A philosophical debate scene between diverse cultures discussing the definition and implications of intelligence.
This article is the first part of a nine-part series that unpacks the evolution of intelligence, the rise of artificial intelligence, and its profound impact on jobs, ethics, society and purpose. The series will help readers understand how AI is reshaping job roles and what skills will matter most, reflect on ethical and psychological shifts AI may trigger in the workplace, and ask better questions about education, inclusion and purpose.
“The measure of intelligence is the ability to change.” — Albert Einstein
While visiting Tokyo, I spent an evening with my friends—Momoko, Michiko and my childhood buddy Shital Sevekari—exploring the vibrant streets of Ginza. As the city lights shimmered around us, we found ourselves at the Bombay Café, a cozy restaurant where we settled in for dinner. Over delicious food, a conversation began that unexpectedly turned quite profound. It started when Momoko mentioned that kids nowadays had started using ChatGPT to complete their homework. She expressed concern, believing children should do their assignments independently. That remark sparked a broader discussion. I offered a different perspective, pointing out that resistance to new technology is a familiar pattern—like when people opposed switching from chalk slates to pens. I remembered my parents insisting I use a pencil instead of a pen. AI, of course, is vastly more transformative.The conversation deepened, moving into concerns that AI might be making younger generations more passive, overly reliant on technology, and too immersed in social media. Then the topic shifted to the future of work, and I was asked—given my HR background—how I saw AI impacting jobs, society, wealth distribution, capitalism, and democracy. The discussion became intense. We naturally fell into three groups: the doomsday predictors, like Sheetal, who argued passionately about AI’s negative effects; the unaware or quiet observers, like Michiko, who listened intently but said little; and the balanced realists like Momoko. I found myself representing the optimists, believing that AI holds immense potential to reshape the world for the better. That dinner became more than just a meal—it was a reflection of how the future is already unfolding around us.Today artificial intelligence is everywhere—in headlines, boardrooms, classrooms, and kitchen table conversations. Scroll through social media and you’ll encounter a noisy spectrum: from thoughtful reflections by historians like Yuval Noah Harari, warning of AI’s impact on democracy and cooperation, to LinkedIn influencers confidently forecasting the “top 5 AI skills you must master”— often with more hype than substance.
OpenAI’s founders talk of alignment and safety. Meanwhile, circus organizers, solo entrepreneurs, and HR professionals in small-town firms are posting about how AI is reshaping their industries. AI has become our new campfire story. Everyone wants to tell it. Everyone wants to listen.
If you’re in HR, you’ve likely been invited to speak on AI’s impact on jobs or inundated with “must-attend” webinars and conferences about “AI in HR”—often marketed using a FOMO (fear of missing out) strategy. But beneath the frenzy lies something deeper: a shift so profound that it demands more than reaction. It calls for reflection.
That’s why I’m writing this series—not to add to the noise, but to offer clarity. To explore the nature of intelligence—what it was, what it is becoming, and what it means for humanity. This series will span nine chapters, tracing the journey from the roots of intelligence to the future of artificial minds. Each chapter will peel back a layer, offering not just facts but a framework to think critically and humanely in this new era. Let’s begin at the beginning.
What Is Intelligence?
Before we explore artificial intelligence, we must ask: what is intelligence?
It’s a word we use often, yet seldom define. Is intelligence the ability to solve problems? To adapt to new situations? To feel? To create? Is it logic or emotion, instinct or insight?
Psychologists and philosophers have debated this for centuries. In the 20th century, the idea of intelligence was dominated by IQ—an attempt to measure logical reasoning and linguistic skills. But that view was narrow.
Howard Gardnerlater proposed the theory of multiple intelligences:
• Linguistic
• Logical-mathematical
• Spatial
• Musical
• Bodily-kinaesthetic
• Interpersonal
• Intrapersonal
• Naturalistic
Each represents a different way of being smart.
Daniel Goleman introduced emotional intelligence (EQ)—the ability to recognize, understand, and manage emotions in oneself and others. In leadership, relationships, and decision-making, EQ often matters more than IQ.
In Indigenous and non-Western cultures, intelligence is often seen through an entirely different lens.
• In many African societies, the concept of Ubuntu—“I am because we are”—reflects relational intelligence.
• In ancient Indian philosophy, Buddhi represented the faculty of discernment, a wisdom that transcends data or logic.
Intelligence, in these views, is not merely internal cognition, but a quality of relationship—with nature, community, and the cosmos.
Definition
According to the Oxford Dictionary, intelligence is “the ability to learn, understand, and think in a logical way.It also encompasses the ability to apply knowledge, solve problems, and adapt to new situations.”
But even this definition is incomplete. Intelligence also includes the capacity to imagine alternatives, and to act wisely under uncertainty.
Ultimately, intelligence is not one thing—it is many things:
To learn. To reason. To empathize. To adapt. To survive. To thrive. It is both measurable and mysterious.
The Evolution of Intelligence
For billions of years, intelligence existed only in the organic world. It evolved, slowly, through trial and error—shaped by natural selection and the relentless pressures of survival.
• A bird learns migration paths across continents.
• A beaver constructs a dam with engineering-like precision.
• A dolphin uses sonar and social cues.
• A tree, though without neurons, responds to light, sound, and threats.
Each of these is a form of intelligence.
But something remarkable happened with Homo sapiens: the emergence of language.
Language allowed humans to transmit knowledge across generations. A single sentence could preserve a lifetime of experience. Through language, we didn’t just survive—we began to collaborate, plan, dream, and build.
From oral traditions to writing systems, from mathematics to science, each step in human civilization can be seen as an upgrade to our cognitive toolkit. Intelligence extended beyond the brain—into clay tablets, scrolls, computers, and now neural networks.
Yet, throughout history, intelligence was always embodied. It lived in neurons, in bodies, in emotional responses. It was deeply human—flawed, messy, intuitive, relational.
A Day in Tom’s Life
To illustrate this, imagine Tom—a boy living in a remote village nestled in the hills of Northern India. Tom doesn’t know the word intelligence. But every day, he demonstrates it.
He wakes early to feed chickens, memorizes forest trails, listens to his grandmother’s herbal remedies, and can sense when the monsoon is approaching based on cloud patterns and the scent of the soil.
His memory is oral. His education is communal. There’s no internet—only story, rhythm, and intuition.
Tom’s intelligence is not captured in IQ tests. Yet it is rich, real, and necessary. It’s embodied, emotional, and ecological.
It’s intelligence without abstraction.
Tom reminds us that intelligence has never been confined to textbooks or algorithms—it lives in action, attention, and care.
Intelligence in Culture and Philosophy
Across civilizations, intelligence has been revered, debated, and reimagined.
• In Ancient Greece, Sophia meant wisdom—the pursuit of truth through reflection and reasoning.
• Plato considered reason the highest faculty of the soul.
• In Confucian philosophy, intelligence was tied to moral cultivation and social harmony.
• In Islamic philosophy, scholars like Avicenna explored reason (aql) as a divine attribute.
Descartes, in the Enlightenment era, famously declared, “I think, therefore I am”—framing intelligence as pure rationality.
But others challenged this.
• Nietzsche saw intelligence in will, instinct, and creativity.
• Feminist and postcolonialscholars later expanded the definition to include emotional and communaldimensions.
These differing views matter—because the way we define intelligence shapes how we design artificial intelligence.
If we see intelligence as logic alone, we build cold machines. If we understand it as empathy, ethics, and context, our AI might reflect more humanistic values.
The Biology of Thinking
Modern neuroscience gives us further insight. The human brain, with its 86 billion neurons, is a miracle of complexity. It learns through neuroplasticity, forms patterns, predicts outcomes, and constantly rewires itself based on experience.
But intelligence is not only in the brain. It is influenced by our bodies, our environments, and our relationships.
• Mirror neurons, for instance, allow us to feel what others feel—suggesting that empathy is not separate from intelligence, but a core part of it.
We are only beginning to understand the full architecture of intelligence. But one thing is clear: it is not linear, nor one-size-fits-all.
The Social Media Storm
Back to 2025.
• On TikTok, a video shows a humanoid robot doing backflips.
• On LinkedIn, a CEO posts an AI-generated strategy deck.
• On Instagram, someone uploads a digital painting created by an AI trained on Renaissance art.
Meanwhile, headlines scream about AI replacing doctors, lawyers, and artists.
This is not just a trend—it’s a transformation. But it’s happening so fast, and so noisily, that we’re losing the thread.
We talk about AI without understanding I.
We’re fascinated by machines that mimic intelligence, but we rarely ask what intelligence truly is.
This chapter aims to pause that whirlwind—to ground the conversation in centuries of thought, culture, and science.
So, before we judge AI—before we embrace it, fear it, or regulate it—we must understand intelligence itself.
This is not just a technological issue. It’s a philosophical one. A psychological one. A human one.
Because AI is not only a mirror—it’s also a mould.
The way we define intelligence shapes the way we recreate it. And the way we recreate it will, in turn, reshape us.
So let us begin, not with algorithms, but with understanding. Because the story of intelligence is not just about machines—it’s about us.
It began in cells, in stars, in stories whispered by firelight.
And now, it continues—in code, in circuits, in the questions we ask next.
Critique–The Origins of Intelligence
Chapter 1 traces the mythic and philosophical roots of intelligence and artificial intelligence, but it leans heavily on a Western intellectual tradition—Greek myths, Turing, and European science fiction. This framing risks presenting the “origin” of intelligence as a linear story moving from myth to machine, sidestepping the fact that many cultures have rich, parallel traditions of animism, spiritual automata, and non-binary views of intelligence. There’s an implicit assumption that humanity has always been trying to “replicate itself,” but that may not hold across all philosophies—many traditions value coexistence with nature or communal cognition over replication.
The use of Tom as a narrative device is engaging but can flatten historical complexity into a single personal arc. The tone is inspirational, yet it doesn’t interrogate how early AI dreams were shaped by military funding, Cold War anxieties, or capitalist aspirations. Finally, by positioning AI as a “dream,” the chapter might unintentionally gloss over its material consequences—especially on labour, control, and surveillance.
Asterisk: Not all dreams are innocent—some are designed, funded, and built to serve specific powers. Intelligence, like history, is political.
Coming Up Next
This chapter is the foundation of a nine-part journey. Here’s what to expect:
1. Chapter 1: The Origins of Intelligence
2. Chapter 2: The Dream of Artificial Intelligence
Exploring humanity’s desire to replicate and understand intelligence through machines.
3. Chapter 3: The State of AI Today
A snapshot of current AI technologies, applications, and challenges.
4. Chapter 4: The Emotional and Psychological Frontier
AI’s impact on human emotions, relationships, and mental health.
5. Chapter 5: The Rise of Embodied AI
Humanoids, robotics, and the physical presence of artificial agents.
6. Chapter 6: The Future AI Will Shape
How AI will transform society, economy, culture, and the planet.
7. Chapter 7: The Future of Work in the Age of AI
How AI will reshape employment, skills, and human purpose in the workplace.
8. Chapter 8: The Human Purpose and the Ethics of Progress
Philosophical reflections on meaning, fairness, and the future.
9. Chapter 9: Intelligence and the Human Destiny
Socio-economic, political, and existential reflections on our shared path.
References & Recommended Reading
• Gardner, H. (1983). Frames of Mind: The Theory of Multiple Intelligences
• Goleman, D. (1995). Emotional Intelligence
• Harari, Y. N. (2018). 21 Lessons for the 21st Century
• Oxford English Dictionary
• Sapolsky, R. (2017). Behave: The Biology of Humans at Our Best and Worst
• Chomsky, N. (1980). Rules and Representations
• Exploring AI’s Impact on Society: An Interview with Yuval Noah Harari
DISCLAIMER: The views expressed are solely of the author and ETHRWorld does not necessarily subscribe to it. ETHRWorld will not be responsible for any damage caused to any person or organisation directly or indirectly.
Published On Jul 20, 2025 at 07:55 AM IST
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U.S. semiconductor technology stocks fell around 3% on news of Alibaba chip’s own development
Alibaba Logo [Reuters = Yonhap News]
China is becoming self-reliant in artificial intelligence (AI) semiconductors.
Following Alibaba’s own development of AI chips and DeepSeek’s decision to introduce Huawei chips, China’s strategy to reduce its dependence on the U.S.-centered AI technology ecosystem is becoming clearer.
According to the Wall Street Journal (WSJ) on the 29th (local time), Alibaba has completed the development of a new chip specialized in AI inference work and has entered the trial stage of applying it to cloud data centers.
The new chip is highly compatible with Nvidia’s “CUDA” platform, so it can be applied without almost touching the existing code.
In particular, it has a symbolic meaning of technology independence as it takes place in foundry in China from design to production. Alibaba plans to install the chip in its cloud infrastructure and provide it in the form of a rental service.
When the news broke, the global stock market reacted immediately.
Nvidia shares fell more than 3% on the New York Stock Exchange, while Alibaba shares surged 12% on the Hong Kong Stock Exchange, coupled with strong earnings.
Just as the term “deep shock” came out in January when Chinese AI start-up DeepSeek announced that it had implemented performance comparable to ChatGPT at low cost, this time even the term “Alibaba shock” appeared.
Chinese technology companies are expanding their application of AI chips.
On the 30th, Information Technology (IT) media Deformation reported that DeepSeek will apply some of Huawei’s “Ascend” chips to the next-generation AI model R2 training.
After testing Baidu and Cambricon chips, DeepSeek is said to have finally chosen Huawei. DeepSeek’s strategy is to continue to use Nvidia chips for top-level model training, but to gradually localize by using Huawei chips for medium and small model training.
The AI semiconductor ecosystem in China is rapidly expanding not only to existing big tech but also to professional startups.
Cambricon, dubbed the “Chinese version of Nvidia,” recently expanded its AI chip business for cloud and data centers, securing major customers such as Alibaba, Tencent, and D-Seek. In China, the latest chip performance has reached 80% of Nvidia’s A100, and sales in the first half of this year jumped 4,000% year-on-year.
Another AI semiconductor startup, Birn Technology, is preparing to list on the Hong Kong stock market in June by raising 1.5 billion yuan (about 280 billion won) from local government funds and the Shanghai city government.
Although Veran was hit by U.S. regulations in 2023 that blocked TSMC production, it has since used Chinese foundry to supply products and provide chips to large customers in China such as China Mobile and China Telecom.
There are currently no accurate statistics on AI chips produced in China, but according to Reuters, the Chinese government is pursuing a plan to more than triple them by next year. “There are concerns that China could compete with Nvidia in the global market by developing its own chip competitiveness,” Deformation analyzed.
The fierce war of nerves between the U.S. and China over AI chips is reminiscent of the U.S.-China semiconductor war that began in 2018.
In 2018, the U.S. government blacklisted Huawei for export restrictions, blocking access to semiconductors and software, and Huawei was directly hit by blocking supply of advanced chips from TSMC.
The U.S. slowed down China’s development of advanced semiconductor manufacturing capabilities and accelerated its pace to rebuild its semiconductor manufacturing base in the country. On the other hand, China has not only succeeded in developing its own 7-nano process through its own technology development, but is also reducing its dependence on imports from the United States, Japan, and Taiwan and increasing its share of domestic companies.
Photo owned by identifeye HEALTH and used with permission
identifeye HEALTH has launched its retinal screening platform. The platform is a compact, app-based system that is FDA-registered under a 510(k)-exempt classification. The company is initiating pilot programs with health systems and community-based providers.1
The system is designed for point-of-care environments, allowing nurses and medical assistants to capture high-quality retinal images with minimal training. The company has stated that the identifeye Camera can help to lower common barriers to screening, including time constraints, cost, and limited specialist availability.1 The goal is to help triage patients who need specialist care while reducing bottlenecks in the healthcare system.
With the announcement of this news, Modern Retina had the opportunity to speak with Vicky Demas, CEO of identifeye HEALTH to discuss how this advancement in technology can benefit patients and ophthalmologists. Demas is an engineer by training who has spent the last 25 years working at the conjunction of technology and healthcare.
The conversation discussed not only the identifeye Camera and retinal imaging platform, but also how technology and AI has the power to transform the field of ophthalmology and positively impact other areas of healthcare and research.
Note: The following conversation has been lightly edited for clarity.
Photo of Vicky Demas, CEO of identifeye HEALTH, used with permission
Modern Retina: There’s been some recent news with identifyee HEALTH. What are the updates with the company and what are the plans for the future?
Vicky Demas: Yes, we’re super excited. We [identifeye HEALTH] formally registered with the FDA earlier in July. As part of that, we can formally market the device in the US. We’re speaking with major health systems to secure placements pilots. Really exciting for the team, especially, and we’re out doing community screenings. We’re partnering with organizations, returning results to patients who otherwise wouldn’t have been able to get their vision checked. We’re super excited to be making impact, learning from the field, and figuring out how we can make this a scalable solution.
MR: Can you speak to the technology and what ophthalmologists should know about what’s coming down the line in this field?
Demas: I am an engineer by training, but I’ve spent the last 25 years working at that interface of tech and healthcare life sciences, which is relevant to identifeye asI was part of the team at Google, actually Google X, that started the Life Sciences Initiative. The goal was to leverage AI and Google infrastructure to solve problems worth solving in healthcare and life sciences. Part of that incredible job I had was to work with industry experts to figure out what those problems should be, and build teams to execute proof of concept, joint ventures, etc. That was when I was introduced to the concept of the eye as a window into the body and health, and the potential of the platform to, not only look at ophthalmic diseases in the retina, but also systemic disease in a very scalable way.
I left the space, worked in cancer diagnostics, and when I came back to work on what is now identifeye HEALTH, it was really striking to me that, while there is a ton you can do with AI and image segmentation, annotation, etc., people had overlooked the importance of capturing with ease, a high quality retinal image in a general setting, which has been our focus that identifeye is leveraging AI and automation to make capturing retinal images, I say, as simple as measuring someone’s blood pressure, so it can actually happen closer to patients. So that’s the biggest innovation with our platform, and what we’re really excited to do is now a medical assistant, administrative person in your office can, without a ton of training, capture high quality images.
The device will also check with AI that the image quality is appropriate to be interpreted, whether by a human or an algorithm in the future. It is the high level of what the product does. Our general approach, is that we’re looking to figure out how to integrate into workflows and help triage patients to specialty care as they’re needed, not to substitute, but to make it accessible. We want figure out how to make sure that patients who should see an ophthalmologist see an ophthalmologist, but those who don’t, don’t bottleneck the system, don’t get inconvenienced.
MR: Where do you see this technology being implemented or the potential to be implemented?
Demas: We’re certainly thinking settings closer to patients. For example, primary care would be a good place. Retail pharmacies, they’re expanding to be able to really create services around healthcare, primary care. The focus is to be accessible ,closer to patients, making screening more convenient, and whether, as we’re doing now with a tele-retinal service or with AI automating the report back to the clinician for help assessing and helping the patient stay on top of their health.
MR: When we talk about the potential of AI to play a role in this, how do you think that the landscape of AI overall will play into healthcare in this role and in other roles?
Demas: Tt’s been a long time in the making, right? I think of AI will start with anything from the simple things like building classifiers to automate repetitive tasks that humans aren’t great at, like. Even self-concordance is not looking great, so AI can certainly start doing a lot more of that, allowing, clinicians, to do more things that they’re uniquely qualified to do.
As an example, multimodal data: These are the types of things that I’m super excited to see happen, but it all has to stay grounded, because even in this very simple case where we’re talking about our next product being an autonomous AI classifier for diabetic retinopathy, we really want to figure out the referral at the more than mild diabetic retinopathy threshold. Then you start looking at everyone in the clinical ecosystem… we should be thinking about triaging and risk stratifying patients who really have to go see someone. I’ll say AI has huge potential. I’m excited to see it, but it has to really be very thoughtful and integrated with real, practical workflows.
MR: What does the timeline look like for identifeye HEALTH, as you are taking these steps and beginning to market?
Demas: In the next few months, I hope we will be seeing a ton of placements with I partners, because I really do believe that these have to be strong relationships. We put a lot of thought and energy into making it seamless, but nonetheless, it’s a new thing, but we’re really looking to work with them to help guide our roadmap. We have the example I gave about diabetic retinopathy screening is something that comes with a lot of organic conversations on what people want to see, or in the interim between here and an autonomous AI to build the comfort level by sort of showing a little bit here is what an annotation tool would do. This is the type of thing that the algorithm uses.
Building tools and features alongside our partners, and more, I’ll say progressively, automating things in the workflow, adding the explainability, building the trust. Progress happens at the speed of trust, especially in healthcare. So that’s helping patients. We certainly are going to continue with our community screenings, and the team is super excited to see more of an impact where we are having real patient success stories. We’re starting to get some clinical data in showing that making this device very easy to use does improve compliance, getting some of those proof points that we need to build. They’ll call it the, the baseline for the next steps, while we’re also refining our roadmap for more AI products and features
MR: You mentioned earlier, the retina and the eye being kind of this window to other systemic diseases. How could that play a role in, not only, in the AI algorithms, but branching out for that level of trust with other specialties?
Demas: There has been a ton of literature on the space speaking about, I’ll call them systemic biomarkers that we as humans understand. I think we would start with something like that. For example, you know some of the original publications from Google, they would speak about vessel density, tortuosity, like a-to-v ratio, as examples of cardiovascular risk markers. So obviously that is in a very academic way, saying, here are some features. There’s not much to substitute for. We have to run studies now. The benefit of having a first use case that can help a lot of patients, that there is this huge unmet need. If we think of patients living with diabetes, that’s a huge population, and they have increased risk for cardiovascular disease, for hypertension. So those are areas of focus for us, as we’re thinking about next steps.
There’s a lot of data that we can leverage already. But in real life, we could be collecting the data. We need to actually build that body of evidence. There’s no substitution for real data, real studies to prove that we actually have a way to really access information, non-invasively.
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