Narrow AI: The Underdog Disrupting the Industry Giants

Image and Text Classification for Supercharged Efficiency

In today’s Caveminds edition…

ML Classification is like an expert guide in a dense forest of data, making swift, informed paths to growth by making optimal decisions.

Today you will learn how this superpower is leveraged.


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Unleashing the Power of AI for Smarter Business Decisions

Imagine navigating a dense forest where each tree represents a piece of data or a customer preference. Without a guide, it's easy to get lost.

There are, however, expert guides helping companies navigate through the forest of information to make intelligent decisions swiftly.

AI-powered classification clears a path, distinguishing between what's relevant and what's not, ensuring businesses can find their way to insights that drive growth and resonate deeply with their audience.

ℹ️ Why This Matters Today:

There are infinite ways to leverage AI. But the buzz makes it hard to separate signal from noise.

Image, voice, and text classification are incredibly crucial to understand and integrate so that your business stays competitive.

🏆 Golden Nuggets:

  • You can quickly train and deploy custom machine learning models for image and text classification, with training times orders of magnitude faster than industry giants like Google, while maintaining comparable accuracy.

  • Don’t build AI for the sake of AI. This is key to understanding the different types of AI functions (generative vs. discriminative) and aligning them with specific business needs.

  • You can leverage AI's potential by strategically using discriminative AI, embracing third-party services for agility, and keeping pace with the industry.

Diving Into New Depths: Literally Every Industry Could Benefit From AI Classification

Did you know that not so many years ago, coral reefs were mapped manually by divers that would dive, count and classify types of coral underwater?

How many years, decades, or centuries would take to track the entire planet if this never changed?

Oscar Beijbom, CTO and cofounder of Nyckel, shares how his company’s inception came from his previous company, CoralNet. Back then, he created a method that would dramatically shift coral reef mapping, thanks to AI.

CoralNet allow scientists to simply upload reef images, and AI handles the classification and analysis swiftly and accurately.

We allowed them to just upload their photos and then start annotating them in the browser. And then we basically had an AI that learned and took over for them.

Oscar shares

This leap from manual labor to AI automation highlights the power of AI-powered classification to streamline processes, save costs, and speed up operations, not just in crucial environmental conservation efforts, but in business too.

Check out this amazing episode of the Caveminds podcast where Osar Beijbom, an expert with over 20 years in the AI industry shares incredible insights:

Impact among industries, sectors are substantial, and use cases are virtually endless. Let’s have a look at them:

💰 Impact On Your Business

From automating waste sorting to streamlining traffic, enhancing customer shopping experiences, and filtering out harmful content, machine learning (ML) can transform every pixel and data point into actionable insights for your business

"And we have several of those types of IoT customers, and marketing customers. So they look at ads and they say, this particular ad, does it have a logo in it or not?

Does it have a face in it or not? And then they can start extracting metadata out of their own ads or someone else's ads and start doing correlation analysis and see what type of content, tend to do better than not."

— Oscar mentions when talking about some use cases.

Virtually every sector will benefit from the correct integration of this technology. Let’s see what’s the current impact on some sectors so you can get a broad perspective of how massive this is:

Retail Sector:

Applications range from demand forecasting, pricing, promotion management, and supply chain optimization that translate into maximized sales and operational efficiency. AI classification showed:

  • Improvement in forecast accuracy of up to 25 percentage points

  • Decrease in out-of-stocks by up to 30%

  • Reduction in stock levels by up to 15%.

This means happy customers who won't be glaring at empty shelves like they just missed the toilet paper rush of 2020. 🧻 

Banking & Financial Services:
  • AI-driven integration can enhance personalization in sales, resulting in up to 15% improvement in cross-selling.

  • Risk management sees a decrease in default rates by up to 0.9 percentage points,

  • Claims handling process accelerates by up to 4x.

This not only streamlines operational efficiency but also significantly improves customer trust and satisfaction.

Basically, it's a win-win for everyone: happy customers, smoother operations, and maybe even a little more free time for your team.

IT Operations:

Benefit from smoother operations, reduced operational costs, and significantly improved service delivery for IT businesses. Here are some key stats:

  • Up to 30% fewer critical failures.

  • 60% reduction in downtime.

  • Volume of event streams decreased by up to 98%, optimizing network diagnostics, anomaly detection, and service ticket management.

It's like giving your IT team superpowers - they can fix things before they break, keep everything running smoothly, and spend less time wrestling with alerts.

💡 Best Use Cases

And use cases or applications are literally endless. Here some of them that hopefully you can relate and apply into your business or get a broader range of possibilities:

  1. Image and Text Classification for Various Industries: This functionality is ideal for content sorting, moderation, real-time labeling, and creating custom classifiers with minimal input.

    Nielsen, managed to cut down by 93% the time required for product information extraction from images, leading to significant time savings.

    Unsplash went to 20K to 40M photo downloads by improve their image tagging process, making their vast photo library more accessible and searchable.

  2. Real-time Content Moderation: You can automate the moderation of user-generated content, ensuring it aligns with brand values and compliance requirements, maintaining quality and safety in your community.

    For instance, the UN automated 91% of the detection of propaganda on social media platforms, significantly increasing efficiency and reaction times.

  3. Marketing and Advertisement Analysis: It’s possible to analyze advertisements for the presence of logos or faces, extract metadata, and perform correlation analysis to understand how different types of content perform. This helps in optimizing marketing strategies and understanding audience engagement better.

  4. IoT Applications: Oscar even shares an example in the context of IoT, where classification is applied to manage indoor gardens. Cameras integrated with the gardens use Nyckel to notify users about their plants' needs, such as watering or flowering, through classification functions. User experience grew through the roof by automating plant care advice.

  5. Automated Customer Support: By classifying customer support inquiries, identify the nature of customer needs to streamline these processes.


David vs Goliath? When GPT-4, Google Vertex and AWS get beaten hard

Our default system leads us to go to the familiar and known, like GPT-4, Google, Amazon, etc.

But what if the not-so-known smaller, could actually outperform these giants?

That’s the case for narrow AI models like Nyckel’s multiple options. You can train faster, sometimes in just seconds, compared to hours or even more for the larger platforms. For example, with one platform clocking in models at speeds 100 to 1000 times faster than AWS.

Accuracy doesn't suffer for speed either. Even with limited data, narrow AI models proved to be more effective, providing an accessible entry point for businesses to harness machine learning without needing deep expertise or resources.

The shift towards Narrow AI represents a classic "David vs Goliath" scenario in the tech world.

For developers and product teams, this means more accessible, easier-to-integrate solutions that don't break the bank. So look out there; there's significant value in focusing on specialized models for specific needs.

💡 Ideas to Marinate:

Consider how AI can transform your business not by chasing the latest tech but by solving real customer problems. Not by relying solely on the biggest players but also in the smaller but specialized experts.

Building Your Own Classification ML Function

Here’s a quick overview of how you could bypass the tedious, manual sorting and labeling of documents and improve the efficiency of document routing by building a classification model. You can thank us later 😉 

⚒️ Actionable Steps

1. Define the Problem:

  • Identify the different document types you want to classify (e.g., invoices, contracts, emails).

  • Decide whether visual features (image classification) or textual content (text classification) are more important for distinguishing the documents.

2. Gather Data:

  • Collect a representative sample of documents for each category you want to classify.

  • Ensure the data size is sufficient for training the model effectively.

  • Annotate the documents with the corresponding category labels.

3. Choose the Right Approach:

  • If documents have distinct visual styles (invoices), use image classification.

  • If the content itself is crucial for classification (legal documents), use text classification with OCR for image-based documents.

4. Select a Platform (consider Nyckel as an example):

  • There are various platforms available for building document classification functions.

  • Choose one that aligns with your technical expertise (pre-built vs. coding) and budget.

5. Train the Model:

  • Follow the platform's instructions to upload your labeled data.

  • Define the model parameters and training configurations.

  • Train the model and monitor its performance.

6. Evaluate and Refine:

  • Assess the model's accuracy on a separate test dataset.

  • Identify areas for improvement (e.g., specific document types the model struggles with).

  • Re-train the model with additional data or adjusted configurations.

7. Deployment and Use:

  • Once satisfied with the model's performance, deploy the function on the chosen platform.

  • Integrate the function into your workflow to automatically classify new documents.

Additional Tips:

  • Start with a smaller number of document categories and expand gradually.

  • Continuously monitor the model's performance and retrain with new data as needed.

  • Leverage features like "invoke capture" to gather data for improvement over time.

If you want to access the full article and tutorial:

  • makes image and text classification easy for everyone. In just a few minutes, you can build an AI model with no machine learning experience needed.

  • helps companies implement AI-powered solutions, with the main focus on AI guidance and AI implementation services.

  • — global leader in combining people and technology to support the artificial intelligence (AI) development lifecycle, from data curation and annotation to quality assurance and model optimization.


OpenAI introduced Voice Engine, a model that generates speech from a 15-second audio sample. It's already powering ChatGPT Voice among other applications.

They're being super careful about rolling it out, thinking about safety and all its implications. For example, they are suggesting that banks might want to rethink using voice authentication since this tech can clone voices so well.

Nevertheless, initial tests show awesome potential in applications like educational support and multilingual content translation.

What’s clear so far: there will be a huge societal adaptation to these technologies.

That’s all for today’s edition!

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