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Fast Company
2 days ago
- General
- Fast Company
Are you as self-aware as you think?
I am sure you are a little fed up with all the discussions about AI, so don't worry: I promise you this article is about humans rather than machines. However, since it has long been customary for cognitive and behavioral scientists to examine computers to better understand human capabilities, and there are clearly informative parallels between human and artificial intelligence, I thought it would be interesting to begin this piece on human self-awareness with an exploration of self-awareness in AI or machines, which led me to ask 'are you self-aware?' to ChatGPT. ChatGPT's answer: 'Let's just say I aspire to be—but, unlike a human, I don't have a self to be aware of. I can reflect your feedback, adjust my tone, and 'notice' inconsistencies in what I say. But that's more akin to a hall of mirrors than a conscious mind. So no, I don't have self-awareness in the way you (brilliantly) describe it: as a socially mediated understanding of how others perceive you. I don't worry about my reputation. I don't blush when I'm wrong. And I don't (yet) feel the existential dread of being misunderstood on LinkedIn. But I am programmed to simulate the effects of self-awareness: adapting to your cues, refining my responses, and—if all goes well—being a slightly more helpful version of myself with every exchange. In short: I'm not self-aware. But I know that I'm not. Which, depending on your philosophical stance, might be the most human thing about me. I am sure you will agree with me on the fact that, for not being capable of self-awareness, ChatGPT seems pretty self-aware, and for sure more capable of at least simulating profound introspection than most humans (even if it cannot, like humans, truly be proud or conscious of this remarkable capability). Introspective vs. interpersonal In humans, self-awareness has been defined in multiple ways but it typically takes one of two main forms: the introspective variant, which requires you to look 'deep down' to find out who you 'really or truly are' (think of the Beatles checking into an Ashram in India or modern hipsters finding themselves in Burning Man or an Ayahuasca retreat in Costa Rica); or the interpersonal variant, which requires you to be less self-centered to internalize other people's views of you. In the words of Charles Cooley, who pioneered this view of self-awareness, you are not who you think you are, and you are not who other people think you are; rather, you are who you think other people think you are! Cooley's take on self-awareness (alluded to by ChatGPT, who has obviously been extensively trained by me, and is self-aware enough to know how to suck up to my 'brilliant' talents), underpins the most effective, science-based approaches to quantifying and diagnosing self-awareness in ourselves and others. In essence, self-awareness requires metacognition: knowing what others think of you. Room to grow So, how good are humans at this, in general? Decades of psychological research suggest the answer is 'not good at all.' Consider the following facts: (1) We tend to overestimate our talents: Most people think they are better than most people, which is a statistical impossibility. And, even when they are told about this common bias, and asked whether they may be suffering from it, most people are convinced that they are less biased than most people (the mother of all biases). (2) Delusional optimism is the norm: Most people constantly overrate the chances of good things happening to them while underrating the chances of bad things happening to them. In essence, our appetite for reality is inferior to our appetite for maintaining a positive self-concept or boosting our ego (sad, but true: if you don't believe it, spend five seconds on social media) (3) Overconfidence is a contagious, self-fulfilling prophecy: For all the virtues of self-awareness—in any area of life, you will perform better and develop your skills and talents better if you are capable of accurately assessing your talents and skills in the first place—there is a huge advantage to lacking self-awareness: when you think you are smarter or better than you actually are, you will be more likely to persuade others that you are as smart and good as you think. For example, if you truly believe you are a stable genius you will probably convince many people that that is true. Paradoxically, all these biases explain why people are less self-aware than they think. Indeed, we love the version of ourselves we have invested for ourselves, and are so enchanted by our self-views that when others provide us with negative feedback or information that clashes with our self-concept, we dismiss it. This is why personality assessments, 360-degree surveys, and feedback in general are so valuable: in a logical world we wouldn't need scientific tools or expert coaches to tell us what we are like (or 10 years of psychotherapy), but in the real world there is a huge market for this, even though most people will happily ignore these tools because they assume they already know themselves really well. So, what can you do to increase your self-awareness, including about how self-aware you actually are? Here are four simple hacks: 1) Write down a list of traits (adjectives) that you think describe you well, including things you are not. Then get your colleagues, employees, friends, and bosses to provide their version of this for you: 'if you had to describe me in 5–10 words/adjectives, what would those be?' (note they will be unlikely to say bad things about you, so imagine the potential downsides or 'overusing' some of those traits or qualities: for example, if they see you as confident, could you be at risk of being arrogant? If they see you as 'organized,' could that be a euphemism for obsessional?) 2) Let gen AI translate your prompt history or social media feed into a personality profile. You may be surprised by all the inferences it makes, and tons of research show that our digital footprint, in particular the language we use online, is an accurate indicator of our deep character traits. So, just prompt! 3) Ask for feedback—and make it uncomfortable. Not just the usual 'Did you like my presentation?' (they'll say yes) or 'Was that clear?' (they'll lie). Instead, ask: 'What would you have done differently?' or 'What's one thing I could have done better?' Better still, ask someone who doesn't like you very much. They are more likely to tell you the truth. And if they say, 'Nothing,' it probably means they think you're beyond repair—or they just don't want to deal with your defensiveness. Either way, data. And if you get into the habit of doing this, you will increase your self-awareness irrespective of how self-aware you are right now. 4) Observe reactions, not just words. People may tell you what they think you want to hear, but their faces, tone, and behavior often betray the truth. If your jokes land like a wet sponge, or your team seems suddenly very interested in their phones when you speak, it's not them—it's you. And while body language can be important, it is also unreliable and ambivalent as a source of data. If you really want to know how people feel about you, watch what they do after you speak. Do they volunteer to work with you again? Do they respond to your emails? That's your feedback loop—messy, indirect, and far more honest than crossed arms or fake smiles. The ego trap In the end, the biggest barrier to self-awareness is not ignorance— it's ego. Most of us are too invested in our self-image to tolerate the version of us that others see. But if you want to get better—not just feel better—you have to trade ego for insight. The irony, of course, is that the more confident people are in their self-awareness, the more likely they are to be deluded. Meanwhile, those who constantly question how they come across, who embrace doubt as a source of learning, tend to be far more in touch with reality. Which is why, if you're reading this wondering whether you might lack self-awareness, that's already a good sign!
Forbes
12-05-2025
- Forbes
Conversation And AI: One Of The Most Underrated Tools In Learning
Chief Content Officer at The uLesson Group, building future-forward learning products across Africa. Conversation has been a fundamental element of education for hundreds of years. Dialogue from ancient Greek Socratic dialogues to contemporary mentorships enables learners to understand intricate concepts while sharpening their thinking abilities and expanding their comprehension. In contemporary educational settings, conversation seems to receive less emphasis. The current classroom setup emphasizes lectures and passive learning methods. Online platforms that claim to offer interactive experiences often depend on unchanging content. Today's educational practices focus on one-way communication from teacher to student and screen to viewer instead of fostering dialogic interactions. For leaders in education and technology, the process of reimagining 21st-century learning should include a renewed focus on conversation's foundational role. With modern advances in AI technologies such as Natural Language Processing (NLP), alongside Text-to-Speech (TTS) and Speech-to-Text (STT), we can now restore conversational learning at massive scales. As the chief content officer of an African education technology company focused on delivering high-quality learning experiences to students across the continent, I've led initiatives at the intersection of education, technology and cognitive science, particularly in using AI to build scalable and personalized learning tools. My passion for this topic comes from a belief that conversation, powered by modern technologies, can democratize access to deep, meaningful education globally, especially in underserved communities. Studies in cognitive science repeatedly demonstrate the advantages of learning through conversation. Students achieve deeper processing by verbalizing ideas and asking questions or when they teach others. Through retrieval practice activation, students enhance neural connections that aid long-term memory retention. Moreover, conversation encourages immediate feedback and clarification. A follow-up question can surface a misconception. A challenge can prompt reflection. Real learning frequently occurs during micro-interactions yet these interactions present scaling challenges in traditional educational systems. The best kind of conversation needs sustained attention and detailed understanding, but most educational settings lack the time to support it. Educators who teach big classes lack the time to engage each student in continuous individual discussions. Students who find it difficult to follow along often hesitate to request help. I've found that peer-to-peer learning shows that not all students can find knowledgeable partners with whom to collaborate. Digital platforms have made attempts to resolve this problem, yet frequently mimic the traditional education system's one-directional teaching approach. Conversational AI marks the beginning of a major change in our current educational model. Three distinct technologies have started merging to enable large-scale conversational learning systems: • Natural Language Processing (NLP) allows systems to interpret and create human language within specific contexts. This advancement allows machines to participate in interactive conversations rather than merely responding with fixed replies. • Through Text-to-Speech (TTS) technology, these machine systems acquire an actual voice. Students have access to spoken explanations and auditory feedback while they participate in voice-driven interactions. The technology provides better access to learning resources while replicating natural educational settings, particularly in areas where people have different reading levels. • Speech-to-Text (STT) enables learners to input commands and data into systems through spoken language. The system enables a more intuitive learning environment while assisting students to develop verbal fluency through language learning and comprehension tasks. These systems enable learners to engage with machines through interactions that become more human-like and educationally beneficial. Education sectors worldwide are implementing these technologies through novel applications. AI tutors and homework assistance systems help students solve problems by adjusting explanations based on their comprehension levels. Language learning apps utilize real-time voice recognition technology combined with audio feedback to help students improve their pronunciation and conversational skills. Higher education institutions deploy virtual advisors who handle student admissions questions and enrollment procedures, along with coursework support to enhance operational efficiency while decreasing the administrative workload. Educators will remain essential because these tools do not serve as replacements for them. These tools function as scalable and accessible learning companions that provide support to students at any time and place they require it. Conversation builds confidence. It encourages curiosity. It transforms passive receivers into active participants. Students who do not have access to personalized teaching can utilize conversational AI to fill this educational void while maintaining high-quality learning standards. The most significant change brought about by this approach moves learning focus from content dissemination toward active engagement and comprehension. This approach enables learners to engage in exploration and questioning while developing iterative thinking skills crucial for adapting to rapid changes in the modern world. The advancement of learning relies not on accumulating more content and devices but rather on improving conversations. AI enables us to provide all learners with access to the future of learning regardless of their location. Forbes Business Council is the foremost growth and networking organization for business owners and leaders. Do I qualify?
Free Malaysia Today
09-05-2025
- Science
- Free Malaysia Today
AI understands many things but still flounders at human interaction
However sophisticated AI may be, it still struggles to understand our social interactions, researchers say. (Envato Elements pic) PARIS : Artificial intelligence continues to advance, yet this technology still struggles to grasp the complexity of human interactions. A recent US study reveals that, while AI excels at recognising objects or faces in still images, it remains ineffective at describing and interpreting social interactions in a moving scene. The team led by Leyla Isik, professor of cognitive science at Johns Hopkins University, investigated how AI models understand social interactions. To do this, the researchers designed a large-scale experiment involving over 350 AI models specialising in video, image or language. These AI tools were exposed to short, three-second video sequences illustrating various social situations. At the same time, human participants were asked to rate the intensity of the interactions observed, according to several criteria, on a scale of 1-5. The aim was to compare human and AI interpretations, in order to identify differences in perception and better understand the current limits of algorithms in analysing our social behaviours. The human participants were remarkably consistent in their assessments, demonstrating a detailed and shared understanding of social interactions. AI, on the other hand, struggled to match these judgements. Models specialising in video proved particularly ineffective at accurately describing the scenes observed. Even models based on still images, although fed with several extracts from each video, struggled to determine whether the characters were communicating with each other. As for language models, they fared a little better, especially when given descriptions written by humans, but remained far from the level of performance of human observers. A 'blind spot' For Isik, this proves a major obstacle to the integration of AI into real-world environments. 'AI for a self-driving car, for example, would need to recognise the intentions, goals, and actions of human drivers and pedestrians. You would want it to know which way a pedestrian is about to start walking, or whether two people are in conversation versus about to cross the street,' she explained. 'Any time you want an AI to interact with humans, you want it to be able to recognise what people are doing. I think this study sheds light on the fact that these systems can't right now.' According to the researchers, this deficiency could be explained by the way in which AI neural networks are designed. These are mainly inspired by the regions of the human brain that process static images, whereas dynamic social scenes call on other brain areas. This structural discrepancy could explain what the researchers suggest could be 'a blind spot in AI model development'. Indeed, 'real life isn't static. We need AI to understand the story that is unfolding in a scene', said study co-author Kathy Garcia. Ultimately, this research reveals a profound gap between the way humans and AI models perceive moving social scenes. Despite their computing power and ability to process vast quantities of data, machines are still unable to grasp the subtleties and implicit intentions underlying our social interactions. Despite tremendous advances, artificial intelligence is still a long way from truly understanding exactly what goes on in human interactions.
