You won't believe how much time this GPT trick saves ⏲️
It's a blessing for mind maps and diagrams.
🗝️ Quick Bytes:
Amazon pours an additional $2.75 billion into AI startup Anthropic
Amazon has invested a total of $4 billion in the AI startup Anthropic, known for its generative AI models, Claude 3, which offer advanced intelligence and new capabilities. These models are available through Amazon Bedrock, and Anthropic is now using AWS as its primary cloud provider, promising AWS customers access to future AI models.
This strategic collaboration aims to enhance customer experiences using generative AI, marking Amazon's largest investment outside its own operations. It positions Amazon in a competitive stance in the rapidly developing AI industry, alongside rivals like OpenAI and Google.
Despite U.S. antitrust regulators scrutinizing such investments, Amazon asserts that its venture investing is compliant with legal standards. This move comes amidst heightened interest in AI technology from both the public and the business sector.
Stability AI CEO resigns to ‘pursue decentralized AI’
Emad Mostaque, CEO and founder of Stability AI, has resigned from his role and the board amid challenges, including disputes with investors and staff departures. Stability AI, known for Stable Diffusion, will now have interim co-CEOs Shan Shan Wong and Christian Laforte.
Mostaque, who co-founded Stability AI in 2019, led it to a $1 billion valuation by 2022 but faced controversies, including financial management issues and a lawsuit over company valuation. His leadership style and statements were often a source of contention.
Following his resignation, Mostaque plans to address power concentration in AI and supports the company's mission. Stability AI is currently undergoing a transition period, with its future direction and leadership under scrutiny as it progresses in the generative AI sector.
Saudi Arabia reportedly in talks with VC firms like Andreessen Horowitz to create mammoth $40 billion AI fund
Saudi Arabia is establishing a $40 billion AI fund as part of its Vision 2030, to transition from an oil-based economy to a tech-driven one. The fund, sourced from the kingdom's Public Investment Fund, aims to make Saudi Arabia a global AI leader.
The kingdom is engaging with notable venture firms like Andreessen Horowitz for partnerships, potentially leading to a Riyadh office. These collaborations aim to position Saudi Arabia prominently in the global AI sector, attracting tech firms and startups.
With investments in chipmakers, data centers, and cloud computing, Saudi Arabia intends to outpace competitors like the UAE in the global AI market. This initiative aligns with attracting significant tech investments, such as Amazon Web Services' $5.3 billion commitment.
🎛️ Algorithm Command Line
If you create mind maps or diagrams I found a trick to save HOURS
In my reality, if something’s not drawn or presented as a diagram, I can’t remember it properly. If you’re like me, then there’s a trick that you can use that eases the time-consuming part of creating and connecting the nodes on your mind maps.
The approach is simple:
1. Ask GPT to transform your current content into an outline for a mind map or prompt it to create it step by step.
Create a detailed mind map or flowchart, ensuring a logical structure with interconnected nodes and concepts. Ensure the flowchart is easy to follow and visually appealing to enhance understanding. Give me the output in Mermaid.2. Always prompt GPT to format the output into the “mermaid code”. Copy all of it.
3. Launch draw.io. This is free, fantastic web-based software for diagrams and flowcharts with many options.
4. Create a new diagram.
5. From the menu in the upper left corner choose Arrange → Insert → Advanced → Mermaid.
6. Paste your code from point 2.
and… that’s it.
Congrats, you just saved a couple of hours.
You can create diagrams like these from scratch, transform your current content, whatever.
Would you give it a try? 🧃
💡Explained
Hi, dear Readers 👋I usually don’t address you directly, but given that recent „Explained” papers come from different authors, it would be nice to also introduce myself. This paper was prepared for you by Agnieszka, a Senior AI Engineer@Chaptr.
I am, as always, happy to find, read and explain recent papers for you. Feel free to reach out to me on LinkedIn.
Reducing the Reversal Curse?
Large language models (LLMs) excel in many tasks, but they suffer from a surprising limitation called the "reversal curse.” This means they struggle to apply knowledge in the opposite direction of how they learned it. Recently, researchers at FAIR Meta proposed a technique called "Reverse Training" to address this issue. (Reverse Training to Nurse the Reversal Curse)
↺ What is the reversal curse?
A few months ago, I explained the paper "The Reversal Curse: LLMs trained on A=B fail to learn B=A". The authors demonstrated that LLMs work well in one specific "direction" but struggle with the reverse. For instance, if a model is trained on "Olaf Scholz is the ninth Chancellor of Germany," it might fail when asked, "Who was the ninth Chancellor of Germany?". The likelihood of the correct answer (“Olaf Scholz”) won't be higher than for a random name. The issue affects all transformer-based auto-regressive language models, such as GPT and Llama architecture.
♟️The strategy
The core idea behind reverse training is basically an offline data augmentation method targeted to reduce the "reversal curse." Training examples are reversed while preserving the integrity of entities or randomly segmenting sequences. This approach doubles the training data by adding reversed versions of examples, which the model learns alongside the original data. The paper categorizes reversals into three types:
Word Reversal: Flipping the entire word order within a sentence.
Entity-Preserving Reversal: Preserving the order of entities (like names) while reversing other words.
Random Segment Reversal: Randomly selected text segments are reversed.
🔬 Experiments
In experiments, they evaluated the effectiveness of reverse training in both pre-training and fine-tuning stages across different tasks. The results show that reverse training significantly enhances model performance on tasks requiring reversed application of knowledge, such as reversing biographies or symbolic reverse tasks, without impairing performance on standard tasks. For instance, reverse training improves recall in biography-related tasks. Importantly, it does not impair performance on standard language benchmarks.
Conclusion
The paper concludes that reverse training represents a promising method for overcoming the limitations of current LLMs in processing information in reverse order. Although enhancing the ability to connect celebrities with their parents in reverse doesn’t seem like a breakthrough, it might be a great way of strengthening relation understanding in LLMs.
🧐 One more thing: Doubling the training data means doubling the training time, which is a very strong limitation of the study. While data augmentation is great, it would be even better if models could understand relations between entities better, from the start.
🗞️ Longreads
I recently discovered this Substack and highly recommend its essays. They offer not only fantastic food for thought but also showcase a very good writing style and mental clarity.
