Back in 2024, the world of artificial intelligence took a bold step forward with the release of the Visual Haystacks (VHs) benchmark. As someone who’s built and managed startups in deeptech, I’ve always been curious about how cutting-edge technologies are pushed to their limits, especially in ways that directly impact fields like healthcare, satellite imaging, and even urban planning. Benchmarks like VHs, which test the ability of AI systems to process and reason over multiple images, are not just academic exercises. They’re roadmaps for the next wave of practical tools that could drive advantages for founders like you and me.
Let’s unpack what this new development means and the lessons startups can draw from it when leveraging AI tools for real-world applications.
Why Visual Haystacks is Different
Until recently, AI models have done a decent job answering questions about a single image. For example, you upload a photo, and the AI can identify objects, describe scenes, or even estimate emotion. But how would an AI fare if tasked with analyzing thousands of images to find one specific element and then draw conclusions based on it? Say you’re looking for a pattern across security camera footage or trying to pick out anomalies in patient scans, most of today’s AI just isn’t built for that level of reasoning.
The VHs benchmark, introduced by researchers from UC Berkeley, aims to address this gap. Unlike earlier benchmarks that focused on simple text-associated tasks like identifying text in an image, VHs tests models where meaningful analysis needs to span across multiple images. For companies in healthtech, logistics, or global analytics, this could be game-changing.
Multi-Image Reasoning: Why Startups Should Care
Here is why this matters. As a founder, you’re exposed to the everyday grind of processing massive data streams, whether for customer behavior analysis, automation, or decision-making. Technology tools that can analyze not just a single data point but multiple, interconnected ones will drastically reshape efficiency and accuracy. This concept of “multi-input” analysis isn’t just for geeks in academia, it applies to everyday business applications like these:
- Healthcare Diagnostics: Imagine trying to diagnose a condition that requires comparing a patient’s current scans with all previous tests. Current diagnostic AI tools often struggle with extracting insights from multiple data points at once. Visual Haystacks measures exactly this: how well an AI system retrieves and integrates real patterns from massive data sets.
- E-commerce Catalog Management: Scaling an online store with thousands of products? Understanding trends or identifying inconsistencies across product images could boost sales conversions.
- Urban Planning: Analyzing aerial or satellite images to monitor areas for new developments or environmental tracking.
Investors are drawn to startups that position themselves as leaders, and it means showing an understanding of the tech’s limitations. You don’t need to expect AI to “think” like a person yet. Insights like these from the VHs benchmark should help shape how you communicate your understanding of AI during pitches.
Shocking Findings You Need To Know
The initial findings from the VHs benchmark highlight serious flaws in existing large multimodal models (LMMs) – the same class of AI driving tools from OpenAI, Google, and others. And here you need to pay attention because this tells us what not to expect or overpromise when incorporating AI into your startup solutions:
- Performance Drops with Scale
When tested on thousands of images, every AI model struggled. Open-source tools like LLaVA could only manage to process up to three images optimally, while API-based tools like GPT-4 hit limits at around 1,000 images. Something most founders don’t recognize is that context limitations aren’t just an AI problem, they’re an infrastructure one. For startups building visual applications, this means navigating size limits carefully while highlighting how you get around them with clever engineering. - Poor Multi-Image Reasoning
Even top proprietary models like GPT-4o and Google’s Gemini didn’t perform better than random guessing when tasked with reasoning across multiple images. This reinforces the need for specialized training and proofs of concept, especially when pitching solutions to industries like healthcare or security. - Sensitive to Distractors
Models demonstrated weaknesses when irrelevant or tricky elements were included in large image sets. As a result, founders building dashboards or analytics tools must keep UIs simple while ensuring better pre-selection of useful insights before involving your AI.
A Practical Guide for Startups: Using AI for Multi-Data Source Tasks
1. Understand AI’s Existing Limits
Don’t rush to adopt an off-the-shelf tool with a buzzword-heavy sales pitch. Current multi-modal systems still have trouble pulling insights across unstructured sets of data. Be clear if your solution involves hundreds or thousands of data elements, plan for testing with tools tailored for large datasets.
2. Pre-Processing is Just as Crucial
Founders underestimate cleaning their data. Whether parsing user behavior, supply chain statistics, or medical results, anything you provide to the AI should be matched seamlessly for the task. Tools like Python libraries, along with pre-trained models like CLIP, can help refine and prepare this data before throwing it at an AI tool.
3. Rethink Your MVP Goals
If real-world applications require a nuanced understanding of connected data, it might make sense to use benchmarks like VHs to stress-test AI systems before full deployment. It’s better than over-promising capabilities or scrambling to account for AI’s shortcomings post-launch.
Common Mistakes When Using Multi-Image AI
If you’re planning to incorporate advanced visual AI systems in your startup, here’s how NOT to trip up:
- Assuming Popular Models Can Do It All
Big tools like ChatGPT aren’t necessarily the right fit for multi-image tasks. Choose specialized tools and retrain them for specific datasets. - Relying Solely on Off-the-Shelf Tools
The results on Visual Haystacks show that out-of-the-box models have significant challenges with complex tasks. Founders need to consider custom training and active involvement in creating task-specific datasets. - Lack of Testing Across Scenarios
A visual tool built on AI should be tested with outlier cases, messy data, “trick questions,” or chaotic inputs, to ensure robust performance.
The Rise of MIRAGE
In response to the challenges highlighted by VHs, the researchers introduced a solution called MIRAGE, which stands for Multi-Image Retrieval Augmented Generation. As the name suggests, it focuses on improving how AI retrieves the right data and makes that data relevant to incoming queries. MIRAGE showed vast improvement on VHs benchmarks, surpassing existing models significantly.
While currently open-source and complex to deploy without technical support, its release marks an exciting opportunity for startups. For companies looking to test drive the AI’s ability to scale reasoning tasks without significant infrastructure investment, this is something to bookmark and explore further.
Wrap-Up
VHs highlights that multi-image reasoning, while still in its early stages, can open doors to practical applications that could transform various industries. If you’re building something in a domain where processing multiple forms of unstructured data is critical, benchmarks like this are invaluable in showing not just what AI can currently do, but where you, as an innovator, need to pay closer attention.
Above all, let’s ensure that while AI grows, it meets real-world standards. Anything less won’t just waste resources but could lead to a rapid evaporation of hard-won investor confidence. Use the lessons drawn from benchmarks like VHs to stay grounded as you build the next big thing, because, as we’ve just learned, even the most advanced AI systems have their limits, and knowing them is the secret edge for us entrepreneurs.
FAQ
1. What is the Visual Haystacks (VHs) benchmark?
The Visual Haystacks (VHs) benchmark is a test designed to evaluate how well AI models can perform multi-image reasoning tasks, such as analyzing large sets of unrelated images and retrieving meaningful insights. Learn about Visual Haystacks
2. Why is multi-image reasoning important?
Multi-image reasoning is essential for real-world applications like medical diagnosis using patient scans, analyzing satellite imagery, and monitoring security footage. It allows AI to draw more complex insights by processing interconnected data across multiple sources.
3. Who created the Visual Haystacks benchmark?
The VHs benchmark was developed by researchers at UC Berkeley, including Tsung-Han Wu, Giscard Biamby, and others. Discover Visual Haystacks on GitHub
4. How does Visual Haystacks differ from existing benchmarks?
Unlike previous benchmarks focused on single-image reasoning, Visual Haystacks challenges AI models to retrieve and analyze information from larger datasets of 1,000 to 10,000 images. This provides a more rigorous test of their multi-image capabilities. Read the official Visual Haystacks blog
5. What are the current limitations of AI models in this field?
AI models struggle with tasks involving large datasets and multi-image reasoning, showing significant performance drops when processing vast image collections, handling distractors, and integrating information across unrelated images. Learn more about these findings
6. What industries can benefit from multi-image reasoning AI?
Industries such as healthcare (diagnostics), urban planning (satellite and aerial image analysis), e-commerce (catalog management), and security (surveillance footage) can benefit from advancements in multi-image reasoning technology.
7. What is MIRAGE, and how is it related to VHs?
MIRAGE (Multi-Image Retrieval Augmented Generation) is a retrieval-based AI framework developed to improve multi-image reasoning tasks. It showed significant performance improvements in the VHs benchmark compared to existing models. Discover MIRAGE
8. Are current large multimodal models effective in multi-image reasoning?
No, most current models like GPT-4o, CLAUDE-3 Opus, and Google’s Gemini-1.5 struggle with long visual context and multi-image reasoning, performing poorly in tasks such as recognizing patterns across multiple images.
9. How does VHs impact AI tool development for startups?
The VHs benchmark highlights existing AI limitations, which can help startups test the constraints of their AI tools and ensure realistic performance claims in applications such as healthcare, security, and e-commerce. Explore Visual Haystacks in detail
10. How can startups prepare for multi-image data challenges?
Startups should understand AI limitations, preprocess data rigorously, and choose the right tools for multi-image tasks. Additionally, using benchmarks like VHs can help stress-test AI tools before deployment.
About the Author
Violetta Bonenkamp, also known as MeanCEO, is an experienced startup founder with an impressive educational background including an MBA and four other higher education degrees. She has over 20 years of work experience across multiple countries, including 5 years as a solopreneur and serial entrepreneur. Throughout her startup experience she has applied for multiple startup grants at the EU level, in the Netherlands and Malta, and her startups received quite a few of those. She’s been living, studying and working in many countries around the globe and her extensive multicultural experience has influenced her immensely.
Violetta Bonenkamp’s expertise in CAD sector, IP protection and blockchain
Violetta Bonenkamp is recognized as a multidisciplinary expert with significant achievements in the CAD sector, intellectual property (IP) protection, and blockchain technology.
CAD Sector:
- Violetta is the CEO and co-founder of CADChain, a deep tech startup focused on developing IP management software specifically for CAD (Computer-Aided Design) data. CADChain addresses the lack of industry standards for CAD data protection and sharing, using innovative technology to secure and manage design data.
- She has led the company since its inception in 2018, overseeing R&D, PR, and business development, and driving the creation of products for platforms such as Autodesk Inventor, Blender, and SolidWorks.
- Her leadership has been instrumental in scaling CADChain from a small team to a significant player in the deeptech space, with a diverse, international team.
IP Protection:
- Violetta has built deep expertise in intellectual property, combining academic training with practical startup experience. She has taken specialized courses in IP from institutions like WIPO and the EU IPO.
- She is known for sharing actionable strategies for startup IP protection, leveraging both legal and technological approaches, and has published guides and content on this topic for the entrepreneurial community.
- Her work at CADChain directly addresses the need for robust IP protection in the engineering and design industries, integrating cybersecurity and compliance measures to safeguard digital assets.
Blockchain:
- Violetta’s entry into the blockchain sector began with the founding of CADChain, which uses blockchain as a core technology for securing and managing CAD data.
- She holds several certifications in blockchain and has participated in major hackathons and policy forums, such as the OECD Global Blockchain Policy Forum.
- Her expertise extends to applying blockchain for IP management, ensuring data integrity, traceability, and secure sharing in the CAD industry.
Violetta is a true multiple specialist who has built expertise in Linguistics, Education, Business Management, Blockchain, Entrepreneurship, Intellectual Property, Game Design, AI, SEO, Digital Marketing, cyber security and zero code automations. Her extensive educational journey includes a Master of Arts in Linguistics and Education, an Advanced Master in Linguistics from Belgium (2006-2007), an MBA from Blekinge Institute of Technology in Sweden (2006-2008), and an Erasmus Mundus joint program European Master of Higher Education from universities in Norway, Finland, and Portugal (2009).
She is the founder of Fe/male Switch, a startup game that encourages women to enter STEM fields, and also leads CADChain, and multiple other projects like the Directory of 1,000 Startup Cities with a proprietary MeanCEO Index that ranks cities for female entrepreneurs. Violetta created the “gamepreneurship” methodology, which forms the scientific basis of her startup game. She also builds a lot of SEO tools for startups. Her achievements include being named one of the top 100 women in Europe by EU Startups in 2022 and being nominated for Impact Person of the year at the Dutch Blockchain Week. She is an author with Sifted and a speaker at different Universities. Recently she published a book on Startup Idea Validation the right way: from zero to first customers and beyond, launched a Directory of 1,500+ websites for startups to list themselves in order to gain traction and build backlinks and is building MELA AI to help local restaurants in Malta get more visibility online.
For the past several years Violetta has been living between the Netherlands and Malta, while also regularly traveling to different destinations around the globe, usually due to her entrepreneurial activities. This has led her to start writing about different locations and amenities from the POV of an entrepreneur. Here’s her recent article about the best hotels in Italy to work from.