Life

‘Never Say Goodbye’: Can AI Bring the Dead Back to Life?

This article delves into the fascinating and controversial world of AI resurrections, exploring how technology is changing the way we cope with grief.

Published

9 months ago

August 26, 2024

AIinAsia

TL;DR:

AI is creating digital ‘resurrections’ of the dead, allowing people to interact with them.
Projects like Replika and StoryFile use AI to mimic the deceased’s communication style.
Experts debate the psychological and ethical implications of these technologies.
Privacy and environmental concerns are significant issues with AI resurrections.

In a world where artificial intelligence can resurrect the dead, grief takes on a new dimension. From Canadian singer Drake’s use of AI-generated Tupac Shakur vocals to Indian politicians addressing crowds years after their passing, technology is blurring the lines between life and death. But beyond their uncanny pull in entertainment and politics, AI “zombies” might soon become a reality for people reeling from the loss of loved ones, through a series of pathbreaking, but potentially controversial, initiatives.

What are AI ‘Resurrections’ of People?

Over the past few years, AI projects around the world have created digital “resurrections” of individuals who have passed away, allowing friends and relatives to converse with them. Typically, users provide the AI tool with information about the deceased. This could include text messages and emails or simply be answers to personality-based questions. The AI tool then processes that data to talk to the user as if it were the deceased.

One of the most popular projects in this space is Replika – a chatbot that can mimic people’s texting styles. Other companies, however, now also allow you to see a video of the dead person as you talk to them. For example, Los Angeles-based StoryFile uses AI to allow people to talk at their own funerals. Before passing, a person can record a video sharing their life story and thoughts. During the funeral, attendees can ask questions and AI technology will select relevant responses from the prerecorded video.

In June, US-based Eternos also made headlines for creating an AI-powered digital afterlife of a person. Initiated just earlier this year, this project allowed 83-year-old Michael Bommer to leave behind a digital version of himself that his family could continue to interact with.

Do These Projects Help People?

When a South Korean mother reunited with an AI recreation of her dead daughter in virtual reality, a video of the emotional encounter in 2020 sparked an intense debate online about whether such technology helps or hurts its users. Developers of such projects point to the users’ agency, and say that it addresses a deeper suffering.

Jason Rohrer, founder of Project December, which also uses AI to stimulate conversations with the dead, said that most users are typically going through an “unusual level of trauma and grief” and see the tool as a way to help cope.

“A lot of these people who want to use Project December in this way are willing to try anything because their grief is so insurmountable and so painful to them.”

The project allows users to chat with AI recreations of known public figures and also with individuals that users may know personally. People who choose to use the service for stimulating conversation with the dead often discover that it helps them find closure, Rohrer said. The bots allow them to express words left unsaid to loved ones who died unexpectedly, he added.

Eternos’s founder, Robert LoCasio, said that he developed the company to capture people’s life stories and allow their loved ones to move forward. Bommer, his former colleague who passed away in June, wanted to leave behind a digital legacy exclusively for his family, said LoCasio.

“I spoke with [Bommer] just days before he passed away and he said, just remember, this was for me. I don’t know if they’d use this in the future, but this was important to me,” said LoCasio.

What are the Pitfalls of This Technology?

Some experts and observers are more wary of AI resurrections, questioning whether deeply grieving people can really make the informed decision to use it, and warning about its adverse psychological effects.

“The biggest concern that I have as a clinician is that mourning is actually very important. It’s an important part of development that we are able to acknowledge the missing of another person,” said Alessandra Lemma, consultant at the Anna Freud National Centre for Children and Families.

Prolonged use could keep people from coming to terms with the absence of the other person, leaving them in a state of “limbo”, Lemma warned. Indeed, one AI service has marketed a perpetual connection with the deceased person as a key feature.

“Welcome to YOV (You, Only Virtual), the AI startup pioneering advanced digital communications so that we Never Have to Say Goodbye to those we love,” read the company’s website, before it was recently updated.

Rohrer said that his grief bot has an “in-built” limiting factor: users pay $10 for a limited conversation. The fee buys time on a supercomputer, with each response varying in computational cost. This means $10 doesn’t guarantee a fixed number of responses, but can allow for one to two hours of conversation. As the time is about to lapse, users are sent a notification and can say their final goodbyes. Several other AI-generated conversational services also charge a fee for use.

Lemma, who has researched the psychological impact of grief bots, says that while she worries about the prospects of them being used outside a therapeutic context, it could be used safely as an adjunct to therapy with a trained professional. Studies around the world are also observing the potential for AI to deliver mental health counselling, particularly through individualised conversational tools.

Are Such Tools Unnatural?

These services may appear to be straight out of a Black Mirror episode. But supporters of this technology argue that the digital age is simply ushering in new ways of preserving life stories, and potentially filling a void left by the erosion of traditional family storytelling practices.

“In the olden days, if a parent knew they were dying, they would leave boxes full of things that they might want to pass on to a child or a book,” said Lemma. “So, this might be the 21st-century version of that, which is then passed on and is created by the parents in anticipation of their passing.”

LoCasio at Eternos agrees.

“The ability for a human to tell the stories of their life, and pass those along to their friends and family, is actually the most natural thing,” he said.

Are AI Resurrection Services Safe and Private?

Experts and studies alike have expressed concerns that such services may fail to keep data private. Personal information or data such as text messages shared with these services could potentially be accessed by third parties. Even if a firm says it will keep data private when someone first signs up, common revisions to terms and conditions, as well as possible changes in company ownership mean that privacy cannot be guaranteed, cautioned Renee Richardson Gosline, senior lecturer at the MIT Sloan School of Management.

Both Rohrer and LoCasio insisted that privacy was at the heart of their projects. Rohrer can only view conversations when users file a customer support request, while LoCasio’s Eternos limits access to the digital legacy to authorised relatives. However, both agreed that such concerns could potentially manifest in the case of tech giants or for-profit companies.

One big worry is that companies may use AI resurrections to customise how they market themselves to users. An advertisement in the voice of a loved one, a nudge for a product in their text.

“When you’re doing that with people who are vulnerable, what you’ve created is a pseudo-endorsement based on someone who never agreed to do such a thing. So it really is a problem with regard to agency and asymmetry of power,” said Gosline.

Are There Any Other Concerns Over AI Chatbots?

That these tools are fundamentally catering to a market of people dealing with grief in itself makes them risky, suggested Gosline – especially when Big Tech companies enter the game.

“In a culture of tech companies which is often described as ‘move fast and break things’, we ought to be concerned because what’s typically broken first are the things of the vulnerable people,” said Gosline. “And I’m hard-pressed to think of people who are more vulnerable than those who are grieving.”

Experts have raised concerns about the ethics of creating a digital resurrection of the dead, particularly in cases where they have not consented to it and users feed AI the data. The environmental impact of AI-powered tools and chatbots is also a growing concern, particularly when involving large language models (LLMs) – systems trained to understand and generate human-like text, which power applications like chatbots.

These systems need giant data centres that emit high levels of carbon and use large volumes of water for cooling, in addition to creating e-waste due to frequent hardware upgrades. A report in early July from Google showed that the company was far behind its ambitious net-zero goals, owing to the demand AI was putting on its data centres.

Gosline said that she understands that there is no perfect programme and that many users of such AI chatbots would do anything to reconnect with a deceased loved one. But it’s on leaders and scientists to be more thoughtful about the kind of world they want to create, she said. Fundamentally, she said, they need to ask themselves one question:

“Do we need this?”

Final Thoughts: The Future of AI and Grief

As AI continues to evolve, so too will its applications in helping people cope with grief. While the technology offers unprecedented opportunities for connection and closure, it also raises significant ethical, psychological, and environmental concerns. It is crucial for developers and users alike to approach these tools with caution and consideration, ensuring that they are used in ways that truly benefit those who are grieving.

Comment and Share:

What do you think about the future of AI and its role in helping people cope with grief? Have you or someone you know used AI to connect with a lost loved one? Share your experiences and thoughts in the comments below. And don’t forget to subscribe for updates on AI and AGI developments.

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Life

Whose English Is Your AI Speaking?

AI tools default to mainstream American English, excluding global voices. Why it matters and what inclusive language design could look like.

Published

2 days ago

May 10, 2025

AIinAsia

TL;DR — What You Need To Know

Most AI tools are trained on mainstream American English, ignoring global Englishes like Singlish or Indian English
This leads to bias, miscommunication, and exclusion in real-world applications
To fix it, we need AI that recognises linguistic diversity—not corrects it.

English Bias In AI

Here’s a fun fact that’s not so fun when you think about it: 90% of generative AI training data is in English. But not just any English. Not Nigerian English. Not Indian English. Not the English you’d hear in Singapore’s hawker centres or on the streets of Liverpool. Nope. It’s mostly good ol’ mainstream American English.

That’s the voice most AI systems have learned to mimic, model, and prioritise. Not because it’s better. But because that’s what’s been fed into the system.

So what happens when you build global technology on a single, dominant dialect?

A Monolingual Machine in a Multilingual World

Let’s be clear: English isn’t one language. It’s many. About 1.5 billion people speak it, and almost all of them do so with their own twist. Grammar, vocabulary, intonation, slang—it all varies.

But when your AI tools—from autocorrect to resume scanners—are only trained on one flavour of English (mostly US-centric, polished, white-collar English), a lot of other voices start to disappear. And not quietly.

Speakers of regional or “non-standard” English often find their words flagged as incorrect, their accents ignored, or their syntax marked as a mistake. And that’s not just inconvenient—it’s exclusionary.

Why Mainstream American English Took Over

This dominance didn’t happen by chance. It’s historical, economic, and deeply structural.

The internet was largely developed in the US. Big Tech? Still mostly based there. The datasets used to train AI? Scraped from web content dominated by American media, forums, and publishing.

So, whether you’re chatting with a voice assistant or asking ChatGPT to write your email, what you’re hearing back is often a polished, neutral-sounding, corporate-friendly version of American English. The kind that gets labelled “standard” by systems that were never trained to value anything else.

When AI Gets It Wrong—And Who Pays the Price

Let’s play this out in real life.

An AI tutor can’t parse a Nigerian English question? The student loses confidence.
A resume written in Indian English gets rejected by an automated scanner? The applicant misses out.
Voice transcription software mangles an Australian First Nations story? Cultural heritage gets distorted.

These aren’t small glitches. They’re big failures with real-world consequences. And they’re happening as AI tools are rolled out everywhere—into schools, offices, government services, and creative workspaces.

It’s “Englishes”, Plural

If you’ve grown up being told your English was “wrong,” here’s your reminder: It’s not.

Singlish? Not broken. Just brilliant. Indian English? Full of expressive, efficient, and clever turns of phrase. Aboriginal English? Entirely valid, with its own rules and rich oral traditions.

Language is fluid, social, and fiercely local. And every community that’s been handed English has reshaped it, stretched it, owned it.

But many AI systems still treat these variations as noise. Not worth training on. Not important enough to include in benchmarks. Not profitable to prioritise. So they get left out—and with them, so do their speakers.

Towards Linguistic Justice in AI

Fixing this doesn’t mean rewriting everyone’s grammar. It means rewriting the technology.

We need to stop asking AI to uphold one “correct” form of English, and start asking it to understand the many. That takes:

More inclusive training data – built on diverse voices, not just dominant ones
Cross-disciplinary collaboration – between linguists, engineers, educators, and community leaders
Respect for language rights – including the choice not to digitise certain cultural knowledge
A mindset shift – from standardising language to supporting expression

Because the goal isn’t to “correct” the speaker. It’s to make the system smarter, fairer, and more reflective of the world it serves.

Ask Yourself: Whose English Is It Anyway?

Next time your AI assistant “fixes” your sentence or flags your phrasing, take a second to pause. Ask: whose English is this system trying to emulate? And more importantly, whose English is it leaving behind?

Language has always been a site of power—but also of play, resistance, and identity. The way forward for AI isn’t more uniformity. It’s more Englishes, embraced on their own terms.

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Business

Build Your Own Agentic AI — No Coding Required

Want to build a smart AI agent without coding? Here’s how to use ChatGPT and no-code tools to create your own agentic AI — step by step.

Published

3 days ago

May 9, 2025

AIinAsia

TL;DR — What You Need to Know About Agentic AI

Anyone can now build a powerful AI agent using ChatGPT — no technical skills needed.
Tools like Custom GPTs and Make.com make it easy to create agents that do more than chat — they take action.
The key is to start with a clear purpose, test it in real-world conditions, and expand as your needs grow.

Anyone Can Build One — And That Includes You

Not too long ago, building a truly capable AI agent felt like something only Silicon Valley engineers could pull off. But the landscape has changed. You don’t need a background in programming or data science anymore — you just need a clear idea of what you want your AI to do, and access to a few easy-to-use tools.

Whether you’re a startup founder looking to automate support, a marketer wanting to build a digital assistant, or simply someone curious about AI, creating your own agent is now well within reach.

What Does ‘Agentic’ Mean, Exactly?

Think of an agentic AI as something far more capable than a standard chatbot. It’s an AI that doesn’t just reply to questions — it can actually do things. That might mean sending emails, pulling information from the web, updating spreadsheets, or interacting with third-party tools and systems.

The difference lies in autonomy. A typical chatbot might respond with a script or FAQ-style answer. An agentic AI, on the other hand, understands the user’s intent, takes appropriate action, and adapts based on ongoing feedback and instructions. It behaves more like a digital team member than a digital toy.

Step 1: Define What You Want It to Do

Before you dive into building anything, it’s important to get crystal clear on what role your agent will play.

Ask yourself:

Who is going to use this agent?
What specific tasks should it be responsible for?
Are there repetitive processes it can take off your plate?

For instance, if you run an online business, you might want an agent that handles frequently asked questions, helps users track their orders, and flags complex queries for human follow-up. If you’re in consulting, your agent could be designed to book meetings, answer basic service questions, or even pre-qualify leads.

Be practical. Focus on solving one or two real problems. You can always expand its capabilities later.

Step 2: Pick a No-Code Platform to Build On

Now comes the fun part: choosing the right platform. If you’re new to this, I recommend starting with OpenAI’s Custom GPTs — it’s the most accessible option and designed for non-coders.

Custom GPTs allow you to build your own version of ChatGPT by simply describing what you want it to do. No technical setup required. You’ll need a ChatGPT Plus or Team subscription to access this feature, but once inside, the process is remarkably straightforward.

If you’re aiming for more complex automation — such as integrating your agent with email systems, customer databases, or CRMs — you may want to explore other no-code platforms like Make.com (formerly Integromat), Dialogflow, or Bubble.io. These offer visual builders where you can map out flows, connect apps, and define logic — all without needing to write a single line of code.

Step 3: Use ChatGPT’s Custom GPT Builder

Let’s say you’ve opted for the Custom GPT route — here’s how to get started.

First, log in to your ChatGPT account and select “Explore GPTs” from the sidebar. Click on “Create,” and you’ll be prompted to describe your agent in natural language. That’s it — just describe what the agent should do, how it should behave, and what tone it should take. For example:

“You are a friendly and professional assistant for my online skincare shop. You help customers with questions about product ingredients, delivery options, and how to track their order status.”

Once you’ve set the description, you can go further by uploading reference materials such as product catalogues, FAQs, or policies. These will give your agent deeper knowledge to draw from. You can also choose to enable additional tools like web browsing or code interpretation, depending on your needs.

Then, test it. Interact with your agent just like a customer would. If it stumbles, refine your instructions. Think of it like coaching — the more clearly you guide it, the better the output becomes.

Step 4: Go Further with Visual Builders

If you’re looking to connect your agent to the outside world — such as pulling data from a spreadsheet, triggering a workflow in your CRM, or sending a Slack message — that’s where tools like Make.com come in.

These platforms allow you to visually design workflows by dragging and dropping different actions and services into a flowchart-style builder. You can set up scenarios like:

A user asks the agent, “Where’s my order?”
The agent extracts key info (e.g. email or order number)
It looks up the order via an API or database
It responds with the latest shipping status, all in real time

The experience feels a bit like setting up rules in Zapier, but with more control over logic and branching paths. These platforms open up serious possibilities without requiring a developer on your team.

Step 5: Train It, Test It, Then Launch

Once your agent is built, don’t stop there. Test it with real people — ideally your target users. Watch how they interact with it. Are there questions it can’t answer? Instructions it misinterprets? Fix those, and iterate as you go.

Training doesn’t mean coding — it just means improving the agent’s understanding and behaviour by updating your descriptions, feeding it more examples, or adjusting its structure in the visual builder.

Over time, your agent will become more capable, confident, and useful. Think of it as a digital intern that never sleeps — but needs a bit of initial training to perform well.

Why Build One?

The most obvious reason is time. An AI agent can handle repetitive questions, assist users around the clock, and reduce the strain on your support or operations team.

But there’s also the strategic edge. As more companies move towards automation and AI-led support, offering a smart, responsive agent isn’t just a nice-to-have — it’s quickly becoming an expectation.

And here’s the kicker: you don’t need a big team or budget to get started. You just need clarity, curiosity, and a bit of time to explore.

Where to Begin

If you’ve got a ChatGPT Plus account, start by building a Custom GPT. You’ll get an immediate sense of what’s possible. Then, if you need more, look at integrating Make.com or another builder that fits your workflow.

The world of agentic AI is no longer reserved for the technically gifted. It’s now open to creators, business owners, educators, and anyone else with a problem to solve and a bit of imagination.

What kind of AI agent would you build — and what would you have it do for you first? Let us know in the comments below!

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Or tap here to try this out now at ChatGPT by tapping here.

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Life

Which ChatGPT Model Should You Choose?

Confused about the ChatGPT model options? This guide clarifies how to choose the right model for your tasks.

Published

3 days ago

May 9, 2025

AIinAsia

TL;DR — What You Need to Know:

GPT-4o is ideal for summarising, brainstorming, and real-time data analysis, with multimodal capabilities.
GPT-4.5 is the go-to for creativity, emotional intelligence, and communication-based tasks.
o4-mini is designed for speed and technical queries, while o4-mini-high excels at detailed tasks like advanced coding and scientific explanations.

Navigating the Maze of ChatGPT Models

OpenAI’s ChatGPT has come a long way, but its multitude of models has left many users scratching their heads. If you’re still confused about which version of ChatGPT to use for what task, you’re not alone! Luckily, OpenAI has stepped in with a handy guide that outlines when to choose one model over another. Whether you’re an enterprise user or just getting started, this breakdown will help you make sense of the options at your fingertips.

So, Which ChatGPT Model Makes Sense For You?

Currently, ChatGPT offers five models, each suited to different tasks. They are:

GPT-4o – the “omni model”
GPT-4.5 – the creative powerhouse
o4-mini – the speedster for technical tasks
o4-mini-high – the heavy lifter for detailed work
o3 – the analytical thinker for complex, multi-step problems

Which model should you use?

Here’s what OpenAI has to say:

GPT-4o: If you’re looking for a reliable all-rounder, this is your best bet. It’s perfect for tasks like summarising long texts, brainstorming emails, or generating content on the fly. With its multimodal features, it supports text, images, audio, and even advanced data analysis.
GPT-4.5: If creativity is your priority, then GPT-4.5 is your go-to. This version shines with emotional intelligence and excels in communication-based tasks. Whether you’re crafting engaging narratives or brainstorming innovative ideas, GPT-4.5 brings a more human-like touch.
o4-mini: For those in need of speed and precision, o4-mini is the way to go. It handles technical queries like STEM problems and programming tasks swiftly, making it a strong contender for quick problem-solving.
o4-mini-high: If you’re dealing with intricate, detailed tasks like advanced coding or complex mathematical equations, o4-mini-high delivers the extra horsepower you need. It’s designed for accuracy and higher-level technical work.
o3: When the task requires multi-step reasoning or strategic planning, o3 is the model you want. It’s designed for deep analysis, complex coding, and problem-solving across multiple stages.

Which one should you pick?

For $20/month with ChatGPT Plus, you’ll have access to all these models and can easily switch between them depending on your task.

But here’s the big question: Which model are you most likely to use? Could OpenAI’s new model options finally streamline your workflow, or will you still be bouncing between versions? Let me know your thoughts!

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