You are reading the article What Are Nft Royalties & How Bitcoin Ordinals Can Help updated in December 2023 on the website Kientrucdochoi.com. We hope that the information we have shared is helpful to you. If you find the content interesting and meaningful, please share it with your friends and continue to follow and support us for the latest updates. Suggested January 2024 What Are Nft Royalties & How Bitcoin Ordinals Can Help
With the emergence of Bitcoin ordinals, and their supposed incompatibility with NFT royalties, it’s worthwhile to review:
What NFT royalties are
How they work
Their benefits and downsides.
This topic is important because whether you’re an artist or an investor of digital art, each Blockchain’s royalty payments could determine whether or not to invest your time and money in it. For example, Bitcoin is fundamentally incompatible with royalties, while Ethereum isn’t. Does that make Ethereum a more attractive blockchain for investors, artists and content creators? Not necessarily.
This article explains the nuances of NFT royalties in more detail, and how bitcoin ordinals treat them.
What are NFT royalties?NFT (non fungible tokens) royalties are mechanisms allowing NFT creators to earn royalty percentage on secondary sales across NFT marketplaces. An NFT royalty provides a continuous income stream for creators and gives them control over their art.
How do NFT royalties work?NFT royalties work via smart contracts, which are self-executing agreements. So when terms and conditions are met, they are enforced on the exchanges.
Once a piece of digital art or a digital artwork is created, the NFT artist can establish a smart contract with a royalty agreement to their preferences (between usually 5-15% of the sale price). When the NFT is sold, the smart contract automatically executes and sends the royalty amount to the creator’s digital wallet. The payment is typically made in cryptocurrency, like ethereum.
The entire transaction is transparent and automated, ensuring timely payments and eliminating manual interventions and intermediaries.
What are the benefits of NFT royalty payments?The following are the benefits of NFT royalty payments:
1. Continuous revenue for creatorsNFT royalties provide artists with an ongoing income stream. This serves as recognition of their original work, and ensures their compensation. This supports their current works that are in development and incentivizes them to keep producing high-quality content.
2. Value distribution for all partiesThe royalty system helps distribute value more fairly within the NFT ecosystem. This is achieved through creators, collectors, speculators, and platforms all profiting from NFT trades in different amounts.
3. Enhanced value propositionRoyalties incentivize creators to promote their work and remain engaged with their audience. This can increase the value of their existing collections which are in owners’ hands. The increased desirability can lead to higher resale prices.
4. Community-buildingNFT owners and creators are part of the same ecosystem. So a thriving market with an active creator community, propelled on by royalties by part, can increase the overall value of the NFT space. Owners’ individual NFTs can indirectly benefit from reputation and value growth of the NFT market.
5. Encouraging authentic and scarce worksRoyalties are tied to market demand that rewards and appreciates originality. So creators are more likely to benefit from the resale of unique, limited edition works. The implication is creators creating catalogs of distinctive, rare digital art, enhancing the value of NFTs held by collectors.
What are the negatives of NFT royalties?The downsides of an NFT royalty system include:
1. Unclear royalty termsNFTs’ royalties terms can vary. And it can be to the owner and creator’s detriment if they are unfamiliar with it. For example, Superare, a marketplace of luxury digital art, gives collectors 90% and creators 10% on secondary sales. But on Binance Smart Chain, the value is on a spectrum of 0-10%. And this year, X2Y2 and Magic Eden have opted to make royalty payments optional, and its inclusion in trade a case-by-case matter between the buyer and the seller.
The lack of consistency across blockchains can lead to over/underestimation of royalty calculation.
2. Platform limitationsNot all platforms, or side-chains, support automatic royalty payments or royalty standards, like ERC-1155 on Ethereum. Incompatible payment mechanisms can lead to confusion and potential disputes if the NFT is created on one blockchain and sold on another one that doesn’t support royalties.
We weren’t able to find any real-life scenario of such an incident. But theoretically, an artist could transfer an NFT from one blockchain to another through bridging services or wrapped tokens, only for the new blockchain to not recognize/support the royalty mechanisms of the original blockchain.
3. Tax implicationsGains from NFT sales may be taxable, depending on the jurisdiction. This can complicate the financial aspects of NFT sales, as buyers and sellers need to understand and comply with applicable tax laws and reporting requirements.
4. Smart contracts’ vulnerabilitiesSmart contracts can have vulnerabilities or errors, such as integer arithmetic, that could lead to disputes or incorrect royalty distribution.
5. Price manipulation & wash tradingMarket players could try and manipulate the NFT market by wash trading or artificially inflating the value of an NFT, ultimately affecting royalty revenue. This can result in mistrust and confusion in the market.
6. Ethical concernsSome NFT creators may use royalties as a way to profit from controversial or objectionable content, which could dissuade potential buyers from purchasing the NFT.
7. Decreased liquidityNFTs and cryptocurrencies are meant to be decentralized and permission-less. Having to pay royalties, or abiding by a royalty policy, complicates a sale and slows the turn around speed.
How do Bitcoin Ordinals address creator royalties?Bitcoin ordinals (i.e. ordinal inscriptions) are minted on satoshis and stored on Bitcoin. To create ordinal inscriptions, you can leverage tools such as chúng tôi Gamma is an established player in the NFT domain, with a 95% market share in Stacks. They have recently also released an Ordinals marketplace for a secured hub of trading ordinal inscriptions.
Bitcoin ordinals natively do not support royalties because they leverage the Bitcoin blockchain. The reasons why Bitcoin cannot natively support royalties include, but aren’t limited to:
For more on NFTs and Ordinal InscriptionsTo learn more about NFTs and Ordinal Inscriptions, read:
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He primarily writes about RPA and process automation, MSPs, Ordinal Inscriptions, IoT, and to jazz it up a bit, sometimes FinTech.
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What Are The Best One
What are the best one-handed Skyrim weapons?
Here are some great one handed Skyrim weapons that you can grab in game.
Hello there, Nords, today we take you through Nord training school, getting you up to speed with martial practices over here in Skyrim. Are you planning on plating a Warrior, perhaps a Rogue, or a Battle Mage, or something entirely different? Well, if the answer is yes, we will guide you through the best one0handed Skyrim weapon.
Note, this article is taking into account anyone who is playing the Skyrim Special Edition, along with the Skyrim 10th Anniversary edition, which launches on November 11. If you’re new to the game or getting rekindled with one of the most popular RPGs of all time, this will get you up to speed in no time.
Best one-handed Skyrim weapons
Blade of Woe
Blade of Woe is one of the most iconic weapons in Elder Scrolls. The blade is part of the Dark Brotherhood (as are quite a few weapons on this list). You can grab the weapon by stealing it from the Dark Brotherhood’s Astrid or by finishing the “With Friends Like These” quest. The great thing about this weapon is it grants the player 10 life on hit, making it a cheeky weapon on harder difficulties.
Chillrend
Chillrend is an ice-themed sword, that has a chance to paralyze opponents. Not to mention it is one of the coolest weapons in the game. Whenever you unsheath the blade from its cabers, ice reigns down from the weapon, hissing as ice cracks and thaws. The weapon is fairly easy to come across, as you can earn it from Mercer Frey, the leader of the Thieves Guild. If you complete the quest, The Pursuit, inside Riftweald Manor, you will get to keep it. Alternatively, you can steal it from a locked box inside the manor. Moreso, the weapon scales as you level up, so you can grab it at level one, and make it your main one-handed weapon.
Dawnbreaker
Paladins, Clerics, rejoice, as there is a weapon for your chivalric, holy warriors. The Dawnbreaker is appreciably golden themed, reflecting the seven in its design. But, while it is visually appealing, it does have its benefits. The sword is a go-to for anyone crusading against all that is unholy and evil in Skyrim. Skeletons and Vampires will take extra damage against you, and will have a chance to ignite in a fiery blaze, turning to ash before your very eyes. Note the power of the sword is from a Daedric Prince, one that is not entirely evil mind you. If you’re interested in this weapon, you can acquire it by completing the Break of Dawn quest.
Dragonbane
Dragonbane is a sword that, as you can imagine, is all about killing Dragons. The weapon grants an extra 40 Damages as you strike at Dragons. The weapon isn’t entirely useless against non Draconic foes either, as it will deal 10 shock damage to those foes instead. Don’t forget to fill those soul gems and replenish its power.
Miraak’s Sword
If you want the strongest damage dealing weapon among the one-handed class, then you might like Miraak’s Sword. Moreso, it has three weights, making it only slightly slower than the Dragonborn Dagger and the Blade of Woe. If you want a Rogue playthrough, you should grab this spooky tentacle weapon, along with the Dragonborn Dagger or Balde of Woe.
Windshear
Windshear is quite a powerful Scimitar, perfect for a character looking to roleplay as a Redguard. You can grab yourself the Winshear by finishing the Hail Sithis quest from the Dark Brotherhood. When you get to the quest, you unlock the Katariah ship, which you can go to during the quest, or after if you are only just learning about this now. The Scimitar boasts 11 Damage, and 10 Weight, and is upgradeable via Steel Smithing. If you’re interested in upgrading this weapon and making it better as your progress, this is one of the best one-handed Skyrim weapons to keep you company.
What Are Jpeg Photos? (And Are They Different From Jpg)
If you have any type of photo on your computer or mobile device, odds are, you’re probably looking at a JPEG file, but what exactly is a JPEG photo?
JPEG files are one of the most common and widely readable image files available. By using lossy compression, JPEG files help reduce the overall size of your image files, without sacrificing image quality. For uploading photos to the web or sharing pictures with others, JPEG is the best file type to use.
What Are Jpeg Photos?
JPEG comes from “Joint Photographic Experts Group”, which is also the name of the committee that created it.
JPEG is the most commonly used format in digital cameras. Furthermore, most images we see online today are JPEGs.
JPEGs are split into two subcategories: Exif (for digital cameras) and JFIF (for storing and transferring)
So what exactly is a jpeg? A Jpeg file format is a format for representing an image. Jpeg photos are smaller versions of the same image and they are typically used in web-based applications. However, JPEGs are not just a file format, but also a lossy compression method.
Lossy Compression is the process of retaining as much visual information as possible, without sacrificing the size of the file. This type of compression discards some original data, so there is some quality loss. In comparison, formats like TIFF, GIF, or PNG use lossless compression meaning there is no data loss, but higher file sizes.
A jpeg image is a digital file with a standardized “picture image” format. The final image discards high-frequency information and color aka everything that our eyes are bad at noticing. Jpeg allows you to use any color space imaginable – RGB, YCbCr, CIELAB, and so on.
The Process Of Jpeg File CompressionThere are three stages of JPEG compression:
1. Chrominance Subsampling – separation of the luminance from the chrominance
2. Discrete Cosine Transform (DCT) & Quantization
3. Run-Length, Delta & Huffman Encoding
This process goes as follows: You take an RGB image and convert it into YCbCr to achieve a separation of the luminance from the chrominance. Then the image goes through downsampling, compressing the frequencies that are less visible to the eye – reducing the amount of color in the image. A loss in hue and saturation will be less visible to the human eye, therefore the downsampling is done only for the color component.
The next step is Discrete Cosine Transformation. This is at the core of how JPEG compression works. It represents image data as the sum of all the cosine waves. In JPEG, each image is split into 8×8 pixel groups (square of pixels) that all have their own cosine transformation.
The 64 modified elements are divided by a Quantization Coefficient and then rounded. In the end, they become integers. This is called quantization and it’s the lossy part of the whole process. Lastly, the image is encoded by using a combination of run-length and Huffman (entropy) coding.
Opening a JPEG file entails decoding it. When we are decoding a JPEG file, all these steps are reversed. You can open a JPEG with almost any program that you can imagine, even across all forms of computers, tablets, and smartphones.
Since JPEGs entail a lossy image compression, some information will ultimately end up being lost. The decoded image will be slightly different than the original RAW file from the camera. The sacrifice of losing a little bit of quality in order to get a smaller file size and increased speed is worth it, especially when the difference between the original and final image is often unnoticeable.
What Are The Advantages Of Jpeg Files?
1. JPEG’s Are An Effective Form Of Image Compression
Compressing a file in Jpeg can save on file space and increase download speed due to the smaller size. But not to worry, JPEGs compress an image while still maintaining the most essential visual characteristics of that photo. So even though you are getting a smaller file, everything will look exactly like it did in-camera after export.
2. Better File ReadabilityJPEG can be opened by most devices and software out there. Most editors, image viewers, browsers, and webpages can support JPEG files, which means that you don’t have to switch to a different format in order to open an image.
JPEGs are compatible with platforms like Microsoft Windows Photos, Apple Preview, Adobe Photoshop, Google Drive, Google Photos, and many more.
3. JPEG’s Retain Color Information Well
Unlike vector images, JPEGs can deliver a wide range of unique shades and tones. They use 8 bits of each color (RGB) and store 24 bits per pixel that can display up to 16 million unique colors.
When it comes to graphics, you would be better off using vector graphics. However, Jpeg image files are a much better way of representing photographs.
4. You Can Control The Compression Amount
A JPEGs quality setting levels are split into low, medium, and high. If the compression rate gets higher (lower quality rating), the images become more pixelated and colors begin to fall apart.
Therefore, a highly compressed JPEG file will noticeably have a much lower quality. There are situations where you might need a lower quality setting though such as for thumbnail images on a website. In most cases, leaving the compression amount to 100 (best quality) will get the job done.
Regardless of which compression you choose, JPEG files make it easy to manually control your compression amount. This type of control isn’t as simple with other image file types.
5. JPEG’s Can Be Shared Straight From Camera
When you’re taking photos on your digital camera or smartphone, JPEG files can be uploaded and shared right after they are taken. With other forms of image files in photography like the RAW file, you need to re-export the file through an editing software before you can share the photo. By capturing JPEG files, your photos are ready to go the moment they are taken!
Jpeg Vs. Jpg – What’s The Difference?
Both JPEG and JPG are the same type of file, each standing for “Joint Photographic Experts Group.” The file extension of JPG came from older windows computers that required a max 3 letter extension name. On modern computers, the JPEG file extension is more commonly seen but some people still refer to these files as JPG.
That’s one of the reasons why we still hear about JPGs and why people get confused when they stumble upon both of these names.
How Do I Convert An Image To Jpeg?
There are a lot of free options out there when it comes to converting an image to JPEG – online, as well as offline. Let’s go through a few of your options.
You can convert an image to a JPEG format by using most image-editing software available out there, like Photoshop or Gimp.
In Photoshop, you can convert an image to JPEG after editing a RAW photograph. A raw image is an uncompressed format that takes longer to read than JPEGs but holds more file information for editing. When doing drastic brightening or color adjustments, the JPEG format is not the most suitable one to use for that. Since there is less file information, the image begins to fall apart as it gets adjusted.
That’s why performing edits on RAW files and then exporting them as JPEGs is the best option when it comes to photo editing!
Using Computer Apps: – How To Convert An Image To JPEG In Photoshop (Paid)
Open and edit your RAW image in Photoshop as you wish.
– Converting An Image To JPEG in Windows Paint or Paint 3d (Free)
Open your image in Paint.
– Exporting As JPEG Using Preview on Mac (Free)
In the Format box select JPEG
Using Free Web-Based Apps:
Choose a File from your computer
Upload the file that you want to convert.
Set convert options (in the Convert to section, choose JPEG)
How Does a JPEG File Look Like?
The first thing that can tell you if an image is a JPEG or not is the filename extension. In most cases, it ends with .jpg or .jpeg.
But now let’s go beyond this detail and talk more about the image and the actual data.
JPEGs are raster images. That means that they’re made out of pixels – as opposed to vector images that are based on mathematical formulas. If you look very closely at a JPEG image, you will see that it’s actually made up of thousands of smaller colored squares, aka pixels.
JPEGs contain all of the graphic information inside the image – brightness levels, color, tone of the image, bitrates, resolution, and so on.
To understand how JPEG data looks like, we have to take a more in-depth look at the process of image compression. Values between 0 and 255 are used to represent a digital photograph. It makes more sense to use fewer bits to represent values that occur more often and more bits to represent values that occur less frequently. This is the process of reducing coding redundancy in JPEG compression! That means that the important information stays there because this color reduction is less obvious to the human eye since people have low acuity for color differences.
At the end of this chroma subsampling process, the color space transformation ends up being almost the same as the original. What does that mean specifically? The final image (output) is going to be a different image. When you access the image on the internet, you will notice it’s a whole lot smaller in size. But it will look very similar to the initial image. So much so, that you won’t be able to tell the difference.
However, you can also get to choose the quality of your final image. The quantization step may result in an image that looks almost identical to the original, but it can also result in a fuzzy one that seems to be comprised of several blocks. It all depends on the level of compression you choose to use. Higher compression means more artifacts.
At the end of the day, the only thing that really matters for you is that you see the .JPEG or .JPG file extension. Without having to do any technical sleuthing, you can identify which of your files are JPEGs simply by their extension!
– Brendan 🙂
What Are Large Language Models (Llms)?
Large Language Models (LLMs) are foundational machine learning models that use deep learning algorithms to process and understand natural language. These models are trained on massive amounts of text data to learn patterns and entity relationships in the language. LLMs can perform many types of language tasks, such as translating languages, analyzing sentiments, chatbot conversations, and more. They can understand complex textual data, identify entities and relationships between them, and generate new text that is coherent and grammatically accurate.
Learning Objectives
Understand the concept of Large Language Models (LLMs) and their importance in natural language processing.
Know about different types of popular LLMs, such as BERT, GPT-3, and T5.
Discuss the applications and use cases of Open Source LLMs.
Hugging Face APIs for LLMs.
Explore the future implications of LLMs, including their potential impact on job markets, communication, and society as a whole.
This article was published as a part of the Data Science Blogathon.
What is a Large Language Model?In contrast, the definition of a language model refers to the concept of assigning probabilities to sequences of words, based on the analysis of text corpora. A language model can be of varying complexity, from simple n-gram models to more sophisticated neural network models. However, the term “large language model” usually refers to models that use deep learning techniques and have a large number of parameters, which can range from millions to billions. These models can capture complex patterns in language and produce text that is often indistinguishable from that written by humans.
How a Large Language Model Is Built?A large-scale transformer model known as a “large language model” is typically too massive to run on a single computer and is, therefore, provided as a service over an API or web interface. These models are trained on vast amounts of text data from sources such as books, articles, websites, and numerous other forms of written content. By analyzing the statistical relationships between words, phrases, and sentences through this training process, the models can generate coherent and contextually relevant responses to prompts or queries.
ChatGPT’s GPT-3 model, for instance, was trained on massive amounts of internet text data, giving it the ability to understand various languages and possess knowledge of diverse topics. As a result, it can produce text in multiple styles. While its capabilities may seem impressive, including translation, text summarization, and question-answering, they are not surprising, given that these functions operate using special “grammars” that match up with prompts.
General ArchitectureThe architecture of Large Language Models primarily consists of multiple layers of neural networks, like recurrent layers, feedforward layers, embedding layers, and attention layers. These layers work together to process the input text and generate output predictions.
The embedding layer converts each word in the input text into a high-dimensional vector representation. These embeddings capture semantic and syntactic information about the words and help the model to understand the context.
The feedforward layers of Large Language Models have multiple fully connected layers that apply nonlinear transformations to the input embeddings. These layers help the model learn higher-level abstractions from the input text.
The recurrent layers of LLMs are designed to interpret information from the input text in sequence. These layers maintain a hidden state that is updated at each time step, allowing the model to capture the dependencies between words in a sentence.
The attention mechanism is another important part of LLMs, which allows the model to focus selectively on different parts of the input text. This mechanism helps the model attend to the input text’s most relevant parts and generate more accurate predictions.
Examples of LLMsLet’s take a look at some popular large language models:
GPT-3 (Generative Pre-trained Transformer 3) – This is one of the largest Large Language Models developed by OpenAI. It has 175 billion parameters and can perform many tasks, including text generation, translation, and summarization.
BERT (Bidirectional Encoder Representations from Transformers) – Developed by Google, BERT is another popular LLM that has been trained on a massive corpus of text data. It can understand the context of a sentence and generate meaningful responses to questions.
XLNet – This LLM developed by Carnegie Mellon University and Google uses a novel approach to language modeling called “permutation language modeling.” It has achieved state-of-the-art performance on language tasks, including language generation and question answering.
T5 (Text-to-Text Transfer Transformer) – T5, developed by Google, is trained on a variety of language tasks and can perform text-to-text transformations, like translating text to another language, creating a summary, and question answering.
RoBERTa (Robustly Optimized BERT Pretraining Approach) – Developed by Facebook AI Research, RoBERTa is an improved BERT version that performs better on several language tasks.
Open Source Large Language ModelsThe availability of open-source LLMs has revolutionized the field of natural language processing, making it easier for researchers, developers, and businesses to build applications that leverage the power of these models to build products at scale for free. One such example is Bloom. It is the first multilingual Large Language Model (LLM) trained in complete transparency by the largest collaboration of AI researchers ever involved in a single research project.
With its 176 billion parameters (larger than OpenAI’s GPT-3), BLOOM can generate text in 46 natural languages and 13 programming languages. It is trained on 1.6TB of text data, 320 times the complete works of Shakespeare.
Bloom ArchitectureThe architecture of BLOOM shares similarities with GPT3 (auto-regressive model for next token prediction), but has been trained in 46 different languages and 13 programming languages. It consists of a decoder-only architecture with several embedding layers and multi-headed attention layers.
Bloom’s architecture is suited for training in multiple languages and allows the user to translate and talk about a topic in a different language. We will look at these examples below in the code.
Other LLMs
We can utilize the APIs connected to pre-trained models of many of the widely available LLMs through Hugging Face.
Hugging Face APIs Example 1: Sentence CompletionLet’s look at how we can use Bloom for sentence completion. The code below uses the hugging face token for API to send an API call with the input text and appropriate parameters for getting the best response.
import requests from pprint import pprint headers = {'Authorization': 'Entertheaccesskeyhere'} # The Entertheaccesskeyhere is just a placeholder, which can be changed according to the user's access key def query(payload): response = requests.post(API_URL, headers=headers, json=payload) return response.json() params = {'max_length': 200, 'top_k': 10, 'temperature': 2.5} output = query({ 'inputs': 'Sherlock Holmes is a', 'parameters': params, }) pprint(output)Temperature and top_k values can be modified to get a larger or smaller paragraph while maintaining the relevance of the generated text to the original input text. We get the following output from the code:
[{'generated_text': 'Sherlock Holmes is a private investigator whose cases ' 'have inspired several film productions'}]Let’s look at some more examples using other LLMs.
Example 2: Question AnswersWe can use the API for the Roberta-base model which can be a source to refer to and reply to. Let’s change the payload to provide some information about myself and ask the model to answer questions based on that.
headers = {‘Authorization’: ‘Entertheaccesskeyhere’}
def query(payload): response = requests.post(API_URL, headers=headers, json=payload) return response.json()
params = {‘max_length’: 200, ‘top_k’: 10, ‘temperature’: 2.5} output = query({ ‘inputs’: { “question”: “What’s my profession?”, “context”: “My name is Suvojit and I am a Senior Data Scientist” }, ‘parameters’: params })
pprint(output)
The code prints the below output correctly to the question – What is my profession?:
{'answer': 'Senior Data Scientist', 'end': 51, 'score': 0.7751647233963013, 'start': 30} Example 3: SummarizationWe can summarize using Large Language Models. Let’s summarize a long text describing large language models using the Bart Large CNN model. We modify the API URL and added the input text below:
headers = {‘Authorization’: ‘Entertheaccesskeyhere’}
def query(payload): response = requests.post(API_URL, headers=headers, json=payload) return response.json()
params = {‘do_sample’: False}
full_text = ”’AI applications are summarizing articles, writing stories and engaging in long conversations — and large language models are doing the heavy lifting.
A large language model, or LLM, is a deep learning model that can understand, learn, summarize, translate, predict, and generate text and other content based on knowledge gained from massive datasets.
Large language models – successful applications of transformer models. They aren’t just for teaching AIs human languages, but for understanding proteins, writing software code, and much, much more.
In addition to accelerating natural language processing applications — like translation, chatbots, and AI assistants — large language models are used in healthcare, software development, and use cases in many other fields.”’
output = query({ ‘inputs’: full_text, ‘parameters’: params })
pprint(output)
The output will print the summarized text about LLMs:
[{'summary_text': 'Large language models - most successful ' 'applications of transformer models. They aren’t just for ' 'teaching AIs human languages, but for understanding ' 'proteins, writing software code, and much, much more. They ' 'are used in healthcare, software development and use cases ' 'in many other fields.'}]These were some of the examples of using Hugging Face API for common large language models.
Future Implications of LLMsIn recent years, there has been specific interest in large language models (LLMs) like GPT-3, and chatbots like ChatGPT, which can generate natural language text that has very little difference from that written by humans. While LLMs have seen a breakthrough in the field of artificial intelligence (AI), there are concerns about their impact on job markets, communication, and society.
One major concern about LLMs is their potential to disrupt job markets. Large Language Models, with time, will be able to perform tasks by replacing humans like legal documents and drafts, customer support chatbots, writing news blogs, etc. This could lead to job losses for those whose work can be easily automated.
However, it is important to note that LLMs are not a replacement for human workers. They are simply a tool that can help people to be more productive and efficient in their work. While some jobs may be automated, new jobs will also be created as a result of the increased efficiency and productivity enabled by LLMs. For example, businesses may be able to create new products or services that were previously too time-consuming or expensive to develop.
LLMs have the potential to impact society in several ways. For example, LLMs could be used to create personalized education or healthcare plans, leading to better patient and student outcomes. LLMs can be used to help businesses and governments make better decisions by analyzing large amounts of data and generating insights.
ConclusionKey Takeaways:
Large Language Models (LLMs) can understand complex sentences, understand relationships between entities and user intent, and generate new text that is coherent and grammatically correct
The article explores the architecture of some LLMs, including embedding, feedforward, recurrent, and attention layers.
The article discusses some of the popular LLMs like BERT, BERT, Bloom, and GPT3 and the availability of open-source LLMs.
Hugging Face APIs can be helpful for users to generate text using LLMs like Bart-large-CNN, Roberta, Bloom, and Bart-large-CNN.
LLMs are expected to revolutionize certain domains in the job market, communication, and society in the future.
Frequently Asked QuestionsQ1. What are the top large language models?
A. The top large language models include GPT-3, GPT-2, BERT, T5, and RoBERTa. These models are capable of generating highly realistic and coherent text and performing various natural language processing tasks, such as language translation, text summarization, and question-answering.
Q2. Why use large language models?
A. Large language models are used because they can generate human-like text, perform a wide range of natural language processing tasks, and have the potential to revolutionize many industries. They can improve the accuracy of language translation, help with content creation, improve search engine results, and enhance virtual assistants’ capabilities. Large language models are also valuable for scientific research, such as analyzing large volumes of text data in fields such as medicine, sociology, and linguistics.
Q3. What are LLMs in AI?
A. LLMs in AI refer to Language Models in Artificial Intelligence, which are models designed to understand and generate human-like text using natural language processing techniques.
Q4. What are LLMs in NLP?
A. LLMs in NLP stand for Language Models in Natural Language Processing. These models support language-related tasks, such as text classification, sentiment analysis, and machine translation.
Q5. What is the full form of LLM model?
A. The full form of LLM model is “Large Language Model.” These models are trained on vast amounts of text data and can generate coherent and contextually relevant text.
Q6. What is the difference between NLP and LLM?
A. NLP (Natural Language Processing) is a field of AI focused on understanding and processing human language. LLMs, on the other hand, are specific models used within NLP that excel at language-related tasks, thanks to their large size and ability to generate text.
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Related
What The Heck Are Sinkholes, Anyway?
Last week, an enormous sinkhole opened up in the middle of a road in Fukuoka, Japan. The sinkhole, which measured nearly 90 feet wide, 100 feet long and 50 feet deep, may have been caused by construction work on a nearby subway line. Fortunately, no one appears to have been seriously injured.
Sinkholes are not usually as spectacular as the one in Fukuoka, but they can cause a lot of damage when they appear. Here’s what you need to know about what causes sinkholes, where they form, and why you shouldn’t fill the one in your backyard with garbage.
What are they?
When geologists refer to sinkholes, they’re usually thinking of holes that form naturally, rather than because of human interference (like subway construction). “The term is often used as sort of a catchall for things that are just ground collapses, but not all ground collapses are in geologic terms true sinkholes,” says Dan Doctor, a research geologist with the U.S. Geological Survey.
True sinkholes are most common in karst landscapes, areas where the underlying rock is made from limestone, gypsum or other material that can be dissolved by water. Over time, groundwater erodes the rock or sediment, forming channels and caves, which can eventually collapse. This is what happened in the National Corvette Museum in Bowling Green, Kentucky in 2014. “They built the Corvette Museum right on top of a fairly large cave passage,” Doctor says. “They didn’t know it, and the roof of the cave collapsed and destroyed several vintage Corvettes.”
This type, known as cover-collapse sinkholes, can appear suddenly. But more often sinkholes form slowly – without a dramatic collapse – as a bowl-shaped depression in the ground. Some develop over thousands of years, and can stick around even longer. “Sinkholes can be animal traps and we often find bones within features like that that go back millions of years,” Doctor says.
And not all sinkholes are gaping chasms. “There are sinkholes that you can step into by accident and just might swallow your leg, and there are sinkholes that can hold the Sistine chapel,” Doctor says.
Sometimes, collapses are also triggered by human activities. “Once you begin to develop and create infrastructure within karst areas…you disrupt the subterranean plumbing,” Doctor says. These manmade “sinkholes” are often caused by construction, mining, or leaking pipes. They can also happen when groundwater is pumped out to supply cities or for irrigation, or ponds are built to hold industrial waste, putting more weight on the supporting rocks below.
Where do they happen?
About 20 percent of the United States lies on karst terrain, making these areas vulnerable to sinkholes. Sinkholes are most common in Texas, Missouri, Kentucky, Alabama, Tennessee, Pennsylvania, and especially Florida.
Map of areas in the contiguous United States that lie over rocks such as limestone that can be dissolved by water, making them vulnerable to sinkholes. U.S. Geological Survey
Sinkholes that appear on private property “quite often become the household dump,” Doctor says. This, he says, is not a wise move. “If they have a groundwater well nearby, they may be contaminating that well with their own waste.”
Sometimes, however, sinkholes can also make an ideal home for plants and animals by trapping cool air or offering shade from the sun. “They form little microclimates,” Doctor says.
Can we predict them?
While we can map which areas are more prone to sinkholes, it’s not really possible to pinpoint when a patch of ground will collapse. Part of the problem is that, to estimate risk, researchers need information about how many sinkholes have opened up over time. “Usually don’t have that information because…as soon as they’re formed they often get filled in and they may not even get reported,” Doctor says.
We do know that sinkholes seem to be tied to heavy rainstorms, hurricanes, droughts, and floods. “When there are large hurricanes that come and affect Florida, sinkholes are almost always problematic,” Doctor says. “With climate changing we might expect that could have some effect but…we don’t have data now to test that.”
There aren’t a lot of options for preventing a sinkhole from opening up on your property. However, “If you’ve done some kind of geophysical survey and you can detect the presence of an underground void, then yes, there are some things you can do,” Doctor says. Engineers might drill a small opening so the hole can be filled in. And if a house hasn’t been built yet, “They’ll probably excavate out until they get down to bare bedrock, and then place beams to cover the span of the void and build on top of that,” Doctor says.
And if you find yourself with a sinkhole in your backyard, a few states, such as Florida, offer ways to report a sinkhole, as well as tips for filling them in—after you’ve called your insurance company.
Here Is What You Can Find In The “Experience Nft” Art Exhibition In Barcelona
Soho Friends Barcelona is hosting a real-life art gallery of NFTs, organized by InvestingNFT.io in partnership with the Missony Art Festival. “Experience NFTs,” powered by chúng tôi is a month-long exhibition that opened on September 30 in Port Vell Studio (Soho Friends Barcelona).
When visiting the venue, which is used for events and as a co-working space, you’ll find NFTs from various categories: contemporary and classic art collections, social impact projects, fashion, graphic works, and more.
The main difference between an NFT exhibition and an art gallery is that you’re roaming in a room full of TV screens instead of canvases, with digital art often showcased in a dynamic display on screens. On top of that, you can scan a QR code to learn more about an art piece or even purchase it on an NFT marketplace.
NFTs are meant to live in the virtual world and aren’t expected to have a physical display. But the more NFTs become mainstream, the more we see them exhibited in public spaces.
NFT exhibition is open for all types of artOne of the most outstanding classical works in the “Experience NFT” exhibit is the digital-NFT version of Gustav Klimt’s painting “The Kiss” (1908). chúng tôi is showcased by the Belvedere museum in Vienna. Other participants include Samuel de Sagas-The King of hearts, Dadara Zero Banknote, Victor Garcia with “The Hoop” basketball court collection, Kiltro Gastrobar NFT community, MonArt NFTs project, CTRL/ART/D with a collection supporting Ukraine, chúng tôi (TIW), The Art Suit marketplace, ONEG fashion collection, Stephen Vineburg, Plastic Breeze by Borja Colom, and a special surprise guest, Chilean artist Guillermo Lorca.
Photos by @monikased
Stephen Vineburg, a digital artist and exhibitor, gave a speech at the event’s opening, covering his digital practice, the piece titled “Been There, Done That,” and he shared his views on the future of the NFT sector.
Vineburg’s piece “Been There, Done That” takes the shape of a discarded chocolate wrapper and is concerned with tourism on two levels. At a conceptual level, it refers to the disposable nature of touristic memories, while on a literal level, it refers to the detritus of mass tourism. The piece began life as a physical sculpture exhibited in a public park in Venice in an event curated by the European Cultural Academy.
The artist has also taken the digital sculpture to the Metaverse, with The Wrapper becoming its first piece of litter located within Decentraland.
Vineburg sees many parallels in digital art, and for the following reasons, the artist believes the digital art revolution will continue despite the recent disruptions to the NFT market:
There is the democratization of art; anyone with a computer can start creating their own art without needing a formal qualification. Similarly, the content can then be immediately distributed through platforms such as Foundation, OpenSea, and SuperRare rather than working through the conventional hierarchy of galleries and physical exhibitions. Finally, information about and assessment of this content can be distributed immediately through blogging and crypto-art channels.
NFTs provide an array of benefits to artistsNFTs provide artists with a way to show off their creativity and innovation, as well as a new way to reach their audience. In addition, NFT art exhibitions can help artists receive recognition.
“Art represented in the form of NFTs motivates and incentivizes artists to push the limits of imagination and creativity by helping eliminate gatekeepers of the traditional art market and, by doing so, increasing the direct profit for the talent,”
said Martin Noam Slutzky, the organizer of “Experience NFTs” and the co-founder of InvestingNFT.
Slutzky also explained that the artist could get direct recognition for his hard work by building an active community and adding utilities to their NFT collection to reward its members.
“This way, NFTs provide artists with a new opportunity to attract art investors and collectors and get attention from brands that would like to collaborate on NFT projects to innovate their image and appeal to the younger generation and even create cross-border social impact projects that are close to their hearts,”
he added.
On October 6, Soho House Barcelona hosted a presentation by Dutch artist Dadara (Daniel Rozenberg), whose work has been inspirational in the NFT space. The artist shared his journey of NFT exploration from philosophical and conceptual points of view.
In the next two weeks, InvestingNFT is organizing two additional presentations and workshops for those interested in NFTs. October 18 (7 pm), “How to create my own NFT collection” by MonartNFT, and October 25 (7 pm), “NFT & Art” by chúng tôi You can visit the NFT gallery at Soho House Barcelona until October 28.
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