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By 2024, AI developments are going to accelerate

Last year we experienced the first phase of awareness and exploration in the world of AI, laying the foundation for the AI era. We have watched a legion of new AI companies launch and existing companies begin to integrate AI into their products and services. In both our private and working lives, we are also discovering AI applications.

By 2024, this development is going to accelerate further. Most companies are going to get serious about integrating AI into their work processes, products and services. We will start to see large-scale experimentation and adoption, with AI becoming prominent in more and more facets of our lives.

Let's see what developments we can expect in 2024 and how they will impact our daily lives. 

  • AI models are getting smarter, more powerful and better 
  • The impact of AI on marketing
  • The emergence of a team of AI assistants
  • Access to proprietary language models
  • New roles and functions in the AI era
  • Apple introduces AI Diary, a harbinger of things to come
  • Large Language Models (LLMs) are hallucinatory dream machines 

One thing is certain: this time next year, our way of living and working will be radically different. But how exactly? Let's find out together.

AI models are getting smarter, more powerful and better

AI models are evolving rapidly and getting smarter, more powerful and better by the day. We are seeing a shift from traditional language models, which are purely text-based, to more sophisticated multimodal AI models. This new generation of models integpricing images, audio, video and code in addition to text. An example of this is Google's recently announced Gemini.

It is expected that ChatGPT-5, OpenAI's successor to GPT-4, will also be a multimodal model and will be launched early next year. There is speculation that GPT-5 will have more advanced features, including some degree of self-awareness and self-correction, but this is still unconfirmed.

This trend represents not only a shift in how AI affects SEO, but more importantly how it changes user behavior. The zero-click answers of AI chatbots offer an instant solution without the need to navigate through links, ads, pop-ups, SEO-optimized text and cookies. And you won't be retargeted. This is undoubtedly going to have a big effect on traditional search engines and website visits.

The impact of AI on marketing

The impact of AI on our marketing efforts is going to be profound. With advanced AI tools at our fingertips, content creation, distribution and customer service, among others, will not only become easier, but also significantly more powerful.

Let's look at 6 marketing areas where AI can be used.

1. Content creation
For many marketers, content creation is the first area they use AI for. They use AI to create (more effectively) content such as text, images, audio and video. The quality of this is going to keep improving, where it is barely, if at all, distinguishable from real. 

2. Content distribution
AI can help us improve visibility on various channels, such as social media, websites, web shop and email marketing. For example, consider an AI tool that adapts content on your website based on the visitor.

3. Online advertising
By using AI for online campaigns, we can analyze huge data sets and learn from user interactions, making our ads more relevant and effective.

4. Customer service
AI-powered chatbots offer a new dimension to customer service, allowing us to respond to customer inquiries quickly and efficiently, at any time of day.

5. Content strategy
By 2024, AI will be indispensable in developing content strategies. Recognizing trends and analyzing behavior will provide us with deeper insights than ever before.

6. Data and insight
AI helps us process mountains of data and gather valuable insights that can strengthen our content marketing strategy and execution.

Wondering what this looks like in practice? Check out a detailed breakdown of the specific marketing aspects where AI can play a rolehere. 

The emergence of a team of AI assistants 

The two most advanced AI models are OpenAI's ChatGPT-4 and Google's Gemini Ultra. These models are currently superior to all other AI models in the world, which do not get beyond the level of ChatGPT-3.5.

In general, the larger the AI model (which is trained on more data and thus requires more expensive computing power to train. For example, training GPT-4 cost more than $100 million!), the "smarter" it is.

In the world of AI, however, we see a difference emerging between generalist and specialized models. The generalist AIs, such as GPT-4 and Gemini, can handle a wide range of tasks. In contrast, the specialized AIs excel at specific tasks, although they are less flexible.

Imagine a team of AI assistants at your disposal. You have general assistants for many different tasks and more specialized assistants for very specific tasks. These AIs not only work for you, but also with each other. They can delegate tasks to each other, depending on who is best suited for a particular task.

For example, if you want to book a trip, a generalist AI assistant can find the perfect destination based on your preferences. It then engages a specialized AI to handle the booking.

The future of AI is not just about using a single smart assistant, but an entire network of AIs working together. This network will consist of both smart, generalist AIs and less sophisticated but specialized assistants.

Curious about how you can prepare your organization for this future? 

Discover the 5 phases of AI maturity in organizations, including the phase of autonomous AI assistants.

Access to proprietary language models

Language models transform the way we interact with technology. These models fall into two categories: "closed source," such as ChatGPT-4 and Google's Gemini, where the source code is not publicly available and the owner retains complete control; and "open source," such as Meta's LLaMA. The open source models, share their source code and training data, making them accessible to all.

With the availability of open source language models (Large Language Models -LLMs), the world is open to smart developers to create effective and specialized AIs. These AIs can help with tasks that previously required complex and expensive programs. We are at the dawn of an era in which language models that respond to and learn from their environment will become the norm, both in our work and in our daily lives.

An interesting new player in this field is Mistral AI. This Paris-based startup, founded by former employees of Google's DeepMind and Meta, recently raised an impressive $415 million. With a valuation of $2 billion in just 10 months! (yes, you read correctly), Mistral AI looks set to become a major player. They focus on developing open, decentralized and accountable AI technology.

Mistral AI has also played an important role in the discussion surrounding the European Union's AI legislation and recently launched a developer platform in beta.

New roles and functions in the AI era

As we enter the AI era, many entrepreneurs are asking the question, "How do I ensure the right capacity and people to make our transition to AI happen?" 

As your AI use m atures, the structure of your team must evolve. This means new roles and functions are needed:

Core function:

Chief AI Officer: Responsible for overarching AI strategy, policy, and implementation. Important is a thorough knowledge of business strategy, change management, and marketing technology procurement.

Must-have roles:

AI Project/Program Manager(s): Drives specific AI projects and programs. Functions as the central hub of the organization with additional AI representatives in each department.


Optional rollers:

Integration expert: Specializes in integrating AI tools into existing processes and systems.
AI developers: For building and maintaining custom AI tools.

Key stakeholders:

Marketing, Sales, Chief Editors, Analysts, Legal, Corporate Culture, and Data Security are critical to the smooth integration of AI into the organization.

It is essential to assemble a versatile team that can harness and apply the capabilities of AI in all aspects of the organization. With an AI steering committee, you can structure and organize this well. The future belongs to companies that embrace these new roles and fully harness the power of AI.

Apple introduces AI-powered Journal

Apple says it is working on advanced AI language models for iPhones and other devices, and has made significant breakthroughs in their implementation and storage. The company's researchers have developed a technique to store AI data in flash memory, making it possible to run complex AI assistants and chatbots on devices with limited memory.

These developments could lead to improved Siri features, real-time translation, and advanced capabilities in photography and augmented reality. Apple aims to integrate AI into as many apps as possible, while focusing on privacy and responsible AI development. 

The launch of the AI-powered Diary app, may be a harbinger of innovative AI applications on the Iphone and other devices.

The Diary app can automatically suggest key moments from your recent locations visited, photos, music and workouts, to help you get started writing. With the update to OS 17.2, the Diary app will be added automatically, fully integrated into the Apple ecosystem.


Apple emphasizes that privacy is central to the development of this app. Data in the Diary app is encrypted while your iPhone is locked, and users have the option to activate additional security measures such as passcode, Face ID or Touch ID. All diary data is end-to-end encrypted and stored in iCloud, so only you have access to your memories.


Interestingly, diary suggestions are generated locally on the device. This means you have complete control over which suggested moments are shared with the app and added to your journal. 

LLMs are hallucinatory dream machines

In closing, I share a fascinating insight from Andrej Karpathy, a leading AI researcher who also recently gave an impressive presentation on the development of LLMs. He highlights the "hallucination" property of AI such as ChatGPT, which often shows up in answers that seem out of context or incorrect. Many people see this as a flaw, but Karpathy offers a different perspective: LLMs are essentially dream machines.

Karpathy explains that what we think of as "hallucination" is in fact the core function of LLMs: they generate ideas and answers based on their training.


'We direct their dreams with prompts. The prompts start the dream, and based on the LLM's hazy recollection of its training documents, most of the time the result goes someplace useful. It's only when the dreams go into deemed factually incorrect territory that we label it a "hallucination." It looks like a bug, but it's just the LLM doing what it always does.'

Karpathy also makes clear the difference with search engines, which only reuse existing information and cannot create new ideas. An LLM is dreaming 100% and has the hallucination problem. A search engine is dreaming 0% and has the creativity problem. He emphasizes that although hallucination is inherent in LLMs, we need to find ways to control this property in AI assistants. 

This vision from Karpathy offers an interesting new perspective on AI, emphasizing the importance of leveraging their unique creative capabilities. Looking ahead to 2024, it is essential to focus on the strengths of AI tools and deploy them where they are most effective for us. By continuously experimenting and regularly testing for improvements, we can make the most of the lightning-fast developing AI technology.

Take a leap forward in your marketing AI transformation every week

Every Friday, we bring you the latest insights, news and real-world examples on the impact of AI in the marketing world. Whether you want to improve your marketing efficiency, increase customer engagement, sharpen your marketing strategy or digitally transform your business, "Marketing AI Friday" is your weekly guide.

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Apple introduces AI-powered Journal

Apple says it is working on advanced AI language models for iPhones and other devices, and has made significant breakthroughs in their implementation and storage. The company's researchers have developed a technique to store AI data in flash memory, making it possible to run complex AI assistants and chatbots on devices with limited memory.

Apple Diary

These developments could lead to improved Siri features, real-time translation, and advanced capabilities in photography and augmented reality. Apple aims to integrate AI into as many apps as possible, while focusing on privacy and responsible AI development.

The launch of the AI-powered Diary app, may be a harbinger of innovative AI applications on the Iphone and other devices.

The Diary app can automatically suggest key moments from your recent locations visited, photos, music and workouts, to help you get started writing. With the update to OS 17.2, the Diary app will be added automatically, fully integrated into the Apple ecosystem.

Apple emphasizes that privacy is central to the development of this app. Data in the Diary app is encrypted while your iPhone is locked, and users have the option to activate additional security measures such as passcode, Face ID or Touch ID. All diary data is end-to-end encrypted and stored in iCloud, so only you have access to your memories.

Interestingly, diary suggestions are generated locally on the device. This means you have complete control over which suggested moments are shared with the app and added to your journal.

Take a leap forward in your marketing AI transformation every week

Every Friday, we bring you the latest insights, news and real-world examples on the impact of AI in the marketing world. Whether you want to improve your marketing efficiency, increase customer engagement, sharpen your marketing strategy or digitally transform your business, "Marketing AI Friday" is your weekly guide.

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AI and the impact on search engines: Welcome to the zero-click world

Developments in the field of AI are having a significant impact on traditional search engines such as Google Search and, consequently, website traffic. Search behavior online is changing rapidly with the emergence of AI tools such as ChatGPT and Perplexity, founded by OpenAI researcher Aravind Srinivas. The "even Googling" is giving way to "even GPTing. What does that mean for you?

With tremendous dominance in search, 9 out of 10, and ad revenue of more than $280 billion last year, Google faces a significant challenge.

My personal experience since the mid-2023s, using AI exclusively as a starting point for my searches (from general information to very specific system user instructions), has caused me to virtually stop using Google Search. I find the user experience with AI chatbots much nicer and more effective. I hear and read from more and more people that they feel the same way. 

This trend represents not only a shift in how AI affects SEO, but more importantly how it changes user behavior. The zero-click answers of AI chatbots offer an instant solution without the need to navigate through links, ads, pop-ups, SEO-optimized text and cookies. And you won't be retargeted. This is undoubtedly going to have a big effect on traditional search engines and website visits.

Understanding and anticipating AI-driven search behavior

It is important to understand the shift to AI-driven search behavior and respond proactively to it. Therefore, consider the following: 

  1. Current search behavior of your target audience: Analyze how your target audience currently searches, what tools they use to do so and what their preferences are. Repeat this regularly. 
  2. Your website's future role: Define the desired role your website plays in a landscape where AI provides immediate answers. How will your site remain relevant and attractive to your visitors?
  3. Adapting your 2024 strategy: Consider whether your strategy adequately addresses the changes in search behavior and the way content is consumed by your audience. This may require a thorough rethinking of your current approach.

Creating AI-friendly and effective content

The goal is to develop content that not only scores well in the new "Search Generative Experience (SGE)" - generating direct, integrated answers to search queries using AI, but also effectively drives visitors to your website.

This includes:

  1. Visibility in AI tools: Make sure your content is visible and attractive to AI tools such as ChatGPT. This means the content should be very distinctive, clear and informative for answering questions asked.
  2. Diversification of content formats: Video and audio content, such as podcasts, will become more important. These formats offer a dynamic and more distinctive way to reach and engage your audience.
  3. Focus on own experiences and unique insights: Share your own experiences, conduct relevant research, and offer unique insights that are also interesting for other media and platforms to publish. This type of content is attractive to AI because it offers unique perspectives and in-depth analysis.

This allows you to respond effectively to the changing dynamics of search engines and AI-driven search behavior. You also simultaneously ensure the relevance and visibility of your content.

Preparing for the future

What actions can you take to prepare for the zero-click world:

  1. Establish an AI steering committee: Create a multidisciplinary team to lead the development and implementation of AI strategies in your organization. This group will act as "discoverers" of new AI capabilities.
  2. Experiment with chat search functionality from Bing and Bard: This provides initial insights into the development of generative search experiences. You can also see the extent to which you yourself are or are not visible on key search terms.
  3. Analyze risks of declining website traffic: Estimate the impact if traffic from Google Search (and paid search) declines starting in 2024.
  4. Determine your ultimate area of expertise: Research which search terms you have a strong position on that you want to protect (the questions you are the very best at answering) and which questions you want to build a dominant position with answers on.
  5. Experiment with other channels: Reduce reliance on SEO and SEA by experimenting with other channels where your target audience is active and expects and accepts you.

The importance of content

While content remains crucial to being found, it is important not to focus on producing more generic content with AI. Instead, a thoughtful and transformative content strategy is required.

We are ready to support you in this and together develop a strategy that fits the new reality of search behavior and content consumption. 

Take a leap forward in your marketing AI transformation every week

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For what aspects of content marketing can you apply AI?

Have you used AI in your marketing efforts in the past year? By 2024, we are going to see an acceleration in the capabilities of AI, with an ever-increasing impact on all facets of marketing, including content creation, distribution, promotion and strategy development.

As marketers, we will have access to advanced AI tools to create countless forms of content, distribute it in the most personal and relevant way possible, as well as be supported by AI assistants to do this as efficiently and effectively as possible.

I am convinced that by the end of next year our way of working will be radically different than it is today - and this applies to the entire marketing landscape.

Let's take a closer look at the specific marketing aspects where AI can play a role and explore the potential risks.

Content creation 

For many marketers, content creation is the first area they use AI for. They use AI to create content (more effectively) such as text, images, audio and video:

  • Text: Generate, summarize and translate as well as answer emails, and respond to social media posts.
  • Images: Creating and editing visual material.
  • Audio: Enhance and edit sound recordings.
  • Video: Edit and modify video content, such as removing or replacing specific parts.
  • Audio to text: Convert audio to text for transcriptions of podcasts, for example.
  • Text to visuals/videos: Create visual content or videos from written text.
  • Images to videos: Converting images into dynamic video content.
  • Text to AI-generated audio: Convert written text to AI-generated audio, even with your own personalized AI voice.

See below an example from my colleague Denis Doeland, this video was created with only written text as input.

However, with the rise of AI-generated content, it is becoming increasingly difficult to distinguish between "real" content, created by humans such as copywriters or designers, and content created with or by AI. This could strain trust in online content, with implications for Google search results, for example, where we are already seeing an increase in AI-generated articles and even images.

If, as expected, AI is also going to be widely used for responses under social media posts, reviews of products and answering emails, trust will decline in those as well. It is therefore crucial for marketers to be transparent about the use of AI and develop strategies for maintaining authenticity and trust in an era when AI content is becoming more common and accepted.

Content distribution

AI can also be used for content distribution to increase visibility and effectiveness on channels such as social media, websites and email marketing. The possibilities are diverse:

  • Content planning: AI can help create a sophisticated content calendar by analyzing data and trends to determine optimal publication times.
  • SEO Optimization: As search engines continue to change, AI can be used to optimize content for both current search engine algorithms and AI-driven tools in which you also want to become visible.
  • Email marketing: AI helps determine the best send times, optimizes subject lines and creates personalized content for different user segments.
  • Personalized content: On websites and in-apps, AI can be used to make personalized product or content recommendations tailored to the individual user's behavior and preferences.

The use of AI in content distribution enables marketers to not only distribute their content more effectively, but also gain deeper insights into the preferences and behavior of their audience, leading to a more aligned and successful marketing strategy.

Online advertising

The use of AI can make online campaigns more efficient and effective. It can analyze large amounts of data and learn from user interactions allowing it to be used for:

  • Targeting: AI helps identify the most appropriate audience for a campaign using advanced data analysis.
  • Ad creation and variation: Choosing the most effective ad executions and creating several variations to maximize response pricing.
  • Personalization: Customize ads to individual users for a more personalized approach.
  • Advertising Space Optimization: Efficient procurement of ad space by predicting the most effective placement and timing.
  • Testing and Optimization: Continually test and adjust campaigns for maximum impact and results.

By using AI, you can not only refine your social advertising strategy, but also make real-time adjustments based on detailed insights, leading to higher engagement and better ROI.

Customer Service

AI is also a powerful tool for improving customer service, making processes more efficient and effective. A common application is the chatbot through websites, chat rooms, instant messaging apps and social media.

Here are some concrete ways AI is improving customer service:

  • Providing information: Chatbots can assist customers in finding specific information on the Web site, such as details about products, services, employees or the company itself.
  • Problem solving: They are able to help customers solve simple problems with products or services, or provide usage instructions.
  • Feedback and experience: Chatbots can actively gather feedback from customers about their experiences with the company, providing valuable insights for continuous improvement.

So the use of AI in customer service goes beyond automation; it is about creating a more personalized, interactive and effective customer experience.

Content strategy

By 2024, AI will play a crucial role in developing content strategy. Through advanced data analysis and trend identification, AI enables marketers to gain deeper insights into what really moves their audiences. This goes far beyond traditional methods and includes:

  • Fan journey analysis: Understanding how the audience's fan journey unfolds and what moments are crucial.
  • Search Terms Research: Identify search terms relevant to the audience, which helps create SEO-optimized content.
  • Touchpoints optimization: Recognize key interaction points and optimize them for better customer experience.
  • Topic Selection: Determine topics that appeal to audiences based on their preferences and behaviors.
  • Story selection: Determine which stories best fit the fan journey.
  • Content concepts and formats: Analyze which content concepts and formats - such as video, blogs, infographics - are most effective.
  • Distribution Choice: Identify the most effective channels to reach audiences, from social media to email marketing to in-house GPTs.

Data and insight

AI can process large amounts of data to identify trends, consumer behavior and market developments. These analyses contribute to data-driven decision-making and help hone content marketing strategies. AI's capabilities include:

  • Predicting customer behavior: Using historical data to predict future customer behavior, which helps anticipate changing needs.
  • Effectiveness Measurement: Measuring the impact of content marketing, from user engagement to conversion.
  • Results improvement: AI algorithms continuously analyze the performance of different content forms and channels to increase effectiveness.
  • Strategic adjustments: Based on the insights gathered, AI can help refine the content marketing strategy to better suit the audience.

This overview is just a starting point; the possibilities of AI in marketing are almost endless. As is becoming clear, AI can play a revolutionary role in virtually every aspect of marketing.

Want to implement AI in your marketing efforts and across your organization?

Here are some essential steps to consider in 2024:

  1. Establish an AI steering committee: Create a team with representatives from each department. These "discoverers" will take the lead in developing and implementing AI strategies in your organization.
  2. Develop an AI policy: Establish both internal and external AI guidelines. Check out our template AI policy for inspiration.
  3. Invest in AI training: Make sure your team has the necessary knowledge and skills to work with AI.
  4. Map work processes: Identify where AI can have the greatest impact and how to deploy it effectively.
  5. Experiment with AI: Test different AI tools to see what works best for you. Consider an "AI Friday" to try out new applications.

Remember: with any major change, and especially something with an impact like AI, success depends on your team's willingness to adapt and take responsibility. It can be valuable to explore the different stages an organization goes through when adopting AI.

Check out "The 5 Phases of AI Implementation in Marketing" for more for more insight and practical tips.

With the right preparation and mindset, AI can be a gamechanger in your marketing strategy, making your business more efficient, effective and data-driven.

Take a leap forward in your marketing AI transformation every week

Every Friday, we bring you the latest insights, news and real-world examples on the impact of AI in the marketing world. Whether you want to improve your marketing efficiency, increase customer engagement, sharpen your marketing strategy or digitally transform your business, "Marketing AI Friday" is your weekly guide.

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Google introduces an AI game-changer with Gemini

Last year, OpenAI, with their groundbreaking ChatGPT, shook up the world of AI. Meanwhile, Google, known as one of the pioneers in AI technology, remained somewhat out of the spotlight. But with the unveiling of game-changer Gemini, they seem poised to take a giant leap forward!

The game is on!

This week, Google introduced Gemini. Unlike current language models such as GPT-4, Gemini is a "multimodal AI model" capable of processing and understanding information from multiple sources in real time, including text, images, video, audio and code.

The introduction looks very impressive and promises applications we haven't seen before with ChatGPT or other AI tools. While the impression is given that this is a real-time demo of how Gemini works, soon after the introduction it turns out that this is not quite so. The video has been edited and there were no real spoken prompts. Google states that "all user prompts and outputs in the video are real but shortened to keep the video concise(read: among other things, the extra promps needed for this output were omitted). The video illustpricing what multimodal user experiences built with Gemini might look like. We created it to inspire developers".

Watch this 6-minute introductory video to get a first impression.

What can Gemini be used for? 

Its multimodality makes Gemini a powerful tool for a wide range of applications, including:

  • Natural language processing (NLP): Gemini can understand and process text, and can be used for tasks such as translations, summaries and answering questions. 
  • Computer vision (CV): Gemini can understand and process images and videos, and can be used for tasks such as object recognition, face recognition and pattern recognition.
  • Audio processing (AP): Gemini can understand and process audio, and can be used for tasks such as speech recognition, music analysis and sound synthesis.
  • Code understanding (CU): Gemini can understand and process code, and can be used for tasks such as code analysis, code generation and code assistance.

How does Gemini perform relative to other AI models such as GPT-4?  

According to Google, Gemini is based on one of the largest and most advanced AI models in the world and also significantly outperforms other AI models, including GPT-4.

For example, in a natural language processing (NLP) test, Gemini scored 20% better than GPT-4, and in a computer vision (CV) test, Gemini scored 15% better than GPT-4.

With a score of 90.0%, Google also says Gemini Ultra is the first model to outperform human experts on MMLU (massive multitask language understanding), which uses a combination of 57 subjects such as math, physics, history, law, medicine and ethics to test both world knowledge and problem-solving skills.

Key differences between Gemini and GPT-4 (according to Google)

  • Multimodality: Gemini is a multimodal AI model, meaning it is capable of processing and understanding information from different sources. For example, it can translate text, taking into account the context of the image associated with the text. Identify objects in an image, taking into account the text written about the object. Write code that performs a particular task, taking into account the audio instructions associated with the code. GPT-4 is a language model, which means that it can only focus on text.
  • Size and complexity: Gemini is the largest and most advanced AI model in the world. GPT-4 is a large language model, but it is not as large or complex as Gemini. Gemini is based on 1.56 trillion parameters, GPT-4 on 1.5 billion parameters. 
  • Different versions: Gemini is available in different versions, with different capacities and capabilities. GPT-4 is currently only available in one version.

Gemini will first be integrated with the chatbot Bard, followed by other Google applications such as Pixel 8 Pro, Search, Ads, Chrome and Duet AI. The amount of applications and data Google has access to (think also Gmail, YouTube, Next camera and Google maps), gives a huge potential and unique position compared to other parties such as OpenAI.

It will first be rolled out in 170 countries outside Europe, so we will have to be patient until we can start using it in the Netherlands as well. 

A side note is that Gemini's Ultra model is compared to GPT-4 and where it scores better these are only a few percentage points. The exact operation of Google's top AI model is still uncertain and is not expected to be rolled out until early 2024, while GPT-4 has been available since March 2023. OpenAI has been developing GPT-5 behind the scenes for a long time, so it will be interesting to see what the applications of this will be and when it is rolled out.

See more about the Gemini launch here. 

Take a leap forward in your marketing AI transformation every week

Every Friday, we bring you the latest insights, news and real-world examples on the impact of AI in the marketing world. Whether you want to improve your marketing efficiency, increase customer engagement, sharpen your marketing strategy or digitally transform your business, "Marketing AI Friday" is your weekly guide.

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Expectations for AI in 2024: Introducing Second Generation Large Language Models (LLMs)

It's December already, and many companies are busy finalizing their plans for the coming year. To what extent is AI an important part of your plans for 2024?

To prepare you for the latest AI developments, I will take you through the evolution and expectations of Large Language Models (LLMs). I'll also give you more background on these fundamental language models that underpin AI systems. 

LLM generation 1

An LLM is an advanced computer program that learns to understand and generate large amounts of text. It is trained on a huge amount of data, including books, articles, and Web sites, to recognize language patterns. This allows it to answer questions, write texts, and even conduct conversations in a way that resembles how people communicate. The model tries to understand the meaning behind words, allowing it to provide relevant and often accurate answers.

A year ago, OpenAI launched ChatGPT, introducing an accessible first-generation language model (LLM 1) worldwide. This launched a wave of innovations, with numerous companies introducing AI applications based on the same foundation.

Although these first-generation language models are revolutionary in their ability to rapidly process and generate language, they suffer from limitations in comprehension and contextual accuracy.

Effective use in our daily operations often still requires significant human guidance and correction. We see this in current applications such as ChatGPT and other available AI tools. These systems respond immediately to prompts, but the responses are not always accurate. .

LLM generation 2 announces itself

The AI industry continues to rapidly evolve, and is on the eve of introducing the next generation of language models: LLM 2.

This generation offers a much more accurate and contextually sensitive understanding. They are better able to interpret nuances and complex language structures, and can arrive at results with greater attention and logic.

A key characteristic is their ability to "think" more independently and deeply, leading to better outcomes. These systems can also improve themselves through experience and feedback.

What to expect from LLM 2 systems:

  • Can read and generate text
  • Real-time Internet access and reference to local files.
  • Integration with existing software infrastructure (calculator, mouse, keyboard, Python)
  • Seeing and generating images and videos.
  • Hearing and speaking (such as GPT voice), including music generation.
  • Customization for specific task execution, adaptation and refinement (such as custom GPTs)
  • Communication with other LLMs.
  • Longer "thinking" processes for optimal results.
  • Self-improvement.
  • More knowledge on subjects than any human being.

Some of these features are already starting to show up in systems such as ChatGPT and Google Bard.

Interested in learning more about these language models?

Andrej Karpathy, a leading AI researcher at OpenAI and former director of AI at Tesla, posted a one-hour video on YouTube, "The Busy Person's Intro to LLMs.

In it, he offers unique insight into projects from leading AI labs. His presentation provides a practical introduction to LLMs, their operation, and visions for the future. Highly recommended! Watch the video here. 

Tips for your AI plans in 2024

How will you implement AI in your organization? Here are some tips to include in your plans for 2024: 

  1. Establish an AI steering committee consisting of representatives (discoverers) from each department in your company. This group takes the lead in developing and implementing AI policies within your organization.
  2. Establish an internal and external AI policy. Check out our template AI policy for inspiration.
  3. Invest in AI training and education for your team.
  4. Map all work processes. Identify where AI can have the greatest impact and plan how to effectively deploy AI here.
  5. Experiment with AI. How about introducing "AI Friday" to test new applications?

With any change, especially one of such great impact and magnitude, successful implementation depends on how well the people within an organization can adapt their behavior and take responsibility.

Therefore, it is useful to examine the different phases an organization goes through when implementing AI. Check out "The 5 phases of AI implementation in marketing" for more insight.

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