Vision-Language Models (VLMs) excel in integrating visual and textual information for vision-centric tasks, but their handling of inconsistencies between modalities is underexplored. We investigate VLMs' modality preferences when faced with visual data and varied textual inputs in vision-centered settings. By introducing textual variations to four vision-centric tasks and evaluating ten Vision-Language Models (VLMs), we discover a \emph{``blind faith in text''} phenomenon: VLMs disproportionately trust textual data over visual data when inconsistencies arise, leading to significant performance drops under corrupted text and raising safety concerns. We analyze factors influencing this text bias, including instruction prompts, language model size, text relevance, token order, and the interplay between visual and textual certainty. While certain factors, such as scaling up the language model size, slightly mitigate text bias, others like token order can exacerbate it due to positional biases inherited from language models. To address this issue, we explore supervised fine-tuning with text augmentation and demonstrate its effectiveness in reducing text bias. Additionally, we provide a theoretical analysis suggesting that the blind faith in text phenomenon may stem from an imbalance of pure text and multi-modal data during training. Our findings highlight the need for balanced training and careful consideration of modality interactions in VLMs to enhance their robustness and reliability in handling multi-modal data inconsistencies.

Making AI more'humble' could not only help improve AI decision making, but could also help inspire ... [ ] more trust in the technology as a whole, and open the door for more useful and mission-critical applications in the future. AI is notoriously difficult to explain, and some deep learning algorithms can be too complex for even their creators to understand their reasoning. This makes it hard to trust what AI is doing, and even harder to find mistakes before it's too late. Having an algorithm stop partway through its reasoning to check with a human-in-the-loop could inspire more trust in AI, and open the door for the technology to be used in more sensitive and mission-critical applications. Injecting some'humility' into AI in this way could not only make AI more trustworthy and change how companies think about AI, but it could also help to demystify AI and reveal it as the logical and reliable technology that it is.

Nearly half of the tasks currently undertaken by humans could already be automated, even at current levels of technology. Within the next decade it is likely large sections of society will be looking for new jobs. People are calling it the fourth industrial revolution or "industry 4.0". The first industrial revolution used steam power to mechanise production. The second used electric power to mass produce products while the third introduced computers to automate production. The fourth revolution is happening now, disruptive technologies including the internet of things, virtual reality, robotics and artificial intelligence are changing the way we interact, work and live.

Is your algorithm fair and unbiased? How can you be sure that the insights it offers are correct? It's a question that's being asked with increasing frequency in the last year. That's because when it comes to machine learning, data goes into a "black box" and insights emerge on the other side. The algorithm itself is inside this so-called black box.

Marketers with blind faith in adtech and martech are at risk of putting advertising in front of people who were going to buy anyway and waste time and money on useless personalisation activities. This was the message from IAG's director of media and technology, Dr Willem Paling, and one-to-one marketing director, Jason Ridge, who took to the stage at CeBIT 2018 to discuss where and when to use AI-enabled martech, and why it's vital to set the right goals around it. Ridge said all consumers expect personalised experiences, and marketers can throw data into AI platforms to ensure the content coming out of their tech engines is personalised. But IAG is starting to ask if it really needs the technology to achieve relevance. "No one will say delivering customers' personalisation isn't a good thing. However, Amazon has hundreds of millions of products, and Facebook has millions of advertisers, and given the opportunity to put a product or content in front of customers, they are left with a dilemma of which product or content to put in front of which customer. So of course it makes sense to use AI or ML to do this," Ridge said.