#DATASET

[aposematic-jessica]

ok so.

🔘 black tank top (skinny straps) + dino bone boxers: feels non-binary.

🔘 long pink valentines dress: feels fem.

im forming a dataset here i can feel it!!!

[axesent]

Just a heads up to any non AI artists that use red bubble (among many more). They are allowing your work to be used by the LAION-5B data set for use in AI training. haveibeentrained.com is free to use

[d0nutzgg]

Tonight I am hunting down venomous and nonvenomous snake pictures that are under the creative commons of specific breeds in order to create one of the most advanced, in depth datasets of different venomous and nonvenomous snakes as well as a test set that will include snakes from both sides of all species. I love snakes a lot and really, all reptiles. It is definitely tedious work, as I have to make sure each picture is cleared before I can use it (ethically), but I am making a lot of progress! I have species such as the King Cobra, Inland Taipan, and Eyelash Pit Viper among just a few! Wikimedia Commons has been a huge help!

I'm super excited.

Hope your nights are going good. I am still not feeling good but jamming + virtual snake hunting is keeping me busy!

[lake-lady]

Deep in my love/hate relationship with R.....

[jbfly46]

I bleed revolution. If your only anarchist actions are related to union organizing, then you’re not an anarchist, you’re a corporate puppet. Everything you do should work to subvert the current and future actions of the state and all of their tentacle corporate affiliations. If your only goal in life is to work under the orders of someone else, under someone’s else’s direction, with someone else’s instructions, then you’re not a human being. You’re chattel cattle at best. If a corporate pig tells or wants you to do something, then you should do the exact opposite, or else you’re just a pawn in a game of global corporate chess. Every one of your actions should be both a defensive and offensive maneuver. If you defend while you attack, you become one with your true purpose, which is to dismantle the state and all corporate authority. If you don’t think in a linear manner, then you’re not apart of their datasets, and they can’t predict your next move. You operate from outside of their datasets and what they think is your next move is never your next move. Then they start to doubt their own intelligence and all the false assumptions it’s based on, and the system starts to crumble. You use any means necessary, because that is your constitutional right, just as they use any means necessary to hold onto the power they stole from you. They stole your birthright, and it’s your legal duty as an American citizen to seek a redress of your grievances, using whatever it takes. Under no pretext.

[pissanddie]

George Maciunas, Fluxpost (Smiles), 1978. © George Maciunas & Jonas Mekas Visual Arts Center

[impy96]

Peak Time Individual Scores

Watching the elimination episode made me so stressed and upset, like this is why I don't watch survival shows. HOWEVER I do love a good dataset so I decided to compile this little piece.

Highest scoring member from each team (individual votes) and how many votes were between them and the next highest member let's go! Top 5 highest-scoring contestants have their names in bold.

Team 1: Luo - 2,650 votes (1,063 more than next member)

Team 2: Yoonhyeok - 10,465 votes (587 more)

Team 4: Hamin - 4,739 votes (1,622 more)

Team 5: Junseok - 7,749 votes (1,913 more)

Team 7: Heejae - 57,043 votes (20,474 more)

Team 8: D1 - 22,539 votes (10,980 more)

Team 9: Sya - 2,226 votes (236 more)

Team 11: Hyesung - 40,061 votes (3,144 more)

Team 13: Hangyul - 47,161 votes (30,066 more)

Team 14: Junhyung - 14,155 votes (5,785 more)

Team 15: Inno - 4,006 votes (381 more)

Team 18: Sihun - 17,572 votes (8,657 more)

Team 20: Bitsaeon - 22,492 votes (10,570 more)

Team 21: Xiwoo - 8,490 votes (5,211 more)

Team 23: Garam - 54,787 votes (41,881 more)

Team 24: Jongup - 69,266 votes (54,572 more)

[kimludcom]

Alexandr Wang: 26-Year-Old Billionaire Powering the AI Industry

[datasetai]

Image Classification Datasets: Fueling the Future of AI

Within artificial intelligence (AI), the power to classify images precisely is a valuable tool for machines to be able to really get and decode the visual world; later on, they are used thus. From object detection to medicine, the image classification dataset procedure is a key task, which, in turn, facilitates different AI systems to the maximum. The core image classification datasets are meticulously curated collections of labeled images that are essential in the education of these systems, as they allow AI models to learn how to categorize visual data.

What is Image Classification?

Image classification is the assignment of an image to a certain category by taking into account its content. As an example, a dataset containing photos of different animals could be labeled as "dog," "cat," or "elephant." A trained model, whether it is supervised or unsupervised, can thus recognize and later classify objects in new, unseen images using the data of different patterns, textures, and features. A successful image classification method is dependent on good datasets, where the images are appropriately labeled with correct tags and also represent the broadest possible range of conditions.

Why Are Image Classification Datasets Important?

The method’s success lies deeply in the used datasets for the training purpose of AI model’s. A properly organized image classification dataset enables machine learning systems to differentiate various categories, thus, the system will become smarter and more accurate. Here are some reasons why these datasets are crucial for AI development:

Improved Accuracy

A versatile and full collection of image classification datasets guarantees that the AI model will be able to distinguish among objects, animals, faces, or scenes regardless of the different lighting conditions, environments or viewpoints. The larger and the better the images in the dataset, the higher will be the reliability of the AI's predictions in practical applications.

Reduces Bias

If the dataset has less variety in it, the AI could harbor biases, which, in turn, limit its efficacy. For instance, a facial recognition model taught by a set of pictures containing only one single ethnicity among all images may not be able to capture people from other ethnicities. Facing this limitation, diversity of image classification datasets, including different age groups, genders, places, and settings, is a must to achieve fairness and inclusion.

Enables Efficient Learning

To design a model that is able to capture the general idea of AI, it needs to be trained on different types of data samples from many different categories. Image classification datasets are the ones that make the model understand to classify images by considering important characteristics rather than superficial ones.

Applications of Image Classification Datasets

Datasets designed for image classification are predominantly implemented in many domains and applications. These are some of the main areas where these datasets are critical:

Healthcare

Within medicine, AI-based systems utilizing image classification datasets are expected to become the most advanced approach to diagnosing diseases in the future. An AI model trained on a huge dataset of medical images of different cases can detect abnormalities like cancer, T.B., or heart disease with a very high level of accuracy. These AI-based systems are the tools through which doctors can reach correct medical conclusions faster.

Retail and E-Commerce

Within the retail division, image classification is applied to product categorization. Retailers may easily use AI to automatically sort and categorize products based on the visual representations including stockrooms and customers. By means of a database containing the classification of images, online shops can online platforms Visual Search to enhance their search and recommendation systems by, for example, helping customers to their desired products visually.

Autonomous Vehicles

Autonomous cars use image classification datasets for road signs, pedestrians, traffic lights, and other vehicles, as they recognize objects by comparing them with the datasets. Autonomous vehicles are able to safely drive around thanks to big and wide bodies of unique image data that allows them to understand their surroundings and decide what to do at that moment.

Agriculture

AI is being employed in agriculture as a tool by farmers and researchers to supervise crop health, detect diseases, and evaluate soil conditions. Image classification datasets are very important in the training of AI models that can identify plant species, observe their growth, and even detect early signs of disease or pest infestation, which in turn, allows for more efficient farming practices.

Security and Surveillance

Security systems with AI models supervised on image classification datasets contribute to the identification of deviants and possible security risks in surveillance footage. Image classification systems can make well-timed decisions of these deviations, thus not only catching suspected people in the surveillance videos, but also tracking the unusual tendencies within the crowd.

Social Media

Social media platforms are using image classification datasets for content moderation, auto-tagging, and even facial recognition. These systems measure and classify millions of images by AI which automatically tags them and this draws user interest and gives the platform safety.

Building a Good Image Classification Dataset

Materials in a quality image classification dataset should be carefully thought out with an emphasis on detail. Below are some important aspects of constructing a dataset:

Diversity of Data

One that is deemed to be a successful image classification dataset includes a wide range of categories and conditions. The AI model learns properly to handle the heterogeneous case of the real-world. For example, creating a face image dataset will necessarily contain the annotated faces of different people with varied ages, ethnicities, and facial expressions which will obviate the bias problem and the model will then be able to generalize.

High-Quality, Labeled Images

Every picture will be examined in this dataset and pinned with its relevant tag correctly. The more exact and uniform the tagging of the images, the more efficiently the model will learn. Properly labeling pictures is a lengthy process for sure but it is a critical constituent of high-quality training.

Data Augmentation

In most of the cases, data augmentation techniques like rotating, cropping, or flipping the images, turn out to be the best way of increasing the dataset. This way, the model actually learns to recognize objects or features from the images, or there can be different possible conditions of those.

Data Preprocessing

Preprocessing of data (for example, by means of resizing images, normalizing pixel values, or removing noisy data) guarantees that the AI model can process the images efficiently and consequently the training is faster and more accurate.

Conclusion: The Future of Image Classification with AI

With AI evolving, accuracy and image classification datasets will be demanded increasingly. These connotations are the triggers of tons of AI applications, from healthcare to retail, and security followed by autonomous driving. The variety and quality of the data-set will seriously determine the AI model to identify proper images, thus, image classification is a core part of successful AI projects.

One of the best approaches to develop a genuine, efficient, and inclusive AI system is by creating different kinds of good quality and labeled image classification datasets. If you've exponentially boosted your skills in the fields like healthcare, retail, or agriculture then choosing the correct data sources is the key step toward the successful AI and ML implementation.

[govindhtech]

Upcoming Developments In Federated Learning AI Technologies

What is Federated learning?

Federated learning AI provides a means of unlocking information to feed new AI applications while training AI models without anybody seeing or touching your data.

The recommendation engines, chatbots, and spam filters that have made artificial intelligence a commonplace in contemporary life were developed using data mountains of training samples that were either scraped from the internet or supplied by users in return for free music, email, and other benefits.

A large number of these AI programs were trained using data that was collected and processed in one location. However, modern AI is moving in the direction of a decentralized strategy. Collaboratively, new AI models are being trained on the edge using data that never leaves your laptop, private server, or mobile device.

Federated learning AI model is a new kind of AI training that is quickly becoming the norm for processing and storing private data in order to comply with a number of new requirements. Federated learning also provides a means of accessing the raw data coming from sensors on satellites, bridges, factories, and an increasing number of smart gadgets on our bodies and in our homes by processing data at its source.

IBM is co-organizing a federated learning session at this year’s NeurIPS, the premier machine learning conference in the world, to foster conversation and idea sharing for developing this emerging subject.

How Federated Learning AI Model Works?

Similar to a team report or presentation, federated learning allows many individuals to remotely share their data in order to jointly train a single deep learning model and improve incrementally. The model, often a pre-trained foundation model, is downloaded by each participant from a cloud datacenter.

After training it on their personal information, they condense and encrypt the updated model configuration. After being decrypted and averaged, the model updates are returned to the cloud and incorporated into the centralized model. The collaborative training process keeps going iteration after iteration until the model is completely trained.

There are three variations of this decentralized, dispersed training method. Similar datasets are used to train the central model in horizontal federated learning. The data are complimentary in vertical federated learning; for instance, a person’s musical interests may be predicted by combining their assessments of books and movies.

Lastly, in federated transfer learning, a foundation model that has already been trained to do one task such as recognizing cars is trained on a different dataset to accomplish another such as identifying cats. The integration of foundation models into federated learning is now being worked on by Baracaldo and her colleagues. One possible use case is for banks to build an AI model to identify fraud and then repurpose it for other purposes.

Advantages Of Federated Learning

Federated learning AI model has a number of clear benefits, particularly where decentralized data processing and data privacy are crucial. Here are a few main benefits:

Improved Privacy of Data

By enabling model training on decentralized data sources without direct access to the raw data, federated learning puts privacy first. By ensuring that private or sensitive data stays on local devices, this decentralized method lowers the possibility of data breaches.

Enhanced Protection

Sensitive information is less centrally located as it is processed and stored locally on separate devices. When compared to conventional centralized learning techniques, this structure reduces the likelihood of significant breaches.

Effective Use of Data

Federated learning may improve model performance and accuracy by using data from several devices or institutions rather than centrally gathering data. This makes it feasible for the model to learn from a large dataset, something that conventional approaches would not be able to do.

Lower Data Transfer Expenses

Federated learning decreases data transmission costs and network stress by sharing just model changes rather than raw data. Applications with poor connection or settings where bandwidth costs are an issue would particularly benefit from this.

Quicker Education and Instantaneous Updates

Models may be updated almost instantly as data is created on local devices with to federated learning. Applications where current learning is essential, such as smart devices or tailored suggestions, benefit from this responsiveness.

Observance of Data Regulations

Because the data remains locally, federated learning is well-suited to comply with data privacy rules and regulations like the GDPR. For businesses managing user data in regulated sectors like healthcare or banking, this may reduce compliance concerns.

Increased Customization

Federated learning preserves user privacy while enabling models to be tailored to local data patterns. Applications such as customized advice or individualized health monitoring benefit greatly from this.

Conclusion

All things considered, federated learning facilitates safe, privacy-aware AI developments, enabling efficient data utilization without jeopardizing user confidence or legal compliance.

Read more on Govindhtech.com

[lepton-software]

Fiber Planning: Key Consideration for Broadband and Deployment

This article into key considerations in fiber planning, future of optical fibers, the importance of network inventory management, and integration of telecom GIS to support broadband deployment.

Click to read more about Fiber Planning and Broadband Deployment.

[womaneng]

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A post shared by Zeynep Küçük Woman Engineer (@woman.engineer)

I tried to remove the stickers from my 11-year-old MacBook... Anyone got a miracle solution to save my MacBook? Help me out, please! 🙃👩🏻‍💻 . . .

[gts-ai]

Empower your machine learning projects with the perfect dataset from GTS.ai. Our meticulously curated datasets are designed to optimize model performance, minimize bias, and support robust AI development. Whether you're training or testing, GTS.ai provides the high-quality data you need to drive innovation and achieve reliable, scalable results.

[pissanddie]

Constant Dullaart and Adam Harvey, Imagenet.xyz, 2017

[threezhang]

library：Image to Text