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Artificial intelligence or AI is a hot topic these days and an oft-used term in nearly every industry. Automation, quality inspection and manufacturing are no exception. Rather, these are a major proving ground for development, experimentation, and implementation of AI for industrial applications. In many instances however, AI is a bit of a catchall term, often referring to the application of some level of computer-assisted decision-making. When we unpack it, the more relevant pieces that people often speak of are machine learning, deep learning, and computer vision individually different but collectively referred to as AI. The discussion stands to benefit from some untangling to enable an improved level of clarity around what each does, why they matter and the value and benefit that machine learning brings to the industrial automation and quality inspection landscapes.

Put simply, computer vision is the process of interpreting information from images or videos in an automated fashion using computers instead of people. The algorithms and models used in computer vision applications tend to be rigid in that they are tailored to find and identify specific items in a scene these could be defects, the absence or presence of something, or incorrect characters on a label. The inspection environment needs to remain static as do the items being inspected. Changes in the variables almost always mean that the model must be updated, reconfigured, and redeployed by humans.

Machine learning on the other hand uses historical data to make decisions related to the interpretation of information from images or videos. Relying on patterns and inference, computers can identify defects or inaccuracies in a scene using algorithms or models that can adapt to changes or to variables in the environment. Initial models are often trained with supervised learning using people to characterize and label pass/fail or good/bad results based on an existing dataset and feeding that information into the model. From that baseline, the system can begin to perform inspection tasks without explicit instructions.

While machine learning is a subset of AI, deep learning should be viewed as a subset of machine learning. Using artificial neural networks, it aims to mimic the learning process of the human brain, eliminating the need for human involvement to learn from its environment. Whereas machine learning algorithms often rely on human correction to help them to overcome mistakes, deep learning leverages high-performance computing to improve through repetition on its own. To accomplish this, deep learning relies on data sets that are significantly larger than those needed to train a machine learning algorithm along with substantially longer processing and training times to achieve reliable accuracy and sophistication.

When we aggregate the various technology subsets computer vision, machine learning and deep learning the result is an artificial intelligence solution that can extend across a multitude of use cases for automation and quality. Machine learning as the potential to play a major role in the successful implementation of AI across the industry, combining traditional computer vision capability with added intelligence that enables increases in productivity, safety and efficiency within applications and tasks that have traditionally been served by human operators. In addition to traditional applications for identifying defects in a product or missed steps in a process, opportunities to create value can be found where processes are subjective, and rules not easily defined or organized into logical steps or decisions. Likewise, scenarios where outcomes are known but the action to be taken is difficult to predict or dependent on many conditions are well-suited to machine learning technology.

The number of robots being sold and deployed continues to grow and is currently setting records annually. Although robots have been used in industrial automation for decades, early functionality consisted of fixed, repetitive routines in a setting that remained static. Today however, Autonomous Mobile Robots (AMRs) extend far beyond the scope of set parameters and due in large part to machine learning, can self-navigate and respond to changes in factory floor configurations, the presence of people or objects as well as other robots. Similarly, robotic arms used in packaging, warehousing and logistics can analyze materials to be loaded onto a pallet or into a carton and can make decisions that result in optimal placement and stacking.

Equipment failure in a production or manufacturing environment often represents one of the worst problems a halted production line which can have a cascading impact on other operations in addition to impacting a manufacturer financially for every minute lost. Machine learning offers the ability to develop and deploy intelligent systems which can monitor and evaluate production equipment continuously. Systems that can self-learn utilize data from production line sensors to identify patterns and anomalies and alert to drops in performance. With these insights, maintenance can be scheduled proactively at cost-effective times rather than reactively when something breaks. The results are improved productivity and a reduction in costly repairs and downtime.

Defect detection is perhaps the most popular use-case for implementing machine vision in an automation or quality inspection application. Like the evolution of robotics, traditional machine vision applications are programmed with rigid instructions configured to look for specific defects in the same product or material in repetitive fashion. Machine learning brings advanced analysis and decision-making to quality inspection by enabling those systems to become more dynamic and tolerant of changes in inspection environments and other variables. For example, in label verification, conventional machine vision is designed to inspect specific fonts or typesets. Utilizing machine learning provides for the ability to inspect varying font styles as well as to make correct pass/fail decisions despite changes in contrast levels and text placement. Advanced machine learning can have the benefit of reduced false positives resulting in less waste, reduced manufacturing costs and higher manufacturer profitability.

The capabilities that are made possible with AI are largely the result of advances in software development and evolution. The potential to enable computers to simulate human interaction through data analysis and pattern identification has dramatically altered the complexity, ability and accuracy of our automation and quality systems. While less talked about, hardware progression makes large-scale deployments possible and until recently, many AI implementations have been limited in terms of where and how they could be used, relying on large computer infrastructure to execute their programming.

Recent advances in peripheral components including CPUs, GPUs, and TPUs, embedded processing and data transfer technologies have created the conditions for dramatic reduction in the physical size, power consumption and cost of deploying machine learning solutions. These advancements, coupled with sophisticated software are accelerating deployment in the form of edge and mobile computing, have made it possible to implement AI in applications where just a few short years ago it was thought impossible. If history is any indication, we are primed for rapid expansion of the market segments and industries served by machine learning along with continued growth in its use across existing and new applications for the foreseeable future, fueled by our drive for greater productivity, efficiency, quality and safety in the work we do and the products we make.

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Still considered an Ethereum (ETH) killer, the native token of the Cardano blockchain ADA is no stranger to sudden price rises and falls. Since its launch in 2017, it saw major surges and nearly as large declines, having in 2021 skyrocketed quickly to $3 before stabilizing below $0.5 by mid-2022.

Last year, however, saw rampant speculation give way as the security, scalability, and sustainability-focused ecosystem finally took off, and after following an overall downward trajectory for more than a year, ADA surged in the final quarter.

By the start of 2024, however, the cryptocurrency entered into a correction and, with it declining nearly 20% since January 1, Finbold decided to consult the AI-driven predictive algorithms of CoinCodex on how ADA might fare by the end of February.

The machine learning algorithms of CoinCodex assess that ADA is likely to trade with significant volatility throughout February albeit on an overall upward trajectory. They estimate the token will rise 8.53% from the press time price of approximately $0.5 and find itself at $0.543316 on February 29.

The platform also estimates that ADA will fall slightly in early March and find itself closer to $0.519478 in one months time.

Given that the token had 14 green days out of the last 30, it, perhaps, isnt surprising that the crypto market sentiment on it is gauged as neutral. The Fear & Greed index the index that tracks investor attitude toward an asset however, records greed when it comes to ADA.

While the future fortunes of ADA are hard to predict, it has recently offered a mostly strong performance. In total, it is up 27.88% in the last 52 weeks, but given the decisive break from its long downtrend in October, it is as much as 70.36% up in the last 6 months.

Zooming into 2024, the token has been in a volatile period of correction following its surge that lasted from mid-October to mid-December and is down 19.62% year-to-date (YTD). More recently, it has again started rising albeit slowly and is 0.63% in the green in the last week but 0.53% in the red in the last 24 hours, having dropped to $0.50.

Disclaimer: The content on this site should not be considered investment advice. Investing is speculative. When investing, your capital is at risk.

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Machine learning algorithm sets Cardano (ADA) price on February 29, 2024 - Finbold - Finance in Bold

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Summary

Machine learning is a transformative branch of artificial intelligence that empowers systems to learn and improve from experience without being explicitly programmed. It involves algorithms that analyze data, learn from it, and make decisions or predictions. Machine learnings capability to adapt to new data independently makes it a cornerstone of modern AI applications, enhancing everything from healthcare diagnostics to personalized consumer experiences.

In this article, we will delve deep into the world of machine learning, providing you with a comprehensive understanding of what it is and how it works. By the end of this article, you will have a solid grasp of the core concepts, algorithms, and real-world applications of machine learning.

Machine Learning (ML) represents a transformative branch of artificial intelligence (AI) that empowers software applications to become more accurate in predicting outcomes without being explicitly programmed to do so. It revolves around the development of algorithms that can process input data and use statistical analysis to predict an output while updating outputs as new data becomes available. The essence of machine learning lies in its ability to learn from data, identify patterns, and make decisions with minimal human intervention.

Machine learning is crucial for its ability to process and analyze vast amounts of data with increasing accuracy and efficiency. Its importance stems from the fact that it enables the automation of decision-making processes and can be applied to a wide range of industries, including healthcare, finance, education, and more. Machine learning algorithms can uncover hidden insights through data without being explicitly programmed where to look, leading to innovations in AI that seem to border on sci-fi.

Moreover, machine learning is fundamental in developing complex models that power modern AI applications, such as natural language processing, self-driving cars, and recommendation systems. By harnessing the power of machine learning, businesses and organizations can improve operational efficiencies, enhance customer experiences, and innovate continuously in an ever-evolving digital landscape.

This technologys significance also lies in its flexibility and scalability, making it a critical tool for tackling complex problems by learning from data patterns and improving over time. As data volumes grow exponentially, machine learnings role becomes increasingly vital in making sense of this information, making predictive analyses more accurate and reliable, and driving forward the capabilities of AI to unlock new possibilities and solutions to complex challenges.

Machine Learning works by using algorithms to analyze data, learn from that data, and make use of the data. The process involves feeding the algorithm training data, which can be labeled (known) or unlabeled (unknown), to develop a model that can make predictions or decisions based on new data. The algorithms performance improves over time through a process of trial and error, adjusting its approach as it learns from the outcomes of its predictions compared to actual results. This iterative learning process enables ML models to increase in accuracy and efficiency, adapting to new data with minimal human intervention.

Machine Learning (ML) is not just a futuristic concept but a present reality, deeply embedded in various aspects of our daily lives and the backbone of numerous future innovations. From enhancing social media experiences to revolutionizing healthcare, MLs applications are vast and varied. Below, we delve into some of these applications, illustrating the breadth of MLs impact on our world:

Machine learning processes vast amounts of data, raising significant data privacy and security concerns. Ensuring the confidentiality and integrity of data while leveraging it for ML applications is paramount. This involves complying with data protection regulations, securing data storage and transfer, and implementing robust access controls.

The deployment of ML systems comes with ethical implications, including bias, fairness, and transparency. Its crucial to develop and train ML models responsibly, ensuring they do not perpetuate or amplify biases present in the training data, and decisions made by algorithms are explainable and fair.

ML models are only as good as the data they are trained on and the assumptions they are built upon. They may not handle novel situations well if those scenarios were not represented in the training data. Furthermore, over-reliance on ML can lead to overlooking simpler, more efficient solutions.

Machine learning stands as a pillar of technological advancement, driving innovation across numerous fields. Its ability to process and learn from vast amounts of data autonomously has opened new avenues for problem-solving and efficiency. As machine learning continues to evolve, its impact on our daily lives and future possibilities expands, marking an era of unprecedented growth in intelligent systems.

What is Machine Learning?

How Does Machine Learning Work?

Why is Machine Learning Important?

What are the Types of Machine Learning?

Can Machine Learning Predict the Future?

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What is Machine Learning and How Does It Work? - Blockchain Council

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Stephany Lapierre founded TealBook with a strong conviction that poor-quality data hinders procurement teams ability to drive efficiency and deliver actionable insights. Now, as TealBooks CEO, Lapierre and her team leverage technologies such as machine learning (ML) and artificial intelligence (AI) to help enterprise organisations access a single, trusted source of supplier data that seamlessly integrates with any data lake or enterprise system.

Whats more, TealBook enables organisations to efficiently centralise their supplier data, consolidating fragmented records from different sources into a single, comprehensive supplier record. TealBook utilizes AI to autonomously collect, verify and enrich supplier data, promoting transparency and delivering actionable insights to procurement teams and their wider businesses.

TealBook was proud to pioneer the utilisation of AI technology to gather and enhance supplier data, transforming the way procurement teams collect and manage vital information.

Procurement teams needed accurate data in a consistent and easily accessible way, Lapierre says. If businesses have better data, their systems will produce better outcomes and, ultimately, procurement will have more value within an organisation.

It wasnt until 2017 when I met TealBooks first CTO, who was fascinated with the evolution of technology that I realised we could achieve this without suppliers having to come to a portal. We could, in fact, build on Google to leverage some of the models, to then find information on businesses and make sense of it in a profile that procurement can consume.

Describing this as the magic moment, Lapierre shares that the AI-enabled TealBook not only allows procurement teams to automate their existing software solutions, but also generates information that is more valuable to customers.

Finding information and matching it to the right company is, therefore, the baseline of how TealBook is utilising AI.

Examples of this include how weve introduced natural language tags to allow our customers to search for normal words rather than codes, and by reducing the amount of checks that humans have to make, so our customers have full confidence in the data provided, adds Lapierre.

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TealBook: Using AI and Machine Learning to Leverage Data - FinTech Magazine

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