environment. An "agent programme" for the agent is created with the aid of AI.

Agent = Agent Program + Architecture

The "autonomous driving" programme for "autonomous vehicles" outfitted with computers, sensors, or similar technology is an illustration of an agent programme. The sensors in the architecture gather environmental "sensor data." The agent programme receives sensor data and returns "actions" that are carried out by the actuators.

Based on the interaction between the agent and the environment and its applicability, agents can be categorized into different types:

• Simple-reflex agent
• Model-based reflex agent
• Goal-based agent
• Utility-based agent
•  Learning agent

1. The condition-action rule is used by the simple-reflex agent. It uses the sensors to gather information about the environment and then acts in accordance with the stated rule in a fully observable environment. The inputs that an intelligent agent is always getting are referred to as "percepts." According to the condition-action rule, an action must be taken in response to a condition. A room cleaner, for instance, only effectively cleans areas of a room that are dirty. In other words, the necessary 'condition' for a room-cleaner to carry out its 'activity' is that there must be dirt in the space.

2. The model-based reflex agent acts in a partially visible environment using a model (environmental knowledge). This model provides data on "how things are in the world right now." Additionally, it keeps a percept history-based internal state that reflects the current context or state. The agent's entire past perceptions are all recorded in the percept history. Based on knowledge about how the world changes and how the agent's activities impact the world, the state is updated.

3. A goal-based agent is more adaptable than reflex- or model-based agents because it explicitly models the information that underlies a choice. A goal is a collection of desirable circumstances. The goal-based agent has the ability to choose an action for accomplishing its objective, which characterises desired scenarios. In order to choose the action from a lengthy list of potential actions, the goal-based agent searches and plans. The goal-based agent is proactive since the chosen action it does is meant to shorten the gap between it and the goal.

4. The utility-based agent is comparable to the goal-based agent and has the ability to measure utility. The utility assessment provides a gauge of objective achievement at a specific condition. In other words, the utility-based agent has the ability to choose actions based on the utility for each state in order to maximise projected utility. The utility provides information on the agent's "happiness," which can be assessed using a utility function. The utility function converts a state into a real number that expresses the level of enjoyment that is connected with it.

5. The learning agent has the ability to learn from previous experiences and act knowing the fundamentals. Over time, the learning agent develops the capacity to act and adapt automatically. The learning agent has several components, including a learning element to learn from the environment, a critic component to receive feedback from critics that describes how well the agent is performing based on a predefined performance standard, a performance element to select an action, and a problem generator to generate actions that will result in novel and educational experiences.

The Procedure

Knowing an AI's algorithm is essential to comprehending how an AI machine functions. Algorithms, to put it simply, are the detailed instructions a machine or system needs to follow in order to carry out the desired purpose. It should be viewed as a manual that instructs a machine on what to do and when to do it. Algorithms, which include methods like machine learning, deep learning, and data science, fuel artificial intelligence.

Statistical methods are used by machine learning (ML) algorithms to input data to an AI system. The AI system can learn and get better over time at executing tasks without having to be particularly designed to do so thanks to statistical methodologies. There are drawbacks to designing an application to respond to each input and output; examples include functional programming, attribute-based programming, and object-oriented programming. There are too many potential unpredictable inputs and outputs in a real-world setting. Machine learning techniques may be used to attain the predictability.

Machine learning algorithms receive a significant amount of data as input. To produce an accurate result, the input data is first pre-processed, or cleaned, to remove unnecessary or redundant information. The input data is processed by machine learning algorithms, which then generate a model with "rules" derived from the input data. The model's reliability may also be examined. If the input data is 100%, for example, the data is divided into, say, 70% of data for building the model and 30% of test data for testing and assessing the model. The 70% of the data are used to build a model.

ML algorithms may receive input data in a variety of modalities, including image, video, audio, and more. Without the requirement for human labelling, deep learning systems can process and analyse various types of unstructured and unlabeled data. Neural networks, such as artificial neural networks that imitate how the human brain functions, can be used to accomplish this. The neural networks aid in clustering, classification, identifying relationships between variables, and identifying patterns in data.

Patenting AI inventions

Patents on AI inventions are required to:

• safeguard technological advancements and problem-solving techniques
• Stop others from using an inventor's AI creation, assist the inventor in establishing legal exclusivity around a patented product, and help the inventor make a profit by licencing the patented product.

To get more information, read entire article on AI inventions