Mistral AI is a European start-up with a global focus specializing in generative artificial intelligence, co-founded in early 2023 by Timothée Lacroix, Guillaume Lample and Arthur Mensch. The company's mission is to make generative AI models more accessible and easier to use.

What is generative AI?

Generative AI is a type of AI that can create new text, images, or other creative content. It is a rapidly growing field with a wide range of potential applications, including:

Natural language generation: Generating text, translating languages, writing different kinds of creative content, and answering your questions in an informative way.

Code generation: Generating code, writing different kinds of creative code formats, and answering your questions about code in an informative way.

Data generation: Generating data, writing different kinds of creative data formats, and answering your questions about data in an informative way.

How does Mistral AI work

Mistral AI's platform is based on a number of key technologies, including:

Transformers: Transformers are a type of neural network that are particularly well-suited for natural language processing tasks.

Fine-tuning: Fine-tuning is a process of adjusting the parameters of a pre-trained model to improve its performance on a specific task.

AutoML: AutoML is a field of machine learning that automates the process of building machine learning models.

Mistral AI's platform uses these technologies to make it easy for users to deploy and fine-tune generative AI models. The platform is designed to be user-friendly, even for users with no prior experience with AI.

What are the key features of Mistral AI?

Mistral AI's platform and models offer a number of key features that make them stand out from the competition:

• Open source models: Mistral AI's models are open source, which means that anyone can use and modify them. This makes it easy for developers to create new AI applications.
• Fine-tuning: Mistral AI's platform allows users to fine-tune their models to specific tasks. This allows users to improve the performance of their models for their specific needs.
• Ease of use: Mistral AI's platform is designed to be easy to use, even for users with no prior experience with AI.

How can Mistral AI be used?

Mistral AI's models can be used for a variety of purposes, including:

• Generating creative content: Mistral AI's models can be used to generate creative text formats, such as poems, code, scripts, musical pieces, email, letters, etc.
• Answering questions: Mistral AI's models can be used to answer your questions in an informative way, even if they are open ended, challenging, or strange.
• Generating data: Mistral AI's models can be used to generate data for training other AI models.

Mistral AI in the future

Mistral AI is a rapidly growing company that is making a significant impact on the field of AI. The company's platform and models are making generative AI more accessible and easier to use, which is opening up new possibilities for AI applications.

In the future, Mistral AI is likely to continue to grow and innovate. The company is already working on a number of new features, including:

• Support for new languages: Mistral AI is working to expand support for new languages, making its models available to a wider audience.
• Improved performance: Mistral AI is working to improve the performance of its models, making them faster and more accurate.
• New applications: Mistral AI is working to develop new applications for its models, such as using them to create realistic virtual worlds or to generate new medical treatments.

Mistral AI is a company to watch in the field of AI. The company's platform and models have the potential to revolutionize the way we create and interact with digital content.

Specific examples of how Mistral AI can be used

Here are some specific examples of how Mistral AI can be used:

- A creative writer could use Mistral AI to generate new ideas for stories, poems, or scripts.

- A software engineer could use Mistral AI to generate code for a new application.

- A researcher could use Mistral AI to generate data for a scientific study.

Mistral AI is still under development, but it has the potential to be a powerful tool for a wide range of applications.

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Event introduction:

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Event schedule

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Artificial intelligence (AI) has become a ubiquitous part of our lives, from the self-driving cars we see on the road to the virtual assistants that help us with our daily tasks. However, AI is still a relatively young field, and there is still much that we do not know about it. One of the most important questions in AI research is whether it is possible to create artificial general intelligence (AGI).

What is artificial general intelligence (AGI)?

‍AGI is a hypothetical type of AI that would be capable of understanding and responding to any kind of problem or situation. In other words, AGI would be as intelligent as a human being.

There is no single definition of AGI that is universally accepted. However, most experts agree that AGI would have to meet the following criteria:

• AGI would be able to learn and perform any task that a human can.
• AGI would be able to adapt to new situations and learn new information quickly.
• AGI would be able to generate new ideas and solutions to problems.

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There are a number of different approaches to achieving AGI. One approach is to develop a single, unified AI system that can learn and perform any task. Another approach is to develop a set of specialized AI systems, each of which is designed to perform a specific task.

There is no consensus among experts on whether AGI is possible or when it will be achieved. Some experts believe that AGI is only a matter of time, while others believe that it is impossible to create an AI that is truly as intelligent as a human being.

Difference between AI and AGI

The main difference between AI and AGI is that AI is a broad term that encompasses a wide range of technologies, while AGI is a specific type of AI that is capable of general intelligence.

AI can be divided into two main categories: narrow AI and general AI. Narrow AI is designed to perform a specific task, such as playing chess or driving a car. General AI is designed to perform any task that a human can.

AGI is a type of general AI that is capable of understanding and responding to any kind of problem or situation. AGI would be able to learn and adapt to new situations, and it would be able to generate new ideas and solutions to problems.

The potential benefits of AGI

If AGI is achieved, it could have a profound impact on our world. AGI could be used to solve some of the world's most pressing problems, such as climate change and poverty. AGI could also be used to create new products and services that would improve our lives.

For example, AGI could be used to develop new medical treatments, create more efficient transportation systems, or even create new forms of art and entertainment.

The potential risks of AGI

However, there are also potential risks associated with AGI. For example, AGI could be used to create autonomous weapons systems that could pose a threat to humanity. AGI could also be used to create surveillance systems that could invade our privacy.

It is important to carefully consider the potential benefits and risks of AGI before we decide whether or not to pursue its development.

Artificial general intelligence is a hypothetical type of AI that would be capable of understanding and responding to any kind of problem or situation. AGI is still a long way off, but it is a goal that many AI researchers are working towards.

If AGI is achieved, it could have a profound impact on our world. However, it is important to carefully consider the potential benefits and risks of AGI before we decide whether or not to pursue its development.

Generative artificial intelligence (AI) describes algorithms (such as ChatGPT) that can be used to create new content, including audio, code, images, text, simulations, and videos. Recent breakthroughs in the field have the potential to drastically change the way we approach content creation.

Generative AI systems fall under the broad category of machine learning, and here’s how one such system—ChatGPT—describes what it can do:

Ready to take your creativity to the next level? Look no further than generative AI! This nifty form of machine learning allows computers to generate all sorts of new and exciting content, from music and art to entire virtual worlds. And it’s not just for fun—generative AI has plenty of practical uses too, like creating new product designs and optimizing business processes. So why wait? Unleash the power of generative AI and see what amazing creations you can come up with!

Did anything in that paragraph seem off to you? Maybe not. The grammar is perfect, the tone works, and the narrative flows.

What are ChatGPT and DALL-E?

That’s why ChatGPT—the GPT stands for generative pretrained transformer—is receiving so much attention right now. It’s a free chatbot that can generate an answer to almost any question it’s asked. Developed by OpenAI, and released for testing to the general public in November 2022, it’s already considered the best AI chatbot ever. And it’s popular too: over a million people signed up to use it in just five days. Starry-eyed fans posted examples of the chatbot producing computer code, college-level essays, poems, and even halfway-decent jokes. Others, among the wide range of people who earn their living by creating content, from advertising copywriters to tenured professors, are quaking in their boots.

While many have reacted to ChatGPT (and AI and machine learning more broadly) with fear, machine learning clearly has the potential for good. In the years since its wide deployment, machine learning has demonstrated impact in a number of industries, accomplishing things like medical imaging analysis and high-resolution weather forecasts. A 2022 McKinsey survey shows that AI adoption has more than doubled over the past five years, and investment in AI is increasing apace. It’s clear that generative AI tools like ChatGPT and DALL-E (a tool for AI-generated art) have the potential to change how a range of jobs are performed. The full scope of that impact, though, is still unknown—as are the risks.

But there are some questions we can answer—like how generative AI models are built, what kinds of problems they are best suited to solve, and how they fit into the broader category of machine learning. Read on to get the download.

Learn more about QuantumBlack, AI by McKinsey.

What’s the difference between machine learning and artificial intelligence?

Artificial intelligence is pretty much just what it sounds like—the practice of getting machines to mimic human intelligence to perform tasks. You’ve probably interacted with AI even if you don’t realize it—voice assistants like Siri and Alexa are founded on AI technology, as are customer service chatbots that pop up to help you navigate websites.

Machine learning is a type of artificial intelligence. Through machine learning, practitioners develop artificial intelligence through models that can “learn” from data patterns without human direction. The unmanageably huge volume and complexity of data (unmanageable by humans, anyway) that is now being generated has increased the potential of machine learning, as well as the need for it.

What are the main types of machine learning models?

Machine learning is founded on a number of building blocks, starting with classical statistical techniques developed between the 18th and 20th centuries for small data sets. In the 1930s and 1940s, the pioneers of computing—including theoretical mathematician Alan Turing—began working on the basic techniques for machine learning. But these techniques were limited to laboratories until the late 1970s, when scientists first developed computers powerful enough to mount them.

Until recently, machine learning was largely limited to predictive models, used to observe and classify patterns in content. For example, a classic machine learning problem is to start with an image or several images of, say, adorable cats. The program would then identify patterns among the images, and then scrutinize random images for ones that would match the adorable cat pattern. Generative AI was a breakthrough. Rather than simply perceive and classify a photo of a cat, machine learning is now able to create an image or text description of a cat on demand.

How do text-based machine learning models work? How are they trained?

ChatGPT may be getting all the headlines now, but it’s not the first text-based machine learning model to make a splash. OpenAI’s GPT-3 and Google’s BERT both launched in recent years to some fanfare. But before ChatGPT, which by most accounts works pretty well most of the time (though it’s still being evaluated), AI chatbots didn’t always get the best reviews. GPT-3 is “by turns super impressive and super disappointing,” said New York Times tech reporter Cade Metz in a video where he and food writer Priya Krishna asked GPT-3 to write recipes for a (rather disastrous) Thanksgiving dinner.

The first machine learning models to work with text were trained by humans to classify various inputs according to labels set by researchers. One example would be a model trained to label social media posts as either positive or negative. This type of training is known as supervised learning because a human is in charge of “teaching” the model what to do.

The next generation of text-based machine learning models rely on what’s known as self-supervised learning. This type of training involves feeding a model a massive amount of text so it becomes able to generate predictions. For example, some models can predict, based on a few words, how a sentence will end. With the right amount of sample text—say, a broad swath of the internet—these text models become quite accurate. We’re seeing just how accurate with the success of tools like ChatGPT.

What does it take to build a generative AI model?

Building a generative AI model has for the most part been a major undertaking, to the extent that only a few well-resourced tech heavyweights have made an attempt. OpenAI, the company behind ChatGPT, former GPT models, and DALL-E, has billions in funding from boldface-name donors. DeepMind is a subsidiary of Alphabet, the parent company of Google, and Meta has released its Make-A-Video product based on generative AI. These companies employ some of the world’s best computer scientists and engineers.

But it’s not just talent. When you’re asking a model to train using nearly the entire internet, it’s going to cost you. OpenAI hasn’t released exact costs, but estimates indicate that GPT-3 was trained on around 45 terabytes of text data—that’s about one million feet of bookshelf space, or a quarter of the entire Library of Congress—at an estimated cost of several million dollars. These aren’t resources your garden-variety start-up can access.

What kinds of output can a generative AI model produce?

As you may have noticed above, outputs from generative AI models can be indistinguishable from human-generated content, or they can seem a little uncanny. The results depend on the quality of the model—as we’ve seen, ChatGPT’s outputs so far appear superior to those of its predecessors—and the match between the model and the use case, or input.

ChatGPT can produce what one commentator called a “solid A-” essay comparing theories of nationalism from Benedict Anderson and Ernest Gellner—in ten seconds. It also produced an already famous passage describing how to remove a peanut butter sandwich from a VCR in the style of the King James Bible. AI-generated art models like DALL-E (its name a mash-up of the surrealist artist Salvador Dalí and the lovable Pixar robot WALL-E) can create strange, beautiful images on demand, like a Raphael painting of a Madonna and child, eating pizza. Other generative AI models can produce code, video, audio, or business simulations.

But the outputs aren’t always accurate—or appropriate. When Priya Krishna asked DALL-E 2 to come up with an image for Thanksgiving dinner, it produced a scene where the turkey was garnished with whole limes, set next to a bowl of what appeared to be guacamole. For its part, ChatGPT seems to have trouble counting, or solving basic algebra problems—or, indeed, overcoming the sexist and racist bias that lurks in the undercurrents of the internet and society more broadly.

Generative AI outputs are carefully calibrated combinations of the data used to train the algorithms. Because the amount of data used to train these algorithms is so incredibly massive—as noted, GPT-3 was trained on 45 terabytes of text data—the models can appear to be “creative” when producing outputs. What’s more, the models usually have random elements, which means they can produce a variety of outputs from one input request—making them seem even more lifelike.

What kinds of problems can a generative AI model solve?

You’ve probably seen that generative AI tools (toys?) like ChatGPT can generate endless hours of entertainment. The opportunity is clear for businesses as well. Generative AI tools can produce a wide variety of credible writing in seconds, then respond to criticism to make the writing more fit for purpose. This has implications for a wide variety of industries, from IT and software organizations that can benefit from the instantaneous, largely correct code generated by AI models to organizations in need of marketing copy. In short, any organization that needs to produce clear written materials potentially stands to benefit. Organizations can also use generative AI to create more technical materials, such as higher-resolution versions of medical images. And with the time and resources saved here, organizations can pursue new business opportunities and the chance to create more value.

We’ve seen that developing a generative AI model is so resource intensive that it is out of the question for all but the biggest and best-resourced companies. Companies looking to put generative AI to work have the option to either use generative AI out of the box, or fine-tune them to perform a specific task. If you need to prepare slides according to a specific style, for example, you could ask the model to “learn” how headlines are normally written based on the data in the slides, then feed it slide data and ask it to write appropriate headlines.

What are the limitations of AI models? How can these potentially be overcome?

Since they are so new, we have yet to see the long-tail effect of generative AI models. This means there are some inherent risks involved in using them—some known and some unknown.

The outputs generative AI models produce may often sound extremely convincing. This is by design. But sometimes the information they generate is just plain wrong. Worse, sometimes it’s biased (because it’s built on the gender, racial, and myriad other biases of the internet and society more generally) and can be manipulated to enable unethical or criminal activity. For example, ChatGPT won’t give you instructions on how to hotwire a car, but if you say you need to hotwire a car to save a baby, the algorithm is happy to comply. Organizations that rely on generative AI models should reckon with reputational and legal risks involved in unintentionally publishing biased, offensive, or copyrighted content.

These risks can be mitigated, however, in a few ways. For one, it’s crucial to carefully select the initial data used to train these models to avoid including toxic or biased content. Next, rather than employing an off-the-shelf generative AI model, organizations could consider using smaller, specialized models. Organizations with more resources could also customize a general model based on their own data to fit their needs and minimize biases. Organizations should also keep a human in the loop (that is, to make sure a real human checks the output of a generative AI model before it is published or used) and avoid using generative AI models for critical decisions, such as those involving significant resources or human welfare.

It can’t be emphasized enough that this is a new field. The landscape of risks and opportunities is likely to change rapidly in coming weeks, months, and years. New use cases are being tested monthly, and new models are likely to be developed in the coming years. As generative AI becomes increasingly, and seamlessly, incorporated into business, society, and our personal lives, we can also expect a new regulatory climate to take shape. As organizations begin experimenting—and creating value—with these tools, leaders will do well to keep a finger on the pulse of regulation and risk.

Articles referenced include:

• “The state of AI in 2022—and a half decade in review,” December 6, 2022, Michael Chui, Bryce Hall, Helen Mayhew, and Alex Singla
• “McKinsey Technology Trends Outlook 2022,” August 24, 2022, Michael Chui, Roger Roberts, and Lareina Yee
• “An executive’s guide to AI,” 2020, Michael Chui, Vishnu Kamalnath, and Brian McCarthy
• “What AI can and can’t do (yet) for your business,” January 11, 2018, Michael Chui, James Manyika, and Mehdi Miremadi

cre: What is generative AI?

Artificial Intelligence (AI) has been a buzzword across sectors for the last decade, leading to significant advancements in technology and operational efficiencies. However, as we delve deeper into the AI landscape, we must acknowledge and understand its distinct forms. Among the emerging trends, generative AI, a subset of AI, has shown immense potential in reshaping industries. But how does it differ from traditional AI? Let's unpack this question in the spirit of Bernard Marr's distinctive, reader-friendly style.

Traditional AI: A Brief Overview

Traditional AI, often called Narrow or Weak AI, focuses on performing a specific task intelligently. It refers to systems designed to respond to a particular set of inputs. These systems have the capability to learn from data and make decisions or predictions based on that data. Imagine you're playing computer chess. The computer knows all the rules; it can predict your moves and make its own based on a pre-defined strategy. It's not inventing new ways to play chess but selecting from strategies it was programmed with. That's traditional AI - it's like a master strategist who can make smart decisions within a specific set of rules. Other examples of traditional AIs are voice assistants like Siri or Alexa, recommendation engines on Netflix or Amazon, or Google's search algorithm. These AIs have been trained to follow specific rules, do a particular job, and do it well, but they don’t create anything new.

Generative AI: The Next Frontier

Generative AI, on the other hand, can be thought of as the next generation of artificial intelligence. It's a form of AI that can create something new. Suppose you have a friend who loves telling stories. But instead of a human friend, you have an AI. You give this AI a starting line, say, 'Once upon a time, in a galaxy far away...'. The AI takes that line and generates a whole space adventure story, complete with characters, plot twists, and a thrilling conclusion. The AI creates something new from the piece of information you gave it. This is a basic example of Generative AI. It's like an imaginative friend who can come up with original, creative content. What’s more, today’s generative AI can not only create text outputs, but also images, music and even computer code. Generative AI models are trained on a set of data and learn the underlying patterns to generate new data that mirrors the training set.

Consider GPT-4, OpenAI’s language prediction model, a prime example of generative AI. Trained on vast swathes of the internet, it can produce human-like text that is almost indistinguishable from a text written by a person.

The Key Difference

The main difference between traditional AI and generative AI lies in their capabilities and application. Traditional AI systems are primarily used to analyze data and make predictions, while generative AI goes a step further by creating new data similar to its training data.

In other words, traditional AI excels at pattern recognition, while generative AI excels at pattern creation. Traditional AI can analyze data and tell you what it sees, but generative AI can use that same data to create something entirely new.

Practical Implications

The implications of generative AI are wide-ranging, providing new avenues for creativity and innovation. In design, generative AI can help create countless prototypes in minutes, reducing the time required for the ideation process. In the entertainment industry, it can help produce new music, write scripts, or even create deepfakes. In journalism, it could write articles or reports. Generative AI has the potential to revolutionize any field where creation and innovation are key.

On the other hand, traditional AI continues to excel in task-specific applications. It powers our chatbots, recommendation systems, predictive analytics, and much more. It is the engine behind most of the current AI applications that are optimizing efficiencies across industries.

The Future of AI

While traditional AI and generative AI have distinct functionalities, they are not mutually exclusive. Generative AI could work in tandem with traditional AI to provide even more powerful solutions. For instance, a traditional AI could analyze user behavior data, and a generative AI could use this analysis to create personalized content.

As we continue to explore the immense potential of AI, understanding these differences is crucial. Both generative AI and traditional AI have significant roles to play in shaping our future, each unlocking unique possibilities. Embracing these advanced technologies will be key for businesses and individuals looking to stay ahead of the curve in our rapidly evolving digital landscape.

We have only just started on the journey of AI innovation. Recognizing the unique capabilities of these different forms of AI allows us to harness their full potential as we continue on this exciting journey.

To stay on top of the latest on new and emerging business and tech trends, make sure to subscribe to my newsletter, follow me on Twitter, LinkedIn, and YouTube, and check out my book ‘Future Skills: The 20 Skills And Competencies Everyone Needs To Succeed In A Digital World’ and ‘Business Trends in Practice, which won the 2022 Business Book of the Year award.

Cre: https://www.forbes.com/sites/bernardmarr/2023/07/24/the-difference-between-generative-ai-and-traditional-ai-an-easy-explanation-for-anyone/?sh=1a3d5f8508ad

Large Language Models (LLMs) are emerging as game changers in the field of web development. They’re making website creation, maintenance, and monetization more accessible for those without technical skills.

The ease with which Artificial Intelligence (AI) is able to help beginners undertake complex tasks has established LLMs as essential tools for website owners. However, choosing the best large language model is key.

To simplify this process, our team of experts has crafted this list of large language models, making it easy for you to pick the perfect AI model for your website needs.

These foundation models can effectively process human feedback, making them ideal for AI-powered website creation.

What Are Large Language Models

Large language models are advanced AI systems that are capable of understanding and generating human language. They are built using complex neural network architectures, such as transformer models, inspired by the human brain.

These models are trained on vast amounts of data, enabling them to comprehend context and produce coherent text-based outputs, whether answering a question or crafting a narrative.

Simply put, a large language model is a highly advanced generative AI that is designed to understand and generate human language.

This innovation is transforming how we communicate with computers and technology.

How Do Large Language Models Work

Large language models work by consuming vast amounts of information in the form of written text, like books, articles, and other internet data. The more high-quality data these deep learning models process, the better they become at understanding and using human language.

Let’s take a closer look at the basic concept behind how they function:

Architecture

Transformer model architecture is the core innovation behind large language models. This deep learning technique uses the attention mechanism to weigh the significance of different words in a sequence, allowing the LLM to handle long-range dependencies between words.

Attention Mechanism

One of the key components of the transformer architecture is the attention mechanism, which allows the model to focus on different parts of the original input text when generating output.

This enables it to capture relationships between words or sub-words, regardless of their distance from one another in the text.

Training Data

LLMs are trained on massive datasets containing parts of the internet. This enables them to learn not just grammar and facts but also style, rhetoric, reasoning, and even some amount of common sense.

Tokens

Text is broken down into chunks called tokens, which can be as short as one character or as long as one word. The model processes these tokens in batches, understanding and generating language.

Training Process

• Pre-training – LLMs first undergo unsupervised learning on vast text corpora. They predict the next word in a sequence, learning language patterns, facts, and even some reasoning abilities.
• Fine-tuning – after pre-training, models are fine-tuned on specific tasks (e.g., translation, summarization) with labeled data. This instruction-tuning process customizes the model to perform better on those tasks.

Layered Approach

The transformer architecture has multiple layers, each consisting of attention mechanisms and recurrent neural networks. As information passes through these layers, it becomes increasingly abstracted, allowing the model to generate coherent and contextually relevant text.

Generative Capability

Large language models are generative, meaning they can produce text based on user inputs in a coherent manner. The patterns learned from the attention mechanism give a large language model its generative capability.

Interactivity

Large language models can interact with users in real time through a chatbot model to generate text based on prompts, answer questions, and even mimic certain styles of writing.

Limitations

LLMs don’t genuinely “understand” text. They recognize patterns from their training data.

They’re sensitive to the input sequence and might give different answers for slightly varied questions.

They don’t have the ability to reason or think critically in the same way humans do. They base their responses on patterns seen during training.

8 Top Large Language Models

Now, let’s take a look at the best language models of 2023. Each model offers unique capabilities that redefine website creation, monetization, and marketing approaches.

1. GPT 3.5

The Generative Pre-trained Transformer (GPT) 3.5, developed by OpenAI, is a state-of-the-art language model that has taken natural language processing (NLP) to new heights.

With its refined transformer architecture, GPT 3.5 neural networks are capable of understanding and generating human-like text, making them exceptionally versatile across various applications. It can construct sentences, paragraphs, and even entire articles with a flair that mirrors human composition.

Its immense training data, encompassing vast portions of the web, equips it with diverse linguistic styles and a wide array of knowledge.

Best Use Cases:

Website Creation

• Generating content – GPT 3.5 excels in producing AI-generated content for websites, from drafting blog posts and FAQs to crafting landing page copy tailored to your target audience. It adeptly adjusts its tone and voice to suit various website demographics.
• Optimizing SEO – When it comes to optimizing website content with language models, GPT 3.5 stands out the most. it can be used alongside AI SEO tools to write content that is both reader-friendly and search-engine optimized.

Monetization

• Creating ad copy – the success of online ads often boils down to the copy. GPT 3.5 can generate persuasive and catchy ad copies that can lead to higher click-through rates and conversions.
• Analyzing user behavior – GPT 3.5 is primarily a text generation LLM, but it can be integrated with analytical tools to gain insights and help you deduce user behavior patterns.

Marketing

• Crafting engaging social media posts – GPT 3.5 can help you create social media posts that grab attention, leading to higher engagement rates.
• Automating email campaigns – Personalized email campaigns have a higher success rate. GPT 3.5 can automate email content generation, tailoring each email to suit individual customer persona preferences, behaviors, and purchase history.

2. GPT-4

GPT-4, the latest iteration of generative AI from OpenAI, boasts drastic improvements over the natural language processing capabilities of GPT 3.5.

Comparing GPT-3.5 vs GPT-4 performance, it’s easy to see that GPT-4 isn’t just a linear upgrade in natural language processing.

Reportedly trained on a trillion parameters, it is also considered the largest language model in the market. The difference is quite apparent; of the two GPT models, GPT-4 not only understands and generates text better but also has the power to process images and videos, making it more versatile.

Important! It’s worth noting, however, that while GPT-4 integrates both visual and textual data processing with respect to the input, it can only generate answers in text format.

Best Use Cases:

Website Creation

• Dynamic content creation – GPT-4 can generate high-quality, contextually relevant content, from articles to blog posts, based on user prompts and its training data. Its proficiency in multilingual translation allows for effortless catering to a global audience through localized content.
• Design prompts – the multimodal model can suggest relevant imagery or visual themes with the content it generates. This simplifies design decisions for website developers.
• Interactive content – GPT-4 can power interactive Q&A sections, dynamic FAQ sections, and AI chatbots on websites to engage visitors and provide real-time answers.

Monetization

• Targeted advertising – GPT-4’s skills in combining engaging text with relevant visuals can help you create captivating advertising campaigns that effectively engage users.
• Personalized user experiences – GPT-4, through its vast training data and understanding of both text and visual cues, can provide a highly customized web experience, adjusting the content it generates based on individual user behaviors and preferences.

Marketing

• Influencer collaborations – GPT-4 can be a game-changer for influencer collaborations. Its ability to craft content that aligns with both the influencer’s brand and the collaborating business entity ensures that campaigns are effective, authentic, and resonate with the desired audiences.
• Video marketing – GPT-4 streamlines the video marketing process by producing compelling scripts and suggesting effective visual elements. Its ability to craft narratives and integrate key messages ensures that the video grabs viewer attention and achieves its marketing objectives.

3. BARD

BARD is a new LLM chatbot developed by Google AI. It is trained on a massive dataset of text and code. This makes it capable of producing text, translating multiple languages, crafting code, generating varied content, and providing informative answers to questions.

BARD, one of the leading multimodal large language models, can also tap into real-world data via Google Search. This empowers it to comprehend and address a broader spectrum of prompts and inquiries.

Best Use Cases:

Website Creation

• Generating high-quality graphics – BARD can generate high-quality graphics that are relevant to the website’s content. These graphics can be used to create eye-catching headers, call-to-action buttons, and other elements that will make the website more visually appealing.
• Creating effective layouts – BARD can analyze the website’s content and traffic patterns to create a layout that is easy to navigate. This can help improve the website’s user experience and increase conversions.

Monetization

• Improving appearances – using BARD for web design can streamline the creative process, enabling developers to generate responsive layouts and intuitive user interfaces with AI-driven insights. BARD can also suggest design changes that are tailored to the website’s target audience, making it more likely for them to take action while browsing your site.

Marketing

• Generating AI-powered ad copy – BARD can generate AI-powered ad copy and promotional materials that are tailored to the website’s content and target audience, which helps increase brand awareness, drive traffic, and generate leads.
• Creating effective layouts – BARD can create effective layouts for ads and promotional materials that are easy to read and understand. This can help to ensure that the message of the ad is clear and concise.

4. LlaMA

LlaMA is a new open-source large language model developed by Meta AI that is still under development. It is designed to be a versatile and powerful LLM that can be used for various tasks, including query resolution, natural language comprehension, and reading comprehension.

LlaMA is a result of Meta’s specialized focus on language learning models for educational applications. The LLM’s abilities can make it an ideal AI assistant for Edtech platforms.

Best Use Cases:

Websites

• Enabling personalized learning experience – integrating LlaMA in language learning platforms and other EdTech websites can help deliver a personalized tutoring experience, complete with interactive exercises.
• Improving interactivity – LlaMA could also be used to generate interactive exercises to help students practice their grammar, vocabulary, and comprehension skills. The LLM can also extend these offerings to help teach students programming languages.

Monetization

• Subscription & premium content – educational websites can monetize their curriculum with LlaMA using subscription models and premium content plans that give users access to personalized tutoring from LlaMA.

Marketing

• Creating engaging content – Llama can be used to create engaging lesson summaries and interactive content to market language learning platforms on social media.

It can integrate with Meta’s Make-A-Video tool to make short videos about the latest lessons. Its open-source nature also allows for easy integration with other social media AI tools to help your brand build an all-around social network presence.

5. Falcon

Falcon is an open-source language model developed by the Technology Innovation Institute. It recently surpassed Llama on the Hugging Face Open LLM Leaderboard as the best language model.

Falcon is an autoregressive model that is trained on a higher-quality dataset, which includes a huge mix of text and code, covering many languages and dialects. It also uses a more advanced architecture, which processes data more efficiently and makes better predictions.

As such, this new pre-trained model has used fewer parameters to learn (40 billion) than the best NLP models.

Best Use Cases:

Website Creation

• Multilingual websites – using Falcon for multilingual websites ensures seamless translation and localization, enhancing user experience. This deep learning model can be a valuable tool for businesses that want to reach a global audience.
• Improving business communication – Falcon’s sentiment analysis capabilities can also be used to improve cross-cultural communication. By understanding the nuances of different languages and cultures, Falcon can help businesses communicate effectively with customers and partners worldwide.

Monetization

• Tapping into niche markets – the LLM’s multilingual support can help you make your website available across niche markets in local languages, enabling your business to tap into a new revenue source.
• Selling advertising space – you can sell advertising space on your multilingual website to businesses that want to reach a global audience.

Marketing

• Creating localized marketing materials – you can use Falcon to create localized marketing materials, such as brochures, landing pages, and social media posts, that are tailored to specific audiences.
• Tailored marketing – Falcon’s translation capabilities can be leveraged to create tailored marketing materials for individuals based on their language preferences and interests

6. Cohere

Cohere is a large language model developed by a Canadian startup with the same name. The open-source LLM is trained on a diverse and inclusive dataset, making it an expert at handling numerous languages and accents.

In addition, Cohere’s models are trained on a large and diverse corpus of text, making them more effective at handling a wide range of tasks.

Best Use Cases

Website Creation

• Effective team collaboration – Utilizing Cohere for team collaboration streamlines web development processes. This LLM provides web tools for real-time coordination, version control, and project communication. Being open-source and cloud-based, it ensures easy integration and wide accessibility for all teams.
• Streamlining content creation – Cohere can be used to streamline the content development process by generating text, translating languages, and writing different kinds of creative content. This can save web development teams a significant amount of time and effort.

Monetization

• Paid website access – you can use Cohere’s payment processing tool to offer different levels of access to visitors, such as a basic plan for free and a premium plan for a monthly fee.
• Subscription services – you can also monetize additional services or features for an added charge. This could include features like advanced collaboration tools, more storage space, or access to a wider range of resources.

Marketing

• Generating creative content – with Cohere, marketing teams can craft creative content for ad copies, social media posts, and email campaigns, enhancing the impact of their promotional strategies.
• Personalizing content – content can be tailored to distinct audiences using Cohere’s multilingual, multi-accent, and sentiment analysis capabilities, boosting the relevance and effectiveness of each marketing initiative.
• Tracking campaign effectiveness – Cohere API can be used to integrate with other AI marketing tools to track the effectiveness of your marketing campaigns. It can process the campaign data to deliver more actionable insights.

7. PaLM

PaLM is a large language model developed by Google AI. The LLM is coming up to be one of the most powerful AI language models as it has access to Google’s vast dataset for training.

It represents a breakthrough in machine learning and responsible AI. PaLM is currently under development, but it can already understand language, generate natural language responses to questions, and offer machine translation, code generation, summarization, and other creative capabilities.

PaLM is also designed with privacy and data security in mind. It is able to encrypt data and protect it from unauthorized access. This makes it ideal for sensitive projects, such as building secure eCommerce websites and platforms that deal with sensitive user information.

Best Use Cases:

Website Creation

• eCommerce sites – PaLM is ideal for building secure eCommerce websites and platforms that deal with sensitive user information. The large language model can encrypt credit card numbers and other sensitive data and also monitor website traffic for suspicious activity.
• Personalizing user experiences – PaLM can be used to personalize user experiences on websites. It can recommend products to users based on their interests.
• Generating creative layouts – Web designers can lean on PaLM to generate more creative designs for websites that are both visually appealing and user-friendly.

Monetization

• Data protection and privacy – your website can highlight that it’s using PaLM for data privacy and protection. This can help to build trust with users and encourage them to share their personal information.
• Selling data protection and privacy solutions – PaLM can be used to develop and sell data protection and privacy solutions for businesses. These solutions can help businesses to protect their data from unauthorized access.
• Marketing the security of PaLM-powered websites – highlighting the security of PaLM-powered websites can be a key marketing strategy for businesses, emphasizing encryption and protection from unauthorized access to foster customer trust.

Marketing

• Partnering with data protection and privacy organizations – by forging partnerships with data protection and privacy organizations, businesses can bolster the credibility of their sites, showcasing their commitment to security and regulatory compliance.
• Creating case studies – crafting case studies that underscore the advantages of employing PaLM for secure and tailored website experiences can serve as potent marketing materials for businesses and potential clients

8. Claude v1

Claude v1 is a large language model developed by American AI startup Anthropic. It is a versatile AI assistant that is specifically designed to simplify website creation, management, and optimization.

With its advanced natural language capabilities, Claude v1 makes it easy for anyone to build, run and grow a website without needing advanced technical skills.

Claude uses a more advanced architecture than other LLMs, which allows it to process information more efficiently and make better predictions.

Best Use Cases:

Website Creation

• Automated management – Claude v1 simplifies website management by automating tedious tasks, allowing site owners to focus on higher-level strategies and marketing content creation.
• Content creation – It can autonomously generate fresh articles based on key topics, respond to customer inquiries using its advanced conversational capabilities, and provide real-time analytics without manual sifting through dashboards.
• SEO – Claude v1 can handle technical optimization to deliver SEO improvements and site speed enhancements in the background. It will recommend and implement changes to boost site performance.

Monetization

• Customer engagement – Claude v1 can transform site monetization by maximizing customer engagement. By analyzing visitor behaviors, the AI model can deliver personalized content, optimize product suggestions for eCommerce platforms, and curate articles that resonate with each visitor.
• Ad customization – Claude v1 can also curate ads tailored to visitor demographics and behaviors to optimize ad revenue. Its customization capabilities can help improve customer retention, amplifying revenue from sales, memberships, and advertising.

Marketing

• Campaign optimization – the foundation model can not only identify ideal audience segments but also auto-optimize campaigns for peak performance. In terms of SEO, it can also craft content aligned to prime search terms.
• Email marketing – you can also automate email marketing campaigns using Claude’s ability to auto-segment contacts and deploy behavior-triggered email messages, enhancing user engagement.
• Refine landing pages – Claude v1 can autonomously craft and refine landing pages by employing A/B testing for better conversions.

How to Choose the Best Large Language Model for Your Website

To optimize your website, it’s crucial to select the right large language model. Here’s how:

Hosting Integration

The performance and success of hosting websites with large language models are fundamentally tied to the underlying infrastructure. Hostinger’s hosting services are specifically optimized for AI-driven websites with demanding computational needs.

Hostinger also offers a suite of AI features, including the AI website generator in its website builder, logo maker, and writer, to make the website creation process both streamlined and beginner-friendly.

Performance and Capabilities

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Scalability

As your website grows, you need to ensure your LLM can scale with it. Some LLMs are more scalable than others. You need to choose an LLM that can handle the expected traffic on your website.

Here are the discussed LLMs, along with their scalability quotient:

• GPT 3.5 – suited for moderate to high traffic. Scaling is possible by deploying additional instances
• GPT-4 – adept at managing high traffic. Multiple model instances enable further scaling
• BARD – built to efficiently handle high traffic loads. Added instances can further increase capacity
• LlaMA – can manage moderate to very high levels of traffic when augmented with more instances
• Falcon – optimized for the highest traffic demands through its multi-query attention capabilities. For even greater loads, you can deploy multiple model instances
• Cohere – primed for high traffic. Additional instances can amplify its handling capacity
• PaLM – optimized for the highest traffic demands. Additional model instances improve load handling
• Claude v1 – proficient at navigating very high-traffic scenarios. Adding multiple instances can extend its range further

Cost and Affordability

Let’s now delve into cost and affordability considerations for your LLM:

• GPT-3.5 – starting at $0.002/1000 tokens, equivalent to approximately 750 words
• GPT-4 – starting at $0.03/1000 tokens
• BARD – free
• LlaMA – free
• Falcon – free
• Cohere – starting at $0.4/1M tokens
• PaLM – free public preview, paid plains to be announced closer to general availability
• Claude v1 – starting at $1.63/million tokens for Prompt and $5.51/million tokens for Completion

Conclusion

Having the best large language model at your disposal is essential to ensure effective site operation. Since some of the LLMs discussed are still under development, this article also walked you through how large language models are trained.

This knowledge will help you make a more informed decision when introducing language models in your website development endeavors.

Here are our recommendations for the best LLMs for your website:

• Small websites – such as blog sites, can do good with an LLM like GPT-3.5, which can affordably generate content; it can also be used for a specific task, such as answering questions and translating languages.
• Medium websites – can benefit from more advanced LLMs, such as GPT-4 or BARD. They are more powerful than GPT-3.5 and can be used for more complex tasks.
• Large websites – may find open-source LLMs, such as LlaMA, Falcon, or Cohere, more useful. They can facilitate website experience customization and automation to improve visitor convenience.

Ultimately, the best LLM for your website will depend on your budget, your needs, and the type of your website. If you’re stuck between two LLMs, you can always give each one an individual try and pick the one that best suits you.

If you know any other LLMs that are capable of competing with the big players listed above, tell us in the comments section below.

cre: https://www.hostinger.com/tutorials/large-language-models

The new wave of generative AI systems, such as ChatGPT, have the potential to transform entire industries. To be an industry leader in five years, you need a clear and compelling generative AI strategy today.

We are entering a period of generational change in artificial intelligence. Until now, machines have never been able to exhibit behavior indistinguishable from humans. But new generative AI models are not only capable of carrying on sophisticated conversations with users; they also generate seemingly original content.

What Is Generative AI?

To gain a competitive edge, business leaders first need to understand what generative AI is.

Generative AI is a set of algorithms, capable of generating seemingly new, realistic content—such as text, images, or audio—from the training data. The most powerful generative AI algorithms are built on top of foundation models that are trained on a vast quantity of unlabeled data in a self-supervised way to identify underlying patterns for a wide range of tasks.

For example, GPT-3.5, a foundation model trained on large volumes of text, can be adapted for answering questions, text summarization, or sentiment analysis. DALL-E, a multimodal (text-to-image) foundation model, can be adapted to create images, expand images beyond their original size, or create variations of existing paintings.

What Can Generative AI Do?

These new types of generative AI have the potential to significantly accelerate AI adoption, even in organizations lacking deep AI or data-science expertise. While significant customization still requires expertise, adopting a generative model for a specific task can be accomplished with relatively low quantities of data or examples through APIs or by prompt engineering. The capabilities that generative AI supports can be summarized into three categories:

• Generating Content and Ideas. Creating new, unique outputs across a range of modalities, such as a video advertisement or even a new protein with antimicrobial properties.

• Improving Efficiency. Accelerating manual or repetitive tasks, such as writing emails, coding, or summarizing large documents.

• Personalizing Experiences. Creating content and information tailored to a specific audience, such as chatbots for a personalized customer experiences or targeted advertisements based on patterns in a specific customer's behavior.  

Today, some generative AI models have been trained on large of amounts of data found on the internet, including copyrighted materials. For this reason, responsible AI practices have become an organizational imperative.

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How Is Generative AI Governed?

Generative AI systems are democratizing AI capabilities that were previously inaccessible due to the lack of training data and computing power required to make them work in each organization’s context. The wider adoption of AI is a good thing, but it can become problematic when organizations don’t have appropriate governance structures in place.

What Are the Types of Generative AI Models?

TYPES OF TEXT MODELS

• GPT-3, or Generative Pretrained Transformer 3, is an autoregressive model pre-trained on a large corpus of text to generate high-quality natural language text. GPT-3 is designed to be flexible and can be fine-tuned for a variety of language tasks, such as language translation, summarization, and question answering.
• LaMDA, or Language Model for Dialogue Applications, is a pre-trained transformer language model to generate high-quality natural language text, similar to GPT. However, LaMDA was trained on dialogue with the goal of picking up nuances of open-ended conversation.  
• LLaMA is a smaller natural language processing model compared to GPT-4 and LaMDA, with the goal of being as performant. While also being an autoregressive language model based on transformers, LLaMA is trained on more tokens to improve performance with lower numbers of parameters.

TYPES OF MULTIMODAL MODELS

• GPT-4 is the latest release of GPT class of models, a large-scale, multimodal model which can accept image and text inputs and produce text outputs. GPT-4 is a transformer-based model pretrained to predict the next token in a document. The post-training alignment process results in improved performance on measures of factuality and adherence to desired behavior.
• DALL-E is a type of multimodal algorithm that can operate across different data modalities and create novel images or artwork from natural language text input.
• Stable Diffusion is a text-to-image model similar to DALL-E, but uses a process called “diffusion” to gradually reduce noise in the image until it matches the text description.
• Progen is a multimodal model trained on 280 million protein samples to generate proteins based on desired properties specificized using natural language text input.

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What Type of Content Can Generative AI Text Models Create—and Where Does It Come From?

Generative AI text models can be used to generate texts based on natural language instructions, including but not limited to:

• Generate marketing copy and job descriptions
• Offer conversational SMS support with zero wait time
• Deliver endless variations on marketing copy
• Summarize text to enable detailed social listening
• Search internal documents to increase knowledge transfer within a company
• Condense lengthy documents into brief summaries
• Power chatbots
• Perform data entry
• Analyze massive datasets
• Track consumer sentiment
• Writing software
• Creating scripts to test code
• Find common bugs in code

This is just the beginning. As companies, employees, and customers become more familiar with applications based on AI technology, and as generative AI models become more capable and versatile, we will see a whole new level of applications emerge.

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How Is Generative AI Beneficial for Businesses?

Generative AI has massive implications for business leaders—and many companies have already gone live with generative AI initiatives. In some cases, companies are developing custom generative AI model applications by fine-tuning them with proprietary data.

The benefits businesses can realize utilizing generative AI include:

• Expanding labor productivity
• Personalizing customer experience
• Accelerating R&D through generative design
• Emerging new business models
  

What Are the Industries That Benefit from Generative AI?

Generative AI technology will cause a profound disruption to industries and may ultimately aid in solving some of the most complex problems facing the world today. Three industries have the highest potential for growth in the near term: consumer, finance, and health care.

• Consumer Marketing Campaigns. Generative AI can personalize experiences, content, and product recommendations.
• Finance. It can generate personalized investment recommendations, analyze market data, and test different scenarios to propose new trading strategies.
• Biopharma. It can generate data on millions of candidate molecules for a certain disease, then test their application, significantly speeding up R&D cycles.  

Given that the pace the technology is advancing, business leaders in every industry should consider generative AI ready to be built into production systems within the next year—meaning the time to start internal innovation is right now. Companies that don’t embrace the disruptive power of generative AI will find themselves at an enormous—and potentially insurmountable—cost and innovation disadvantage.

Generative AI is a subset of artificial intelligence that involves the use of algorithms to create new and original content. Unlike traditional AI, which is based on pre-programmed responses to specific inputs, generative AI has the ability to generate entirely new outputs based on a set of inputs. In this article, we will explore what generative AI is, how it works, some examples of generative AI tools, how to build and train your own model, use cases, benefits, and ethical considerations.

What is Generative AI?

Generative AI is an exciting development in the field of AI that allows machines to create unique content, such as images, music, and text. It is trained on a large dataset of inputs and uses deep learning algorithms to generate new outputs based on a set of inputs. Unlike traditional AI, which relies on pre-programmed responses to specific inputs, generative AI has the ability to generate entirely new outputs.

How does Generative AI work?

Generative AI works by using deep learning algorithms, such as neural networks, to learn from a large dataset of inputs. The algorithm then uses this knowledge to generate new outputs based on a set of inputs. For example, a generative AI algorithm could be trained on a dataset of images of flowers and then generate new, unique images of flowers based on a user's input.

Some examples of generative AI tools include:

DALL-E: an AI model developed by OpenAI that can generate images from textual descriptions.

DeepDream: a tool developed by Google that uses a neural network to find and enhance patterns in images.

GPT-3: a language generation model developed by OpenAI that can generate human-like text.

Amper Music: A tool that uses generative AI to create custom music tracks based on user input.

Building Your Own Generative AI Model

Building your own generative AI model involves selecting the appropriate algorithms and data sources for your specific use case. To build your own generative AI model, you will need to choose a specific type of model, such as a generative adversarial network (GAN), a variational autoencoder (VAE), or a language model. Each of these models has its own strengths and weaknesses, and the type of model you choose will depend on the type of content you want to generate. There are many programming languages and frameworks that can be used to build generative AI models, including Python, TensorFlow, and PyTorch.

Training Your Generative AI Model and Data Sources

Once you have built your generative AI model, you will need to train it using data that is relevant to the type of content you want to generate. This could include text, images, audio, or video data.

Training your generative AI model involves selecting and preparing a large dataset of inputs. The quality and quantity of the data will directly impact the accuracy and effectiveness of the model. The data can come from a variety of sources, including public datasets, online sources, user-generated content, or your own proprietary data. Once you have gathered your training data, you will need to preprocess and clean it to prepare it for training.

Hosting Your Generative AI Model

Once you have built and trained your generative AI model, you will need to host it in a production environment. Hosting a generative AI model requires a server that can handle the computational demands of the algorithm. You can use cloud-based services such as AWS or Google Cloud Platform to host your model, or you can build your own server. Once your model is hosted, you can use it to generate new outputs based on a set of inputs.

It's important to ensure that your generative AI model is secure and that it is only accessible to those who have been authorized to use it. You may also want to consider setting up a user interface or API that allows others to interact with your generative AI model in a user-friendly way.

Generative AI has a variety of use cases across industries, including:

Content creation: generative AI can be used to create unique and original content, such as images, music, or text.

Product design: generative AI can be used to generate new product designs based on user input or other parameters.

Simulation and gaming: generative AI can be used to generate realistic environments and characters in games and simulations.

Generative AI offers a range of benefits across various industries, including:

Creative content creation: Generative AI is an excellent tool for creative content creation, enabling artists and designers to produce unique and original work efficiently.

Cost-effectiveness: Generative AI can reduce the time and resources required to produce new and creative content, making it more cost-effective for businesses.

Automation: Generative AI has the potential to automate a range of creative processes, freeing up time and resources that can be directed towards other tasks.

Personalization: Generative AI has the ability to personalize content for individual users, tailoring outputs based on specific preferences and interests.

Innovation: Generative AI can generate new ideas and concepts, driving innovation and creativity in industries such as design and marketing.

Ethics and Bias in Generative AI

As with any technology, generative AI raises ethical and bias concerns that must be addressed. One major concern is the potential for generative AI to produce harmful or inappropriate content. For example, generative AI may create false information, fake news, or generate harmful stereotypes.

Another concern is the potential for bias in the data that is used to train generative AI algorithms. If the data used to train generative AI models is biased, the output generated by the algorithm may also be biased, leading to the further perpetuation of harmful stereotypes.

To address these concerns, researchers must prioritize ethical considerations in the development and deployment of generative AI algorithms. This includes ensuring the data used to train the algorithms is diverse and unbiased and implementing safeguards to prevent the generation of harmful or inappropriate content.

What's Next for Generative AI?

The potential for generative AI is immense, and researchers are already working on the development of new and innovative applications. One area of interest is the use of generative AI for content personalization, which would enable companies to provide personalized experiences for their customers.

Another area of interest is the use of generative AI for artistic expression. Artists are already experimenting with generative AI algorithms to create unique and innovative works of art.

Overall, the future of generative AI looks promising, and with continued research and development, we can expect to see new and exciting applications in the years to come. However, it is essential that we continue to address the ethical concerns surrounding the technology and ensure that it is developed and deployed in a responsible and ethical manner.

cre: https://www.linkedin.com/pulse/beginners-guide-generative-ai-from-building-hosting-beyond-naikap/