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Artmajeur.com, one of the world's largest online galleries just banned Crawlers from conducting AI machine learning in order to protect artists' creative rights

Samuel Charmetant - Artmajeur CEO

Being a platform dedicated to showcasing creators' work, Artmajeur recognizes the significance of preserving their intellectual property and creative rights. As a result, we have decided to discontinue allowing crawlers to do machine learning on our website. The ultimate goal of this decision is to protect our artists' work and ensure that their labor is not exploited for the benefit of others.

Artmajeur Gallery actively advocates the proper evolution of artificial intelligence while protecting artists' rights. It was a difficult decision to no longer allow crawlers to perform machine learning on our website. We recognize that there are huge benefits to machine learning in the digital world, as well as compelling reasons to crawl websites. But, the potential harm is simply too great to ignore.

Samuel Charmetant - Artmajeur CEO: "I am told the use of AI technologies comes with great promises to make the world a better place. However, we at Artmajeur are committed to protecting our artists and their unique works. I am determined to fight against the theft of their artwork by crawlers who steal their content."

Artists devote a significant amount of time, effort, and emotion to their work. Every piece is a manifestation of their unique point of view and artistic vision. When this work is shared on Artmajeur, it is done with the expectation that it will be seen and appreciated by others. But, without the artist's permission, it is not intended to be taken and used for other purposes.

Crawlers who scan our website for data may steal images, writing, and other submitted work from artists. The content can then be used for a variety of purposes, including generating new content for a commercial gain. The fact that artists are frequently unaware that their work has been used in this manner can result in missed opportunities and lost profits.

We are actively preserving our artists' intellectual property rights by no longer allowing crawlers to perform machine learning on our website. Our decision is consistent with our commitment to provide artists with a safe and supportive environment in which to display their work and interact with other members of the art world. We acknowledge that this decision may have an impact on people who employ machine learning for legitimate purposes. Yet, we believe that protecting our artists' creative rights exceeds any potential downsides by a large degree. We invite anyone who is interested in using our website for study or other reasons to contact us so that we can discuss their needs.

We look forward to continuing to provide safe and responsible support to the artistic community.

Samuel CharmetantARTMAJEUR+33 9 50 95 99 66press@artmajeur.comVisit us on social media:FacebookTwitterLinkedIn

IA (2022) Painting by Laure Bollinger

Artmajeur Gallery Artworks

Artmajeur.com, one of the world's largest online galleries, just banned Crawlers from conducting machine learning

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Artmajeur.com, one of the world's largest online galleries, just banned Crawlers from conducting AI Machine - EIN News

The supply chain as we know it continues to evolve, and that is due in part to learning from the effects of the industry impacts over the last few years. As a result, more advanced supply chain technologies are focusing on the use of artificial intelligence primarily machine learning (ML) in various areas of their operations, and it is proving to be one of the most profound technologies enabling significant improvements in supply chains.

ML allows technology to teach itself over time, so that it can improve predictions, recommendations and decisions, but how does it relate to the supply chain exactly? In theory, it is nothing new, but in terms of the supply chain, it continues to prove beneficial in the advancement of digitization through data cleaning and supply chain planning, procurement and execution.

ML can deliver several benefits for supply chain management, one regarding an issue plaguing many companies today too much or too little inventory. The pandemic limited inventory due to backlogs, and now, many are finding they have too much inventory. With ML, organizations do not need to hold as much inventory because it optimizes the flow of products from one place to another. As a result, costs are reduced due to quality improvement and waste reduction, and products arrive in the marketplace just in time for sale as a result of upstream optimization.

Dealing with suppliers is one of the most challenging parts of supply chain management (SCM). With ML, supplier relationship management becomes easier due to simpler, proven administrative practices. ML can be implemented to analyze the types of contracts, documentation and other areas that lead to the best outcomes from suppliers and use those as a basis for future agreements and administration. Stakeholders get more insight into meaningful information, allowing for continual improvement and easier problem solving.

Quality is vital to good SCM as waste and faulty products create unnecessary rework and increase costs. ML can monitor how quality varies over time and suggest improvements. This doesnt just apply to materials and products. It can track other areas such as shipping, supplier and third-party quality.

ML isnt perfect, of course. It depends on reliable, high-quality and timely information, and lack of access to good data can cause significant issues. Supply chain managers need to have a robust approach to collecting and analyzing their data.

All organizations in the supply chain should provide information in a consistent way, and, where possible, SCM software should integrate with supplier and manufacturer systems to automatically collect and process data. There will need to be some human interaction with ML, especially with the quality of the data being collected. Supply chain information should be checked and audited periodically to ensure quality.

Machine learning models should be tested and checked to make sure outputs and suggestions are aligned with business needs and expectations.

For retailers, stock level analysis through ML can identify when products are declining in popularity and are reaching the end of their life in the retail marketplace. Price analysis can be compared to costs in the supply chain and retail profit margins to establish the best combination of pricing and customer demand. Also, upstream delays can be identified, allowing for contingency planning or alternative sourcing, and retailers can lower storage costs due to not having to hold as much stock.

Food manufacturers can use ML to conduct an analysis of commodity prices and weather patterns to optimize harvesting. Manufacturers can also increase speed to market by optimizing contracts and reducing turnaround times with upstream organizations.

The industry continues to focus on supply chain technologys role moving forward, and leaders will only benefit by riding the wave that ML is creating.

Glenn Jones is SVP of Product Marketing at Blume Global. He has a proven track record of growing businesses by building and leading R&D and product marketing organizations to define, develop, position and sell highly innovative and high value enterprise solutions delivered in the cloud. He was formerly the COO of Sweetbridge, the CTO of Steelwedge Software and also held leadership positions at other supply chain software companies including Elementum, E2Open and i2 Technologies.

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How Machine Learning Optimizes the Supply Chain - Talking Logistics

Machine Learning as a Service Market

Stay up to date with Machine Learning as a Service Market research offered by HTF MI.

Criag Francis

According to HTF Market Intelligence, the Global Machine Learning as a Service market to witness a CAGR of 39.25% during forecast period of 2023-2028. The market is segmented by Global Machine Learning as a Service Market Breakdown by Application (Network Analytics and Automated Traffic Management, Augmented Reality, Predictive Maintenance, Fraud Detection and Risk Analytics, Marketing and Advertising, Others) and by Geography (North America, South America, Europe, Asia Pacific, MEA). The Machine Learning as a Service market size is estimated to increase by USD 288.71 Million at a CAGR of 39.25% from 2023 to 2028. The report includes historic market data from 2017 to 2022E. Currently, market value is pegged at USD 13.95 Million.

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Definition: The Machine Learning as a Service (MLaaS) market refers to the provision of cloud-based platforms or services that enable organizations to leverage machine learning capabilities without the need for in-house expertise, infrastructure, or data storage. MLaaS providers offer a range of services, including tools for data preprocessing, model training and evaluation, deployment, and maintenance. The market also includes providers of pre-built machine learning models, APIs, and software development kits (SDKs) that enable developers to build intelligent applications and automate business processes. MLaaS can help organizations of all sizes to reduce the cost and complexity of adopting machine learning and accelerate their time-to-market for AI-powered solutions.

Market Trends: Growing Adoption of Machine Learning Services in Healthcare and Research Oriented Marketing Campaigns and Customer-centric Communication

Market Drivers: Lack of Technical Expertise to Deploy Machine Learning Services

Market Opportunities: Increasing Data Volume and Growing IoT Application and Consistent Retraining of Algorithms

The titled segments and sub-section of the market are illuminated below: The Study Explore the Product Types of Machine Learning as a Service Market:

Key Applications/end-users of Machine Learning as a Service Market: Network Analytics and Automated Traffic Management, Augmented Reality, Predictive Maintenance, Fraud Detection and Risk Analytics, Marketing and Advertising, Others

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With this report you will learn: Who the leading players are in Machine Learning as a Service Market? What you should look for in a Machine Learning as a Service What trends are driving the Market About the changing market behaviour over time with strategic view point to examine competition Also included in the study are profiles of 15 Machine Learning as a Service vendors, pricing charts, financial outlook, swot analysis, products specification &comparisons matrix with recommended steps for evaluating and determining latest product/service offering.

List of players profiled in this report: Google [United States], IBM Corporation [United States], Microsoft Corporation [United States], Amazon Web Services [United States], BigML [United States], FICO [United States], Yottamine Analytics [United States], Ersatz Labs [United States], Predictron Labs [United Kingdom], H2O.ai [United States], AT&T [United States], Sift Science [United States]

Who should get most benefit from this report insights? Anyone who are directly or indirectly involved in value chain cycle of this industry and needs to be up to speed on the key players and major trends in the market for Machine Learning as a Service Marketers and agencies doing their due diligence in selecting a Machine Learning as a Service for large and enterprise level organizations Analysts and vendors looking for current intelligence about this dynamic marketplace. Competition who would like to benchmark and correlate themselves with market position and standings in current scenario.

Make an enquiry to understand outline of study and further possible customization in offering https://www.htfmarketintelligence.com/enquiry-before-buy/global-machine-learning-as-a-service-market

Quick Snapshot and Extracts from TOC of Latest Edition Overview of Machine Learning as a Service Market Machine Learning as a Service Size (Sales Volume) Comparison by Type (2023-2028) Machine Learning as a Service Size (Consumption) and Market Share Comparison by Application (2023-2028) Machine Learning as a Service Size (Value) Comparison by Region (2023-2028) Machine Learning as a Service Sales, Revenue and Growth Rate (2023-2028) Machine Learning as a Service Competitive Situation and Current Scenario Analysis Strategic proposal for estimating sizing of core business segments Players/Suppliers High Performance Pigments Manufacturing Base Distribution, Sales Area, Product Type Analyse competitors, including all important parameters of Machine Learning as a Service Machine Learning as a Service Manufacturing Cost Analysis Latest innovative headway and supply chain pattern mapping of leading and merging industry players

Get Detailed TOC and Overview of Report @ https://www.htfmarketintelligence.com/report/global-machine-learning-as-a-service-market

Thanks for reading this article; you can also get individual chapter-wise sections or region-wise report versions like North America, MINT, BRICS, G7, Western / Eastern Europe, or Southeast Asia. Also, we can serve you with customized research services as HTF MI holds a database repository that includes public organizations and Millions of Privately held companies with expertise across various Industry domains.

Criag FrancisHTF Market Intelligence Consulting Pvt Ltd+1 434-322-0091sales@htfmarketintelligence.comVisit us on social media:FacebookTwitterLinkedIn

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Machine Learning as a Service Market to Predicts Huge Growth by 2029: Google, BigML, FICO - EIN News

Artificial intelligence has become an integral part of our daily lives in today's technology-driven world. Although some people use neural networks and deep learning interchangeably, their advancements, features, and applications vary.

So what are neural networks and deep learning models, and how do they differ?

Neural networks, also known as neural nets, are modeled after the human brain. They analyze complex data, complete mathematical operations, look for patterns, and use the information gathered to make predictions and classifications. And just like the brain, AI neural networks have a basic functional unit known as the neuron. These neurons, also called nodes, transfer information within the network.

A basic neural network has interconnected nodes in the input, hidden, and output layers. The input layer processes and analyzes information before sending it to the next layer.

The hidden layer receives data from the input layer or other hidden layers. Then, the hidden layer further processes and analyzes the data by applying a set of mathematical operations to transform and extract relevant features from the input data.

It is the output layer that delivers the final information using the extracted features. This layer may have one or more nodes, depending on the data collection type. For binary classificationa yes/no problemthe output will have one node presenting a 1 or 0 result.

There are different types of AI neural networks.

Feedforward neural networks, mostly used for facial recognition, transfer information in one direction. This means every node in one layer is linked to every node in the next layer, with information flowing unidirectionally until it reaches the output node. This is one of the simplest types of neural networks.

This form of neural network aids theoretical learning. Recurrent neural networks are used for sequential data, like natural language and audio. They are also used for text-to-speech applications for Android and iPhones. And unlike feedforward neural networks that process information in one direction, recurrent neural networks use data from the procession neuron and send it back into the network.

This return option is critical for times when the system releases wrong predictions. Recurrent neural networks can attempt to find the reason for incorrect outcomes and adjust accordingly.

Traditional neural networks have been designed to process fixed-size inputs, but convolutional neural networks (CNNs) can process data of varying dimensions. CNNs are ideal for classifying visual data like images and videos of different resolutions and aspect ratios. They are also very useful for image recognition applications.

This neural network is also known as a transposed convolutional neural network. It is the opposite of a convolutional network.

In a convolutional neural network, input images are processed through convolutional layers to extract important features. This output is then processed through a series of connected layers, which carry out classificationassigning a name or label to an input image based on its features. This is useful for object identification and image segmentation.

However, in a deconvolutional neural network, the feature map that was formerly an output becomes the input. This feature map is a three-dimensional array of values and is unspooled to form the original image with an increased spatial resolution.

This neural network combines interconnected modules, each performing a specific subtask. Each module in a modular network consists of a neural network primed to tackle a subtask like speech recognition or language translation.

Modular neural networks are adaptable and useful for handling input with widely varying data.

Deep learning, a subcategory of machine learning, involves training neural networks to automatically learn and evolve independently without being programmed to do so.

Is deep learning artificial intelligence? Yes. It is the driving force behind many AI applications and automation services, helping users carry out tasks with little human intervention. ChatGPT is one of those AI applications with several practical uses.

There are many hidden layers between the input and output layers of deep learning. This allows the network to perform extremely complex operations and continually learn as the data representations pass through the layers.

Deep learning has been applied to image recognition, speech recognition, video synthesis, and drug discoveries. In addition, it has been applied to complex creations, like self-driving cars, which use deep learning algorithms to identify obstacles and perfectly navigate around them.

You must feed large amounts of labeled data into the network to train a deep-learning model. This is when backpropagation occurs: adjusting the weights and biases of the networks neurons until it can accurately predict the output for new input data.

Neural networks and deep learning models are subsets of machine learning. However, they differ in various ways.

Neural networks are usually made up of an input, hidden, and output layer. Meanwhile, deep learning models comprise several layers of neural networks.

Though deep learning models incorporate neural networks, they remain a concept different from neural networks. Applications of neural networks include pattern recognition, face identification, machine translation, and sequence recognition.

Meanwhile, you can use deep learning networks for customer relationship management, speech and language processing, image restoration, drug discovery, and more.

Neural networks require human intervention, as engineers must manually determine the hierarchy of features. However, deep learning models can automatically determine the hierarchy of features using labeled datasets and unstructured raw data.

Neural networks take less time to train, but feature lower accuracy when compared to deep learning; deep learning is more complex. Also, neural networks are known to interpret tasks poorly despite fast completion.

Deep learning is a complex neural network that can classify and interpret raw data with little human intervention but requires more computational resources. Neural networks are a simpler subset of machine learning that can be trained using smaller datasets with fewer computational resources, but their ability to process complex data is limited.

Though used interchangeably, neural and deep learning networks are different. They have different methods of training and degrees of accuracy. Nonetheless, deep learning models are more advanced and produce results with higher accuracy, as they can learn independently with little human interference.

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Neural Networks vs. Deep Learning: How Are They Different? - MUO - MakeUseOf