Artificial Intelligence (AI) is the science of making machines that can think like humans. It can do things that are considered “smart.”
Many diverse problems have been solved by artificial intelligence programs. Some examples are finding the winning move (or sequence of moves) in a board game, devising mathematical proofs, and manipulating “virtual objects” in a computer-generated world.
AI is classified into three main types: Narrow AI, General AI, and Super AI. Each type of AI has its unique characteristics, capabilities, and limitations
One of the central aims of AI is to develop systems that can analyze large datasets, identify patterns, and make data-driven decisions. This ability to solve problems and make decisions efficiently is invaluable across various industries, from healthcare and finance to transportation and manufacturing.
John McCarthy is considered as the father of Artificial Intelligence. John McCarthy was an American computer scientist. The term “artificial intelligence” was coined by him. He is one of the founders of artificial intelligence, together with Alan Turing, Marvin Minsky, Allen Newell, and Herbert A.
These technologies not only save time, but also potentially save lives by minimizing human error and ensuring a safer working environment. In addition, automating repetitive tasks in design, planning, and management with AI frees up human workers to focus on more complex and creative aspects.
The key elements of AI include Natural language processing (NLP), Expert systems, Robotics, Intelligent agents, and Computational intelligence.
Natural Language Processing (NLP)
NLP is a branch of AI that allows machines to use and understand human language. It is built into products such as automatic language translators used in multilingual conferences, text-to-speech translation, speech-to-text translation, and knowledge extraction from text. This technology is used to scan data in the form of raw language such as handwriting, voice, and images into contextually relevant structures and relationships that can easily be integrated with other structured data for more efficient analysis in subsequent processes.
Expert systems are machines or software applications that provide explanation and advice to users through a set of rules provided by an expert. The rules are programmed into software to reproduce the knowledge for non-experts to solve a range of actual problems. Examples of this are found in the fields of medicine, pharmacy, law, food science, and engineering, and maintenance. In the oil and gas industry, expert systems have been used from exploration through production, from research through operations, and from training through fault diagnosis.
Robotics
Intelligent robots are mechanical structures in various shapes that are programmed to perform specific tasks based on human instructions. Depending on the environment of use (land, air, and sea), they are called drones and rovers. In the petroleum industry, they have been used in innovative and beneficial ways: in production; to connect different segments of drill pipes during drilling, in underwater welding to conduct underwater maintenance and repair tasks; in exploration to map outcrops for building digital models for geologists; and in field operations to inspect remote sites and challenging terrains that are potentially dangerous for humans to navigate. Some of the benefits derived from the use of robots in the oil and gas industry include improving safety, increasing productivity, automating repetitive tasks, and reducing operational costs by diminishing downtime.
Intelligent Agents
Multi-agent systems (MAS) is a subfield of AI that builds computational systems capable of making decisions and take actions autonomously. These systems are capable of maintaining information about their environment and making decisions based on their perception about the state of the environment, their past experiences, and their objectives. Agents can also interface with other agents to collaborate on common goals. They emulate human social behavior by sharing partial views of a problem, enabling collaboration, and cooperating with other agents to make appropriate and timely decisions to reach desired objectives. Agents have been implemented successfully, mostly in the manufacturing industries, and are proven to have potential benefits in the petroleum industry.
Computational Intelligence
Computational Intelligence is the computational aspect of AI that focuses on utilizing and deriving value from data. It uses the knowledge-discovery and data-mining processes to develop Machine Learning (ML) workflows to learn from historical data and predict future events. There are several algorithms designed to build ML models. Examples are artificial neural networks, decision trees, random forests, support vector machines, extreme learning machines, fuzzy logic types I and II, adaptive neuro fuzzy inference systems (popularly known as ANFIS), Gaussian-process regression, Bayesian belief network, and K-nearest neighbor. Data science can be defined as the new and continuously evolving field that uses various scientific methods, processes, algorithms, and systems to extract knowledge, patterns, or insights from data.
Supervised Machine Learning
Supervised ML algorithms learn patterns from historical examples (called training data) to generate the outcome of future events. It involves building and training a model for a specific application using a set of input data with their corresponding target values. The model is able to predict outcomes for new inputs after sufficient training. An example is to build a relationship between wireline logs as input and a specific reservoir property (such as porosity) from historical data to predict the porosity values for a new or uncored well. Typical applications of this method are regression and classification.
Unsupervised Machine Learning
In contrast to supervised, unsupervised ML algorithms make inferences from events without prior classification or labels. They infer a function, usually based on some distance metric, to discover a hidden structure from unlabeled data. An expert can thus derive meanings that lead to new insights. An example is to use historical wireline log data to compartmentalize a reservoir into zones based on the density of the data points. An expert may then interpret the sections as different lithologies. A typical application of this is clustering.
Hybrid Machine Learning
Hybrid or mixed ML algorithms combine supervised and unsupervised methods to solve a problem especially where there are uncertainties in human knowledge. Either one could come first. A typical application could start with supervised learning and the predicted output could then be clustered to reveal certain hidden patterns. Another application could start by assigning clusters to an input data to generate an output that will form the basis for a new prediction to achieve a supervised learning objective.
Kick-Starting a Career in Artificial Intelligence
Getting started with a new venture involves two major steps: acquiring knowledge and applying the knowledge through practical implementation. Some of the basic requirements to learn AI include a knowledge in statistics, probability theory, mathematics (linear/non-linear algebra), and programming. The first step to practicing AI is to take relevant courses. Available learning platforms include Udemy, Coursera, Edx, and Springboard. It is strongly recommended to lay hands on practical applications to solidify the foundational training in these courses.
Investing in relevant books on probability, statistics, mathematics, and coding would complement the courses. Working on as many projects as possible and collaborating with other AI enthusiasts would help to accelerate the learning curve. Participating in ML competitions, hackathons, and datathons could also help.
AI, along with other technologies, sprang from the emergence of the fourth industrial revolution. AI technologies are here to stay and have become part of our personal lives and business processes. Industries, including oil and gas, are already experiencing transformation. There are still challenges to be addressed with more research, innovative thinking, and collaborative efforts.
Starting with AI and any of its elements is not difficult. The journey of a thousand miles starts with one bold step. Training, followed by practical implementation, will help make your journey from an enthusiast to expert easy.
