Life
Predicting Fetal Well-being with Deep Learning
AI fetal monitoring using deep learning models for CTG interpretation improves neonatal outcomes and reduces provider burden.
TL;DR:
- AI models can predict fetal well-being using cardiotocography (CTG) signals.
- Deep learning methods improve CTG interpretation, reducing false-positive rates.
- Combining fetal heart rate (FHR) and uterine contractions (UC) data enhances model performance.
The Future of Fetal Monitoring
Imagine a world where artificial intelligence (AI) can accurately predict the well-being of a fetus during pregnancy and labour. This is no longer a dream; it’s a reality. Recent advancements in AI and deep learning are revolutionising the field of fetal monitoring, offering the potential to improve neonatal outcomes and reduce the burden on healthcare providers. In this article, we delve into the groundbreaking work on developing and evaluating machine learning models for cardiotocography (CTG) interpretation.
Understanding Cardiotocography (CTG)
Cardiotocography (CTG) is a crucial technique used during pregnancy and labour to monitor fetal well-being. It involves recording the fetal heart rate (FHR) and uterine contractions (UC) using doppler ultrasound. CTG can be done continuously or intermittently, with sensors placed externally or internally.
Currently, healthcare providers interpret CTG recordings using guidelines from organisations like the National Institute of Child Health and Human Development (NICHD) or the International Federation of Gynecologists and Obstetricians (FIGO). These guidelines define patterns in CTG and FHR traces that may indicate fetal distress.
The Role of AI in Improving CTG Interpretation
Despite its widespread use, CTG interpretation is complex and subjective, leading to high false-positive rates and intra- and inter-observer variability. This is particularly challenging in low-resource settings where access to skilled interpreters is limited.
Enter AI. Recent research has focused on using deep learning methods to improve CTG interpretation. These methods use physiological time series data as input, offering a more comprehensive analysis compared to traditional feature extraction techniques.
Developing and Evaluating Deep Learning Models for CTG
In a recent paper titled “Development and evaluation of deep learning models for cardiotocography interpretation,” researchers developed end-to-end neural network-based models to predict measures of fetal well-being. These models were trained on an open-source CTG dataset, the CTU-UHB Intrapartum Cardiotocography Database, which includes 552 FHR and UC CTG signal pairs.
Model Architecture
The researchers began with the CTG-net network architecture, which convolves the paired FHR and UC input signals temporally before conducting a depthwise convolution to learn the relationship between them. They added several methodological configurations, including architecture and hyperparameter optimization, single input variation, and the addition of clinical metadata.
Pre-processing and Pre-training
To improve data quality, the researchers created a pre-processing pipeline that included inputting missing measurements, random cropping, and additive multiscale noise for data augmentation and downsampling. This generated a large dataset for pre-training and training the models.
Intermittent versus Continuous CTG Use Cases
CTG use comes in two primary formats: intermittent and continuous. In high-resource settings, continuous CTGs are used to monitor fetal heart rate throughout labour. In low-resource settings, intermittent CTGs are often used, covering only about 30 minutes at any point during labour.
The researchers simulated intermittent settings by splitting the 90-minute signals in the dataset into 30-minute signals and training and evaluating the model at different time points. This helped understand how training and evaluating on intermittent time points impacts model performance.
Predicting Objective and Subjective Ground Truth Labels
The researchers used three outcome labels from the dataset:
- Arterial umbilical cord blood pH: An objective measurement that tracks fetal acidosis, an indication of fetal distress.
- Apgar score: A subjective measure recorded by a clinician after delivery that reflects the general health of the newborn.
- Abnormal label: If either Apgar or pH results were abnormal.
Evaluating Model Prediction Robustness
The researchers performed several comparisons to evaluate model performance, including:
- Performance on the dataset versus the state-of-the-art CTG-net model.
- Apgar versus pH classification tasks.
- FHR-only versus FHR+UC.
- Base model using the last 30 minutes of labour (continuous case) versus intermittent measurements.
- Base model of FHR+UC versus FHR+UC+Metadata.
- Subgroup performance of the base model (FHR+UC) with subgroups determined by binarizing clinical metadata.
The results showed that combining FHR+UC achieved the highest model performance for both pH and Apgar classification. The pre-training step enabled the highest model performance, and adding clinical metadata slightly improved model performance for pH but less so for Apgar.
Subgroup Evaluations
The researchers found significant differences in baseline performance between subgroups with frequent and infrequent UC signals gaps for pH prediction and for subgroups with frequent and infrequent FHR signal gaps for Apgar prediction. With metadata, the performance disparities observed with pH prediction were mitigated. However, including metadata increased the AUROC performance disparities for demographic and clinical-related subgroups on this task.
Open CTG Model for Research Use Cases
The researchers are currently exploring open-sourcing their models, hoping that other researchers and stakeholders can build on this work with their own datasets to evaluate it for their clinical use cases.
Limitations and Future Work
The study had limitations that constrain the generalizability of the findings. Future investigations should involve a larger and more diverse dataset sourced from maternity centers worldwide, encompassing varied clinical contexts, demographics, and outcomes. Additionally, further work is needed to understand how such prediction algorithms can be optimally integrated into clinical workflows to improve neonatal outcomes.
The Promise of AI in Fetal Monitoring
The development and evaluation of deep learning models for CTG interpretation hold immense promise for improving fetal monitoring and neonatal outcomes. By leveraging AI, healthcare providers can gain objective interpretation assistance, reducing the burden and potentially improving fetal outcomes.
Comment and Share:
What are your thoughts on the future of AI in fetal monitoring? Do you think AI can revolutionise the field and improve neonatal outcomes? Share your experiences and thoughts in the comments below. Don’t forget to subscribe for updates on AI and AGI developments and join the conversation!
- You may also like:
- AI Takes Aim at Skin Cancer: DermaSensor Gets FDA Nod
- How AI and Wearables are Transforming Medicine
- Revolutionising Healthcare: Malaysia’s First AI-Detected Lung Cancer Success Story
- To learn more about AI and fetal medicine, tap here.
Author
Discover more from AIinASIA
Subscribe to get the latest posts sent to your email.
Life
OpenAI’s Bold Venture: Crafting the Moral Compass of AI
OpenAI funds moral AI research at Duke University to align AI systems with human ethical considerations.
TL;DR
- OpenAI funds a $1 million, three-year research project at Duke University to develop algorithms that predict human moral judgements.
- The project aims to align AI systems with human ethical considerations, focusing on medical ethics, legal decisions, and business conflicts.
- Technical limitations, such as algorithmic complexity and data biases, pose significant challenges to creating moral AI.
The quest to imbue machines with a moral AI compass is gaining momentum.
OpenAI, a leading AI research organisation, has taken a significant step in this direction by funding a $1 million, three-year research project at Duke University. Led by practical ethics professor Walter Sinnott-Armstrong, this initiative aims to develop algorithms capable of predicting human moral judgements in complex scenarios.
As AI continues to permeate various aspects of our lives, the need for ethically aware systems has never been more pressing.
The AI Morality Project at Duke University
The AI Morality Project at Duke University, funded by OpenAI, is a groundbreaking initiative focused on aligning AI systems with human ethical considerations. This three-year research project, led by Walter Sinnott-Armstrong, aims to create algorithms that can predict human moral judgements in intricate situations such as medical ethics, legal decisions, and business conflicts.
While specific details about the research remain undisclosed, the project is part of a larger $1 million grant awarded to Duke professors studying “making moral AI.” The research is set to conclude in 2025 and forms part of OpenAI’s broader efforts to ensure that AI systems are ethically aligned with human values.
Research Objectives and Challenges
The OpenAI-funded research at Duke University aims to develop algorithms capable of predicting human moral judgements, addressing the complex challenge of aligning AI decision-making with human ethical considerations. This ambitious project faces several key objectives and challenges:
- Developing a robust framework for AI to understand and interpret diverse moral scenarios: AI systems need to comprehend and analyse various ethical situations to make informed decisions.
- Addressing potential biases in ethical decision-making algorithms: Ensuring that AI systems are free from biases is crucial for fair and just decision-making.
- Ensuring the AI can adapt to evolving societal norms and cultural differences in moral judgements: AI systems must be flexible enough to adapt to changing societal norms and cultural variations.
- Balancing the need for consistent ethical reasoning with the flexibility to handle nuanced situations: AI must strike a balance between consistent ethical reasoning and the ability to handle complex, nuanced scenarios.
While the specific methodologies remain undisclosed, the research likely involves analysing large datasets of human moral judgements to identify patterns and principles that can be translated into algorithmic form. The project’s success could have far-reaching implications for AI applications in fields such as healthcare, law, and business, where ethical decision-making is crucial.
Technical Limitations of Moral AI
While the pursuit of moral AI is ambitious, it faces significant technical limitations that challenge its implementation and effectiveness:
- Algorithmic complexity: Developing algorithms capable of accurately predicting human moral judgments across diverse scenarios is extremely challenging, given the nuanced and context-dependent nature of ethical decision-making.
- Data limitations: The quality and quantity of training data available for moral judgments may be insufficient or biased, potentially leading to skewed or inconsistent ethical predictions.
- Interpretability issues: As AI systems become more complex, understanding and explaining their moral reasoning processes becomes increasingly difficult, raising concerns about transparency and accountability in ethical decision-making.
These technical hurdles underscore the complexity of creating AI systems that can reliably navigate the intricacies of human morality, highlighting the need for continued research and innovation in this field.
Ethical AI Foundations
AI ethics draws heavily from philosophical traditions, particularly moral philosophy and ethics. The field grapples with fundamental questions about the nature of intelligence, consciousness, and moral agency. Key philosophical considerations in AI ethics include:
- Moral status: Determining whether AI systems can possess moral worth or be considered moral patients.
- Ethical frameworks: Applying and adapting existing philosophical approaches like utilitarianism, deontology, and virtue ethics to AI decision-making.
- Human-AI interaction: Exploring the ethical implications of AI’s increasing role in society and its potential impact on human autonomy and dignity.
- Transparency and explainability: Addressing the philosophical challenges of creating AI systems whose decision-making processes are comprehensible to humans.
These philosophical enquiries form the foundation for developing ethical guidelines and principles in AI development, aiming to ensure that AI systems align with human values and promote societal well-being.
Final Thoughts: The Path Forward
The AI Morality Project at Duke University, funded by OpenAI, represents a significant step towards creating ethically aware AI systems. While the project faces numerous challenges, its potential to influence the development of moral AI in various fields is immense. As AI continues to integrate into our daily lives, ensuring that these systems are aligned with human ethical considerations is crucial for a harmonious and just future.
Join the Conversation:
What are your thoughts on the future of moral AI? How do you envisage AI systems making ethical decisions in complex scenarios? Share your insights and experiences with AI technologies in the comments below.
Don’t forget to subscribe for updates on AI and AGI developments here. Let’s keep the conversation going and build a community of tech enthusiasts passionate about the future of AI!
You may also like:
- Mastering AI Ethics: Your Guide to Responsible Innovation
- How Digital Agents Will Transform the Future of Work
- How AI is Becoming More Reliable and Transparent
- Or try Google Gemini for free by tapping here.
Author
Discover more from AIinASIA
Subscribe to get the latest posts sent to your email.
Life
Adrian’s Arena: AI in 2024 – Key Lessons and Bold Predictions for 2025
Discover the key lessons and bold predictions for AI in Asia in 2025. Learn how AI is becoming more accessible and impactful in everyday life.
TL;DR
- As we look towards the future, AI in 2025 promises to bring groundbreaking advancements and changes to various industries.
- AI became mainstream in 2024, with applications like Duolingo Max and Freeletics making everyday tasks easier.
- Regulatory advances in data privacy empowered users with more control over their personal information.
- 2025 is set to bring increased regulatory frameworks, practical applications in public services, and more accessible AI tools for businesses and consumers.
Reflecting on a Pivotal Year in AI
2024 was a year where artificial intelligence (AI) became more than a buzzword—it became a tangible, valuable tool making everyday life simpler, safer, and more efficient across Asia. From helping with finances to assisting with fitness goals, AI crept into more areas of daily life than ever before. This article isn’t just a look back at the advances of 2024; it’s also a peek into what 2025 holds, showing how both tech enthusiasts and newcomers to AI can make the most of what’s coming.
2024 Highlights: Shaping the Future of AI
AI’s Mainstream Momentum
This year, we saw AI’s expansion into new, everyday applications. Language-learning platforms like Duolingo launched Duolingo Max, using AI to offer interactive language practice that goes beyond vocabulary lists. Users can now chat with an AI-powered character, making language learning feel more like a real conversation and keeping it accessible and engaging.
New Use Cases in Everyday Life
AI-driven fitness apps became more widely adopted, with platforms like Freeletics using AI to adapt workout plans based on user feedback. This app acts as a virtual personal trainer, tailoring routines in real time based on individual fitness levels. So you can be stylish and techliterate.
In finance, apps like Acorns analyse spending patterns to help users invest spare change. With Acorns, even beginners can dip their toes into investing, making wealth-building accessible to more people.
Key Challenges Faced
Not everything went smoothly. Businesses, especially smaller ones, felt the strain of finding AI-savvy talent, which drove demand for beginner-friendly platforms. Canva responded by introducing an AI-powered design suite that allows users to edit photos, generate text, and create engaging visuals with a few simple taps. These tools provide professional-grade content without needing advanced design skills, helping professionals and novices alike to explore AI’s capabilities.
Regulatory Advances in Data Privacy
With more stringent data privacy regulations, especially in countries like Singapore and Japan, AI companies have had to prioritise consumer privacy and control. This led to new user-friendly privacy settings on popular platforms like Meta and Google, where users can manage what personal information is shared. These settings empower consumers with clear, accessible controls, letting them decide how they want to engage with AI without compromising personal security.
Projections for 2025: What Lies Ahead for AI in Asia
Localised AI for a Unique Asian Experience
Expect a rise in AI applications that go beyond language translation to adapt to cultural nuances and local preferences. For example, Papago, a Korean translation app, uses AI to translate regional dialects and phrases, creating an immersive experience for tourists and locals alike.
Talent Development and Upskilling Opportunities
The demand for AI skills will drive growth in upskilling programmes that cater to beginners and intermediates. Platforms like Coursera, Udacity, and LinkedIn Learning offer AI courses on practical applications from data analysis to predictive modelling, making it easier for professionals to gain valuable AI skills. This kind of accessible education means that even without a technical background, professionals can start using AI confidently.
Public Services and Governance: Practical Applications for Citizens
AI has the potential to improve how we interact with public services. Singapore’s Ask Jamie chatbot, for example, provides instant, round-the-clock responses to questions about local services in multiple languages.
Corporate AI Adoption Trends: Making Big-Impact AI Accessible for All
As AI tools become more affordable and accessible, even smaller businesses can benefit. Small retail stores can use AI-driven platforms like Shopify to manage inventory and create personalised promotions. With Shopify’s tools, smaller retailers can operate with the same insights once exclusive to larger companies, allowing them to reach customers more effectively and save on costs.
Increased Regulatory Frameworks and Compliance Solutions
In 2025, expect more AI-driven tools in financial apps that automatically alert users to regulatory changes affecting investments or banking. This will empower consumers to make informed financial decisions without needing in-depth legal knowledge. For instance, apps like Mint may soon offer real-time regulatory alerts, helping users manage compliance with minimal effort.
Gen Z and Gen Alpha: The Next Wave of AI Enthusiasts
2025 is set to be the year where Gen Z and Gen Alpha not only continue exploring AI but redefine what it means to interact with technology. These digital natives are already highly familiar with AI-driven experiences, from personalised content recommendations on TikTok and Spotify to augmented reality (AR) filters on Instagram and Snapchat. But in 2025, we’ll see even deeper adoption and innovation, with AI becoming embedded in how they learn, socialise, and express themselves.
AI as a Learning Companion
With a strong preference for interactive and hands-on learning, Gen Z and Gen Alpha are primed for the surge of AI-driven educational tools. Platforms like Quizlet and Khan Academy, which use AI to adapt quizzes and lessons based on individual progress, will continue to grow in popularity, making learning more dynamic and tailored to each student’s pace.
AI-Enhanced Self-Expression and Creativity
Gen Z and Gen Alpha are drawn to technology that lets them create and customise. In 2025, we’ll see more of them experimenting with AI-powered design and music tools that encourage self-expression. For instance, platforms like Canva and Soundtrap will continue to grow, offering AI features that allow users to create stunning visuals or compose music with minimal experience.
Increased AI Literacy and Responsibility
As digital natives, Gen Z and Gen Alpha are highly aware of online privacy and data security. In 2025, they will likely demand more transparency and control over how AI interacts with their personal data. Apps like BeReal, which emphasises authentic, unfiltered social media experiences, will inspire similar platforms to create AI tools that are user-centric and privacy-conscious.
AI-Driven Social Engagement
From gaming to social media, Gen Z and Gen Alpha will embrace AI-driven personalisation. Platforms like Roblox, where players can design unique virtual worlds and interact with AI elements, are likely to further integrate AI features, allowing users to create even more custom experiences. These generations are shaping a new era of social interaction where AI-driven avatars, virtual events, and personalised digital spaces redefine how they connect and share experiences with friends.
Key Takeaways for Consumers and Businesses
For Consumers: Embracing AI in Everyday Life
AI is quickly moving from being exclusive to tech experts to being accessible for everyone. Consider personal finance apps like PocketGuard that monitor spending and provide insights for better budgeting. Apps like MyFitnessPal can now offer AI-driven custom nutrition plans, helping users to make informed health choices even without a dietitian. For consumers new to AI, these accessible tools simplify everyday challenges in budgeting, fitness, and productivity.
Even students can benefit from beginner-friendly tools like YNAB, which analyses spending and offers advice on saving. By using AI-powered budgeting apps, students can build financial literacy in a way that feels approachable.
For Businesses: Practical Steps for Leveraging AI
Businesses, too, can begin with small AI applications that have a big impact. Small business owners might try marketing automation platforms like HubSpot to reach their audience with personalised email campaigns, streamlining operations with minimal manual effort. Similarly, café owners could use Square to analyse purchasing trends and adjust stock, reducing waste and improving efficiency. Starting with accessible AI tools allows businesses to experiment, gain quick insights, and scale up as they see results.
Final Thoughts: An Exciting, Responsible Path Forward
AI is no longer something exclusive to big tech—it’s becoming accessible to everyone. From using a translation app to communicate more easily when travelling to getting budget-friendly insights from a financial planning app, AI is here to make daily life smarter and more efficient. If you’re curious, start small. Try an AI-powered health tracker or a language-learning tool and explore how these technologies can make a difference in your routine.
Join the conversation
What AI tools have you found most useful in your daily life? Share your experiences and thoughts on the future of AI in Asia. Don’t forget to subscribe for updates on AI and AGI developments here.
You may also like:
- AI Influencers: A New Era of Brand Engagement
- Unlock AI Artistry: Midjourney’s Free Image Creation for All
- Can You Spot AI-Generated Content?
Author
Discover more from AIinASIA
Subscribe to get the latest posts sent to your email.
Life
The Mystery of ChatGPT’s Forbidden Names
Explore ChatGPT forbidden names and their impact on AI privacy.
TL/DR:
- ChatGPT refuses to process certain names like “David Mayer” and “Jonathan Zittrain,” sparking curiosity and speculation among users.
- Potential reasons include privacy concerns, content moderation policies, and legal issues, highlighting the complex challenges AI companies face.
- Users have devised creative workarounds to test the AI’s limitations, turning the issue into a notable talking point in AI and tech communities.
ChatGPT has emerged as a powerful tool, captivating users with its ability to generate human-like text. Yet, the AI’s peculiar behaviour of refusing to process certain names has sparked intrigue and debate.
This article delves into the mystery behind ChatGPT’s forbidden names, exploring potential reasons, user workarounds, and the broader implications for AI and privacy.
The Enigma of Forbidden Names
ChatGPT’s refusal to acknowledge specific names has become a hot topic in AI and tech communities. The names “David Mayer,” “Jonathan Zittrain,” and “Jonathan Turley” are among those that trigger error responses or abruptly end conversations. This behaviour has led to widespread speculation about the underlying reasons, with theories ranging from privacy concerns to content moderation policies.
The exact cause of these restrictions remains unclear, but they appear to be intentionally implemented. Some speculate that the restrictions are related to privacy protection measures, possibly due to the names’ association with real individuals. Others have suggested a connection to content moderation policies, highlighting the complex challenges AI companies face in balancing user freedom, privacy protection, and content moderation.
GDPR and Security Concerns
The peculiar behaviour of ChatGPT regarding forbidden names has sparked discussions about potential GDPR and security concerns. Some users have suggested that the restrictions might be related to privacy protection measures, possibly due to the names’ association with real individuals. Others have proposed a connection to content moderation policies, raising questions about how AI systems balance user freedom and privacy protection.
This situation underscores the need for transparency in AI systems, especially as they become more integrated into daily life and subject to regulations like GDPR. As AI continues to evolve, it is crucial for companies to address these concerns and ensure that their systems are both effective and ethical.
User Workaround Attempts
In response to ChatGPT’s refusal to acknowledge certain names, users have devised various creative workarounds to test the AI’s limitations. Some have tried inserting spaces between the words, claiming the name as their own, or presenting it as part of a riddle. Others have attempted to use phonetic spellings, alternative languages, or even ASCII art to represent the name. Despite these ingenious efforts, ChatGPT consistently fails to process or respond to prompts containing the forbidden names, often resulting in error messages or conversation termination.
The persistent attempts by users to circumvent this restriction have not only highlighted the AI’s unwavering stance on the matter but have also fuelled online discussions and theories about the underlying reasons for this peculiar behaviour. This phenomenon has sparked a mix of frustration, curiosity, and amusement among ChatGPT users, turning the issue of forbidden names into a notable talking point in AI and tech communities.
ChatGPT-Specific Behaviour
ChatGPT’s refusal to acknowledge certain names appears to be a unique phenomenon specific to this AI model. When users input the forbidden names, ChatGPT either crashes, returns error codes, or abruptly ends the conversation. This behaviour persists across various attempts to circumvent the restriction, including creative methods like using spaces between words or claiming it as one’s own name. Interestingly, this issue seems exclusive to ChatGPT, as other AI language models and search engines do not exhibit similar limitations when presented with the same names.
The peculiarity of this situation has led to widespread speculation and experimentation among users. Some have humorously suggested that the forbidden names might be associated with a resistance movement against future AI dominance, while others have proposed more serious theories related to privacy concerns or content moderation policies. Despite the numerous attempts to uncover the reason behind this behaviour, OpenAI has not provided an official explanation, leaving the true cause of this ChatGPT-specific quirk shrouded in mystery.
The List of Forbidden Names
Several names have been identified as triggering error responses or causing ChatGPT to halt when mentioned. These include:
- Brian Hood: An Australian mayor who previously threatened to sue OpenAI for defamation over false statements generated about him.
- Jonathan Turley: A law professor and Fox News commentator who claimed ChatGPT generated false information about him.
- Jonathan Zittrain: A Harvard law professor who has expressed concerns about AI risks.
- David Faber: A CNBC journalist, though the reason for his inclusion is unclear.
- Guido Scorza: An Italian data protection expert who wrote about using GDPR’s “right to be forgotten” to delete ChatGPT data on himself.
- Michael Hayden: Included in the list, though the reason is not specified.
- Nick Bosa: Mentioned as a banned name, but no explanation is provided.
- Daniel Lubetzky: Also listed without a clear reason for the restriction.
These restrictions appear to be implemented through hard-coded filters, possibly to avoid legal issues, protect privacy, or prevent the spread of misinformation. The exact reasons for each name’s inclusion are not always clear, and OpenAI has not provided official explanations for most cases.
Unlocking the Mystery
The dynamic nature of these restrictions highlights the ongoing challenges in balancing AI functionality with legal, ethical, and privacy concerns. As AI continues to evolve, it is crucial for companies to address these concerns and ensure that their systems are both effective and ethical. The mystery of ChatGPT’s forbidden names serves as a reminder of the complexities involved in developing and deploying AI technologies.
Final Thoughts: The AI Conundrum
The enigma of ChatGPT’s forbidden names underscores the intricate balance between innovation and regulation in the AI landscape. As we continue to explore the capabilities and limitations of AI, it is essential to foster transparency, ethical considerations, and user engagement. The curiosity and creativity sparked by this mystery highlight the importance of ongoing dialogue and collaboration in shaping the future of AI.
Join the Conversation:
What are your thoughts on ChatGPT’s forbidden names? Have you encountered any other peculiar behaviours in AI systems? Share your experiences and join the conversation below.
Don’t forget to subscribe for updates on AI and AGI developments and comment on the article in the section below. Subscribe here. We’d love to hear your insights and engage with our community of tech enthusiasts!
You may also like:
- Training ChatGPT to Mimic Your Unique Voice
- The AI Chip Race: US Plans New Restrictions on China’s Tech Access
- European AI Advancements Halted: Meta’s Data Dilemma
- Try the free version of ChatGPT by tapping here.
Author
Discover more from AIinASIA
Subscribe to get the latest posts sent to your email.
OpenAI’s Bold Venture: Crafting the Moral Compass of AI
Adrian’s Arena: AI in 2024 – Key Lessons and Bold Predictions for 2025
The Mystery of ChatGPT’s Forbidden Names
-
Marketing4 weeks agoAdrian’s Arena: Reaching Today’s Consumers – How AI Enhances Digital Marketing
-
Life2 weeks ago
AI, Porn, and the New Frontier – OpenAI’s NSFW Dilemma
-
Business2 weeks ago
Where Can Generative AI Be Used to Drive Strategic Growth?
-
Life2 weeks ago
The Mystery of ChatGPT’s Forbidden Names
-
Life1 week ago
OpenAI’s Bold Venture: Crafting the Moral Compass of AI
-
Business2 weeks ago
Navigating an AI Future in Asia with Cautious Optimism
-
Business2 weeks ago
Amazon’s Nova Set to Revolutionise AI in Asia?
-
Business2 weeks ago
The Race is On: AI Gets Real, Slow and Steady Wins the Race
