Life
AI Decodes the Language of DNA: Revolutionising Genomics and Personalised Medicine
AI decodes DNA, revolutionising genomics and personalised medicine with GROVER.
Published
3 months agoon
By
AIinAsia
TL;DR:
- AI language model GROVER decodes DNA as a language, revealing hidden genetic functions.
- GROVER identifies gene promoters, protein binding sites, and epigenetic information.
- This breakthrough could revolutionise genomics and personalised medicine.
Imagine if you could read the language of life itself. That’s precisely what researchers have achieved with GROVER, an AI language model trained on human DNA. This innovative approach treats DNA as a language, learning its rules and context to extract biological meanings, such as gene promoters and protein binding sites. This breakthrough could revolutionise genomics and personalised medicine by unlocking hidden layers of genetic information.
Understanding DNA as a Language
DNA contains the foundational information needed to sustain life. Understanding how this information is stored and organised has been one of the greatest scientific challenges of the last century. With GROVER, a new large language model trained on human DNA, researchers could now attempt to decode the complex information hidden in our genome.
Developed by a team at the Biotechnology Center (BIOTEC) of Dresden University of Technology, GROVER treats human DNA as a text, learning its rules and context to draw functional information about the DNA sequences. This new tool, published in Nature Machine Intelligence, has the potential to transform genomics and accelerate personalised medicine.
The Complexity of DNA
Since the discovery of the double helix, scientists have sought to understand the information encoded in DNA. 70 years later, it is clear that the information hidden in the DNA is multilayered. Only 1-2% of the genome consists of genes, the sequences that code for proteins.
“DNA has many functions beyond coding for proteins. Some sequences regulate genes, others serve structural purposes, most sequences serve multiple functions at once. Currently, we don’t understand the meaning of most of the DNA,” says Dr. Anna Poetsch, research group leader at the BIOTEC.
AI and Large Language Models
Large language models, like GPT, have transformed our understanding of language. Trained exclusively on text, these models have developed the ability to use language in many contexts.
“DNA is the code of life. Why not treat it like a language?” says Dr. Poetsch.
The Poetsch team trained a large language model on a reference human genome. The resulting tool named GROVER, or “Genome Rules Obtained via Extracted Representations”, can be used to extract biological meaning from the DNA.
“GROVER learned the rules of DNA. In terms of language, we are talking about grammar, syntax, and semantics. For DNA this means learning the rules governing the sequences, the order of the nucleotides and sequences, and the meaning of the sequences. Like GPT models learning human languages, GROVER has basically learned how to ‘speak’ DNA,” explains Dr. Melissa Sanabria, the researcher behind the project.
The DNA Dictionary
“DNA resembles language. It has four letters that build sequences and the sequences carry a meaning. However, unlike a language, DNA has no defined words,” says Dr. Poetsch.
DNA consists of four letters (A, T, G, and C) and genes, but there are no predefined sequences of different lengths that combine to build genes or other meaningful sequences.
To train GROVER, the team had to first create a DNA dictionary. They used a trick from compression algorithms.
“This step is crucial and sets our DNA language model apart from the previous attempts,” says Dr. Poetsch.
“We analysed the whole genome and looked for combinations of letters that occur most often. We started with two letters and went over the DNA, again and again, to build it up to the most common multi-letter combinations.
“In this way, in about 600 cycles, we have fragmented the DNA into ‘words’ that let GROVER perform the best when it comes to predicting the next sequence,” explains Dr. Sanabria.
The Promise of AI in Genomics
GROVER promises to unlock the different layers of genetic code. DNA holds key information on what makes us human, our disease predispositions, and our responses to treatments.
“We believe that understanding the rules of DNA through a language model is going to help us uncover the depths of biological meaning hidden in the DNA, advancing both genomics and personalised medicine,” says Dr. Poetsch.
Potential Applications
GROVER has the potential to advance genomics and personalised medicine, offering insights into human biology and disease. The model identifies gene promoters, protein binding sites, and epigenetic information, enhancing understanding of DNA’s non-coding regions.
DNA language model GROVER learns sequence context in the human genome. Deep-learning models that learn a sense of language on DNA have achieved a high level of performance on genome biological tasks. Genome sequences follow rules similar to natural language but are distinct in the absence of a concept of words.
The defined dictionary of tokens in the human genome carries best the information content for GROVER. Analysing learned representations, we observed that trained token embeddings primarily encode information related to frequency, sequence content and length.
Some tokens are primarily localised in repeats, whereas the majority widely distribute over the genome. GROVER also learns context and lexical ambiguity. Average trained embeddings of genomic regions relate to functional genomics annotation and thus indicate learning of these structures purely from the contextual relationships of tokens.
This highlights the extent of information content encoded by the sequence that can be grasped by GROVER.
On fine-tuning tasks addressing genome biology with questions of genome element identification and protein–DNA binding, GROVER exceeds other models’ performance. GROVER learns sequence context, a sense for structure and language rules. Extracting this knowledge can be used to compose a grammar book for the code of life.
Concluding Thoughts: The Future of Genomics
The development of GROVER marks a significant milestone in the field of genomics. By treating DNA as a language, AI models can uncover hidden layers of genetic information, offering new insights into disease predispositions and treatments. This breakthrough could revolutionise personalised medicine, providing tailored treatments based on an individual’s genetic makeup.
Comment and Share:
What do you think about the potential of AI in decoding the language of DNA? How do you see this technology impacting the future of personalised medicine? Share your thoughts and experiences in the comments below. Don’t forget to subscribe for updates on AI and AGI developments.
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- To learn more about GROVER tap here.
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Adrian’s Arena: AI and the Global Shift – What Trump’s 2024 Victory Means for AI in Asia
With Trump’s 2024 re-election, Asian nations might push for self-reliant AI ecosystems, regional partnerships, and stronger privacy standards.
Published
19 hours agoon
November 6, 2024
TL;DR
- Donald Trump’s 2024 presidential win could reshape AI development in Asia by prompting self-reliant AI ecosystems, more regional partnerships, and increased privacy standards.
- Asian nations may accelerate AI innovation and talent development to reduce reliance on U.S. tech, particularly as they anticipate shifts due to this result.
- Asian companies are positioned to thrive, offering privacy-compliant, localised AI insights that align with Asia’s unique market dynamics during this new Trump era.
What now for AI?
The re-election of Donald Trump to the U.S. presidency is sure to have profound global impacts, particularly in areas like artificial intelligence (AI). In Asia, where AI adoption is already soaring, Trump’s approach to foreign policy, technology, and economic partnerships may drive significant shifts in both public and private AI ventures. This focus includes Donald Trump’s 2024 election win and subsequent implications on AI in various sectors.
This article explores how the changing political landscape could reshape AI in Asia and how businesses are poised to navigate and leverage these shifts.
AI Regulation and Innovation: A Push for Autonomy
Trump’s leadership may spur a greater focus on AI autonomy in Asia, encouraging countries to develop homegrown AI solutions across various industries. For example, healthcare data analytics in Singapore, fintech solutions in India, and consumer insights platforms in Japan could see accelerated development as these nations prioritise self-reliance.
Several companies in Asia are well-positioned to contribute, offering privacy-compliant AI insights that help brands tailor messaging without relying on U.S.-based tech giants.
Trade Policies and Tech Partnerships: Redrawing Lines
With Trump’s trade policies likely to maintain a “protectionist” edge, tech partnerships across the Pacific may become more complex, leading Asia’s leading economies to bolster regional AI collaborations. This may foster tighter partnerships within Asia, where companies can provide high-impact AI solutions tailored to local consumer behaviours and trends.
Research Funding and Education: A New Wave of Asian Talent
The expected restrictions on U.S. visas for Asian students and researchers could spark a wave of investment in AI education and talent retention across Asia. AI companeis can support this talent surge by offering real-world, Asia-specific AI applications, from data analytics to customer insights and digital advertising.
Practical programs in Asia, especially in Singapore —offer hands-on AI training—equip professionals with critical skills for driving regional innovation, positioning Asia as a powerhouse for AI expertise.
AI-Powered Defense and Cybersecurity: Strengthening Regional Security
As Asian nations fortify their defences in response to Trump’s renewed focus on military alliances, AI-driven cybersecurity solutions are expected to see considerable growth. AI companies in Asia are poised to address emerging threats with precision and speed.
For instance, Asian technology could support national cybersecurity initiatives by identifying threat patterns in real-time across public data sources, providing governments and enterprises with actionable insights for safeguarding critical infrastructure.
Privacy and Data Ownership: Asia’s Standards vs. the U.S. Approach
Asia’s data governance standards are set to diverge further from those in the U.S., especially with Trump’s preference for lighter tech regulation. This shift aligns with ad tech’s approach to delivering privacy-compliant audience insights, offering Asia-based companies a way to engage their customers effectively without compromising data security.
Impact on the AI Talent Pipeline: Challenges and Opportunities
Trump’s immigration policies could impact the AI talent pipeline to the U.S., pushing many skilled AI professionals to remain in Asia. Companies can leverage this shift by tapping into local AI talent for projects that require regional expertise, particularly in the Donald Trump 2024 election AI context.
By prioritising local talent, companies can ensure that solutions align with Asia’s unique market demands, from local consumer insights to culturally resonant AI-driven advertising.
As a result, Asian companies and their partners can benefit from deeper market understanding, making their campaigns more impactful across Asia.
A Shift Towards Pan-Asian AI Standards
With Trump’s policies creating a potential divide in AI development approaches, Asian countries may push for unified AI standards within the region. By aligning AI governance across economies, Asia could build a formidable framework that encourages innovation while ensuring ethical usage and robust privacy protections.
Countries like Japan, South Korea, and Singapore are already leaders in setting high AI standards, and an Asia-wide approach could help establish a distinctive identity in the global AI community.
This alignment would also reduce friction for companies operating across multiple Asian markets, fostering an interconnected ecosystem that accelerates growth and adaptability.
The Rise of Localised AI Applications
As trade and regulatory landscapes shift, there’s an increased incentive for Asian companies to design AI solutions that cater to local languages, cultural nuances, and consumer behaviours. Localisation has always been a critical factor for success in Asia, and AI is no exception.
From natural language processing that understands regional dialects to AI-driven marketing insights that resonate with unique consumer mindsets, tailored AI applications could see a significant boost.
This emphasis on localisation not only enhances user experience but also ensures that AI remains relevant and effective in each unique market across the continent
Conclusion: A New Era for AI in Asia
The Trump presidency may catalyse a new chapter for AI in Asia. As Asian nations brace for potential shifts in trade and technology policies, they are well-positioned to accelerate regional AI innovation, self-sufficiency, and collaboration.
By investing in local talent, fostering privacy-compliant solutions, and collaborating across the region, companies like SQREEM are driving Asia’s transformation into a global AI powerhouse.
While the future may be uncertain under a second new era of Trump, we know at least it won’t be boring for the AI industry!
Join the Conversation
As AI in Asia surges towards autonomy and privacy-first innovation, will Trump’s policies drive the region to outperform the U.S. in tech advancements? Or are we on the cusp of a global AI divide? Please share your thoughts and don’t forget to subscribe for updates on AI and AGI developments.
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- Read more about Trump’s impact on AI at our good friends The Diplomat
Author
-
Adrian is an AI, marketing, and technology strategist based in Asia, with over 25 years of experience in the region. Originally from the UK, he has worked with some of the world’s largest tech companies and successfully built and sold several tech businesses. Currently, Adrian leads commercial strategy and negotiations at one of ASEAN’s largest AI companies. Driven by a passion to empower startups and small businesses, he dedicates his spare time to helping them boost performance and efficiency by embracing AI tools. His expertise spans growth and strategy, sales and marketing, go-to-market strategy, AI integration, startup mentoring, and investments. View all posts
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Protect Your Writing from AI Bots: A Simple Guide
This article explains how to protect your writing from AI bots using the robots.txt file, and discusses the copyright issues surrounding AI models.
Published
2 days agoon
November 5, 2024By
AIinAsia
TL;DR:
- AI models like ChatGPT use vast amounts of text, often without permission.
- The New York Times has sued OpenAI for copyright infringement.
- You can protect your writing by editing your robots.txt file.
The Rise of AI and Its Hunger for Words
Artificial Intelligence (AI) is transforming the world, but it comes with challenges. AI models like ChatGPT require enormous amounts of text to train. For instance, the first version of ChatGPT was trained on about 300 billion words. That’s equivalent to writing a thousand words a day for over 800,000 years!
But where does all this text come from? Often, it’s scraped from the internet without permission, raising serious copyright concerns.
The Case of The New York Times vs. OpenAI
In a high-profile case, The New York Times sued OpenAI, the company behind ChatGPT, for copyright infringement. The lawsuit alleges that OpenAI scraped millions of articles from The New York Times and used them to train its AI models. Sometimes, these models even reproduce chunks of text verbatim.
“OpenAI made three hundred million in August and expects to hit $3.7 billion this year.” – The New York Times
This raises a crucial question: How would you feel if AI models were using your writing without permission?
The Looming Content Crisis
AI companies face a potential content crisis. A study by Epoch AI suggests that AI models could run out of human-generated content as early as 2026. This could lead to stagnation, as AI models need fresh content to keep improving.
“The AI field might face challenges in maintaining its current pace of progress once it drains the reserves of human-generated writing.” – Tamay Besiroglu, author of the Epoch AI study
Protecting Your Writing: The robots.txt File
So, how can you protect your writing? The solution lies in a simple text file called robots.txt. This file tells robots (including AI bots) what they can and can’t access on your website.
Here’s how it works:
- User-agent: This is the name of the robot. For example, ‘GPTBot’ for ChatGPT.
- Disallow: This means ‘no’.
- The slash (/): This means the whole website or account.
So, if you want to block ChatGPT from accessing your writing, you would add this to your robots.txt file:
User-agent: GPTBot
Disallow: /
How to Edit Your robots.txt File
If you have your own website, you can edit the robots.txt file to block AI bots.
Here’s how:
- Using the Yoast SEO plugin: Go to Yoast > Tools > File Editor.
- Using FTP access: The robots.txt file is in the root directory.
- Using the WP Robots Txt plugin: This is a simple, non-technical solution. Just go to Plugins > Add New, then type in ‘WP Robots Txt’ and click install.
Once you’re in the robots.txt file, copy and paste the following to block common AI bots:
User-agent: GPTBot
Disallow: /
User-agent: ChatGPT-User
Disallow: /
User-agent: Google-Extended
Disallow: /
User-agent: Omgilibot
Disallow: /
User-agent: ClaudeBot
Disallow: /
User-agent: Claude-Web
Disallow: /
The Common Crawl Dilemma
Common Crawl is a non-profit organisation that creates a copy of the internet for research and analysis. Unfortunately, OpenAI used Common Crawl data to train its AI models. If you want to block Common Crawl, add this to your robots.txt file:
User-agent: CCBot
Disallow: /
The Future of AI and Copyright Law
The future of AI and copyright law is uncertain. Until the laws change, the best way to protect your writing is to block AI bots using the robots.txt file.
“Until they change copyright laws and intellectual property laws and give the rights to he with the most money — your words are yours.”
Comment and Share:
How do you feel about AI models using your writing without permission? Have you checked your robots.txt file? Share your thoughts and experiences below. And don’t forget to subscribe for updates on AI and AGI developments!
- You may also like:
- NYT vs OpenAI copyright lawsuit is like Hollywood’s early fight against VCRs
- Google’s AI Overviews: A New Era of Information Theft?
- Stop AI art theft: Digital watermarking with Nightshade
- To learn more about robots.txt files, tap here.
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AI at the Polls: Is Technology Steering the 2024 US Election?
As Americans cast their votes tomorrow, artificial intelligence will play a quiet but powerful role behind the scenes.
Published
3 days agoon
November 4, 2024By
AIinAsia
TL;DR:
- Campaign ads, social media feeds, and even “news” popping up in swing states are being shaped by AI’s invisible hand
- Campaigns in 2024 aren’t just reaching voters; they’re diving deep into our digital footprints
- AI brings campaigns closer to voters, it also makes it easier than ever to spread misinformation
A New Political Battleground—Inside the AI-Powered Election
As Americans cast their votes tomorrow, artificial intelligence will play a quiet but powerful role behind the scenes. Campaign ads, social media feeds, and even “news” popping up in swing states are being shaped by AI’s invisible hand. This isn’t just the next step in election tech; it’s a dramatic leap that could change the game forever. Is AI enhancing democracy, or are we giving it the keys to the whole democratic car?
1. Supercharging Campaigns: Microtargeting to the Extreme
Let’s face it—if you feel like your social media feeds are eerily personal, that’s not a coincidence. Campaigns in 2024 aren’t just reaching voters; they’re diving deep into our digital footprints to send messages so tailored they feel like personal letters. Thanks to AI, campaigns can slice the electorate into precise segments, tapping into anxieties, interests, and even specific local issues.
In battleground states like Arizona and Pennsylvania, this tech-driven targeting reaches a fever pitch. AI sifts through oceans of data—social media interactions, browsing habits, even purchase history—to craft ads that connect directly with you, personally.
“Campaigns are increasingly leveraging sophisticated machine learning algorithms to analyse vast quantities of voter data, refining their strategies with pinpoint accuracy,” notes MIT Technology Review (source).
With AI knowing so much, it raises an interesting (if slightly chilling) question: where’s the line between effective campaigning and outright manipulation?
2. The Double-Edged Sword: AI, Deepfakes, and Digital Misinformation
Here’s the darker side. While AI brings campaigns closer to voters, it also makes it easier than ever to spread misinformation. AI-generated deepfakes—fake videos that look so real you wouldn’t know they’re fake—have added a surreal twist to this election. Imagine seeing a video of a candidate saying something outrageous… and then realising it never actually happened.
“Deepfakes have made the spread of disinformation much easier and more convincing, raising concerns about the future of truth in politics,” the Brookings Institution warns (source).
AI’s power to create convincing fakes isn’t just a technical marvel; it’s a fundamental threat to truth in politics. Without strict regulations or ways to fact-check in real-time, we’re left wondering how many people will cast their vote based on a lie.
3. Predictive Polling: AI, Sentiment Analysis, and the All-Seeing Eye
If you thought AI was only influencing what you see online, think again. Polling has evolved far beyond traditional methods. This election, campaigns are using AI-driven sentiment analysis to tap into public moods in real time, keeping a pulse on issues that resonate with voters minute by minute.
“Sentiment analysis enables campaigns to see beyond traditional polling, observing shifts in public mood and identifying emerging concerns as they happen,” reports the Pew Research Center (source).
Let’s say economic concerns are heating up in Georgia; Trump’s team could amplify ads focusing on job growth in just hours. Or Harris’s camp could hone in on climate change in Michigan based on AI-driven insights from yesterday’s online conversations. This real-time fine-tuning isn’t just impressive—it’s a little mind-bending. Can polls really capture the pulse of the nation, or are we just seeing what AI’s algorithms want us to?
4. Mobilising the Masses: AI Nudges and Digital Persuasion
Getting people to the polls has always been crucial, and AI’s here to make sure more people than ever get nudged, reminded, and maybe even guilt-tripped into voting. AI-driven models predict not only who’s likely to vote but also who might need a little extra encouragement. Campaigns can then send targeted texts, emails, or even pop up on your social feed reminding you to “make your voice heard.”
The Atlantic remarks on AI’s power in mobilisation, stating, “AI has transformed voter outreach into an exact science, enabling campaigns to efficiently target and mobilise segments of the electorate that might otherwise stay home” (source).
For instance, Harris’s campaign has deployed AI to boost turnout among younger voters in key states, while Trump’s team uses it to rally dedicated supporters in traditionally red zones. AI doesn’t just follow you online; it’s practically waiting outside your door with a “Don’t forget to vote” sign. This kind of outreach raises a fascinating question about voter autonomy—are we freely deciding to vote, or are we being nudged by an algorithm?
5. Navigating the Ethical Minefield: Can Democracy Keep Up?
Here’s where it all gets tricky. While AI offers stunning capabilities for reaching, engaging, and mobilising voters, it also opens up new doors for potential misuse. From deepfakes to ultra-targeted political ads, AI is testing the limits of what’s fair game in political campaigns.
With regulations still trying to catch up, we’re left with a significant blind spot.
“Current frameworks for AI regulation are woefully inadequate, leaving a critical gap in safeguarding electoral processes,” states the Harvard Political Review (source).
AI has handed campaigns a powerful toolkit, but with great power comes… well, you know the rest. Without real oversight, there’s a real risk of crossing ethical lines, leaving voters questioning whether their choices are truly their own or just the echoes of an algorithm.
A Glimpse into Asia’s Future?
As AI’s influence in US elections becomes clear, Asia’s political landscape might not be far behind. In a region where social media is booming and governments increasingly leverage AI for everything from citizen services to surveillance, the potential for AI-driven election strategies is immense. Imagine a world where voter preferences in Tokyo, Jakarta, or Delhi are meticulously profiled, and campaign ads are hyper-personalised to every demographic, language, and cultural nuance. But here’s the question for Asia: with AI’s rapid adoption and limited oversight, who will control this powerful tool—governments, political parties, or the people? The US election offers a glimpse of how AI can shape democracy, but will Asia be able to harness this power responsibly, or could it open doors to unprecedented political manipulation? The stakes are high, and the path ahead remains uncharted.
Join the Conversation
How do you think AI will impact elections in Asia? Will it drive democracy forward or lead to new challenges in political manipulation? Leave a comment or subscribe for AI in Asia updates.
You may also like:
- AI Voice Cloning: A Looming Threat to Democracy
- AI Chatbots Struggle with Real-Time Political News: Are They Ready to Monitor Elections?
- AI-Fakes Detection Is Failing Voters in the Global South
- To learn more about the role of AI in the US elections, tap here.
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