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I used to think artificial intelligence was just about silicon chips and code. Then I discovered something that completely shattered that assumption: A laboratory dish containing living human brain cells that learned to play Pong in just minutes (along the lines of ~5–20 minutes).

While tech giants pour billions into faster processors and more sophisticated algorithms, a small group of scientists has been quietly growing actual intelligence.

Not simulating it. Not programming it. Growing it.

And what they’ve discovered will fundamentally change how we think about the future of computing.

The Dish That Changed Everything

In 2022, researchers at Cortical Labs achieved something that sounds like science fiction. They successfully grew human neurons in a laboratory dish, placed them on a grid of thousands of tiny electrodes, and connected this biological system to a simplified version of Pong.

The results were staggering.

These lab-grown neurons didn’t just learn to play the game but mastered it in mere minutes (and yes, they didn’t reach the deterministic expert level seen in trained AIs). Human brain cells were controlling the paddle, tracking the ball, and showing adaptive behaviour. The neurons received sensory input about the ball’s position through electrical stimulation and responded by firing collectively to move the paddle up or down.

But here’s where it gets really interesting: when researchers tested with non-neural cells (HEK293T kidney cells) as a control group, these cells did not learn to play Pong. But, they performed at media control (i.e., baseline) levels and showed no evidence of learning or improved performance over time. So, specialized neural architecture (brain cells) outperformed other tissues: kidney cells and non-neural controls did not learn or adapt as neurons did.

Why Your Laptop Can’t Compete

The energy efficiency alone should make Silicon Valley executives lose sleep. These biological computers could plausibly achieve the same learning outcomes as traditional silicon-based systems with less energy (because biological brains are orders of magnitude more energy efficient than artificial neural networks)

Think about that for a moment. While companies like Google and OpenAI are building massive data centres that consume the power equivalent of small cities, a petri dish of neurons can now run a basic closed-loop system with a pre-set reward signal structure. This is much simpler than tasks handled by commercial AI or data centres, but it is a first step into an interesting direction.

The learning mechanism is elegantly simple yet profoundly sophisticated. When the neurons successfully hit the ball, they receive predictable, gentle electrical stimulation across all electrodes, essentially a biological reward signal. Miss the ball, and they get four seconds of chaotic, unpredictable stimulation that acts as negative feedback.

Within this reward-punishment cycle, something remarkable happens: The neurons begin reorganizing themselves. They increase coordinated firing patterns between sensory (input) and motor (output) electrode regions. Functional connectivity (but not direct synaptic mapping) increased as the neural cultures learned to play Pong. The system becomes more coordinated and goal-directed. It’s optimization happening at the cellular level, in real time.

The Cyberpunk Reality We’re Already Living

What caught my attention wasn’t just the scientific breakthrough  but  it was discovering that Cortical Labs is already commercializing this technology. You can visit their website right now and purchase biological computers powered by living neurons.

We’re not talking about some distant future possibility. This is happening today.

The implications extend far beyond gaming demonstrations. These hybrid biological-digital systems could revolutionize drug testing, offering researchers the ability to study how compounds affect neural computation in real time. Brain-computer interfaces could become fully integrated biological systems rather than clunky external devices.

Consider the possibilities: If you study neurological diseases using actual living neural networks, developing treatments that work with biological intelligence rather than against it, or creating computing systems that adapt and evolve like living organisms.

What This Means for the AI Arms Race

While major tech companies compete to build larger language models and faster processors, biological computing represents a completely different approach to intelligence. Instead of simulating consciousness, we’re working with actual conscious material.

This raises deep questions about the nature of intelligence itself. When human neurons outperform mouse neurons in these experiments, we’re seeing evidence that different types of biological intelligence have distinct computational capabilities. The structural differences between species translate into measurable performance differences in artificial environments.

But it also forces us to confront uncomfortable questions about consciousness and ethics. If we’re using actual neural tissue to perform computational tasks, what are our responsibilities toward these biological systems? When does a collection of neurons become something more than just organic hardware?

A Gentle Earthquake of AI Innovation

The most fascinating aspect of this story is how quietly it’s unfolding. While mainstream media focuses on ChatGPT and Silicon Valley’s latest AI announcements, researchers are literally growing intelligence in laboratories around the world.

This isn’t just another incremental improvement in processing power. It’s a fundamental shift from artificial intelligence to biological intelligence operating in digital environments. We’re witnessing the emergence of true hybrid systems that blur the line between living organisms and computing machines.

Companies like Cortical Labs aren’t just building better computers. No, they’re pioneering an entirely new form of technology that could make traditional silicon-based systems look as outdated as vacuum tubes soon.

What Happens Next?

The trajectory is clear here. Biological computing will likely become mainstream within the next decade. The energy efficiency alone makes it economically inevitable, especially as traditional computing approaches physical limits.

But the real question isn’t whether this technology will succeed  but whether we’re prepared for a world where the line between artificial and biological intelligence first blurs and then disappears entirely.

As I write this, somewhere in a laboratory, neurons grown from human stem cells are learning, adapting, and solving problems. They’re not following pre-programmed instructions or executing algorithms. They’re demonstrating adaptive, reward-driven pattern changes. Just some steps away from thinking.

The age of silicon intelligence is ending before it really began. The age of biological computing has already started.

The only question that remains is how you will be part of this revolution, or if you will be disrupted by it?

Research Freedom lets you become a smarter researcher in around 5 minutes per week.

Further Reading

* In vitro neurons learn and exhibit sentience when embodied in a simulated game-world. Kagan, Brett J. et al. Neuron, Volume 110, Issue 23, 3952–3969.e8

What did you think of this week’s issue?

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Three stories reached our screen this week. Each one stopped me cold.

A politician stood before her nation’s parliament. She held up an image. Not any image, but a deepfake nude of herself. Generated by AI. Created without her consent. She showed the world what violation looks like in the digital age.

Halfway around the globe, the Pope spoke about children. About dignity. About the sacred space between human hearts that no algorithm should ever touch. His words carried the weight of millennia, yet they addressed a threat barely a couple of years old.

And in a laboratory, scientists celebrated. They had taught machines to see rain before it falls. To map storms that haven’t even formed yet. To peer into the sky’s intentions with startling accuracy. Amazing.

Three moments. Three choices about who we become when we build tools that think.

The courage to show your wounds

New Zealand MP Laura McClure did something remarkable. She took her violation and made it visible. Not hidden. Not buried. Not dismissed with bureaucratic language about “emerging technologies” and “policy frameworks.” None of that.

She said: Look. This is what happens to us now. We are powerless against the machines.

There’s a particular kind of bravery in turning pain into purpose. In saying yes, this happened to me, and I will not let it happen to you in darkness. The deepfake was meant to shame her into silence. Instead, she used it to break silence open.

We live in times when our faces can be stolen. Our voices copied. Our bodies simulated without permission. The technology exists. The harm is real. The question becomes: Do we pretend this isn’t happening, or do we face it with clear eyes?

If you are interested in learning more about this, we’re teaching a Masterclass on how to avoid these modern harms of AI.

A sacred boundary

The Pope’s words matter. They still do, because they draw a line. Not against progress. Not against innovation. But around something that matters more than efficiency or capability.

Human dignity.

Children need space to grow without algorithms watching. And that space is becoming harder to carve out. Youth deserve to discover themselves without machines predicting their futures. There are territories of the heart that should remain unmapped by artificial minds.

This message wasn’t targeting fear. It was radiating wisdom. About knowing that not everything that can be built, should be built. Not everything that can be automated should be automated. Some spaces belong to us alone. Singular.

Touching the sky with AI

Then there’s the rain. Nature’s tears. Washing the world clean. But ever more dangerous as Nature’s cries have turned into torrents that are hard to predict.

Scientists can now peer into the atmosphere and see storms before they form. They can map rainfall with precision that would have seemed magical just years ago. They’re teaching machines to read the sky’s moods like an ancient farmer reading cloud formations.

This power fills me with wonder. The ability to understand weather at a global scale. To predict floods. To anticipate droughts. To maybe, someday, guide rain where it’s needed most. Fascinating.

But power always asks questions. Who decides where the rain falls? Who controls the algorithms that read the sky? How do we use tools this profound without losing our sense of wonder at the storm itself?

Forward we go

Here’s what these three stories taught me:

Change happens whether we’re ready or not. AI will continue to grow. Deepfakes will continue to threaten. Machines will continue to learn new capabilities. Weather prediction will become weather influence.

But we still choose how to respond.

We can choose courage over hiding. We can speak truth about harm instead of pretending it doesn’t exist. We can draw boundaries around what matters most. We can use powerful tools while protecting sacred spaces.

We can build the future instead of just letting it happen to us.

We notice when technology serves us and when it doesn’t. We pay attention to which innovations protect human dignity and which ones threaten it. We stay awake to the choices being made in boardrooms and laboratories that will shape our children’s world. And we have to.

This is engaged awareness. The practice of staying human in an increasingly artificial world.

The machines are learning. But so are we. We must.

We’re learning to be brave like that politician. To be wise like the Pope. To be thoughtful about power like those scientists.

The future isn’t something that happens to us. It’s something we create, choice by choice, story by story, boundary by boundary.

Rain falls where it will. But we decide what we do when we get wet.

Research Freedom makes you a smarter researcher with AI.

What did you think of this week’s issue?

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Let’s be honest: the AI tool market is chaos. Every week brings another revolutionary platform promising to change your academic life. Most academics I know are paying for multiple subscriptions, switching between platforms, and feeling overwhelmed by the complexity. There are many AI tools, but frontier models often suffice for your academic work. In this episode of the AI Academics podcast, we tackle the overwhelming array of AI research tools facing academics.

We show you the power of free tools like Google’s NotebookLM, and AI Studio. We demonstrate how these tools can simplify academic tasks such as creating mind maps from lengthy papers and building Socratic partners for critical reflection; no coding required.

NotebookLM’s Mindmaps. Upload any PDF and get an instant topological overview of the content. Think of it as generating a table of contents with hidden subsections for documents that don’t have clear structure. Laws, regulations, and dense academic papers often lack clear navigation. NotebookLM solves this by creating visual maps of complex information.

Google AI Studio. Here’s where things get interesting. We show how AI Studio can become your Socratic partner for critical reflection. This addresses a key question we were asked: “How can we become critical thinkers with AI?” Instead of just asking AI for answers, you use it for reflection. Reflecting involves back-and-forth dialogue. You can’t learn critical thinking from AI alone, but it can help guide your reflection.

The right prompt is a force multiplier. Engage AI in scholarly dialogue. Ask follow-up questions. Challenge its responses. Use it to explore different perspectives on your research. Refining your prompts increases efficiency. It can transform your workflow, allowing you to use the full power of frontier LLMs.

Simplifying your tools can enhance workflow efficiency. Instead of juggling multiple paid subscriptions, you can:

* Save money: Potentially hundreds of dollars per year in subscription fees

* Reduce complexity: Focus on mastering two powerful tools instead of learning many mediocre ones

* Improve results: Access frontier LLMs that often outperform specialized paid tools

* Enhance critical thinking: Use AI as a reflection partner, not just an answer machine

Our episode encourages academics to improve their research strategies and use these accessible tools to enhance their workflows effectively. It’s like a mini webinar. The episode closes with a promise of future discussions and resources to further assist academics in the AI landscape. Relying on AI tools entirely isn’t feasible, especially for critical tasks like writing a dissertation

The future of academic AI use isn’t to collect more tools but to master the right ones.

Geoffrey Hinton said “become a plumber” if you're worried about AI.

He was only half-joking. But he showed us something important about the future of work.

1. From Execution to Evaluation

Before: You research, analyze, and write reports.

Now: AI generates drafts, you provide critical judgment.

Your value: Knowing what's wrong, not just what's right.

2. From Knowledge to Curation

Before: You needed to know everything in your field.

Now: You need to know which AI outputs to trust.

Your value: Pattern recognition across AI-generated content.

3. From Individual to Orchestrator

Before: You completed tasks solo.

Now: You manage multiple AI agents working simultaneously.

Your value: Strategic thinking about workflow design.

4. From Technical to Ethical

Before: Focus on getting the work done.

Now: Focus on ensuring the work should be done.

Your value: Moral reasoning and consequence evaluation

5. From Production to Innovation

Before: You spent 80% of time on routine tasks.

Now: AI handles routine, you focus on breakthrough thinking.

Your value: Asking questions nobody thought to ask.

Most important skills that will continue to matter

Critical thinking beats technical knowledge. Ethics trump efficiency. Human judgment remains irreplaceable. At least for the next little while

So, what does our future look like?

AI won’t steal your job by default. But someone using AI better than you might. The difference isn’t technical skills. It’s knowing when to trust the machine and when to override it. The entry level is disappearing, and instead, we are training to orchestrate tasks together with our artificial buddies.

What skill are you developing to stay ahead of the AI curve? Let us know in the comments.

Research Freedom lets you become a smarter researcher in around 5 minutes per week.

What did you think of this week’s issue?

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💖 Newbie? Welcome here. Start with these three resources for Research Freedom.

In the episode 5 of the AI Academics Podcast, hosted by Professor Dr. Lennart Nacke and his co-host Vugar Ibrahimov, the duo discusses AI's role in generating creative chess pieces, AI tools for reading academic papers, and evolving internet behaviour influenced by AI. The episode also emphasizes ethical integration and effective use of AI tools, concerns about data privacy, and the future of AI-driven search engines. The team highlights their AI research tools webinar and encourages listeners to explore their Substack and Medium writings for more insights.

00:00 Welcome to the AI Academics Podcast

00:20 Black Friday and Cyber Monday Deals

00:59 Gen Chess: A Creative AI Experiment

04:27 Exploring 11 Reader and GenFM

05:53 Notebook LM and Privacy Concerns

18:54 Generative AI in Browsers

24:33 The Future of AI-Powered Search

27:35 Wrapping Up and Webinar Promotion

In this episode, I chat with Vugar Ibrahimov to explore how AI is reshaping academic writing. We dive into practical tools like Napkin AI for creating social media content, and discuss the double-edged sword of AI writing assistance - from improving productivity to potentially dulling our writing skills. Our conversation takes interesting turns as we debate AI's role in creativity and critical thinking, explore ethical considerations, and share insights on using AI tools effectively while preserving human creativity in our work. We also tackle the growing concern of over-reliance on AI and discuss strategies for maintaining a healthy balance between human and machine input in academic writing.

You sit at your desk. Fingers hovering over the keyboard. The human prompts, the machine responds. ChatGPT stares back at you. The cursor waits while ChatGPT thinks. That rainbow wheel is doing more loops than a rollercoaster having an existential crisis. You type a prompt, but the response misses the mark completely. Sound familiar?

AI tools transform how we work—but only if we know how to talk to them. Think of prompting as learning a new language. You wouldn’t walk into a French café and expect perfect service with broken phrases from your high school textbook. The same goes for AI. Let us show you how to order exactly what you want.

Context shapes meaning

And meaning creates context. Most self-proclaimed AI experts write prompts like they’re sending a text message to a stranger. A quick “write me a literature review” gets you about as far as “food please” gets you in a restaurant. Your AI assistant needs context—rich, specific, carefully crafted context.

Start with a clear frame. Tell the AI who you are, what you’re working on, and what you need. Plant the seed, and watch the garden grow. Give it boundaries, expectations, and examples. It’s not unlike showing a research scholar their first steps. The more specific you are, the better your results.

Tools serve you

Or you end up serving tools. Not every AI fits every task. Claude 3.5 Sonnet shines at complex analysis. GitHub Copilot accelerates coding. HeyGen helps create educational videos. Pick your tool like you’d pick your research method—match it to your objective. We discussed this at length in our webinar.

Start small. Small steps make great journeys, but great journeys also begin with the smallest of steps. Test different tools on manageable tasks. Compare their outputs. Build your toolkit gradually, and you’ll develop an instinct for which assistant suits which job. Shop for AI tools like you’re casting characters for another ‘The Office’ reboot.

Frame with precision. Frame with intent.

Frame with clarity. Prompts work best when they follow a clear structure. Here's my tested approach:

* Set the stage with context

* Define the specific task

* Provide examples of what you want

* Specify format and length

* Ask for step-by-step reasoning

* Request specific frameworks or approaches

Think of it as writing a detailed research proposal. Format those citations while you’re juggling flaming chainsaws, APA style. The clearer your requirements, the better your results.

Build better conversations

Stop treating AI like a vending machine. Single-shot prompts rarely give you the best results. Instead, build a conversation. Start broad, then narrow down. Ask follow-up questions. Challenge assumptions. Guide the AI toward your goal through dialogue.

These tools predict text based on patterns. Help them understand your patterns through clear, structured conversation.

Need more guidance? Check out our webinar. We discuss practical applications of AI tools in academic work.

Until next time.

Welcome to episode 2 of the AI Academics Podcast. In this episode, we discuss the latest AI tools that can significantly enhance your academic workflow. We discuss ten reliable AI tools and their practical applications in academia. Additionally, we explore the emerging issue of AI clones and deepfakes, sharing a real-life example of how someone's identity was misused for propaganda purposes. Join us for an insightful conversation.

Exciting travels aside, we've got two major points to cover today. We’ll talk about ten reliable AI tools that can enhance your academic workflow, plus dive into a serious discussion about AI clones. Yes, AI clones are real, and one researcher found herself cloned for purposes she never agreed to.

Breaking Down the Tool List

Here’s a tool list that I tweeted recently. These aren’t sponsored picks—just my personal favourites. Let’s run through them, starting with TypingMind:

TypingMind

This AI user interface (UI) emerged as a favourite of mine, offering a smooth UI to access major APIs like GPT, Gemini, and Claude. The ability to install local versions, like LLaMa or Mistral, on your computer makes it a must-have for academics looking to improve their workflows.

Consensus

Perfect for initial literature searches, Consensus gives quick summaries and references, making it ideal for kick-starting any research project. Yes, Vugar (video) and I have both done detailed reviews on it, so check those out!

Jenni AI

Jenni AI stands out as a co-writing assistant. It had a steep learning curve, but once you get used to it, the tool can be a great companion. It’s perfect for breaking the blank page syndrome and offers a conversation-like interface to help you develop your writing.

PaperPal

PaperPal comes in handy when working in Word. It offers paraphrasing options and gives writing suggestions that can make your work more fluid and concise. It's a lifesaver when dealing with Word documents.

Writefull

If you’re working in LaTeX and using Overleaf, Writeful is your go-to tool. It suggests edits on the go and integrates seamlessly with your writing flow. It helps you maintain clarity and coherence, making academic writing less of a chore.

Otter.ai

Otter.ai isn’t just for meeting transcriptions. With its Otter Bot feature, you can chat with your transcripts, making it perfect for lectures and notes review.

SciteAI

Vugar’s favourite, SciteAI, brings smart citations and writing assistance to the foreground. It even narrows down sources into supporting, contrasting, or just mentioning—adding an extra layer of refinement to your research.

Perplexity

For those simple queries that need a refined touch, Perplexity replaces Google for many, giving AI-generated responses that are detailed and insightful.

Speechify

While I have reservations about its pricing, Speechify's text-to-speech feature in your own voice is unique and adds fun to academic reading.

AI Clones: A Growing Concern

The spotlight today also falls on a viral phenomenon. Imagine waking up one day to find your face and voice cloned for purposes you never intended. That's exactly what happened to creator Olga Loiek who woke up to find her likeness used for propaganda in China. Scary, right? This sheds light on the darker side of AI. It shows us how important it is to build AI tools responsibly. Tools like 11Elevenlabs and HeyGen already allow making perfect voice and face clones, but do so with your consent.

Next week, our online thesis workshop aims to help people get unstuck with thesis writing. Run together with Ali Hindi, this session is packed with useful tips for anyone working on a thesis. Get the recording if you missed it.

Final Thoughts

We hope you found value in today's mini AI tools webinar. Follow us on X, LinkedIn, YouTube, or Medium. The AI Academics Podcast is also on Substack. Don’t miss rating us on Apple Podcasts, Spotify, or YouTube. A little love goes a long way.

We’re taking a short break next week, but don’t worry—Vugar and I will be back with more AI insights. Until then, keep geeking out with the best AI tools that matter to your academic life.

Thank you for joining us today. Enjoy your summer, and we’ll see you in the next session!

Say Thanks

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In this episode of the AI Academics podcast, hosts Dr. Lennart Nacke and Vugar Ibrahimov discuss the recent Apple announcement about 'Apple Intelligence' and its implications for AI integration into Apple's ecosystem, emphasizing privacy and the cooperation with OpenAI's ChatGPT. The conversation covers the advancements in tools for academics, ethical implications of AI, and how AI can be used to improve workflow while maintaining the human element in critical thinking and decision-making processes. The episode also explores how AI could reshape academic research and the importance of understanding which tasks should be delegated to AI. Live audience questions are addressed as well.

Timestamps:

00:00 Introduction to the AI Academics Podcast

00:27 Going Live: New Platforms and Venues

01:21 Apple Intelligence Announcement

06:49 Privacy Concerns and AI Integration

12:54 AI Tools for Academics

22:15 Pushback on AI Tools in Academia

23:12 Critical Thinking vs. AI Assistance

25:46 Integrating AI in Education

28:04 Ethical and Practical Considerations of AI

31:00 Environmental Impact of AI

33:47 Coding in the Age of AI

38:44 The Future of AI and Human Tasks

41:29 Q&A and Final Thoughts

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