Facebook launches 'Your Algorithm' feature for feed personalization using Artificial Intelligence
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Olhar Digital
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Facebook launches 'Your Algorithm' feature for feed personalization using Artificial Intelligence

Facebook is introducing a feature called 'Your Algorithm,' which gives users the ability to specify which topics they would like to see more or less frequently in both the Feed and Reels. This functionality uses artificial intelligence (AI) to refine suggestions based on these declared preferences.

This addition expands the modes of engagement with the social network's algorithm. Instead of relying exclusively on metrics such as likes, comments, or viewed videos to infer interests, the system will now accept written commands provided by the users themselves.

The feature had already been implemented on Instagram, where it allows users to add and manage topics of interest in a specific section, accessible through the three-dot menu on posts. Within this same menu, users can select the options 'I am interested' or 'I am not interested.' These buttons can also be displayed directly under certain posts, and the user's response may lead Facebook to ask additional questions to better understand their preference.

Another available option is allowing the user to describe in their own words the type of content they wish to find with more or less frequency. According to Meta, recommendations will be adjusted in real time, with potential modifications during the same usage session.

Currently, Facebook uses behavioral indicators to estimate each individual's interests; likes, comments, and watched videos are factors that determine which posts appear in the feed. However, Meta is now integrating large language models (LLMs), which have the capacity to interpret instructions in natural language and consider word context.

In practice, this means the user can directly express what they are looking for, overcoming the need to only signal preferences through passive interactions with posts. The company claims that this combination makes recommendations much more adaptable.

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Technology allows AI to detect errors in understanding human intentions from brain signals
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olhardigital.com.br

Technology allows AI to detect errors in understanding human intentions from brain signals

Researchers have developed a technology capable of recognizing when artificial intelligence (AI) has misinterpreted human intentions by analyzing brain signals. The goal of this development is to enable AI systems to recognize the state of 'that was not what I meant' without the need for verbal clarification from the user.

The work was conducted by scientists from Korea Advanced Institute of Science and Technology (KAIST) in collaboration with Microsoft Research Asia. This technology, named Neural Value Alignment (NVA), uses a brain-computer interface to analyze data obtained through electroencephalography (EEG), allowing AI to correct its behavior in real time.

The core idea arose from a common problem in human-machine interaction: the same human action can correspond to different goals, and the same goal can be achieved in multiple ways. For example, a person picking up a cup does not necessarily mean the intention to drink from it; the person might want to pass the object, wash it, or simply place it elsewhere.

Similarly, if the goal is to quench thirst, a person might pick up a cup, find a bottle of water, or ask someone to bring a drink. This ambiguity between action and goal leads to AI systems potentially interpreting the action correctly but being wrong about the true desire of the human, or understanding the goal but choosing a different method of achieving it than expected by the user.

During experiments, researchers discovered different patterns in brain waves when RPE, SPE, or both states occurred simultaneously. They then applied deep learning methods to the EEG signals captured. This allowed the system to classify the human's reaction to AI actions.

In practice, this technology can distinguish between two scenarios: 'you misunderstood my goal' and 'you understood the goal, but did it in the wrong way.' The decoded signals are then used as feedback for the AI's decision-making process. Upon detecting SPE, the system interprets that the goal is correct, but the strategy needs modification. Meanwhile, RPE indicates that the goal itself was misunderstood, and the AI must try to ascertain the human's intention again.

According to KAIST, simulations showed that the method adapts faster than existing approaches, even under uncertain conditions such as a sudden change in the human's goal or the absence of some neural feedback. The study was published in August 2026 in the journal IEEE Transactions on Cybernetics under the title Neural Value Alignment: Human–AI Collaboration Under Goal–Action Ambiguity.

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