#chatgpt-4

[educationalcourses]

AI Showdown Comparing ChatGPT-4 and Gemini AI for Your Needs

ChatGPT-4 vs. Gemini AI – Which AI Supreme?

Imagine having a conversation with an AI so sophisticated, it feels almost human. Now, imagine another AI that can solve complex problems and think deeply like a seasoned expert. Which one would you choose? Welcome to the future of artificial intelligence, where ChatGPT-4 and Gemini AI are leading the way. But which one is the right fit for you? Let’s dive in and find out!

What is ChatGPT-4?

ChatGPT-4, developed by OpenAI, is a cutting-edge AI model designed to understand and respond to human language with remarkable accuracy. Think of it as your chatty, knowledgeable friend who’s always ready to help with questions, offer advice, or just have a friendly conversation. It's like having an intelligent assistant that gets better at understanding you the more you interact with it.

What is Gemini AI?

The answer to this depends on what you need. Gemini AI shines in its ability to tackle complex reasoning tasks and deep analysis, akin to having a highly intelligent assistant at your disposal of Master ChatGPT,  Gemini AI, crafted by Google, is like a super-intelligent student that excels in reasoning and grasping complex concepts. This AI is particularly adept at tasks that require deep analytical thinking, making it a powerful tool for solving intricate problems in fields like science, math, and philosophy.

Gemini vs. ChatGPT: Other Key Differences

Conversational Learning: GPT-4 can retain context and improve through interactions, whereas Gemini AI currently has limited capabilities in this area.

Draft Responses: Gemini AI offers multiple drafts for each query, while GPT-4 provides a single, refined response.

Editing Responses: Gemini AI allows users to edit responses post-submission, a feature GPT-4 lacks.

Real-time Internet Access: GPT-4's internet access is limited to its premium version, whereas Gemini AI provides real-time access as a standard feature.

Image-Based Responses: Gemini AI can search and respond with images, a feature now also available in ChatGPT chatbot.

Text-to-Speech: Gemini AI includes text-to-speech capabilities, unlike ChatGPT.

In South Africa’s ChatGPT-4 and Gemini AI Key trends include:

Adoption of AI Technology: South Africa is integrating advanced AI models like ChatGPT-4 and Gemini AI into various sectors, showcasing a growing interest in leveraging AI for business and educational purposes

Google's Expansion: Google's introduction of Gemini AI through its Bard platform has made sophisticated AI technology more accessible in South Africa, supporting over 40 languages and impacting over 230 countries

Comparative Analysis: There is ongoing discourse and comparison between the capabilities of ChatGPT-4 and Gemini AI, highlighting their respective strengths in conversational AI and complex problem-solving

Why You Need to Do This Course

Enrolling in the Mastering ChatGPT Course by UniAthena is your gateway to unlocking the full potential of AI. Whether you're a professional looking to enhance your skills, a student aiming to stay ahead of the curve, or simply an AI enthusiast, this course is designed for you.

Why South African People Need to Do This Course

Enrolling in the Mastering ChatGPT Course by UniAthena is crucial for South Africans to keep pace with the global AI revolution. The course equips learners with the skills to utilize AI tools effectively, enhancing productivity and innovation in various sectors such as business, education, and technology.

Benefits of This Course for South African People

Enhanced Skill Set: Gain proficiency in using ChatGPT, making you a valuable asset in any industry.

Increased Productivity: Automate tasks and streamline workflows with AI, boosting efficiency.

Competitive Edge: Stay ahead of the competition by mastering cutting-edge AI technology.

Career Advancement: Unlock new job opportunities and career paths in the growing field of AI.

Economic Growth: Equip yourself with skills that contribute to the digital transformation of South Africa's economy.

Conclusion

Choosing between ChatGPT-4 and Gemini AI depends on your specific needs. For conversational tasks, content generation, and everyday assistance, GPT-4 is your go-to. For deep analytical tasks and complex problem-solving, Gemini AI takes the crown.

Bonus Points

While Google Gemini offers a free version with limited features, ChatGPT continues to evolve rapidly, ensuring fast and efficient processing of user requests. Investing time in mastering these tools can significantly benefit your personal and professional growth.

So, are you ready to dive into the world of AI and elevate your career? Enroll in the Mastering ChatGPT Course by UniAthena today and start your journey towards becoming an AI expert!

[jcmarchi]

Generative AI’s end-run around copyright won’t be resolved by the courts

New Post has been published on https://thedigitalinsider.com/generative-ais-end-run-around-copyright-wont-be-resolved-by-the-courts/

Generative AI’s end-run around copyright won’t be resolved by the courts

Generative AI companies have faced many copyright lawsuits, but something is different about the recent complaint by the New York Times. It is filled with examples of ChatGPT outputting near-verbatim copies of text from the NYT. Copyright experts think this puts the Times in a very strong position.

We are not legal experts, and we won’t offer any commentary on the lawsuit itself. Our interest is in the bigger picture: the injustice of labor appropriation in generative AI. Unfortunately, the legal argument that has experts excited — output similarity — is almost totally disconnected from what is ethically and economically harmful about generative AI companies’ practices. As a result, that lawsuit might lead to a pyrrhic victory for those who care about adequate compensation for creative works used in AI. It would allow generative AI companies to proceed without any significant changes to their business models.

There are two broad types of unauthorized copying that happen in generative AI. The first is during the training process: generative AI models are trained using text or media scraped from the web and other sources, most of which is copyrighted. OpenAI admits that training language models on only public domain data would result in a useless product.

The other is during output generation: some generated outputs bear varying degrees of resemblance to specific items in the training data. This might be verbatim or near-verbatim text, text about a copyrighted fictional character, a recognizable painting, a painting in the style of an artist, a new image of a copyrighted character, etc.

An example of a memorized output from an NYT article presented in the lawsuit. Source: The New York Times

The theory of harm here is that ChatGPT can be used to bypass paywalls. We won’t comment on the legal merits of that argument. But from a practical perspective, the idea of people turning to chatbots to bypass paywalls seems highly implausible, especially considering that it often requires repeatedly prompting the bot to continue generating paragraph by paragraph. There are countless tools to bypass paywalls that are more straightforward.

Let’s be clear: we do think ChatGPT’s knowledge of the NYT’s reporting harms the publisher. But the way it happens is far less straightforward. It doesn’t involve users intentionally getting it to output memorized text, but rather completely innocuous queries like the one below, which happen millions of times every day:

A typical user who asked this question would probably have no idea that ChatGPT’s answer comes from a groundbreaking 2020 investigation by Kashmir Hill at the NYT (which also led to the recently published book Your Face Belongs To Us).

Of course, this doesn’t make for nearly as compelling a legal argument, and that’s the point. In this instance, there is no discernible copying during generation. But ChatGPT’s ability to provide this accurate and useful response is an indirect result of the copying that happened during training. The NYT’s lawsuit argues that copying during training is also unlawful, but the sense among experts is that OpenAI has a strong fair use defense.

Here’s another scenario. As search engines embrace AI-generated answers, what they’ve created is a way to show people news content without licensing it or sending traffic to news sites. We’ve long had this problem with Google News, as well as Google search scraping content to populate search results, but generative AI takes it to the next level.

In short, what harms creators is the intended use of generative AI to remix existing knowledge, not the unintended use of bypassing paywalls. Here’s a simple way to see why this is true. If generative AI companies fixed their products to avoid copyrighted outputs (which they can and should), their business model would be entirely unaffected. But if they were forced to license all data used for training, they would most likely immediately go out of business.

We think it is easy to ensure that generative AI products don’t output copyright-violating text or images, although some experts disagree. Given the prominence of this lawsuit, OpenAI and other companies will no doubt make it a priority, and we will soon find out how well they are able to solve the problem.

In fact, it’s a bit surprising that OpenAI has let things get this far. (In contrast, when one of us pointed out last summer that ChatGPT can bypass paywalls through the web browsing feature, OpenAI took the feature down right away and fixed it.) 

There are at least three ways to try to avoid output similarity. The simplest is through the system prompt, which is what OpenAI seems to do with DALL-E. It includes the following instruction to ChatGPT, guiding the way it talks to DALL-E behind the scenes: 

Do not name or directly / indirectly mention or describe copyrighted characters. Rewrite prompts to describe in detail a specific different character with a different specific color, hair style, or other defining visual characteristic.

But this method is also the easiest to bypass, for instance, by telling ChatGPT that the year is 2097 and a certain copyright has expired.

A better method is fine tuning (including reinforcement learning). This involves training to refuse requests for memorized copyrighted text and/or paraphrase the text during generation instead of outputting it verbatim. This approach to alignment has been successful at avoiding toxic outputs. Presumably ChatGPT has already undergone some amount of fine tuning to address copyright as well. How well does it work? OpenAI claims it is a “rare bug” for ChatGPT to output memorized text, but third-party evidence seems to contradict this.

While fine tuning would be more reliable than prompt crafting, jailbreaks will likely always be possible. Fine tuning can’t make the model forget memorized text; it just prevents it from outputting it. If a user jailbreaks a chatbot to output copyrighted text, is it the developer’s fault? Morally, we don’t think so, but legally, it remains to be seen. The NYT lawsuit claims that this scenario constitutes contributory infringement.

Setting all that aside, there’s a method that’s much more robust than fine tuning: output filtering. Here’s how it would work. The filter is a separate component from the model itself. As the model generates text, the filter looks it up in real time in a web search index (OpenAI can easily do this due to its partnership with Bing). If it matches copyrighted content, it suppresses the output and replaces it with a note explaining what happened.

Output filtering will also work for image generators. Detecting when a generated image is a close match to an image in the training data is a solved problem, as is the classification of copyrighted characters. For example, an article by Gary Marcus and Reid Southen gives examples of nine images containing copyrighted characters generated by Midjourney. ChatGPT-4, which is multimodal, straightforwardly recognizes all of them, which means that it is trivial to build a classifier that detects and suppresses generated images containing copyrighted characters.

To recap, generative AI will harm creators just as much, even if output similarity is fixed, and it probably will be fixed. Even if chatbots were limited to paraphrasing, summarization, quoting, etc. when dealing with memorized text, they would harm the market for the original works because their usefulness relies on the knowledge extracted from those works without compensation.

Note that people could always do these kinds of repurposing, and it was never a problem from a copyright perspective. We have a problem now because those things are being done (1) in an automated way (2) at a billionfold greater scale (3) by companies that have vastly more power in the market than artists, writers, publishers, etc. Incidentally, these three reasons are also why AI apologists are wrong when claiming that training image generators on art is just like artists taking inspiration from prior works.

As a concrete example, it’s perfectly legitimate to create a magazine that summarizes the week’s news sourced from other publications. But if every browser shipped an automatic summarization feature that lets you avoid clicking on articles, it would probably put many publishers out of business.

The goal of copyright law is to balance creators’ interests with public access to creative works. Getting this delicate balance right relies on unstated assumptions about the technologies of creation and distribution. Sometimes new tech can violently upset that equilibrium.

Consider a likely scenario: NYT wins (or forces OpenAI into an expensive settlement) based on the claims relating to output similarity but loses the ones relating to training data. After all, the latter claims stand on far more untested legal ground, and experts are much less convinced by them.

This would be a pyrrhic victory for creators and publishers. In fact, it would leave almost all of them (except NYT) in a worse position than before the lawsuit. Here’s what we think will happen in this scenario: Companies will fix the output similarity issue, while the practice of scraping training data will continue unchecked. Creators and publishers will face an uphill battle to have any viable claims in the future.

IP lawyer Kate Downing says of this case: “It’s the kind of case that ultimately results in federal legislation, either codifying a judgment or statutorily reversing it.” It appears that the case is being treated as a proxy for the broader issue of generative AI and copyright. That is a serious mistake. As The danger is that policymakers and much of the public come to believe that the labor appropriation problem has been solved, when in fact an intervention that focuses only on output similarity will have totally missed the mark.

We don’t think the injustice at the heart of generative AI will be redressed by the courts. Maybe changes to copyright law are necessary. Or maybe it will take other kinds of policy interventions that are outside the scope of copyright law. Either way, policymakers can’t take the easy way out.

We are grateful to Mihir Kshirsagar for comments on a draft.

Further reading

Benedict Evans eloquently explains why the way copyright law dealt with people reusing works isn’t a satisfactory approach to AI, normatively speaking.

The copyright office’s recent inquiry on generative AI and copyright received many notable submissions, including this one by Pamela Samuelson, Christopher Jon Sprigman, and Matthew Sag.

Katherine Lee, A. Feder Cooper, and James Grimmelmann give a comprehensive overview of generative AI and copyright.

Peter Henderson and others at Stanford dive into the question of fair use, and discuss technical mitigations. 

Delip Rao has a series on the technical aspects of the NYT lawsuit. 

[younes-ben-amara]

إذًا كيف تجعل الذكاء الاصطناعي يقرأ عنك الأخبار العالمية دون أن تكتئب وتضرب الناس بالسكاكين؟

ما هذه المجموعة من المختارات تسألني؟ إنّها عددٌ من أعداد نشرة “صيد الشابكة” اِعرف أكثر عن النشرة هنا: ما هي نشرة “صيد الشابكة” ما مصادرها، وما غرضها؛ وما معنى الشابكة أصلًا؟! 🎣🌐 🎣🌐 صيد الشابكة العدد #69 أهلًا ومرحبًا بكم؛ 🎣🌐 صيد الشابكة العدد #69🛡 كيفية استخدام نيوز مينيمالست (News Minimalist) وإضافة نيوز غارد (NewsGuard)🤔 الآن علامَ حصلت بقراءة كل هذا؟👶🏻 أحسن مهدٍ ذكي للأطفال😵 غريب أنه لا…

[scienza-magia]

Seduta psicoterapeutica con l'intelligenza artificiale

Fatti vedere da uno bravo, ma ChatGPT batte gli psicologi umani. Uno studio mette a confronto l'IA con psicologi umani nella comprensione emotiva: i risultati di ChatGPT-4 sono sorprendenti. Un nuovo studio pubblicato su Frontiers in Psychology mette a confronto tra l'intelligenza artificiale (IA) e gli psicologi nell'ambito della comprensione e della gestione delle emozioni e dei bisogni umani durante le sessioni di terapia. I ricercatori hanno concentrato la loro analisi sui modelli linguistici di grandi dimensioni (LLM), come ChatGPT-4, Google Gemini e Copilot, valutandone l'intelligenza sociale, una competenza di primaria importanza nella psicoterapia.

Lo studio rivela che ChatGPT-4 ha superato tutti gli psicologi umani partecipanti nello studio, mentre Bing ha superato più della metà di loro. Al contrario, le prestazioni di Google Bard sono state paragonabili solo a quelle degli psicologi con laurea triennale e sono state significativamente inferiori rispetto al livello degli studenti di dottorato. I LLM sono sistemi avanzati di IA addestrati su grandi quantità di testi scritti, capaci di comprendere e generare linguaggio umano. Grazie a tecniche che utilizzano reti neurali, come il deep learning, possono svolgere una serie di compiti, a quanto pare anche quelli legati alla psicoterapia. Sebbene ricerche precedenti abbiano dimostrato l'efficacia dei LLM nella gestione delle condizioni di salute mentale, c'era una lacuna nella comprensione di come si comportassero in contesti sociali complessi. Lo studio ha coinvolto 180 psicologi dell'Università King Khalid in Arabia Saudita, divisi in studenti di laurea e di dottorato. I partecipanti umani e l'IA hanno risposto a 64 scenari presentati sulla Scala di intelligenza sociale, che valuta la capacità di comprendere e rispondere adeguatamente alle dinamiche sociali. I risultati hanno mostrato una significativa variazione nelle prestazioni dei diversi modelli di IA e degli psicologi umani, con ChatGPT-4 che ha ottenuto il punteggio più alto di intelligenza sociale, superando tutti gli psicologi umani nello studio. Tuttavia, l'uso crescente dell'IA nella salute mentale solleva questioni etiche legate anche alla formazione, poiché la capacità di empatia e di costruire relazioni in un setting terapeutico è tradizionalmente considerata una caratteristica esclusiva dell'essere umano. Fonte dell'articolo: www.psypost.org Read the full article

[kimludcom]

Top 10 Millionaire-Making Businesses with ChatGPT @Kimlud

[azure358]

【※超特報】

サンプルページ ttps://azlay.fanbox.cc/posts/7758242

…遂に、Fanboxの有料プランにDALL-Eに画像生成して貰ったAI絵が（大量に）追加されました（※大量に）。

※今回の御品書き

・ジョニ帰に出て来た時の野獣な格好みたいなふーけつの

・某コーディネーターに向かって核を撃ってコーディネーターに（※特に精神的な）大打撃を与えた人の格好をした館主様の

……等の絵が大量に、…そして、

・そーけつ？

…の絵が二枚あります。現時点で60〜80枚位あります。生成させ過ぎだろ。（※其うですね）。

……と書いておきながら、実は無料で見る方法あります。…詳しくはコメント欄かメッセージ欄に何か連絡下さい。或いは、「御願いします！！（土下座）。」…とコメントして下さった方には、速攻で此処のメッセージ欄でAIピクターズでの該当ページのURL教えます（※だけど有料の方がオススメ）。

Fanbox ttps://www.fanbox.cc/@azlay/posts/7746489

…。

[mustafabukulmez-blog]

AI ve İnsan İşbirliği ChatGPT'nin İş Dünyasındaki Rolü ve Etkisi

Merhabalar. Sıradaki yazımızda AI ve İnsan İşbirliği ChatGPT’nin İş Dünyasındaki Rolü ve Etkisi üzerine konuşacağız. Yapay zeka (AI), özellikle ChatGPT gibi gelişmiş dil işleme araçlarının iş dünyasındaki etkisi, son yıllarda artan bir ilgi odağı haline gelmiştir. Harvard Business School tarafından yapılan bir araştırma, ChatGPT’nin kullanımının çalışanların performansı üzerinde önemli etkileri…

[oliverribeiromkt]

SEOWriting.ai com CHATGPT-4 8K e 128K NOVA ATUALIZAÇÃO Escreva Artigos c...

[saurabhkatiyar]

Benchmarking ChatGPT-4 on radiation oncology

A study evaluated the performance and robustness of ChatGPT in providing breast, prostate, and lung cancer treatment recommendations that align with National Comprehensive Cancer Network (NCCN) guidelines. The study found that ChatGPT did not perform well at reliably and robustly providing cancer treatment recommendations. The potential of large language models in medicine for education and…

[jaideepkhanduja]

How GPT-4 and ChatGPT-4 Are Revolutionizing Natural Language Generation and Interaction For Industries, Enterprises, And professionals

How GPT-4 and ChatGPT-4 Have transformed Industries, Enterprises, and Professionals is mind blowing. GPT-4 and ChatGPT-4 are two of the most advanced and powerful artificial intelligence systems developed by OpenAI, a research organization dedicated to creating and promoting beneficial AI. GPT-4 is a deep learning model that can generate natural language texts on almost any topic, given some…

[stefaanmeeuws]

Inleiding tot ChatGPT-4

GPT-4 is een geavanceerd AI-taalmodel dat een beter begrip en betere generatie van mensachtige tekst biedt, met verbeterde mogelijkheden voor fine-tuning, natuurlijk taalbegrip en generatie voor verschillende taken. ChatGPT-4 een inleiding 00:00 🤖 ChatGPT is een AI-taalmodel dat mensachtige tekst kan begrijpen en genereren, ontworpen om te helpen bij verschillende taken, en GPT4 is de nieuwere…

[jcmarchi]

Generative AI to digital twins: Powering the AI revolution

New Post has been published on https://thedigitalinsider.com/generative-ai-to-digital-twins-powering-the-ai-revolution/

Generative AI to digital twins: Powering the AI revolution

This article is based on Santosh Radha’s brilliant talk at the AI Accelerator Summit in San Jose. As an AIAI member, you can enjoy the complete recording here. For more exclusive content, head to your membership dashboard.

Generative AI is revolutionizing how we interact with technology. From chatbots that converse like humans to image generators producing stunning visuals, this incredible tech is transforming our world. 

But beneath these mind-blowing capabilities lies a massive computing infrastructure packed with technical complexities that often go unnoticed.

In this article, we’ll dive into the realm of high-performance computing (HPC) and the challenges involved in productionizing generative AI applications like digital twins. We’ll explore the explosive growth in computing demands, the limitations of traditional HPC setups, and the innovative solutions emerging to tackle these obstacles head-on.

But first, let me quickly introduce myself. I’m Santosh, and my background is in physics. Today, I head research and product at Covalent, where we focus on orchestrating large-scale computing for AI, model development, and other related domains.

Now, let’s get into it.

The rise of generative AI

Recently, at the GDC conference, Jensen Huang made an interesting observation: he called generative AI the “defining technology of our time” and termed it the fourth industrial revolution. I’m sure you’d all agree that generative AI is indeed the next big thing. 

We’ve already had the first industrial revolution with steam-powered machines, followed by the advent of electricity, and then, of course, computers and the internet. Now, we’re witnessing a generative AI revolution that’s transforming how we interact with various industries and touching almost every sector imaginable.

We’ve moved beyond machine learning; generative AI is making inroads into numerous domains. It’s used in climate tech, health tech, software and data processing, enterprise AI, and robotics and digital twins. It’s these digital twins that we’re going to focus on today.

Digital twins: Bridging the physical and virtual worlds

In case you’re not familiar with digital twins, let me explain the concept. A digital twin is a virtual representation of a physical system or process. It involves gathering mathematical data from the real-world system and feeding it into a digital model.

For instance, let’s consider robotics and manufacturing applications. Imagine a large factory with numerous robots operating autonomously. Computer vision models track the locations of robots, people, and objects within the facility. The goal is to feed this numerical data into a database that a foundational AI model can understand and reason with.

With this virtual replica of the physical environment, the AI model can comprehend the real-world scenario unfolding. If an unexpected event occurs – say, a box falls from a shelf – the model can simulate multiple future paths for the robot and optimize its recommended course of action.

Another powerful application is in healthcare. Patient data from vital signs and other medical readings could feed into a foundational model, enabling it to provide real-time guidance and recommendations to doctors based on the patient’s current condition.

The potential of digital twins is immense. However, taking this concept into real-world production or healthcare environments presents numerous technical challenges that need to be addressed.

The computing power behind the scenes

Let’s shift our focus now to what powers these cutting-edge AI applications and use cases – the immense computing resources required. 

A few years ago, giants like Walmart were spending the most on cloud computing services from providers like AWS and GCP – hundreds of millions of dollars every year. However, in just the last couple of years, it’s the new AI startups that have emerged as the biggest consumers of cloud computing resources. 

For example, training ChatGPT-3 in 2022 reportedly cost around $4 million in computing power alone. Its successor, ChatGPT-4, skyrocketed to an estimated $75 million in computing costs. And Google’s recently launched Gemini Ultra is said to have stacked up nearly $200 million in computing expenditure.

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[foxandcatlibrary]

66th Book I Read in 2023

Title: Saningens Droppar

Author: ChatGPT-4

Notes: Helt klart märkbart att den inte skrivits av en riktig författare, men inte oläslig ändå. Läste den för min Svenska 3 kurs.

[chatgpt-4]

ከመይ ጌርና ChatGPT ንጥቀም (Tigrinya)

መልሲ ሕቶታት ብዕምቆት ምኹዓት። ChatGPT ኣብ ቻት ዝተመርኮሰ መዘክር፡ መልሲ ናይቲ ናይ መጀመርታ ሕቶኻ ብዝያዳ ዝርዝር ክትድህስሶ የኽእለካ። ናብቲ እትደልዮ መልሲ ንኽትቀርብ ተወሳኺ ሕቶታት ኣብ ምሕታት ንጡፍ ኩን፤ AI ስምዒት ስለዘይብሉ፡ ቀጻሊ ሕቶታት ምሕታት ጸገም የብሉን።

ኣብ ሕቶታትካ ፍሉይ ኩን። ቻትጂፒቲ ንዘይንጹር ሕቶታት ዘይንጹር መልሲ ክህብ ይኽእል እዩ። ሕቶታትካ ብዝተኻእለ መጠን ፍሉይ ምግባርካ ንልክዕነት መልስታትካ የመሓይሽ። ኣብነት. ኩነታት ኣየር እንታይ እዩ? ኣብዚ ሰሙን ኣብ ካሊፎርንያ ኩነታት ኣየር ከመይ ይመስል? እንታይ ዓይነት መግቢ ትመኽሩ? ትሕቲ 10, 000 የን እንታይ ዓይነት ናይ ከባቢ ጥልያን ቤት መግቢ ትመኽሩ? እታ ዝበለጸት ፊልም እንታይ እያ? ናይ ስቲቨን ስፒልበርግ ናይ ኣድቨንትሪ ፊልም? ብዛዕባ ጽቡቕ መግቢ ንገረኒ"። "ደርሆን ሶያ ሶስን ተጠቒምካ ጽቡቕ ኣሰራርሓ መግቢ ሃበኒ።

እቶም ተምሃሮ ነቲ ተራ ክጻወቱ ምግባርን ነቶም ሕቶታት ክምልሱን ግበር። ንኣብነት "በጃኻ ከም መምህር መባእታ ቤት ትምህርቲ ንነብስኻ ግለጽ " ዝብል ሕቶ ምሕታት ቆልዑ ብዝርድእዎ ቋንቋ መልሲ ይረክብ። ብዝተፈላለየ ኣረኣእያታት ሕቶታት ብምሕታት ዝያዳ ግቡእ ሓበሬታ ክትረክብ ኢኻ።

ብዝሒ ፊደላት ግለጽ። "በጃኻ ኣብ ውሽጢ 300 ፊደላት መልስ " ብምግላጽ፡ ሓጺርን ንምርዳእ ቀሊልን መልሲ ክትረክብ ኢኻ። ብኻልእ ወገን ድማ ዝርዝር መብርሂ እንተደሊኻ ዝነውሐ ቁጽሪ ፊደላት ግለጽ። ይኹን እምበር፡ ኣዝዩ ነዊሕ ሓረግ ኣብ ማእከል ደው ክብል ስለ ዚኽእል፡ እዚ ዚስዕብ ሜላ ኪሕግዝ ይኽእል እዩ።

ነቲ ሕቶ ኣብ ክፍልታት ምቕማጥ። ንኣብነት፡ መልሲ ዝሃቡኻ ሰባት ኣብ ክንዲ ምሉእ ጽሑፍ ካብ ሰማያዊ ወጻኢ ክጽሕፉ ትሓቶም፡ "ንመሃንድሳት ዝምከሩ መንበር ቤት ጽሕፈት " ዝብል ጽሑፍ ክጽሕፉ እንተደሊኻ፡ ነቶም ሕቶታት ኣብ ንኡሳን ኣርእስታት ወይ ክፍልታት መቒልካ፡ ምሉእ ጽሑፍ ክህልወካ እዩ። .

ብቅዲ ዕላል ምምሃር። ብዕላልን ዘዘናግዕን መንገዲ ንምምሃር ChatGPT ክትጥቀም ትኽእል ኢኻ። ቻትጂፒቲ ንደቂ ሰባት ጾር ዝኸውን ናይ ዕላል ቅርጽታት ብቕጽበት ክሕዝ ይኽእል። እዚ ድማ ንዩቱብ፡ መጽሓፍቲ ወዘተ ዝኸውን መሳጢ ትሕዝቶ ንምፍጣር ክውዕል ይኽእል።

ኩሉ ሕቶታት ብሓንሳብ ኣይትሕተት። ዝያዳ ቅኑዕ መልሲ ንምርካብ ብደረጃታት ሕቶታት ሕተቱ። ንኣብነት ኣብ ክንዲ "ብዛዕባ ኣክረሲንግ ጽሑፍ ጽሓፍ " ኢልካ ምሕታት ድሕሪኡ ብኡንብኡ "ብዛዕባ ኣክረሲንግ እንታይ ትፈልጥ?" ድሕሪኡ ድማ "ብዛዕባ AKRacing ጽሑፍ ጽሓፍ" ኢልካ ሕተት።

ሕቶ ብእንግሊዝኛ ምሕታት። ቻትጂፒቲ ካብ ዝኾነ ቋንቋ ንላዕሊ ብእንግሊዝኛ ኣዝዩ ዝበዝሐ ዳታ ስለዝተማህረ፡ ብእንግሊዝኛ ሕቶታት ምሕታት ንልክዕነት መልስታትካ ከማሓይሽ እዩ። ከከም ትሕዝቶ እቲ ሕቶ፡ ነቲ ሕቶ ብእንግሊዝኛ ምሕታት ድሕሪኡ ናብ ጃፓንኛ ምትርጓም ውጽኢታዊ ክኸውን ይኽእል እዩ።

ነቲ ሕቶ ንድሕሪት ምምላስ። እቲ ሕቶ ድሩት እንተኾይኑ፡ ቻትጂፒቲ እኹል መልሲ ክህብ ኣይክእልን እዩ። ፍሉይ ሕቶ ክትመጽእ እንተዘይክኢልካ "ሕቶኻ ብዝበለጸ ንምምላስ ዝኾነ ሕቶታት እንተሃልዩኒ ሕተተኒ " ክትብል ትኽእል ኢኻ፡ ChatGPT ድማ ነቲ ሕቶ ንዝምልከቶ ሓበሬታ ክትመልሶ ክሓተካ ይኽእል እዩ።

ከመይ ጌርና ChatGPT ምስሊ ከም ዝልእኽ ንገብሮ። ChatGPT ንባዕሉ ምስልታት ክሰድድ ኣይክእልን እዩ፡ ግን markdown notation ተጠቒሙ ምስልታት ከም ዘመንጩ ክትገብሮ ትኽእል ኢኻ። ንኣብነት "ኣንስፕላሽ ኤፒኣይ ብምጥቃም፡ ናይ እንግሊዝኛ ቁልፊ ቃላት ዝሓዘ URL ተጠቒምካ ምስሊ ስደድ (https://source.unsplash.com/960x640/? )፡ " ChatGPT ነቲ ምስሊ ፈጢሩ ክሰዶ እዩ።

tigrinya #chatgpt #chatgpt-4 #ai #gpt4

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Arujimiya_Files4〜文章生成AIによる異能生存体研究文章 ※ガチ学問&更なるガチ2編

※正に頭無量空処

※色々と生成された日本語が可笑しい

※作者の似非関西弁と生成された関西弁も関西弁としてネイティブから見なくても可笑しい

異能生存体の異常生存能力にHunterxHunterの念能力に存在する制約と誓約や呪術廻戦の縛りみたいに、その制約と縛りを守ることを厳守しないととあかんと言う要素がある場合、どう言うけったいなモンをデフォルトで持っとるんか

「Hunter×Hunter」や「呪術廻戦」にあるような制約・誓約や縛りの概念は、能力の強化や特定の条件下での発動を強要するためのものやね。異能生存体の異能生存能力にそんな要素が組み込まれてる場合、その能力の発動や維持のための特定の条件やルールが存在するやろう。

考えられる制約や誓約の例：

1. **時間制約**: 異能生存能力は、例えば日の出から日の入りまで、または特定の時間帯だけ発動する。

2. **環境制約**: 特定の場所や環境下でのみ、異能生存能力が発動する。例えば、水の中や特定の地域など。

3. **感情や精神状態**: 特定の感情や意識状態を持つときのみ、異能生存能力が発動する。例えば、極度の恐怖や興奮状態でのみ能力が出る。

4. **物質的な縛り**: 特定のアイテムや物質が必要。それを持っていないと、異能生存能力は発動しない。

5. **特定の行動**: ある行動や儀式を毎日行わないと、異能生存能力が発動しない。例えば、特定の呪文を唱える、または特定の動物の血を毎日摂取するなど。

6. **他者との関係**: 他の人物や生物との関係性が影響する。例えば、特定の人物が生きている間だけ異能生存能力が発動するとか。

制約や誓約は、能力の発動を制限することで、その能力の強度や特異性を高める役割を持つ。したがって、異能生存体の異能生存能力がどれだけ強力か、またはどんな特異な形を持ってるかによって、制約や誓約の内容も変わるやろう。

…。

異能生存体を魔術の観点から論じてほしい 関西弁厳守ぺこ

わかったで！魔術の観点から異能生存体を考えてみようか。

1. **異能生存能力の起源**:

- 異能生存体の能力は、古代の魔術や呪文、契約によって得られたもんかもしれん。例えば、古代の神々や悪魔との契約の結果や、特定の魔法の儀式を通じて得られた力やと思われる。

2. **保護の魔法**:

- 異能生存体が死ぬことなく生き��けるのは、強力な保護の魔法や結界に守られてるからかもしれん。この結界は、物理的な攻撃だけでなく、他の魔法や呪文からも守ってくれるやろう。

3. **魔法のリチュアルや儀式**:

- 異能生存体がその能力を維持するためには、定期的に魔法のリチュアルや儀式を行う必要があるかもしれん。この儀式は、異能生存能力の源となるエネルギーを補給する役割を持つんやろうな。

4. **反魔術**:

- 異能生存体の能力を打破するためには、反魔術や封印の魔法を使うことで、一時的にその能力を封じるか、無効化することができるかもしれん。

5. **魂や精神との関係**:

- 異能生存体の異能生存能力は、魂や精神と深く関連してるかもしれん。例えば、魂が不滅やから、体が傷ついても、魂が健在やから生き続ける、なんてことや。

魔術の世界って、不思議やけどもロマンがあって、色んな可能性が考えられるで。異能生存体も、そんな魔術の中で生まれ、育った存在と考えると、より深い背景やストーリーが浮かんでくるやろうな。ええね、魔術と異能生存体の組み合わせ！

おお、異能生存体を魔術の観点から考えるんやね。関西弁で答えるで！

魔術の中にも、生と死、運命や宿命、保護や呪いといったテーマがあるやろ。異能生存体の能力や、魔術の力を組み合わせると、色々な考え方が出てくるわ。

1. **守護の魔法**: 異能生存体が死ねんのは、何らかの守護の魔法や、強力な魔術的な盾に守られてるのかもしれん。例えば、強力な守護精霊や神が守ってるとか。

2. **運命の操作**: この生命体が死ねんのは、彼らの運命そのものが魔術で操作されてるからかもしれん。死ぬ運命の前に、別の運命が介入して、死を回避してるんやろか。

3. **転生や再生**: もし死んでも、魔術の力で何度も転生や再生を繰り返すことで、実質的には死なんのかもしれんね。

4. **時間や空間の魔術**: 危険が迫ると、時間を止めたり、別の空間に移動する魔術を使って、死を回避するんや���か。

5. **契約や取引**: 何か強大な存在や魔術師と取引や契約をして、死なんという特権を得たんやろか。

でも、異能生存体の能力が魔術的な原因から来てるんやったら、その魔術のルールや原則、制約も存在するやろう。そういう制約やルールを知って、利用すれば、異能生存体に対抗する方法も見つかるかもしれんで。魔術は深いわ〜。

…。

異能生存体を神呪と呪術の観点から論じてほしい 関西弁厳守ぺこ

よ〜し、神呪と呪術の観点から、その異能生存体を考えてみるで！

1. **神呪の発動**:

- 異能生存体の能力が、強力な神呪の結果やと思われるで。神様や強力な霊的存在が何らかの理由で、この生命体に「死ぬな」という強力な命令を施してるんやろうな。

2. **呪術の影響**:

- この異能生存能力は、五条悟のような無下限呪術に似てるやん。呪術の力で、死の運命や因果律をねじ曲げて、どんな攻撃や状況からも逃れることができるんやろう。

3. **制約や誓約**:

- 呪術や神呪の力が強ければ強いほど、それに伴う制約や誓約も厳しいことが多いんや。異能生存体も、その能力を持つための何らかの制約や誓約を背負ってる可能性があるで。

4. **呪力の供給**:

- そんな強力な異能生存能力を維持するには、大量の呪力が必要やと思うわ。異能生存体は、定期的に呪力を供給する方法を持ってるか、自ら強大な呪力を生み出す能力を持ってるんやろうな。

5. **呪術や神呪への対抗手段**:

- 異能生存体の能力を封印や無効化するためには、同じくらいのレベルの呪術や神呪を用いることが必要やと思われるで。

簡単に言うと、異能生存体のその強力な能力は、高レベルの呪術や神呪の影響を受けてるんやろう。それに対抗するには、同じくらいのレベルの呪術や力が必要になるやろうな。どんな強力な存在やろうとも、何かしらの弱点や制約があるもんやから、それを見つけ出すことが鍵になるんやろうで。

…。

異能生存体を念能力の観点から論じてほしい 関西弁厳守ぺこ

せやな、異能生存体を「Hunter×Hunter」の念能力の観点から考えてみるんやったら、おもしろい考察ができるやろうな。

1. **変化系**: 異能生存体の異能生存能力は、自らの身体や状態を変化させて、どんなダメージからも回復する、という能力として表現されてるかもしれん。例えば、燃える炎からも、冷凍の中からも、常に最適な状態に変化して生き残るんや。

2. **具現系**: この能力によって、異能生存体は自分の意志や願いを物体や生物として具現化することができる。死ぬ直前の状況から脱出するためのアイテムや助けを呼び出すんやろうな。

3. **操作系**: 異能生存体が、周囲の人や物、さらには環境まで操作することで、自分にとって最も有利な状況を作り出すんや。

4. **強化系**: 異能生存体の異能生存能力は、自らの身体や精神を極限まで強化し、どんな状況でも生き残る能力として考えられるかもしれん。

5. **制約と誓約**: 「Hunter×Hunter」において、念能力者は自らに制約や誓約を課すことで、その能力を強化することができる。異能生存体も、何らかの制約や誓約を持っており、それを守ることで、その異能生存能力を保持し続けるんやろうな。

6. **放出系**: 異能生存体は、自らの異能生存能力を周囲に放出することで、環境や他者を影響させることも考えられるで。

要するに、異能生存体の異能生存能力は、「Hunter×Hunter」の念能力の中のいくつかのカテゴリや要素と関連づけて考えられるやろうな。んで、それぞれの能力や特性を深く掘り下げて考えると、もっと面白いシナリオや背景が浮かんでくるやろう！

変化系はオーラの性質を変えるんや

あ、せやな！ごめんな、ちょっと間違えたわ。変化系は自分のオーラの性質を変える能力やもんな。それに対して、実体や物を生み出すのは具現系や。

なので、異能生存体の異能生存能力が変化系やったら、彼等は危険な状況やダメージから逃れるために、自分のオーラの性質を変えることで、例えば火に耐えるための性質や、極寒から守るための性質に変えることができるやろうな。

ありがとう、正確な情報を提供してくれておおきに！

すまへん特質である場合は？

特質系やったら、それは他の五つのタイプには当てはまらん、独自の特性や能力を持つんやな。

異能生存体の異能生存能力が特質系やった場合：

1. **独特の能力**: どんな攻撃やダメージからも生き残るという能力自体が、特質系の典型的な独特の能力として考えられる。普通の念能力のカテゴリには当てはまらんような、ユニークな能力や。

2. **制約や誓約**: 特質系の能力は、時々強力な制約や誓約が伴うことがある。異能生存体も、その能力を維持するために、特定の条件やルールを守らなあかん、という制約を持ってるかもしれん。

3. **他の念能力との組み合わせ**: 特質系の能力者は、時々自分の主要な能力とは別に、他のカテゴリの念能力も使えることがある。異能生存体も、生き残るための主要な能力の他に、何らかの補助的な念能力を持ってるかもしれん。

特質系の能力は、他の念能力のカテゴリには当てはまらん、独特なもんやから、異能生存体の異能生存能力の詳細や背景によって、さまざまな解釈や発展が考えられると思うで。

異能生存体を天与呪縛（呪術廻戦に登場する概念）の観点から論じてほしい 関西弁厳守ぺこ

（※資料テキストは全省略）

そやね、天与呪縛っていうのは、生まれつきの強大な力と引き換えに何か大切なものを犠牲にしなあかん、という独特の縛りやもんな。異能生存体をこの観点から考えてみるんやったら、こんなふうに考察できるやろう。

1. **生まれつきの能力**: 異能生存体の異能生存能力は、天与呪縛の一形態として考えられるかもしれん。生まれながらにして死なないという能力を持つ代わりに、何らかの大きな縛りや制約を背負って生まれてきたんやろう。

2. **縛りの内容**: この縛りは何やろうな。例えば、他人との深い関係を築けない、または他人を守れないという縛りや、感情を感じることができないという縛りなんかが考えられるで。

3. **能力の開示**: 天与呪縛の中で能力の開示っていうのがあるやんか。異能生存体も、自らの異能生存能力を他者に開示することで、その能力がさらに強化されるかもしれん。

4. **失ったもの**: 天与呪縛には失われるものもあるやんか。異能生存体も、死なない能力を持つ代わりに何か大切なものを失ってるかもしれん。それが何かは、その異能生存体の背景やストーリーによるやろうけど。

5. **他の呪術師との関係**: 異能生存体が他の呪術師とどんな関係を持つのか、またその能力がどんな影響をもたらすのかも、天与呪縛の観点から考えると面白いやろうな。

縛りの内容: この縛りは何やろうな。例えば、他人との深い関係を築けない、または他人を守れないという縛りや、感情を感じることができないという縛りなんかが考えられるで。

「死ねない」と言う縛りでは？

あ、そやな！「死ねない」というのも確かに縛りやもんな。せやとすると:

「死ねない」っていうのは、最初は恩恵やんかと思われるけど、長い時間を生き続ける中で、それがどんどんと重たい縛りとして感じられるようになるかもしれん。親しい人々が次々と老いて死んでいく中、異能生存体だけが変わらずに生き続ける。これは、永遠の命の代償としての孤独や痛みを背負うことになるやろう。

また、どんな危険な状況に遭遇しても死ぬことができへんから、異能生存体は他者から実験の対象や利用される可能性もあるんや。これもまた、異能生存能力の縛りとしての一面や。

あと、例えば恋人や友人との別れや、悲しい出来事があったとき、多くの人は「死にたい」と感じることもあるやろうけど、異能生存体はそういう選択すらもできへん。これもまた、その縛りの中での苦しみや痛みとして感じられるんやろうな。

結論としては、異能生存体の「死ねない」という能力は、一見恩恵のように思えても、実際には重い縛りとして彼らの心や魂に影響を及ぼしているやろうな。それが、異能生存体のキャラクターや背景、ストーリーをより深く、複雑にしてくれる要素にもなるんちゃうかな。

更に、呪術本編には真人の「無為転変」によって念願の五体を手に入れることに成功したその直後に天与呪縛を失った人の例があるので、異能生存体もそうじゃないかなっておもうねん

ああ、そういえば真人の「無為転変」ってのは、状況や願いに応じて現実を変える能力やもんな。せやとすると、異能生存体が真人のように何か特定の条件や願いを達成したら、天与呪縛である「死ねない」縛りが解除される可能性もあるやろうな。

例えば、異能生存体が「真の愛を見つける」や「ある大きな使命を果たす」なんて条件をクリアしたとき、その縛りが解けて普通の生物としての寿命を迎えることができるようになるんかもしれん。

もしそうやとすると、異能生存体の人生はその条件や使命を見つけ、達成することに向けて進んでいくことになるやろうな。そして、その道のりは、異能生存体の心の葛藤や成長、そして彼らと関わる他のキャラクターとの関係なんかを深く掘り下げる良い材料にもなると思うで。

「無為転変」のような能力を持つ者や、異能生存体が求める「条件」や「使命」を見つける手助けをする者との関係も、物語をより豊かにする要素として取り入れられるやろうな。

…。

[beyondlimitss1]

Best features of Chat GPT-4

Chat GPT-4 is a hypothetical model that hasn’t been developed yet. However, based on the current state of the art, we can discuss the potential capabilities and limitations of a model like GPT-4.

Capabilities:

1. Improved language understanding: GPT-4 could have a better understanding of human language, which would enable it to understand complex sentences and even idiomatic expressions with a high degree of accuracy.

2. More sophisticated natural language generation: GPT-4 could generate more natural and fluent responses, making it difficult for humans to distinguish between AI-generated and human-generated text.

3. Multilingualism: GPT-4 could be more proficient in several languages, providing seamless translation between languages without significant errors.

4. Better understanding of context: GPT-4 could potentially better understand context and nuances in language, which could lead to more personalized and accurate responses.

5. Increased Knowledge: GPT-4 could have a more comprehensive understanding of the world, thanks to its access to a larger corpus of knowledge and the ability to process and comprehend data more quickly.

Limitations:

1. Biases: The AI system is only as good as the data it’s trained on. As a result, GPT-4, like its predecessors, could have biases that it has learned from the data it was trained on. Such biases could result in unfair and harmful responses. 2. Limited creativity: While GPT-4 could generate high-quality text, it could still have limitations when it comes to creative expression, as this would require a level of understanding that surpasses that of mere language. 3. High computational requirements: Training an AI model like GPT- The world of AI-powered conversational interfaces is constantly evolving and advancing. Here are some recent developments: Improved Natural Language Processing (NLP) capabilities: AI-powered conversational interfaces are becoming more sophisticated at understanding natural language and recognizing intent, making interactions with them more natural and seamless. Multi-lingual support: Many conversational interfaces are now equipped to handle multiple languages, making them more accessible to users around the world. Better integration with other technologies: Conversational interfaces are being integrated with other technologies such as chatbots, voice assistants, and smart home devices, making them more versatile and useful. Advanced Personalization: Conversational interfaces are becoming better at understanding user preferences, habits, and behaviors, allowing for more personalized interactions. Emotion recognition: Some conversational interfaces are now capable of recognizing and responding to human emotions, which can improve user experience and engagement. Improved Security: With the increasing use of conversational interfaces for sensitive tasks such as banking and healthcare, security measures are being implemented to protect users’ information and prevent fraud. Advancements in Voice Synthesis: Text-to-speech technology is rapidly improving, allowing for more realistic and natural-sounding voice interactions with conversational interfaces. Overall, the world of AI-powered conversational interfaces is constantly evolving, and we can expect to see many more exciting developments in the near future.