#Generative Pre-trained Transformer

[hasnainhyder1996]

Revolutionizing Presentation Creation: How AI is Transforming PowerPoint Generation

In today’s fast-paced professional environment, creating impactful presentations has become an essential skill. However, the traditional process of designing PowerPoint slides can be tedious and time-consuming. Enter AI-powered presentation generation tools, a game-changer for professionals and students alike. With innovations like AI Presentation Maker by Leaveit2AI, creating engaging and visually appealing slides is now easier than ever.

The Challenge of Traditional Presentation Creation

Crafting an effective PowerPoint presentation requires a delicate balance between content, design, and storytelling. Traditional methods often demand hours of manual work—researching topics, structuring content, aligning visuals, and tweaking layouts to perfection. For many, this process diverts valuable time away from core tasks, leading to stress and inefficiency.

How AI Revolutionizes Presentation Creation

AI tools for presentation generation are designed to streamline and enhance the entire process. These platforms leverage advanced algorithms to understand topics, structure content, and apply professional design principles, producing ready-to-use slides in minutes. Here’s how AI-driven solutions are transforming presentation creation:

Automated Content Structuring AI algorithms analyze your input topic and automatically generate a logical content flow. Whether you need bullet points, detailed paragraphs, or definitions, AI tools can structure your content coherently.

Professional Design Made Effortless AI tools offer a variety of themes and templates, ensuring a polished and professional appearance. They can also suggest visuals, infographics, and animations to enhance engagement.

Time Efficiency What once took hours can now be achieved in a fraction of the time. AI tools free up valuable time, allowing users to focus on the message rather than the mechanics of design.

Customization and Flexibility Modern AI tools allow users to customize their presentations to align with personal or brand-specific requirements. From fonts and color schemes to content tone, users can easily tweak outputs to meet their needs.

Accessibility for All AI-powered tools democratize the process of presentation creation, making it accessible to individuals without design or technical expertise.

Key Features of AI Presentation Maker by Leaveit2AI

One standout platform in this domain is the AI Presentation Maker by Leaveit2AI. Here’s what sets it apart:

Intelligent Topic Analysis: Simply input your topic, and the tool generates slides with relevant and structured content.

Customizable Themes and Content: Users can select from multiple themes and specify content types, ensuring presentations align with their objectives.

Image Integration: The platform seamlessly incorporates visuals, making slides more dynamic and engaging.

Export Options: Presentations can be exported in multiple formats, including PowerPoint and PDF, with editing options available in Google Slides.

User-Friendly Interface: Designed for ease of use, it caters to professionals, students, and educators looking for quick and effective presentation solutions.

Applications Across Industries

AI-powered presentation tools are finding applications in a variety of fields:

Business: For client pitches, internal meetings, and sales presentations.

Education: Helping teachers and students create compelling lectures and project presentations.

Freelancers: Simplifying the creation of portfolios or proposals.

Nonprofits: Creating impactful visuals for campaigns or donor meetings.

The Future of Presentation Creation

As AI continues to evolve, the possibilities for presentation creation are endless. Future advancements could include real-time audience feedback analysis, voice-to-slide capabilities, and even VR-powered immersive presentations.

Conclusion

AI-powered presentation tools like AI Presentation Maker by Leaveit2AI are revolutionizing how we create and deliver content. By automating the design process and providing smart customization options, these tools are empowering users to create professional-quality presentations effortlessly. Whether you're a seasoned professional or a student preparing for a class project, AI presentation generation is here to make your work easier, faster, and better.

Why spend hours on slides when AI can do it for you? Embrace the future of presentation creation and experience the difference today!

Visit AI Presentation Maker by Leaveit2AI to explore the possibilities.

[enterprise-knowledge-advisor]

Contact Enterprise Knowledge Advisor: Your Information Mining Solution (celebaltech.com)

[drcpanda12]

New Post has been published on https://www.knewtoday.net/the-rise-of-openai-advancing-artificial-intelligence-for-the-benefit-of-humanity/

The Rise of OpenAI: Advancing Artificial Intelligence for the Benefit of Humanity

OpenAI is a research organization that is focused on advancing artificial intelligence in a safe and beneficial manner. It was founded in 2015 by a group of technology luminaries, including Elon Musk, Sam Altman, Greg Brockman, and others, with the goal of creating AI that benefits humanity as a whole.

OpenAI conducts research in a wide range of areas related to AI, including natural language processing, computer vision, robotics, and more. It also develops cutting-edge AI technologies and tools, such as the GPT series of language models, which have been used in a variety of applications, from generating realistic text to aiding in scientific research.

In addition to its research and development work, OpenAI is also committed to promoting transparency and safety in AI. It has published numerous papers on AI ethics and governance and has advocated for responsible AI development practices within the industry and among policymakers.

Introduction to OpenAI: A Brief History and Overview

An American artificial intelligence (AI) research facility called OpenAI is made as a non-profit organization. OpenAI Limited Partnership is its for-profit sister company. The stated goal of OpenAI’s AI research is to advance and create a benevolent AI. Microsoft’s Azure supercomputing platform powers OpenAI systems.

Ilya Sutskever, Greg Brockman, Trevor Blackwell, Vicki Cheung, Andrej Karpathy, Durk Kingma, John Schulman, Pamela Vagata, and Wojciech Zaremba created OpenAI in 2015; the inaugural board of directors included Sam Altman and Elon Musk. Microsoft invested $1 billion in OpenAI LP in 2019 and another $10 billion in 2023.

Brockman compiled a list of the “top researchers in the field” after meeting Yoshua Bengio, one of the “founding fathers” of the deep learning movement. In December 2015, Brockman was able to bring on nine of them as his first workers. In 2016, OpenAI paid business compensation rather than nonprofit payments to its AI researchers, but not salaries that were on par with Facebook or Google.

Several researchers joined the company because of OpenAI’s potential and mission; one Google employee claimed he was willing to leave the company “partly because of the very strong group of people and, to a very big extent, because of its mission.” Brockman said that advancing humankind’s ability to create actual AI in a secure manner was “the best thing I could imagine doing.” Wojciech Zaremba, a co-founder of OpenAI, claimed that he rejected “borderline ridiculous” offers of two to three times his market value in order to join OpenAI.

A public beta of “OpenAI Gym,” a platform for reinforcement learning research, was made available by OpenAI in April 2016. “Universe,” a software platform for assessing and honing an AI’s general intelligence throughout the universe of games, websites, and other applications, was made available by OpenAI in December 2016.

OpenAI’s Research Areas: Natural Language Processing, Computer Vision, Robotics, and More

In 2021, OpenAI will concentrate its research on reinforcement learning (RL).

Gym

Gym, which was introduced in 2016, intends to offer a general-intelligence benchmark that is simple to deploy across a wide range of environments—similar to, but more extensive than, the ImageNet Large Scale Visual Recognition Challenge used in supervised learning research. In order to make published research more easily replicable, it aims to standardize how environments are characterized in publications on AI. The project asserts that it offers a user-friendly interface. The gym may only be used with Python as of June 2017. The Gym documentation site was no longer maintained as of September 2017, and its GitHub page was the site of ongoing activity.

RoboSumo

In the 2017 virtual reality game RoboSumo, humanoid meta-learning robot agents compete against one another with the aim of learning how to move and shoving the rival agent out of the arena. When an agent is taken out of this virtual environment and placed in a different virtual environment with strong gusts, the agent braces to stay upright, indicating it has learned how to balance in a generic fashion through this adversarial learning process. Igor Mordatch of OpenAI contends that agent competition can lead to an intelligence “arms race,” which can improve an agent’s capacity to perform, even outside of the confines of the competition.

Video game bots

In the competitive five-on-five video game Dota 2, a squad of five OpenAI-curated bots known as OpenAI Five is utilized. These bots are trained to compete against human players at a high level solely by trial-and-error techniques. The first public demonstration took place at The International 2017, the yearly premier championship event for the game, where Dendi, a professional Ukrainian player, lost to a bot in a real-time one-on-one matchup before becoming a team of five. Greg Brockman, CTO, revealed after the game that the bot had learned by competing against itself for two weeks in real-time, and that the learning software was a step toward developing software that could perform intricate jobs like a surgeon.

By June 2018, the bots had improved to the point where they could play as a full team of five, defeating teams of amateur and semi-professional players. OpenAI Five competed in two exhibition games at The International 2018 against top players, but they both lost. In a live demonstration game in San Francisco in April 2019, OpenAI Five upset OG, the current global champions of the game, 2:0.During that month, the bots made their last public appearance, winning 99.4% of the 42,729 games they participated in over a four-day open internet competition.

Dactyl

In 2018 Dactyl uses machine learning to teach a Shadow Hand, a robotic hand that resembles a human hand, how to manipulate actual objects. It uses the same RL algorithms and training code as OpenAI Five to learn totally in simulation. Domain randomization, a simulation method that exposes the learner to a variety of experiences rather than attempting to match them to reality, was used by OpenAI to address the object orientation problem. Dactyl’s setup includes RGB cameras in addition to motion tracking cameras so that the robot may control any object simply by looking at it. In 2018, OpenAI demonstrated that the program could control a cube and an octagonal prism.

2019 saw OpenAI present Dactyl’s ability to solve a Rubik’s Cube. 60% of the time, the robot was successful in resolving the puzzle. It is more difficult to model the complex physics introduced by items like Rubik’s Cube. This was resolved by OpenAI by increasing Dactyl’s resistance to disturbances; they did this by using a simulation method known as Automated Domain Randomization (ADR),

OpenAI’s GPT model

Alec Radford and his colleagues wrote the initial study on generative pre-training of a transformer-based language model, which was released as a preprint on OpenAI’s website on June 11, 2018. It demonstrated how pre-training on a heterogeneous corpus with lengthy stretches of continuous text allows a generative model of language to gain world knowledge and understand long-range dependencies.

A language model for unsupervised transformers, Generative Pre-trained Transformer 2 (or “GPT-2”) is the replacement for OpenAI’s first GPT model. The public initially only saw a few number of demonstrative copies of GPT-2 when it was first disclosed in February 2019. GPT-2’s complete release was delayed due to worries about potential abuse, including uses for creating fake news. Some analysts questioned whether GPT-2 posed a serious threat.

It was trained on the WebText corpus, which consists of little more than 8 million documents totaling 40 gigabytes of text from Links published in Reddit contributions that have received at least three upvotes. Adopting byte pair encoding eliminates some problems that can arise when encoding vocabulary with word tokens. This makes it possible to express any string of characters by encoding both single characters and tokens with multiple characters.

GPT-3

Benchmark results for GPT-3 were significantly better than for GPT-2. OpenAI issued a warning that such language model scaling up might be nearing or running into the basic capabilities limitations of predictive language models.

Many thousand petaflop/s-days of computing were needed for pre-training GPT-3 as opposed to tens of petaflop/s-days for the complete GPT-2 model. Similar to its predecessor, GPT-3’s fully trained model wasn’t immediately made available to the public due to the possibility of abuse, but OpenAI intended to do so following a two-month free private beta that started in June 2020. Access would then be made possible through a paid cloud API.

GPT-4

The release of the text- or image-accepting Generative Pre-trained Transformer 4 (GPT-4) was announced by OpenAI on March 14, 2023. In comparison to the preceding version, GPT-3.5, which scored in the bottom 10% of test takers,

OpenAI said that the revised technology passed a simulated law school bar exam with a score in the top 10% of test takers. GPT-4 is also capable of writing code in all of the major programming languages and reading, analyzing, or producing up to 25,000 words of text.

DALL-E and CLIP images

DALL-E, a Transformer prototype that was unveiled in 2021, generates visuals from textual descriptions. CLIP, which was also made public in 2021, produces a description for an image.

DALL-E interprets natural language inputs (such as an astronaut riding on a horse)) and produces comparable visuals using a 12-billion-parameter version of GPT-3. It can produce pictures of both actual and unreal items.

ChatGPT and ChatGPT Plus

An artificial intelligence product called ChatGPT, which was introduced in November 2022 and is based on GPT-3, has a conversational interface that enables users to ask queries in everyday language. The system then provides an answer in a matter of seconds. Five days after its debut, ChatGPT had one million members.

ChatGPT Plus is a $20/month subscription service that enables users early access to new features, faster response times, and access to ChatGPT during peak hours.

Ethics and Safety in AI: OpenAI’s Commitment to Responsible AI Development

As artificial intelligence (AI) continues to advance and become more integrated into our daily lives, concerns around its ethics and safety have become increasingly urgent. OpenAI, a research organization focused on advancing AI in a safe and beneficial manner, has made a commitment to responsible AI development that prioritizes transparency, accountability, and ethical considerations.

One of the ways that OpenAI has demonstrated its commitment to ethical AI development is through the publication of numerous papers on AI ethics and governance. These papers explore a range of topics, from the potential impact of AI on society to the ethical implications of developing powerful AI systems. By engaging in these discussions and contributing to the broader AI ethics community, OpenAI is helping to shape the conversation around responsible AI development.

Another way that OpenAI is promoting responsible AI development is through its focus on transparency. The organization has made a point of sharing its research findings, tools, and technologies with the wider AI community, making it easier for researchers and developers to build on OpenAI’s work and improve the overall quality of AI development.

In addition to promoting transparency, OpenAI is also committed to safety in AI. The organization recognizes the potential risks associated with developing powerful AI systems and has taken steps to mitigate these risks. For example, OpenAI has developed a framework for measuring AI safety, which includes factors like robustness, alignment, and transparency. By considering these factors throughout the development process, OpenAI is working to create AI systems that are both powerful and safe.

OpenAI has also taken steps to ensure that its own development practices are ethical and responsible. The organization has established an Ethics and Governance board, made up of external experts in AI ethics and policy, to provide guidance on OpenAI’s research and development activities. This board helps to ensure that OpenAI’s work is aligned with its broader ethical and societal goals.

Overall, OpenAI’s commitment to responsible AI development is an important step forward in the development of AI that benefits humanity as a whole. By prioritizing ethics and safety, and by engaging in open and transparent research practices, OpenAI is helping to shape the future of AI in a positive and responsible way.

Conclusion: OpenAI’s Role in Shaping the Future of AI

OpenAI’s commitment to advancing AI in a safe and beneficial manner is helping to shape the future of AI. The organization’s focus on ethical considerations, transparency, and safety in AI development is setting a positive example for the broader AI community.

OpenAI’s research and development work is also contributing to the development of cutting-edge AI technologies and tools. The GPT series of language models, developed by OpenAI, have been used in a variety of applications, from generating realistic text to aiding in scientific research. These advancements have the potential to revolutionize the way we work, communicate, and learn.

In addition, OpenAI’s collaborations with industry leaders and their impact on real-world applications demonstrate the potential of AI to make a positive difference in society. By developing AI systems that are safe, ethical, and transparent, OpenAI is helping to ensure that the benefits of AI are shared by all.

As AI continues to evolve and become more integrated into our daily lives, the importance of responsible AI development cannot be overstated. OpenAI’s commitment to ethical considerations, transparency, and safety is an important step forward in creating AI that benefits humanity as a whole. By continuing to lead the way in responsible AI development, OpenAI is helping to shape the future of AI in a positive and meaningful way.

Best Text to Speech AI Voices

[sitenesia]

Bagaimana ChatGPT Bekerja? Apa SihChat-GPT: Sistem Generasi Teks Berbasis AI dari OpenAI itu?

Bagaimana ChatGPT Bekerja? Apa Sih Chat-GPT: Sistem Generasi Teks Berbasis AI dari OpenAI itu? Seperti Apa Arsitektur dan Kelebihannya? Dalam era digital yang semakin maju ini, teknologi kecerdasan buatan (Artificial Intelligence/AI) telah menjadi bagian penting dalam kehidupan sehari-hari. Salah satu perkembangan terbaru dalam bidang AI adalah ChatGPT, yang telah menarik perhatian banyak…

[twinkdrama]

[enterprise-knowledge-advisor]

Leveraging the Power of AI: How Celebal's EKA Can Revolutionize Enterprise Knowledge Management 

In today's data-driven world, businesses are constantly accumulating information from various sources. This includes emails, documents, presentations, and more. Managing and effectively utilizing this vast knowledge base can be a significant challenge. Here's where Celebal Technologies' Enterprise Knowledge Advisor (EKA) comes in. EKA is a revolutionary information mining solution powered by OpenAI's Generative Pre-trained Transformer (GPT-3) technology, designed to empower businesses to unlock the true potential of their internal knowledge. 

What is EKA and How Does it Work?  

EKA is an AI-powered information retrieval system that goes beyond simple keyword searches. It leverages the capabilities of GPT-3, a cutting-edge AI language model, to understand the context and intent behind user queries. This allows EKA to deliver highly relevant and insightful results, even for complex or nuanced questions.  

Here's a breakdown of how EKA works:  

Deep Knowledge Ingestion: EKA seamlessly integrates with various enterprise data sources, ingesting and indexing a wide range of documents, emails, and other internal content. 

Advanced Natural Language Processing (NLP): It utilizes NLP techniques to comprehend the meaning and relationships within the ingested data. This enables EKA to not only identify relevant documents but also understand the context and connections between them. 

AI-powered Search and Retrieval: When a user submits a query, EKA employs its AI capabilities to analyze the query and user intent. It then retrieves the most pertinent information from the indexed knowledge base, considering not just keywords but also the context and relationships within the data.  

Intelligent Information Delivery: It presents the retrieved information in a user-friendly and informative way. It can highlight key points, summarize findings, and even suggest related content that might be valuable to the user. 

Benefits of Utilizing EKA for Enterprise Knowledge Management 

GPT-3 OpenAI-powered EKA offers a multitude of advantages for businesses seeking to optimize their knowledge management practices. Here are some of the key benefits: 

Enhanced Search Accuracy and Relevance: EKA's AI-powered search capabilities deliver highly relevant results that directly address user queries. This eliminates the need for users to sift through irrelevant information, saving them valuable time and effort. 

Improved User Engagement: EKA's intuitive interface and intelligent information delivery make it easy for users to find the information they need. This can lead to increased user engagement with the knowledge base and a more informed workforce. 

Boosted Productivity: By providing users with quick and easy access to the information they need, EKA can significantly improve employee productivity. Less time spent searching for information translates to more time dedicated to core tasks and strategic initiatives. 

Knowledge Democratization: EKA empowers all employees, regardless of their technical expertise, to access and utilize the organization's knowledge base effectively. This fosters a culture of knowledge-sharing and collaboration.  

Data-driven Decision-making: With EKA, businesses can leverage their internal knowledge to make more informed decisions. EKA's ability to surface relevant insights and connections within the data can provide valuable guidance for strategic planning and problem-solving. 

A Real-World Example of EKA's Impact 

According to Celebal Technologies, a major media conglomerate using EKA reported a significant increase of 25% in user engagement with their internal knowledge base. This demonstrates the effectiveness of EKA in making information more accessible and user-friendly, ultimately leading to a more informed and productive workforce. 

The Future of Enterprise Knowledge Management with EKA 

EKA represents a significant leap forward in the realm of enterprise knowledge management. As AI technology continues to evolve, we can expect EKA's capabilities to become even more sophisticated. Here are some potential future advancements: 

Advanced Personalization: EKA could personalize search results and information delivery based on individual user preferences and past search behavior. 

Integration with Cognitive Tools: EKA could integrate with other cognitive tools and applications, allowing for a more seamless flow of information and knowledge within the organization. 

Enhanced Knowledge Graph Capabilities: EKA's ability to understand relationships and connections within data could be further refined, enabling more advanced knowledge graph functionalities. 

Conclusion 

Celebal Technologies’ Enterprise Knowledge Advisor represents a significant advancement in enterprise knowledge management. By leveraging the power of GPT-3 OpenAI and the Generative Pre-trained Transformer model, EKA offers a comprehensive information mining solution that enhances decision-making, improves efficiency, and provides a competitive advantage. Organizations across various industries can benefit from the transformative capabilities of EKA, unlocking the full potential of their data assets. As businesses continue to navigate an increasingly data-driven world, tools like EKA will be essential in driving innovation and success. To learn more about EKA, schedule a free consulting session with the experts at enterprisesales@celebaltech.com.

[shaileshrathod]

GPT-4 (Generative Pre-trained Transformer 4) નો ઉપયોગ

GPT-4 (Generative Pre-trained Transformer 4) નો ઉપયોગ અનેક ક્ષેત્રોમાં કરવામાં આવે છે અને તે બહુ ઉપયોગી સાબિત થાય છે. અહીં કેટલાક મુખ્ય ઉપયોગો જણાવ્યા છે:1. **લેખન અને સામગ્રી સર્જન**: લખાણની રચના, બ્લોગ પોસ્ટ્સ, લેખ, કવિતાઓ અને સ્ક્રિપ્ટ્સ જેવા મલ્ટિમીડિયા સામગ્રી બનાવવા માટે.2. **ગ્રાહક સેવા**: ચેટબોટ્સ અને વર્ચ્યુઅલ અસિસ્ટન્ટ્સ તરીકે ગ્રાહકોને ત्वरિત અને ��સરકારક જવાબો આપવા માટે.3. **અનુસંધાન…

[twinkdrama]

[uplymedia]

Boost Your Business Edge with GPTs

A Deep Dive into the World of Customized GPTs FeatureStandard GPTsCustom GPTsKnowledge BaseBroad and generalSpecialized and focusedRelevanceGeneral-purposeHighly contextualApplicationsWide-rangingIndustry-specificCustom GPT Types Hello, dear reader! Today, We are thrilled to guide you through the fascinating world of Custom Generative Pre-trained Transformers, commonly known as Custom GPTs.…

[jor-elthatendswell]

Today I learned the T in ChatGPT stands for "transformer" . Clearly a Decepticon plot...

[startheskelaton]

Do you have a timelime for your sparkplug au?

Yes and It's LONG. This isn't even all of it but it's what I have written out at least

Timeline: important plot points 

Pre war

Orion pax and D-16 are born 

Both experience the loss of parental guardian 

D-16 is taken to the pit 

Orion pax breaks into the pit, he and Dee start the foundations of the Decepticon cause 

Revolution starts to take shape on Cybertron 

Orion is killed in an attempt to stop D from falling down the slippery slope of a rage filled warmonger 

Orion is brought back as Optimus prime 

OG Ultra Magnus makes him a general in the Autobot ranks 

During war 

War goes on for like…. Long ass time

Autobots land on earth 

Main decepticons fallow suit and step up shop due to amount of resources

Governments make deals with different factions in place of protection and access to weapon technology

Eventually Megatron has a “oh fuck” moment when he invades an illegal mining operation in central Africa. It puts into perspective how far he has fallen, seeing his commanders make deals with those who profited off the mines, just like those in power back on cybertron. 

Midlife crisis, Megatron leaves the Decepticon cause, he takes Soundwave with him. Declares he will do whatever he needs in order to free those enslaved on this planet

Megatron joins the Autobots, this cases Prowl to leave and switch sides 

Battles pick up heat as both sides are desperate 

Millionaires and those in places of high power use Cybertronian technology to flee earth and live in space as earth is being destroyed

Starscream kills Optimus Prime in an attempt to kill Megatron. Both sides retreat as a result 

Optimus splits the matrix and gives it to Hotrod and bumblebee, 

Rodimus prime and Vespa Prime are born 

Optimus Prime’s spark is put in a reformation chamber with parts of Megatron in an attempt to bring him back 

Treaties are made and the decepticons take Cybertron as the Autobots stay on earth 

Post war 

Decepticons start rebuilding Cybertroinian society 

Shockwave finishes creating a replacement for Soundwave

Soundblaster is born 

The first sparkling born on Cybertron in millenia emerges

Nightflyer is born 

Due to the splitting of the matrix of leadership, dormant energon on earth awakens and allows for new sparklings to emerge 

The attempt the revive Optimus prime’s spark fails 

Sparkplug is born 

A new era 

Earth 

The Autobots have made it their mission to help reform the planet and help the humans rebuild 

Rodimus prime leaves earth in a hope to find some kind of explanation for why he feels like everything is in the shitter 

Subsections of colonies start to pop up, some keep to themselves, others work directly with the autobots, some hate transformers entirely 

Railroads are made more efficient in order to transport supplies across countries 

Earth starts to heal with the help of the matrix, forests grow and temperatures fall to a normal level 

Major cities act as sanctuaries for the human population, help from other alien races arrives as well, helping earth to become a space traveling hub 

Cybertron 

The Decepticons no longer go by that name, no longer wanting to be associated with the past. They go by Workers of Prime 

Shockwave has put together a complicated and purposeful chain of command and leadership that he sits at the top of. However Starscream is the “king” of Cybertron, while being a puppet 

Prowl takes care of enforcing laws and regulations to the planet. Along with trying to unite the cities with one another 

Cybertron now has a entertainment industry, focused on promoting good morals to the population along with keeping bots distracted 

Cybertron only communicate with it’s colony planets, trying to form a stronger relationship between all transformers 

“Peace times” (start of the story) 

Sparkplug is currently working as the assistant of Ratchet under the blessing of Megatron and Elita one 

Sparkplug trains in her free time to be a scout and will sneak off every once and a while to play basement concerts 

On Cybertron, Nightflyer is top of his class while training to be a high guard soldier. He is chosen by Shockwave to go on a mission to earth and infiltrate the Autobots

Cybertron is in desperate need of resources 

Nightflyer lands on earth and pretends to be a Decepticon defector, Sparkplug is wary of him 

Nightflyer manages to become an Autobot and meets Sparkplug during the scout tryouts 

Reluctantly Sparkplug is passed but gets put on the Energon transportation and quality control team, she’s fine with this as she just wanted to see the world 

Nightflyer gets put on a mission team, meeting Landlot, Defender and other bots his age. 

During this time we get our first mentions of a cult ran by a former Decepticon that’s turning humans into purple energon

Both Spark and Night explore earth and meet new and old bots.

Example: Sparkplug gets to know earth born transformers, Nightflyer gets to meet bots like Skyfire 

Back at the base, Sparkplug gets annoyed with how much fanfare Nightflyer is getting, while she still gets treated like a sparkling 

She breaks Night’s social mask and gets to know the real him. A romance between the two starts to form 

Shockwave informs Nightflyer that they’re sending a team to take over the main Autobot base 

Shockwave employs the DJD to help in the Autobot attack 

Return to war 

Sparkplug confesses to Nightflyer, Nightflyer returns the feeling as he does like her.

Right before the invasion he tries to convince her that living on Cybertron wouldn’t be so bad. Sparkplug refutes that she likes Earth and that Cybertron would probably hate her.

The DJD and a group of seakers make their way to Earth and start fucking shit up

Big dramatic reveal to the characters that Nightflyer was a spy all along and is Starscream’s ward

Things are going in the bad guy’s favor until Tarn realizes Sparkplug is part Megatron.

 He orders his men to capture her and kill everyone else, as they have a new leader of the Decepticon cause 

The battle becomes even more messy as sides are switched and the Autobots and seekers are now fighting to survive

While attempting to help Megatron fight off Tarn, Sparkplug is grabbed by Soundblaster, who hopes to bring her back to Shockwave in order to get in his favor. 

Space distortion happens when Skywarp tries to help get Soundblaster out of there and accidentally sends him and Sparkplug halfway across the universe. 

This cases the DJD to leave as they are now looking for Sparkplug, and the seekers(after getting beaten by the DJD) are taken prisoner for now 

Depression but in space 

 Sparkplug and Soundblaster are in the middle of nowhere on a deserted planet. After trying to restrain one another, they realize they need to help each other if they wanna survive this mess

Back on earth, Rodimus comes back from space due to getting a SOS message, he is yelled at by his family

Acidstorm, Slipstream and Airachnid are absolutely furious about being stuck on earth for the time being. Nightflyer is currently being used as a verbal punching bag for the Autobots 

Back in Space, Sparkplug and Soundblaster start to develop a chemistry as they learn more about each other.

Rodiums takes it upon himself to get Sparkplug back as a way to make up for leaving everyone years ago. This is a big reference to “the lost light”, characters like megatron, rodimus, swerve and others join, along with some OC’s like Nanabah (native american sharpshooter) who forms a friendship with Preceptor, and Lobo (the lowrider transformer born on earth) 

Rodimus takes nightflyer under his wing in an attempt to reform him  

In space, Spark and Soundblaster start to feel romantic feelings for one another. However this is interrupted by them getting found by the DJD.

Soundwave takes it upon himself to split off from Rodimus group as he might be able to locate her better through his mind powers (I don’t know, it's all space magic man)

Sparkplug properly meets Tarn and is quickly given a new frame and alt mode against her will. 

Soundwave finds the DJD with Spark on it and sneaks on, However he is caught, and even if he fights well, he can’t fight off all of them.

Tarn forces Sparkplug to finally give into her anger when he kills Soundwave in front of her and lets her kill him. 

Spark takes on the name “Megatron” and is then forced to eat Tarn’s spark in an act of dominance. She is now the leader of the DJD 

There will be more to come!!! this is not all of it

Part two

[imsobadatnicknames2]

You once said you mostly like playing Dwarf Fortress in adventure mode nowadays, right? Any adventure mode tips you can give for a total noob?

Okay I'm not any kind of expert by any means, but...

For combat, there are weapon skills (e.g. Crossbowman, Swordsman, Hammerman, Bowman) which determine how good you are at using a specific weapon, but there are also two I like to call "parent skills": Fighter, which determines how good you are at using melee weapons in general, and Archer, which determines how good you are at using ranged weapons in general (as well as attacking with thrown objects). Attacking with a sword uses (and trains) both your Fighter and Swordsman skills. For character creation I think it's better to put points into Fighter and/or Archer, and train in the use of specific weapons during play. That way, if you ever need to switch to a different weapon from the specific one you're trained in you'll still be able to use it competently.

Always put one point into the Reader skill, otherwise your character will be illiterate. Since the only way to train skills in game is by using them, there is no in-game way to ever learn to read if you start with an illiterate character.

There is currently no implemented in-game way to fulfill the needs to be with family, be with friends, or make romance in adventure mode, so you should avoid creating a character with a personality and set of values that gives them these needs, otherwise they will inevitably become distracted from being unable to fulfill them. Also, the need to eat a good meal is technically possible but extremely hard to fulfill (since it requires either eating an extremely valuable meal, or a meal made with one of your character's randomly selected preferred ingredients) so you should probably avoid it too.

For purposes of trading, carrying small high-value items such as gems or high-quality crafts is a lot more useful than carrying coins around, since coins don't have any monetary value outside of the civilization that minted them, so you can only use certain coins for trade in certain sites.

However, with a high Thrower (or Archer) skill, coins make for a surprisingly decent and easily replenishable thrown weapon.

In certain climates, the water in your waterskin may freeze at night, or even stay frozen all the time. This took me a while to figure out back in the day, but: If you need to drink but your water is frozen, you'll need to interact with an adjacent empty space to make a campfire there, and then interact with the campfire and select the ice to heat it (or, in pre-steam versions, press g and then choose the option to make a campfire, and then while standing next to it press I to open advanced interactions with your inventory and then select the ice and choose the option to heat it)

If you find it annoying to constantly have to find food and water, play as a goblin, since goblins don't need to eat or drink.

I haven't tested if it works the same way in the post-steam versions, but in iirc performing anything at a tavern and then talking to the tavern keeper about your performance would get them to give you a discount on your room and drinks, regardless of the actual quality of the performance. I don't think this has been changed, but still.

Offloading a site by moving in travel mode will instantly heal you of all temporary damage, such as wounds, broken bones, bleeding, etc. If you're bleeding out during combat you can avoid dying by running away from your enemies until you're far away enough to initiate travel mode and then moving in any direction.

The only way to heal permanent damage such as lost body parts or severed nerves is to become a werebeast, since your body will be completely restored every time you transform. You can become a werebeast by getting bitten by one and surviving (the bite has to tear at least one tissue layer or it won't pass on the curse), or by getting cursed either by toppling a statue at a temple dedicated to a deity you worship, or by rolling one of the divination dice found at shrines three times (although when you get cursed it's randomized if you become either a vampire or a werebeast). However, being a werebeast will make you vulnerable against a random metal, and transforming will unequip and drop all your worn items (including backpacks and pouches) unless the size of your werebeast form is relatively similar to your normal size (plus destroy all non-leather clothing you're wearing regardless of size change)

If you don't start with a high Armor User skill, wearing a full set of armor can actually be more harm than good, since a low Armor User skill makes you more susceptible to the armor's encumbrance penalty, and makes you tire more easily while wearing armor, making it harder to dodge attacks and get attacks in.

However, any leather clothing counts as armor for the purposes of training, and doesn't have encumbrance penalties. So if you don't have a high armor user skill you should equip yourself with maybe a metal helmet, gauntlets, and or/boots, and then put on as much leather clothing as you can, so you can avoid the penalties while you train the skill to the point that you can wear a full set of metal armor.

[just-a-little-cellist]

Can I request Commander Wolffe x reader, who's a Jedi knight and Plo Koon's former padawan? Reader is from an alien race that can turn into a giant wolf and shares a gentle nature toward the clones like their master. Reader is also an absolute fighting unit and tall (Like a gentle giant) Bonus if reader uses their enhanced sense of smell to locate things and finds Wolffe's scent most pleasant. What if reader uses their wolf form to warm up Wolffe and his brothers when they get trapped in some snow planet? (You can put fem! reader if gender has to be specific. )

(omg this is such a cute idea! also this is my first ever Star Wars piece with one of my favourite copy paste men?? so excited <33 this is also sort of a pre-relationship build up thing, so if you'd like me to do a follow up I'd be more than happy to! hope you enjoy!)

(Wolffe x gender neutral reader (can be read as platonic) - warning for brief mentions of fighting, no detail though)

His general is the one to introduce the two of you, Master Plo stating that his former padawan would be 'shadowing' until they were assigned their own battalion.

Wolffe had an immediate respect for you - your sweet smile and offer of a handshake were somewhat unusual, since many of the higher ups chose to keep their distance from the clones where they could help it, but you seemed genuine, so he returned the handshake and nodded politely.

Your introduction alone made it very clear that you had trained under Master Plo. Your temperament was very similar, with the same aura of patience, kindness and wisdom that the general had.

You also seemed eager to get to know the soldiers you were serving with, making sure to remember all of their names and asking questions about them.

The Wolfpack responded well to you, Wolffe had to admit. While he was somewhat hesitant having a new face around, it was good to see his men's morale so high.

They all seemed to trust you and your abilities. While he knew not to underestimate you, especially given your towering height, he would not be convinced completely until he saw you fight.

The first callout to battle had you buzzing with anticipation, and Wolffe watched you warily as you whispered something to the general, to which he shook his head in response.

There was no time to wonder though, as your ship soon touched down and you were thrust onto the battlefield not long after.

You didn't remember the name of the planet, but it was layered in snow, and the glow of blaster bolts and lightsabers was all that guided you in the blizzard you had landed in.

Everything was moving so quickly, and you did your best to put yourself between your men and the enemy fire wherever you could, but it soon became clear that they would overwhelm you in this weather. The more men you saw fall to the ground, the more your hope fell.

You couldn't see well enough in this form. You knew that, the general knew that. Plo Koon had told you not to shift unless it was a last resort, you had asked on the way there - though with the enemy fire now surrounding you, surely this counted?

A quick nod from him confirmed your thinking and, while your former master gave Wolffe the most warning he could, you shifted into wolf form.

It was relieving, to suddenly have your senses be so sharp when you could barely tell where you were stood seconds earlier, and you were easily able to tell where the enemy horde stood. There were stragglers in other directions, but they were soon picked off by the clones.

Your attackers were clearly startled by your transformation (reasonable enough), but their moment's hesitation was enough to allow you to spring forward and send the majority scattering with a sweep of one enormous paw. You felt your claws tear through some, and the ones left intact were swiftly finished off with your teeth. Any that were smart enough to run didn't make it far either.

The sounds of blaster fire ceased once your swift massacre had ended, and you slowly padded across the snow, lifting your nose to the air to find the only ones left were your men.

Far fewer of your men than you would've liked.

Plo Koon watched with mirth in his eyes as you approached the clones, their expressions fixed in awe, aside from Wolffe, who seemed uncharacteristically nervous.

You supposed you couldn't blame him. But still, to will him to trust you, you lay down in front of him, blood-stained muzzle pressed into the snow at his feet while he stared, perplexed, down at you.

"They are still aware in this form, Commander. You need not worry."

The comforting words from the general seemed to placate Wolffe momentarily, then he turned to you.

"We must find shelter quickly. We'll never make it back to the ship with so many injured."

You dipped your head in understanding, and gestured south. It seemed like a smudge on the horizon to the others, but you could make out the silhouettes and smell the scent of the forest enough to know it was your best option.

Not having a better choice, they agreed to follow you, and those still standing began helping up the injured clones. Seeing this, you huffed and nodded at your back.

Plo nodded in understanding. "They will carry the injured. We can move faster that way."

Wolffe, still watching you warily, started helping the others climb onto your back, not willing to waste time even in his distrust. He could ask questions later, but the general trusted you, so he could too.

Moving in this form was certainly faster, and it was not long before you were amidst a cluster of trees and helping down the clones on your back. It wasn't perfect, but you were more shielded from the blizzard.

Wolffe and the general were at the back of the pack, ensuring you weren't followed. They didn't seem confident in the condition of the men, and you tilted your head in question.

The commander looked up at you, seemingly frustrated. "We have no supplies. We'll freeze if we stay here."

You huffed in reply and padded over behind the cluster of injured clones, laying down and using your nose to nudge them closer to your fur. Some of them nestled closer to your warmth on instinct, and you looked back at Wolffe expectantly.

"You can't be serious..."

Comet in particular seemed to be happy with this arrangement though, and quickly dove into your mountain of fur beside his brothers.

Plo patted Wolffe on the shoulder and urged him to sit, humming in amusement at the look on his face as you draped your tail over the small group of clones.

You were... useful. Wolffe couldn't deny that. Useful and very, very warm.

As he drifted to sleep far faster than usual, he thought maybe he could get used to this...

[twinkdrama]

[iminseriousdebt]

GENLOSS RAMBLE

Heyo! This is a little ramble I needed to make before the founders cut comes out!  yipee!

(GENERATION LOSS SPOILERS)

/

/

/

/

/

/

/

/

/

/

/

/

/

/

/

/

So we can see in the above images the methods Showfall Media is using to control gl!Sneeg gl!Charlie and gl!Ranboo, they use an already pre-existing technology called an Electroencephalogram (EEG). Now this technology has been in use for decades, and essentially how it works is that it uses electrodes placed onto your scalp combined with a conductive gel to measure the electrical activity in your brain, these electrical signals are usually referred to as “brain waves” and these brainwaves can be subdivided into four categories, Gamma (greater than 30 Hz), Beta (13-30 Hz), Alpha (8-12 Hz), Theta (3.5-7.5 Hz), and Delta (0.1-3.5 Hz)

These different brainwaves are generally assosiated with different emotions, awareness levels, brain activities, etc. Now if Showfall Media has installed these onto sneeg, charlie, and ranboo, that means they have access to their thoughts and feelings, but brainscanning isn’t an absolute precise device, it still takes a lot of human effort and time to properly interpret the brainwaves. If Showfall somehow had a tool to easily interpret the signals they could much more easily operate, say, a live show. Lucky for them there is already a real life solution to this problem, kinda.

Its called Brain Generative Pre-Training Transformer, or BrainGPT for short.  What its goal is, is to act as an assist tool for human neurologists to use in real neuroscience cases and case studies, what it does is it uses a Large Language Model (LLM) full of pre-existing human research papers and other neuroscience knowledge too vast for human comprehension. And whenever a neurologist hands BrainGPT a prompt, (such as anomalous finding or to asses the fields understanding of a certain topic) , “would generate likely data patterns reflecting its current synthesis of the scientific literature”  (braingpt.org)

Now in regards to Generation Loss, what this means is that Showfall Media potentially has acces to this sort of technology, and would be able to use it in the production of their shows, now BrainGPT has a good way to go before its widely avalable. But in the genloss au, it can be far into development at this point, and be available for companies to use in whatever way they see fit.

Now reading and decoding brain signals is one thing, but to mind control someone is far beyond what is capable today, but Showfall Media has somehow developed technology to do so, the way I’m guessing they did it is that they produced certain brainwaves from the electrodes on the actors heads to give them the emotional reactions they needed for the show. I can’t exactly get into the technical stuff cause I’m not a neurologist, but its just a hunch on how I think they did it.

As for the mind controlling devices themselves, I feel there’s a more subtextual reason as to why those objects in particular are chosen as the devices that are central to the show’s operation. Ranboo’s mask has been a heavy emphasis throughout Gen 1 TSE,

Its been a central figure in not only generation loss’ marketing, but also ranboo’s marketing, because when you think of ranboo one of the first things that pops up is the mask, atleast in the wider public’s eye.

But these general associations not only exist with Ranboo, with Slimcicle you usually think of the wide brim glasses, with Sneeg its his backwards cap, and this is with the other cast members too when their introduced on the spinning carousel in episode 2. Furthermore, with Niki it’s that’s she's just so nice, with Austin its that he’s just a gay guy,  and with Vinny and Ethan these associations don’t really exist. So, with Vinny he's just the “hoarder”, and Ethan isn't even introduced. And then there's Jerma, who is relinquished to a goofy character with a weird voice and a strange sense of humour which sort of fits his public image.

But what I wanna mention with Ranboo’s mask specifically is that with the three images shown on the genloss twitter of the control devices, sneeg’s is just a hat, like theres nothing special about it, just a hat with electrodes on it, when you take it off he’s completely in control of himself. But, with charlie’s it’s a good bit harder to just take it off. His glasses are drilled into his skull connected to electrodes which are also implanted in his skull, with an additional feature of a speaker in his jaw. But if you remove the glasses, there would be a lot of bleeding and his vision would be impaired, but he would still be a free man.

But with Ranboo, poor, poor, Ranboo… Like Charlie, they have electrodes implanted on to their brain connected to a switch on the back of their skull (which also may or may not also be connected to their spine, idk its hard to tell). These sprout wires that thread through the mask and lead into their throat, and the mask piece itself is sewn shut onto their SKIN.

Now this makes me wonder, why is Ranboo so heavily guarded when the other are (relatively) easy to set free? Is it because Ranboo is an integral part of the show and therefore high risk? Is it because Showfall needed extra resources for the chat to be able to control them?... Or is it because Ranboo tried to escape so many times before that they were forced to disfigure them to such an extreme degree, and yet somehow, SOMEHOW, they are able to resist, whether it be tapping SOS on their hand when they're on full control mode or shanking a Showfall employee with a dagger, Ranboo, Resists. But Showfall will never let them leave. Or they will? Idk founders cut hasn’t come out yet as of writing this, anyway ramble over. You can leave now.

[neurospring]

History and Basics of Language Models: How Transformers Changed AI Forever - and Led to Neuro-sama

I have seen a lot of misunderstandings and myths about Neuro-sama's language model. I have decided to write a short post, going into the history of and current state of large language models and providing some explanation about how they work, and how Neuro-sama works! To begin, let's start with some history.

Before the beginning

Before the language models we are used to today, models like RNNs (Recurrent Neural Networks) and LSTMs (Long Short-Term Memory networks) were used for natural language processing, but they had a lot of limitations. Both of these architectures process words sequentially, meaning they read text one word at a time in order. This made them struggle with long sentences, they could almost forget the beginning by the time they reach the end.

Another major limitation was computational efficiency. Since RNNs and LSTMs process text one step at a time, they can't take full advantage of modern parallel computing harware like GPUs. All these fundamental limitations mean that these models could never be nearly as smart as today's models.

The beginning of modern language models

In 2017, a paper titled "Attention is All You Need" introduced the transformer architecture. It was received positively for its innovation, but no one truly knew just how important it is going to be. This paper is what made modern language models possible.

The transformer's key innovation was the attention mechanism, which allows the model to focus on the most relevant parts of a text. Instead of processing words sequentially, transformers process all words at once, capturing relationships between words no matter how far apart they are in the text. This change made models faster, and better at understanding context.

The full potential of transformers became clearer over the next few years as researchers scaled them up.

The Scale of Modern Language Models

A major factor in an LLM's performance is the number of parameters - which are like the model's "neurons" that store learned information. The more parameters, the more powerful the model can be. The first GPT (generative pre-trained transformer) model, GPT-1, was released in 2018 and had 117 million parameters. It was small and not very capable - but a good proof of concept. GPT-2 (2019) had 1.5 billion parameters - which was a huge leap in quality, but it was still really dumb compared to the models we are used to today. GPT-3 (2020) had 175 billion parameters, and it was really the first model that felt actually kinda smart. This model required 4.6 million dollars for training, in compute expenses alone.

Recently, models have become more efficient: smaller models can achieve similar performance to bigger models from the past. This efficiency means that smarter and smarter models can run on consumer hardware. However, training costs still remain high.

How Are Language Models Trained?

Pre-training: The model is trained on a massive dataset to predict the next token. A token is a piece of text a language model can process, it can be a word, word fragment, or character. Even training relatively small models with a few billion parameters requires terabytes of training data, and a lot of computational resources which cost millions of dollars.

Post-training, including fine-tuning: After pre-training, the model can be customized for specific tasks, like answering questions, writing code, casual conversation, etc. Certain post-training methods can help improve the model's alignment with certain values or update its knowledge of specific domains. This requires far less data and computational power compared to pre-training.

The Cost of Training Large Language Models

Pre-training models over a certain size requires vast amounts of computational power and high-quality data. While advancements in efficiency have made it possible to get better performance with smaller models, models can still require millions of dollars to train, even if they have far fewer parameters than GPT-3.

The Rise of Open-Source Language Models

Many language models are closed-source, you can't download or run them locally. For example ChatGPT models from OpenAI and Claude models from Anthropic are all closed-source.

However, some companies release a number of their models as open-source, allowing anyone to download, run, and modify them.

While the larger models can not be run on consumer hardware, smaller open-source models can be used on high-end consumer PCs.

An advantage of smaller models is that they have lower latency, meaning they can generate responses much faster. They are not as powerful as the largest closed-source models, but their accessibility and speed make them highly useful for some applications.

So What is Neuro-sama?

Basically no details are shared about the model by Vedal, and I will only share what can be confidently concluded and only information that wouldn't reveal any sort of "trade secret". What can be known is that Neuro-sama would not exist without open-source large language models. Vedal can't train a model from scratch, but what Vedal can do - and can be confidently assumed he did do - is post-training an open-source model. Post-training a model on additional data can change the way the model acts and can add some new knowledge - however, the core intelligence of Neuro-sama comes from the base model she was built on. Since huge models can't be run on consumer hardware and would be prohibitively expensive to run through API, we can also say that Neuro-sama is a smaller model - which has the disadvantage of being less powerful, having more limitations, but has the advantage of low latency. Latency and cost are always going to pose some pretty strict limitations, but because LLMs just keep geting more efficient and better hardware is becoming more available, Neuro can be expected to become smarter and smarter in the future. To end, I have to at least mention that Neuro-sama is more than just her language model, though we only talked about the language model in this post. She can be looked at as a system of different parts. Her TTS, her VTuber avatar, her vision model, her long-term memory, even her Minecraft AI, and so on, all come together to make Neuro-sama.

Wrapping up - Thanks for Reading!

This post was meant to provide a brief introduction to language models, covering some history and explaining how Neuro-sama can work. Of course, this post is just scratching the surface, but hopefully it gave you a clearer understanding about how language models function and their history!