Image processing refers to a collection of techniques that apply specific operations on an image in order to create an improved version of it or additionally manipulate digital images to make the process of extracting information using computer vision algorithms simpler or quicker.
Image Annotation is the process of labeling images in order to train a machine learning model. Annotation is a prerequisite for all supervised machine learning models. Deep learning is a subset of machine learning methods that uses artificial neural networks to extract high-level features from raw input data.
We offer a number of AI powered drone applications to automate the processing of high-resolution aerial images in agriculture, greenhouse, ecology, habitat monitoring and environmental conservation. Our applications compile the drone imagery, generating customer-requested reports with high precision and efficiency, ultimately accelerating knowledge synthesis by removing the tedious aspects of dealing with large and complex data.
In this project, using machine learning and Saiwa Anomaly Detection service, we detect and localize the location of micro and macro defects on a casting line, including: crack, frost, frost patch, longitude frost and mold oscillation. We have successfully implemented Aluminum Surface Defect Detection solution for CastTechnology in Canada. This solution is delivered via a simple user interface where users can run the defect detection APIs.
Corrective Exercise is a technique that leverages an understanding of anatomy, kinesiology, and biomechanics to address and fix movement compensations and imbalances to improve the overall quality of movement during workouts and in everyday life. This technique is used to help assess and determine the root cause of imbalances and faulty movement patterns that lead to issues with posture, balance, and total body coordination. In this project, a corrective exercise mobile application was developed for Android platform. For more details, please visit project dedicated page.
Detection and Surveillance of European Water Chestnut (EWC)
European Water Chestnut (Trapa natans) is an invasive floating-leaved aquatic plant that is capable of out-competing native species and altering Ontario’s aquatic ecosystems. Ducks Unlimited Canada (DUC) performs surveillance and control of this species in eastern Ontario in attempts to eradicate existing populations and prevent the establishment of new ones. To enhance DUC‘s capability for UAV-based surveillance of EWC through machine learning, we at Saiwa implemented the EWC detector software. Please visit here for more details of this project.
Saiwa is a start-up that uses a service-oriented platform to deliver artificial intelligence (AI) and machine learning (ML) services and solutions. Despite the fact that the Saiwa story began in January 2021, we had a lengthy journey that motivated us to design this product. We are pleased to inform that we have made three versions of our platform public so far, and we are expanding our collection of services by closely monitoring new state-of-the-art AI and ML technologies.
The Evolution of Artificial Intelligence has caused it to become one of the most prominent buzzwords in the technology world today, and for a good reason. In recent years, we have seen remarkable advances in AI that were once the stuff of science fiction. Experts predict that AI is a crucial factor of production that could transform how work is done across industries and create new opportunities for growth. In fact, according to a PWC analysis, AI has the potential to contribute $15.7 trillion to global economic growth by 2035. With China and the United States expected to benefit the most, accounting for around 70% of the worldwide impact, we can anticipate a revolutionized economy in the coming years.
What is Artificial Intelligence?
Artificial intelligence (AI) is a broad field of computer science and engineering that focuses on developing computers capable of performing activities that normally require human intelligence, such as learning, problem-solving, decision-making, and natural language processing. AI systems use algorithms and statistical models to evaluate and learn from data, allowing them to improve their performance over time without being explicitly programmed.
The evolution of artificial intelligence has brought about a range of techniques, including rule-based systems, machine learning, deep learning, natural language processing, computer vision, and robotics. These techniques have enabled practical applications such as speech recognition, image and video analysis, autonomous vehicles, medical diagnosis, fraud detection, and predictive analytics. With continuous innovation and progress, AI has become a rapidly evolving field. As it advances, it has the potential to revolutionize numerous industries and fundamentally transform the way we live and work.
According to Arend Hintze, assistant professor of integrative biology, computer science, and engineering at Michigan State University, there are four categories of AI. These categories start with task-specific intelligent systems, widely used today, and work their way up to sentient systems, which are still hypothetical. These are the categories.
These AI systems are task-specific and have no memory. To illustrate, consider Deep Blue, the IBM chess software that defeated Garry Kasparov in the 1990s. Deep Blue can recognize pieces on a chessboard and make predictions, but it cannot do this because it has no memory. 2. Limited memory These devices collect historical data and constantly add to their memory.
These devices collect information from the past and continually add it to their memory. Although their memory is limited, they have enough experience or knowledge to make wise decisions. For example, this system can recommend a restaurant based on its collected geographical information.
Theory of mind
In psychology, there is a concept known as the theory of mind. When applied to AI, it suggests that technology will be socially intelligent enough to understand emotions. This type of AI will be able to anticipate human actions and infer motives, a necessary skill for AI systems to function as vital members of human teams.
AI programs are aware of this category because they understand who they are. Self-aware machines are aware of their states. No such AI currently exists.
History of Artificial Intelligence
Although artificial intelligence has existed for thousands of years, its potential was not recognized until 1950. Some scientists, physicists and intellectuals had the idea of artificial intelligence in their heads, but an English scientist named Alan Turing suggested that humans solve problems and make decisions with the help of available information and correct reasons. The complexity and difficulty of computers was an obstacle to their development. They had to adapt fundamentally before they could expand. In fact, machines can execute commands but not store them. While funding was also a problem in 1974 and during the evolution of artificial intelligence, computers became popular and could store data faster and cheaper than today.
Evolution of Artificial Intelligence
Here is a timeline depicting the evolution of artificial intelligence over the last six decades since its inception.
1956: The term “artificial intelligence” is first used by John McCarthy at the first AI conference at Dartmouth College. (McCarthy later created the Lisp language.) Later that year, Allen Newell, J.C. Shaw, and Herbert Simon developed Logic Theorist, the first working AI software.
1967: The Mark 1 Frank Rosenblatt’s Perceptron was the first neural network-based system to “learn” through error. About a year later, Marvin Minsky and Seymour Papert published Perceptrons, which became a seminal work on neural networks and, for a time, served as a critique of further neural network research.
1997: World chess champion Garry Kasparov was defeated in a match by the supercomputer known as Deep Blue. The development of this large computer by IBM was a significant achievement.
2015: An advanced deep neural network, called a convolutional neural network, is used by Baidu’s Minwa supercomputer to recognize and classify images more accurately than the average person.
2020: During the early stages of the SARS-CoV-2 (COVID-19) epidemic, Baidu provides the LinearFold AI algorithm to scientific, medical, and vaccine development teams. The program can predict the RNA sequence of a virus in just 27 seconds, 120 times faster than previous methods.
Artificial Intelligence Research Today
In today’s world, the evolution of artificial intelligence continues and expands day by day. According to the surveys conducted, artificial intelligence research has grown by 12% in the last five years. In the field of artificial intelligence development, Europe is the largest and most diverse continent with significant international cooperation.
The recent development of AI programs
These are a few of the most recent advancements in the Evolution of Artificial Intelligence and how they can influence different economic sectors that you should be aware of.
Generative Pre-trained Transformer 3
OpenAI, a machine learning and artificial intelligence research group has developed the state-of-the-art language model GPT-3 (Generative Pre-trained Transformer 3).
The GPT-3 was trained on a large dataset of written text, including books, papers, and web pages. As a result, it can perform natural language processing (NLP) tasks such as translation, summarization, and question-answering in multiple languages. It uses deep neural networks to create text that mimics human speech and provides logical and relevant contextual answers to questions posed in natural language.
The evolution of artificial intelligence has led to significant progress in computer vision, which enables machines to identify and categorize objects, individuals, and events in images and videos. With recent advancements in this field, robots can now perform tasks that were once exclusive to humans, such as recognizing emotions and objects. Convolutional neural networks have been a crucial development in deep learning for processing visual data. Moreover, computer vision has the potential to benefit various industries, including manufacturing, retail, and entertainment. It can also be utilized to create highly accurate virtual environments that respond to augmented and virtual reality user movements.
Artificial Intelligence Advancements in Healthcare
The Evolution of Artificial Intelligence has transformed personalized medicine by using AI algorithms to generate customized treatment plans based on a patient’s medical history and genetic makeup. AI has also paved the way for developing new drugs and predicting which patients are likely to respond best to a specific therapy, resulting in better patient outcomes and cost reduction.
Medical imaging algorithms have significantly improved diagnostic accuracy by enabling faster and more precise diagnoses of diseases from X-rays, MRIs, and CT scans, thus facilitating earlier and more effective treatments.
AI has the potential to revolutionize patient monitoring, reduce healthcare expenses, and enhance medical efficiency. However, ethical and transparent implementation and addressing privacy concerns and algorithmic bias must be prioritized.
The evolution of artificial intelligence has led to the creation of self-driving cars and other autonomous systems through reinforcement learning, where AI agents are trained by rewarding or punishing specific behaviors. The technique’s three key components are the agent, environment, and reward signal. The agent interacts with its surroundings and makes decisions, while the physical environment provides feedback to the agent in the form of reward signals, which are beyond the agent’s control. Google’s Deep Mind has leveraged reinforcement learning to develop algorithms that can compete in games such as Go and Chess. The agent learns by making mistakes and maximizing rewards from the environment. Reinforcement learning can also be applied to teach autonomous robots to navigate and to train virtual assistants to converse.
Explainable Artificial Intelligence
The evolution of artificial intelligence has brought about one of the most significant challenges in the field, which is commonly referred to as the “black box” problem. Explainable AI (XAI) aims to tackle this problem, which arises when many AI algorithms accurately make predictions but are difficult to understand internally. This creates issues, particularly in high-stakes business applications. The primary goal of XAI is to develop AI systems that are not only precise but also transparent and comprehensible.
With accurate and transparent AI systems produced by XAI, predictions or judgments can be explained in terms that people can understand. Standard methods for developing understandable AI include “interpretable models” and “post hoc” reasoning. Providing illustrations, stories written in everyday language, or interactive user interfaces that allow users to learn more about how the AI system makes decisions are all ways to provide these explanations.
As artificial intelligence evolves, it will significantly impact our lives and work. Therefore, staying up to date with these developments is crucial, as they will be necessary for our future. It is also essential to consider the ethical implications of AI to ensure that these technologies are implemented responsibly and transparently for the benefit of humanity.
Artificial Intelligence in The Future
The evolution of artificial intelligence has led to significant growth in the AI market, as forecasted by a recent study by Grand View Research, which predicts that it will reach a value of $390.9 billion by 2025, growing at a CAGR of 46.2%. The integration of AI into various applications is a significant driving force behind this growth. Advances in speech and image recognition, which are widely used in our daily lives across multiple devices and functions, are key contributors to the global market expansion. Moreover, the development of better image recognition technology is vital for the growth of robotics, drones, and self-driving cars.
Artificial Intelligence in Different Industries
AI systems have a wide range of practical applications nowadays. Some of the most typical examples are provided below:
AI in business
Machine learning algorithms are being incorporated into analytics and customer relationship management (CRM) platforms to figure out how to serve customers better. Chatbots have been integrated into websites to provide instant assistance to customers. The rapid development of generative artificial intelligence technologies is expected to have far-reaching effects, including eliminating jobs, a revolution in product design, and disrupting business models.
AI in healthcare
The most significant investments are in reducing costs while improving health outcomes. Organizations use machine learning to identify diseases faster and more accurately than humans. This technology uses patient data and other available data sources to construct a hypothesis, providing a confidence rating scheme. Many AI technologies are being used to anticipate, manage and understand pandemics such as COVID-19.
AI in education
Automating grading with artificial Intelligence can free up time for other instructional tasks. Students can work independently because their needs can be assessed and met. Artificial intelligence tutors can provide extra help to students to keep them on track. In addition, technology can change where and how students learn, possibly even replacing some professors.
AI in finance
Artificial Intelligence disrupts fintech companies in personal finance software like Intuit Mint and TurboTax. Apps like these collect personal information and provide financial advice. Other technologies like IBM Watson have been used in home buying. Today, artificial intelligence systems automate a significant percentage of Wall Street trading.
AI in Security
Given the current hype surrounding artificial intelligence and machine learning in the security industry, potential customers should exercise caution. However, as the evolution of artificial intelligence continues, these technologies are proving useful for various cybersecurity tasks, such as detecting anomalies, reducing false positives, and conducting behavioral threat analysis.
Organizations are increasingly utilizing machine learning approaches, including in security information and event management (SIEM) software and related areas, to identify and track suspicious behavior that may indicate a potential attack. AI can detect new and evolving threats much earlier than human operators and previous technology iterations by analyzing data and using logical reasoning to identify similarities to known malicious code.
Self-driving cars are definitely one of the things you will see in the future. The near future has been mentioned about this issue. This means that this issue will not be among science fiction, and self-driving cars are already a reality. According to the latest predictions, there will be about 33 million cars with self-driving capabilities on the roads by 2040.
Transportation and Travel
In travel, artificial intelligence can help provide the best route recommendations for drivers and remote travel reservations. People can navigate more easily, thanks to artificial intelligence. Travel companies using artificial intelligence will also benefit from smartphones.
saiwa is an online platform which provides privacy preserving artificial intelligence (AI) and machine learning (ML) services, from local (decentralized) to cloud-based and from generic to customized services for individuals and companies to enable their use of AI in various purposes with lower risk, without the essence of a deep knowledge of AI and ML and large initial investment.