
On this page you can find detailed information about all AIstrova models and datasets, from the latest breakthroughs to future releases.

<br>

# Navigation
1. [Future Releases & Queue](#future-releases--queue)
   - [1. AIstrova Safesearch v4](#1-aistrova-safesearch-v4)
   - [2. Culturally Aware Content Moderator v7](#2-culturally-aware-content-moderator-v7)
   - [3. Malphish Eater v4](#3-malphish-eater-v4)
   - [4. Self-harm Moderator](#4-self-harm-moderator)
   - [5. 24/7 Emotional Support](#5-247-emotional-support)
   - [6. [No Name] Malware Classification](#6-no-name-malware-classification)
   - [7. Security Guard v2](#7-security-guard-v2)
3. [Latest Releases & Breakthroughs](#latest-releases--breakthroughs)
   - [1. AIstrova Safesearch v3.1](#1-aistrova-safesearch-v31)

<br>

Currently, AIstrova strives to build and train models on massively multilingual datasets (i.e., about 100 languages) that are representative of real-world scenarios, with a strong focus on supporting the 15 most common languages on Discord.

<br>

# Future Releases & Queue
🚀 All production-ready & open-source products listed below are in the form of Name, followed by the purpose of the product.


### 1. AIstrova Safesearch v4
Accurately scans images, videos, and GIFs for gore & graphic content, sexually explicit content, and hateful memes.
Users can apply the "suggestive sensitivity" parameter to the "sexually explicit" prediction based on personal preferences.

Advantages:
- Over 300% of Google Safesearch customizations
- Consistently achieves 130% to 150% of Google Safesearch's accuracy on our Safesearch benchmark (i.e. very consistent in its predictions)
  - Details and usage of our Safesearch benchmark will be made public when this model is released
  - Benchmark does not include content that are highly subjective to minimize bias
- A more reliable, open source alternative to YouTube's filter (e.g. address parents' complaints to YouTube, address the issue of inconsistent predictions for different YouTubers with the same content)


### 2. Culturally Aware Content Moderator v7
A text moderator that has seen **18 categories of toxicity** (Propaganda, Cyberbullying, Harrasement, Violent Extremism, Sexting, Emotional Abuse, Emotional Manipulation, Psychological Abuse, Identity Hate/Homophobia, Hate Speech, Misogyny, Offensive Insults, Obscene, Threats, Racisim, Doxing, Trolling, and Others),
**3 categories of spam** (junk mail, text spam, and unsolicited advertisements),
and **over 1,000** scam methods (as mentioned by Discord Safety and Aura Digital Security 101), as well as the corresponding legitimate emails or messages.

It makes 6 predictions for each text `toxic`, `neutral`, `spam`, `ham`, `scam`, and `legitimate`.

Q: How is this different from any other toxicity or spam classification model on HuggingFace or Discord?

A: None of them can accurately detect scam content. Plus, this content moderator has seen all types of unwanted text content reported online to date - August 12th, 2023 - from **over 5,200,000** comments, tweets, messages, and emails in 65-95 languages.
It also understands the context of the conversation. To further increase accuracy, the labels on the dataset are a range of values from 0 to 1, rather than just binary labels.

We also took cultural considerations into account. For example, in the U.S., casual use of profanity is generally not considered toxic unless it's very serious or directed at others to intentionally cause harm.

Using this model alone, we can help Discord reduce 97-98% of their Harassment & Bullying and Hateful Conduct user reports.

### 3. Malphish Eater v4

*I am still semi-automatically cleaning the datasets and checking the URLs...*.

*I have no idea why Mcafee flags URLs as phishing for websites that have been down for at least 7-11 days or URLs that are no longer phishing (e.g. https://att-100351-103068.weeblysite.com/).

*Google doesn't use machine learning at all, so it's understandable that they blacklist safe URLs.

*In our benchmark, we will show that Mcafee only checks the URLs and uses beginner-level machine learning, which is why Mcafee is very good at producing false positives and can still produce false negatives.
For example, it flagged several non-malware dataset files and dataset pages while I was **casually** browsing the web.
Also, "http://discord-o.com/", "https://discorcl.club", "https://beacons.ai/onedriveoffice", and several other phishing sites are not flagged at all.

This ensemble model will significantly outperform Mcafee in terms of false positives and achieves higher accuracy in detecting malicious URLs, not to mention Google Safe Browsing and Discord's filters.
Phishing, malware, and defacement URLs in the dataset appeared as early as 2008 and as late as today. We cover **over 3,500,000** URLs (and websites) written in around 40 languages.

Using this with #1 and #7, we can help Discord reduce user reports with Deceptive Practices and Identity and Authenticity by 98-99%.

Q: How does AIstrova plan to combat DM scams? I'm curious because Discord bots can't see DM messages.

A: The idea is to give Discord users a "vaccine" against scams.
We will train an artificial scammer on our task #1 dataset, capable of generating 110+ types of text message and email scams, and expose Discord users to each type of scam through meaningful conversations and illustrations, one at a time.

### 4. Self-harm Moderator
This deep learning model classifies conversations into three categories: 'healthy', 'depressed' and 'suicidal', in 10-15 languages.

Using this with #5, we can at least reduce some of Discord's Self Harm reports each quarter.

### 5. 24/7 Emotional Support
Currently, top-of-the-line products like GPT-4 struggle to provide effective emotional support, even with a complex prompt. ChatGPT and GPT-4 are not even generally acceptable as standalone counseling services, according to the American Psychological Association (APA).
AIstrova will train an efficient and reliable model to respond effectively and timely to users of different ages in need of support.

### 6. [No Name] Malware Classification
Classifies all types of malware that existed in the 2000s and appeared as late as April 2023

### 7. Security Guard v2
Aims to achieve comparable and higher accuracy on Violent videos classification than AIstrova Safesearch.

Classifies videos into categories of `no violence`, `PG 13 violence`, and `graphic violence`.

### 8. [No Name] Organized Crime Detection
Dealing with issues of regulated or illegal activity on Discord. This task came last because it won't be as effective unless Discord implements it.

*Other projects not shown to avoid overflow*

<br>

Q: AIstrova wants to excel in machine learning for almost all human tasks, but why do I see mostly classification tasks in the Queue?

A: What you see in the Queue are the 7 projects I will complete in the next 15 days or so.
Next there'll be even more complex projects like TranslateFXL, which aims to support more languages than DeepL Translate, while achieving an accuracy higher than Google Translate and ChatGPT (even with FlowGPT.com prompt engineering) in all its supported languages.

Another example is ClydeX (with options for fast, normal, and slow text generation), which has all the features of Discord Clyde, except that it can answer **all** your questions about Discord with over 300-1000% more detail and proper citations.
Running on 2 CPU cores, the fast version of ClydeX should generate about the same number of tokens per second as Clyde.
In addition, ClydeX's answers may be closer to human writing than ChatGPT's. Unlike ChatGPT, which has a data cutoff of September 2021, ours is trained on data up to today.

Note that we may not release the actual ClydeX, but we will definitely release the base model that we used to fine-tune the model on our Discord Q&A dataset.

**There are others I'm working on at the same time. So stay tuned!**


<br>

# Latest Releases & Breakthroughs
🚀 All production-ready & open-source products listed below are in the form of Name, followed by the highlights of the product.

### 1. AIstrova Safesearch v3.1
[[Demo](https://huggingface.co/spaces/aistrova/safesearch-v3.1)] [[Model](https://huggingface.co/aistrova/safesearch-v3.1)] [Paper]

Summary:
  - The World's Most Accurate & Efficient Safesearch Algorithm

Highlights:
  - Has over 300% of the features and customizations of Google Safesearch, while achieving higher overall accuracy on images, videos, and GIFs
  - Trained on 4,210,000+ real photos, AI-generated images, and GIFs
  - Accurately classifies video content up to any length
license:apache-2.0 region:us
{"license": "apache-2.0"}
TAGS
#license-apache-2.0 #region-us 


On this page you can find detailed information about all AIstrova models and datasets, from the latest breakthroughs to future releases.

<br>

# Navigation
1. Future Releases & Queue
   - 1. AIstrova Safesearch v4
   - 2. Culturally Aware Content Moderator v7
   - 3. Malphish Eater v4
   - 4. Self-harm Moderator
   - 5. 24/7 Emotional Support
   - [6. [No Name] Malware Classification](#6-no-name-malware-classification)
   - 7. Security Guard v2
3. Latest Releases & Breakthroughs
   - 1. AIstrova Safesearch v3.1

<br>

Currently, AIstrova strives to build and train models on massively multilingual datasets (i.e., about 100 languages) that are representative of real-world scenarios, with a strong focus on supporting the 15 most common languages on Discord.

<br>

# Future Releases & Queue
 All production-ready & open-source products listed below are in the form of Name, followed by the purpose of the product.


### 1. AIstrova Safesearch v4
Accurately scans images, videos, and GIFs for gore & graphic content, sexually explicit content, and hateful memes.
Users can apply the "suggestive sensitivity" parameter to the "sexually explicit" prediction based on personal preferences.

Advantages:
- Over 300% of Google Safesearch customizations
- Consistently achieves 130% to 150% of Google Safesearch's accuracy on our Safesearch benchmark (i.e. very consistent in its predictions)
  - Details and usage of our Safesearch benchmark will be made public when this model is released
  - Benchmark does not include content that are highly subjective to minimize bias
- A more reliable, open source alternative to YouTube's filter (e.g. address parents' complaints to YouTube, address the issue of inconsistent predictions for different YouTubers with the same content)


### 2. Culturally Aware Content Moderator v7
A text moderator that has seen 18 categories of toxicity (Propaganda, Cyberbullying, Harrasement, Violent Extremism, Sexting, Emotional Abuse, Emotional Manipulation, Psychological Abuse, Identity Hate/Homophobia, Hate Speech, Misogyny, Offensive Insults, Obscene, Threats, Racisim, Doxing, Trolling, and Others),
3 categories of spam (junk mail, text spam, and unsolicited advertisements),
and over 1,000 scam methods (as mentioned by Discord Safety and Aura Digital Security 101), as well as the corresponding legitimate emails or messages.

It makes 6 predictions for each text 'toxic', 'neutral', 'spam', 'ham', 'scam', and 'legitimate'.

Q: How is this different from any other toxicity or spam classification model on HuggingFace or Discord?

A: None of them can accurately detect scam content. Plus, this content moderator has seen all types of unwanted text content reported online to date - August 12th, 2023 - from over 5,200,000 comments, tweets, messages, and emails in 65-95 languages.
It also understands the context of the conversation. To further increase accuracy, the labels on the dataset are a range of values from 0 to 1, rather than just binary labels.

We also took cultural considerations into account. For example, in the U.S., casual use of profanity is generally not considered toxic unless it's very serious or directed at others to intentionally cause harm.

Using this model alone, we can help Discord reduce 97-98% of their Harassment & Bullying and Hateful Conduct user reports.

### 3. Malphish Eater v4

*I am still semi-automatically cleaning the datasets and checking the URLs...*.

*I have no idea why Mcafee flags URLs as phishing for websites that have been down for at least 7-11 days or URLs that are no longer phishing (e.g. URL

*Google doesn't use machine learning at all, so it's understandable that they blacklist safe URLs.

*In our benchmark, we will show that Mcafee only checks the URLs and uses beginner-level machine learning, which is why Mcafee is very good at producing false positives and can still produce false negatives.
For example, it flagged several non-malware dataset files and dataset pages while I was casually browsing the web.
Also, "URL "URL", "URL and several other phishing sites are not flagged at all.

This ensemble model will significantly outperform Mcafee in terms of false positives and achieves higher accuracy in detecting malicious URLs, not to mention Google Safe Browsing and Discord's filters.
Phishing, malware, and defacement URLs in the dataset appeared as early as 2008 and as late as today. We cover over 3,500,000 URLs (and websites) written in around 40 languages.

Using this with #1 and #7, we can help Discord reduce user reports with Deceptive Practices and Identity and Authenticity by 98-99%.

Q: How does AIstrova plan to combat DM scams? I'm curious because Discord bots can't see DM messages.

A: The idea is to give Discord users a "vaccine" against scams.
We will train an artificial scammer on our task #1 dataset, capable of generating 110+ types of text message and email scams, and expose Discord users to each type of scam through meaningful conversations and illustrations, one at a time.

### 4. Self-harm Moderator
This deep learning model classifies conversations into three categories: 'healthy', 'depressed' and 'suicidal', in 10-15 languages.

Using this with #5, we can at least reduce some of Discord's Self Harm reports each quarter.

### 5. 24/7 Emotional Support
Currently, top-of-the-line products like GPT-4 struggle to provide effective emotional support, even with a complex prompt. ChatGPT and GPT-4 are not even generally acceptable as standalone counseling services, according to the American Psychological Association (APA).
AIstrova will train an efficient and reliable model to respond effectively and timely to users of different ages in need of support.

### 6. [No Name] Malware Classification
Classifies all types of malware that existed in the 2000s and appeared as late as April 2023

### 7. Security Guard v2
Aims to achieve comparable and higher accuracy on Violent videos classification than AIstrova Safesearch.

Classifies videos into categories of 'no violence', 'PG 13 violence', and 'graphic violence'.

### 8. [No Name] Organized Crime Detection
Dealing with issues of regulated or illegal activity on Discord. This task came last because it won't be as effective unless Discord implements it.

*Other projects not shown to avoid overflow*

<br>

Q: AIstrova wants to excel in machine learning for almost all human tasks, but why do I see mostly classification tasks in the Queue?

A: What you see in the Queue are the 7 projects I will complete in the next 15 days or so.
Next there'll be even more complex projects like TranslateFXL, which aims to support more languages than DeepL Translate, while achieving an accuracy higher than Google Translate and ChatGPT (even with URL prompt engineering) in all its supported languages.

Another example is ClydeX (with options for fast, normal, and slow text generation), which has all the features of Discord Clyde, except that it can answer all your questions about Discord with over 300-1000% more detail and proper citations.
Running on 2 CPU cores, the fast version of ClydeX should generate about the same number of tokens per second as Clyde.
In addition, ClydeX's answers may be closer to human writing than ChatGPT's. Unlike ChatGPT, which has a data cutoff of September 2021, ours is trained on data up to today.

Note that we may not release the actual ClydeX, but we will definitely release the base model that we used to fine-tune the model on our Discord Q&A dataset.

There are others I'm working on at the same time. So stay tuned!


<br>

# Latest Releases & Breakthroughs
 All production-ready & open-source products listed below are in the form of Name, followed by the highlights of the product.

### 1. AIstrova Safesearch v3.1
[Demo] [Model] [Paper]

Summary:
  - The World's Most Accurate & Efficient Safesearch Algorithm

Highlights:
  - Has over 300% of the features and customizations of Google Safesearch, while achieving higher overall accuracy on images, videos, and GIFs
  - Trained on 4,210,000+ real photos, AI-generated images, and GIFs
  - Accurately classifies video content up to any length
# Navigation
1. Future Releases & Queue
   - 1. AIstrova Safesearch v4
   - 2. Culturally Aware Content Moderator v7
   - 3. Malphish Eater v4
   - 4. Self-harm Moderator
   - 5. 24/7 Emotional Support
   - [6. [No Name] Malware Classification](#6-no-name-malware-classification)
   - 7. Security Guard v2
3. Latest Releases & Breakthroughs
   - 1. AIstrova Safesearch v3.1

<br>

Currently, AIstrova strives to build and train models on massively multilingual datasets (i.e., about 100 languages) that are representative of real-world scenarios, with a strong focus on supporting the 15 most common languages on Discord.

<br> # Future Releases & Queue
 All production-ready & open-source products listed below are in the form of Name, followed by the purpose of the product. ### 1. AIstrova Safesearch v4
Accurately scans images, videos, and GIFs for gore & graphic content, sexually explicit content, and hateful memes.
Users can apply the "suggestive sensitivity" parameter to the "sexually explicit" prediction based on personal preferences.

Advantages:
- Over 300% of Google Safesearch customizations
- Consistently achieves 130% to 150% of Google Safesearch's accuracy on our Safesearch benchmark (i.e. very consistent in its predictions)
  - Details and usage of our Safesearch benchmark will be made public when this model is released
  - Benchmark does not include content that are highly subjective to minimize bias
- A more reliable, open source alternative to YouTube's filter (e.g. address parents' complaints to YouTube, address the issue of inconsistent predictions for different YouTubers with the same content) ### 2. Culturally Aware Content Moderator v7
A text moderator that has seen 18 categories of toxicity (Propaganda, Cyberbullying, Harrasement, Violent Extremism, Sexting, Emotional Abuse, Emotional Manipulation, Psychological Abuse, Identity Hate/Homophobia, Hate Speech, Misogyny, Offensive Insults, Obscene, Threats, Racisim, Doxing, Trolling, and Others),
3 categories of spam (junk mail, text spam, and unsolicited advertisements),
and over 1,000 scam methods (as mentioned by Discord Safety and Aura Digital Security 101), as well as the corresponding legitimate emails or messages.

It makes 6 predictions for each text 'toxic', 'neutral', 'spam', 'ham', 'scam', and 'legitimate'.

Q: How is this different from any other toxicity or spam classification model on HuggingFace or Discord?

A: None of them can accurately detect scam content. Plus, this content moderator has seen all types of unwanted text content reported online to date - August 12th, 2023 - from over 5,200,000 comments, tweets, messages, and emails in 65-95 languages.
It also understands the context of the conversation. To further increase accuracy, the labels on the dataset are a range of values from 0 to 1, rather than just binary labels.

We also took cultural considerations into account. For example, in the U.S., casual use of profanity is generally not considered toxic unless it's very serious or directed at others to intentionally cause harm.

Using this model alone, we can help Discord reduce 97-98% of their Harassment & Bullying and Hateful Conduct user reports. ### 3. Malphish Eater v4

*I am still semi-automatically cleaning the datasets and checking the URLs...*.

*I have no idea why Mcafee flags URLs as phishing for websites that have been down for at least 7-11 days or URLs that are no longer phishing (e.g. URL

*Google doesn't use machine learning at all, so it's understandable that they blacklist safe URLs.

*In our benchmark, we will show that Mcafee only checks the URLs and uses beginner-level machine learning, which is why Mcafee is very good at producing false positives and can still produce false negatives.
For example, it flagged several non-malware dataset files and dataset pages while I was casually browsing the web.
Also, "URL "URL", "URL and several other phishing sites are not flagged at all.

This ensemble model will significantly outperform Mcafee in terms of false positives and achieves higher accuracy in detecting malicious URLs, not to mention Google Safe Browsing and Discord's filters.
Phishing, malware, and defacement URLs in the dataset appeared as early as 2008 and as late as today. We cover over 3,500,000 URLs (and websites) written in around 40 languages.

Using this with #1 and #7, we can help Discord reduce user reports with Deceptive Practices and Identity and Authenticity by 98-99%.

Q: How does AIstrova plan to combat DM scams? I'm curious because Discord bots can't see DM messages.

A: The idea is to give Discord users a "vaccine" against scams.
We will train an artificial scammer on our task #1 dataset, capable of generating 110+ types of text message and email scams, and expose Discord users to each type of scam through meaningful conversations and illustrations, one at a time. ### 4. Self-harm Moderator
This deep learning model classifies conversations into three categories: 'healthy', 'depressed' and 'suicidal', in 10-15 languages.

Using this with #5, we can at least reduce some of Discord's Self Harm reports each quarter. ### 5. 24/7 Emotional Support
Currently, top-of-the-line products like GPT-4 struggle to provide effective emotional support, even with a complex prompt. ChatGPT and GPT-4 are not even generally acceptable as standalone counseling services, according to the American Psychological Association (APA).
AIstrova will train an efficient and reliable model to respond effectively and timely to users of different ages in need of support. ### 6. [No Name] Malware Classification
Classifies all types of malware that existed in the 2000s and appeared as late as April 2023 ### 7. Security Guard v2
Aims to achieve comparable and higher accuracy on Violent videos classification than AIstrova Safesearch.

Classifies videos into categories of 'no violence', 'PG 13 violence', and 'graphic violence'. ### 8. [No Name] Organized Crime Detection
Dealing with issues of regulated or illegal activity on Discord. This task came last because it won't be as effective unless Discord implements it.

*Other projects not shown to avoid overflow*

<br>

Q: AIstrova wants to excel in machine learning for almost all human tasks, but why do I see mostly classification tasks in the Queue?

A: What you see in the Queue are the 7 projects I will complete in the next 15 days or so.
Next there'll be even more complex projects like TranslateFXL, which aims to support more languages than DeepL Translate, while achieving an accuracy higher than Google Translate and ChatGPT (even with URL prompt engineering) in all its supported languages.

Another example is ClydeX (with options for fast, normal, and slow text generation), which has all the features of Discord Clyde, except that it can answer all your questions about Discord with over 300-1000% more detail and proper citations.
Running on 2 CPU cores, the fast version of ClydeX should generate about the same number of tokens per second as Clyde.
In addition, ClydeX's answers may be closer to human writing than ChatGPT's. Unlike ChatGPT, which has a data cutoff of September 2021, ours is trained on data up to today.

Note that we may not release the actual ClydeX, but we will definitely release the base model that we used to fine-tune the model on our Discord Q&A dataset.

There are others I'm working on at the same time. So stay tuned!


<br> # Latest Releases & Breakthroughs
 All production-ready & open-source products listed below are in the form of Name, followed by the highlights of the product. ### 1. AIstrova Safesearch v3.1
[Demo] [Model] [Paper]

Summary:
  - The World's Most Accurate & Efficient Safesearch Algorithm

Highlights:
  - Has over 300% of the features and customizations of Google Safesearch, while achieving higher overall accuracy on images, videos, and GIFs
  - Trained on 4,210,000+ real photos, AI-generated images, and GIFs
  - Accurately classifies video content up to any length
TAGS
#license-apache-2.0 #region-us 
 # Navigation
1. Future Releases & Queue
   - 1. AIstrova Safesearch v4
   - 2. Culturally Aware Content Moderator v7
   - 3. Malphish Eater v4
   - 4. Self-harm Moderator
   - 5. 24/7 Emotional Support
   - [6. [No Name] Malware Classification](#6-no-name-malware-classification)
   - 7. Security Guard v2
3. Latest Releases & Breakthroughs
   - 1. AIstrova Safesearch v3.1

<br>

Currently, AIstrova strives to build and train models on massively multilingual datasets (i.e., about 100 languages) that are representative of real-world scenarios, with a strong focus on supporting the 15 most common languages on Discord.

<br> # Future Releases & Queue
 All production-ready & open-source products listed below are in the form of Name, followed by the purpose of the product. ### 1. AIstrova Safesearch v4
Accurately scans images, videos, and GIFs for gore & graphic content, sexually explicit content, and hateful memes.
Users can apply the "suggestive sensitivity" parameter to the "sexually explicit" prediction based on personal preferences.

Advantages:
- Over 300% of Google Safesearch customizations
- Consistently achieves 130% to 150% of Google Safesearch's accuracy on our Safesearch benchmark (i.e. very consistent in its predictions)
  - Details and usage of our Safesearch benchmark will be made public when this model is released
  - Benchmark does not include content that are highly subjective to minimize bias
- A more reliable, open source alternative to YouTube's filter (e.g. address parents' complaints to YouTube, address the issue of inconsistent predictions for different YouTubers with the same content) ### 2. Culturally Aware Content Moderator v7
A text moderator that has seen 18 categories of toxicity (Propaganda, Cyberbullying, Harrasement, Violent Extremism, Sexting, Emotional Abuse, Emotional Manipulation, Psychological Abuse, Identity Hate/Homophobia, Hate Speech, Misogyny, Offensive Insults, Obscene, Threats, Racisim, Doxing, Trolling, and Others),
3 categories of spam (junk mail, text spam, and unsolicited advertisements),
and over 1,000 scam methods (as mentioned by Discord Safety and Aura Digital Security 101), as well as the corresponding legitimate emails or messages.

It makes 6 predictions for each text 'toxic', 'neutral', 'spam', 'ham', 'scam', and 'legitimate'.

Q: How is this different from any other toxicity or spam classification model on HuggingFace or Discord?

A: None of them can accurately detect scam content. Plus, this content moderator has seen all types of unwanted text content reported online to date - August 12th, 2023 - from over 5,200,000 comments, tweets, messages, and emails in 65-95 languages.
It also understands the context of the conversation. To further increase accuracy, the labels on the dataset are a range of values from 0 to 1, rather than just binary labels.

We also took cultural considerations into account. For example, in the U.S., casual use of profanity is generally not considered toxic unless it's very serious or directed at others to intentionally cause harm.

Using this model alone, we can help Discord reduce 97-98% of their Harassment & Bullying and Hateful Conduct user reports. ### 3. Malphish Eater v4

*I am still semi-automatically cleaning the datasets and checking the URLs...*.

*I have no idea why Mcafee flags URLs as phishing for websites that have been down for at least 7-11 days or URLs that are no longer phishing (e.g. URL

*Google doesn't use machine learning at all, so it's understandable that they blacklist safe URLs.

*In our benchmark, we will show that Mcafee only checks the URLs and uses beginner-level machine learning, which is why Mcafee is very good at producing false positives and can still produce false negatives.
For example, it flagged several non-malware dataset files and dataset pages while I was casually browsing the web.
Also, "URL "URL", "URL and several other phishing sites are not flagged at all.

This ensemble model will significantly outperform Mcafee in terms of false positives and achieves higher accuracy in detecting malicious URLs, not to mention Google Safe Browsing and Discord's filters.
Phishing, malware, and defacement URLs in the dataset appeared as early as 2008 and as late as today. We cover over 3,500,000 URLs (and websites) written in around 40 languages.

Using this with #1 and #7, we can help Discord reduce user reports with Deceptive Practices and Identity and Authenticity by 98-99%.

Q: How does AIstrova plan to combat DM scams? I'm curious because Discord bots can't see DM messages.

A: The idea is to give Discord users a "vaccine" against scams.
We will train an artificial scammer on our task #1 dataset, capable of generating 110+ types of text message and email scams, and expose Discord users to each type of scam through meaningful conversations and illustrations, one at a time. ### 4. Self-harm Moderator
This deep learning model classifies conversations into three categories: 'healthy', 'depressed' and 'suicidal', in 10-15 languages.

Using this with #5, we can at least reduce some of Discord's Self Harm reports each quarter. ### 5. 24/7 Emotional Support
Currently, top-of-the-line products like GPT-4 struggle to provide effective emotional support, even with a complex prompt. ChatGPT and GPT-4 are not even generally acceptable as standalone counseling services, according to the American Psychological Association (APA).
AIstrova will train an efficient and reliable model to respond effectively and timely to users of different ages in need of support. ### 6. [No Name] Malware Classification
Classifies all types of malware that existed in the 2000s and appeared as late as April 2023 ### 7. Security Guard v2
Aims to achieve comparable and higher accuracy on Violent videos classification than AIstrova Safesearch.

Classifies videos into categories of 'no violence', 'PG 13 violence', and 'graphic violence'. ### 8. [No Name] Organized Crime Detection
Dealing with issues of regulated or illegal activity on Discord. This task came last because it won't be as effective unless Discord implements it.

*Other projects not shown to avoid overflow*

<br>

Q: AIstrova wants to excel in machine learning for almost all human tasks, but why do I see mostly classification tasks in the Queue?

A: What you see in the Queue are the 7 projects I will complete in the next 15 days or so.
Next there'll be even more complex projects like TranslateFXL, which aims to support more languages than DeepL Translate, while achieving an accuracy higher than Google Translate and ChatGPT (even with URL prompt engineering) in all its supported languages.

Another example is ClydeX (with options for fast, normal, and slow text generation), which has all the features of Discord Clyde, except that it can answer all your questions about Discord with over 300-1000% more detail and proper citations.
Running on 2 CPU cores, the fast version of ClydeX should generate about the same number of tokens per second as Clyde.
In addition, ClydeX's answers may be closer to human writing than ChatGPT's. Unlike ChatGPT, which has a data cutoff of September 2021, ours is trained on data up to today.

Note that we may not release the actual ClydeX, but we will definitely release the base model that we used to fine-tune the model on our Discord Q&A dataset.

There are others I'm working on at the same time. So stay tuned!


<br> # Latest Releases & Breakthroughs
 All production-ready & open-source products listed below are in the form of Name, followed by the highlights of the product. ### 1. AIstrova Safesearch v3.1
[Demo] [Model] [Paper]

Summary:
  - The World's Most Accurate & Efficient Safesearch Algorithm

Highlights:
  - Has over 300% of the features and customizations of Google Safesearch, while achieving higher overall accuracy on images, videos, and GIFs
  - Trained on 4,210,000+ real photos, AI-generated images, and GIFs
  - Accurately classifies video content up to any length
passage: TAGS
#license-apache-2.0 #region-us 
# Navigation
1. Future Releases & Queue
   - 1. AIstrova Safesearch v4
   - 2. Culturally Aware Content Moderator v7
   - 3. Malphish Eater v4
   - 4. Self-harm Moderator
   - 5. 24/7 Emotional Support
   - [6. [No Name] Malware Classification](#6-no-name-malware-classification)
   - 7. Security Guard v2
3. Latest Releases & Breakthroughs
   - 1. AIstrova Safesearch v3.1

<br>

Currently, AIstrova strives to build and train models on massively multilingual datasets (i.e., about 100 languages) that are representative of real-world scenarios, with a strong focus on supporting the 15 most common languages on Discord.

<br># Future Releases & Queue
 All production-ready & open-source products listed below are in the form of Name, followed by the purpose of the product.### 1. AIstrova Safesearch v4
Accurately scans images, videos, and GIFs for gore & graphic content, sexually explicit content, and hateful memes.
Users can apply the "suggestive sensitivity" parameter to the "sexually explicit" prediction based on personal preferences.

Advantages:
- Over 300% of Google Safesearch customizations
- Consistently achieves 130% to 150% of Google Safesearch's accuracy on our Safesearch benchmark (i.e. very consistent in its predictions)
  - Details and usage of our Safesearch benchmark will be made public when this model is released
  - Benchmark does not include content that are highly subjective to minimize bias
- A more reliable, open source alternative to YouTube's filter (e.g. address parents' complaints to YouTube, address the issue of inconsistent predictions for different YouTubers with the same content) passage: ### 2. Culturally Aware Content Moderator v7
A text moderator that has seen 18 categories of toxicity (Propaganda, Cyberbullying, Harrasement, Violent Extremism, Sexting, Emotional Abuse, Emotional Manipulation, Psychological Abuse, Identity Hate/Homophobia, Hate Speech, Misogyny, Offensive Insults, Obscene, Threats, Racisim, Doxing, Trolling, and Others),
3 categories of spam (junk mail, text spam, and unsolicited advertisements),
and over 1,000 scam methods (as mentioned by Discord Safety and Aura Digital Security 101), as well as the corresponding legitimate emails or messages.

It makes 6 predictions for each text 'toxic', 'neutral', 'spam', 'ham', 'scam', and 'legitimate'.

Q: How is this different from any other toxicity or spam classification model on HuggingFace or Discord?

A: None of them can accurately detect scam content. Plus, this content moderator has seen all types of unwanted text content reported online to date - August 12th, 2023 - from over 5,200,000 comments, tweets, messages, and emails in 65-95 languages.
It also understands the context of the conversation. To further increase accuracy, the labels on the dataset are a range of values from 0 to 1, rather than just binary labels.

We also took cultural considerations into account. For example, in the U.S., casual use of profanity is generally not considered toxic unless it's very serious or directed at others to intentionally cause harm.

Using this model alone, we can help Discord reduce 97-98% of their Harassment & Bullying and Hateful Conduct user reports.### 3. Malphish Eater v4

*I am still semi-automatically cleaning the datasets and checking the URLs...*.

*I have no idea why Mcafee flags URLs as phishing for websites that have been down for at least 7-11 days or URLs that are no longer phishing (e.g. URL

*Google doesn't use machine learning at all, so it's understandable that they blacklist safe URLs.

*In our benchmark, we will show that Mcafee only checks the URLs and uses beginner-level machine learning, which is why Mcafee is very good at producing false positives and can still produce false negatives.
For example, it flagged several non-malware dataset files and dataset pages while I was casually browsing the web.
Also, "URL "URL", "URL and several other phishing sites are not flagged at all.

This ensemble model will significantly outperform Mcafee in terms of false positives and achieves higher accuracy in detecting malicious URLs, not to mention Google Safe Browsing and Discord's filters.
Phishing, malware, and defacement URLs in the dataset appeared as early as 2008 and as late as today. We cover over 3,500,000 URLs (and websites) written in around 40 languages.

Using this with #1 and #7, we can help Discord reduce user reports with Deceptive Practices and Identity and Authenticity by 98-99%.

Q: How does AIstrova plan to combat DM scams? I'm curious because Discord bots can't see DM messages.

A: The idea is to give Discord users a "vaccine" against scams.
We will train an artificial scammer on our task #1 dataset, capable of generating 110+ types of text message and email scams, and expose Discord users to each type of scam through meaningful conversations and illustrations, one at a time.### 4. Self-harm Moderator
This deep learning model classifies conversations into three categories: 'healthy', 'depressed' and 'suicidal', in 10-15 languages.

Using this with #5, we can at least reduce some of Discord's Self Harm reports each quarter. passage: ### 5. 24/7 Emotional Support
Currently, top-of-the-line products like GPT-4 struggle to provide effective emotional support, even with a complex prompt. ChatGPT and GPT-4 are not even generally acceptable as standalone counseling services, according to the American Psychological Association (APA).
AIstrova will train an efficient and reliable model to respond effectively and timely to users of different ages in need of support.### 6. [No Name] Malware Classification
Classifies all types of malware that existed in the 2000s and appeared as late as April 2023### 7. Security Guard v2
Aims to achieve comparable and higher accuracy on Violent videos classification than AIstrova Safesearch.

Classifies videos into categories of 'no violence', 'PG 13 violence', and 'graphic violence'.### 8. [No Name] Organized Crime Detection
Dealing with issues of regulated or illegal activity on Discord. This task came last because it won't be as effective unless Discord implements it.

*Other projects not shown to avoid overflow*

<br>

Q: AIstrova wants to excel in machine learning for almost all human tasks, but why do I see mostly classification tasks in the Queue?

A: What you see in the Queue are the 7 projects I will complete in the next 15 days or so.
Next there'll be even more complex projects like TranslateFXL, which aims to support more languages than DeepL Translate, while achieving an accuracy higher than Google Translate and ChatGPT (even with URL prompt engineering) in all its supported languages.

Another example is ClydeX (with options for fast, normal, and slow text generation), which has all the features of Discord Clyde, except that it can answer all your questions about Discord with over 300-1000% more detail and proper citations.
Running on 2 CPU cores, the fast version of ClydeX should generate about the same number of tokens per second as Clyde.
In addition, ClydeX's answers may be closer to human writing than ChatGPT's. Unlike ChatGPT, which has a data cutoff of September 2021, ours is trained on data up to today.

Note that we may not release the actual ClydeX, but we will definitely release the base model that we used to fine-tune the model on our Discord Q&A dataset.

There are others I'm working on at the same time. So stay tuned!


<br># Latest Releases & Breakthroughs
 All production-ready & open-source products listed below are in the form of Name, followed by the highlights of the product.