Skip to main content

This Blog and the Tools Used (+ how to build one of your own)

 



I've tested many dozens of AI tools and features during the research and writing of this blog. I'd like to have utilised even more, if I had a powerful enough graphics card to download and execute different LLMs then I would have. My Dell workstation though is limited by 32GB and a 4GB NVIDIA Quadro card with ageing Xeon processor. My Dell laptop has 16GB, a 12th Gen i7 and 4GB 3050. These are averagely powerful pieces of hardware, but are insufficient to run much in the way of Open Source AI models, at least ones that I'd use as regular tools. 

I state this to demonstrate that most people's usage of AI tools is likely to be through 'cloud' services, such as the new (clunky) ChatGPT4 Apple OS phone app. Which is a shame, as it does hinder people's understanding of how these tools work, and what sort of libraries they are dependent upon. This may ease up, as more smaller and capable LLMs become available, but it's probably a bit too late. Why is this of any importance at all? Well I'd suggest that a semblance of understanding how these tools work is of benefit to comprehending their strengths and weaknesses.

For those interested there follows a list of the tools I've used in preparing for, and writing this blog. Not all are AI.

  • Research and writing time: circa 83 hours per month. Without using the tools below, this would add a significant amount of time to the process. Have the AI tools themselves increased my efficiency by the claimed 40%, as often touted? Certainly, especially in searching for new white papers etc. Chat PDF has been very useful too, once I have downloaded a PDF.
  • Research tools used: Elicit, Consensus, Google Scholar, DuckDuck Go, 
  • Preparation Tools: YouTube Transcript, Chat PDF, Perplexity, 
  • Visualisation Tools: DALL·E, via Bing, DreamLike, 
  • Podcast Tool: Hindenburg Pro
  • Writing Tools: Notepad ++, LibreOffice, Initially also Bard for writing the initial background posts to this blog. Bard is also used, on occasion, late at night to correct grammatical mistakes when I'm just too tired to be bothered!
There remain few useful AI based text summarisers available, all are very limited in the amount of text that can be input (I expect this to change as the token size of LLMs increase) and, to be frank, many do an adequate job at best. 

Code your own tools


For those interested in installing and using their own PDF chat, and have also gain an understanding of how such tools work in practice, than this new video from Prompt Engineering will prove invaluable.




It would be good if and when more tools become available for researchers, of a higher quality. Automated source referencing within Word Processors, such as found in Perplexity would be a very useful addition, as an example. 

Tools I want to try out

Pi, Notion AI, Superus, DocuAsk, which may solve the size of file limitations I am currently experiencing? Adobe Firefly, SoundDraw, and finally Descript. which I've been looking at for a while now, but still haven't go around to using it. 

What sort of tools would you propose? What tasks could narrow AI help best with in your own blog / paper / article writing, research?

Labels:

Comments

Post a Comment

Popular posts from this blog

The Whispers in the Machine: Why Prompt Injection Remains a Persistent Threat to LLMs

 Large Language Models (LLMs) are rapidly transforming how we interact with technology, offering incredible potential for tasks ranging from content creation to complex analysis. However, as these powerful tools become more integrated into our lives, so too do the novel security challenges they present. Among these, prompt injection attacks stand out as a particularly persistent and evolving threat. These attacks, as one recent paper (Safety at Scale: A Comprehensive Survey of Large Model Safety https://arxiv.org/abs/2502.05206) highlights, involve subtly manipulating LLMs to deviate from their intended purpose, and the methods are becoming increasingly sophisticated. At its core, a prompt injection attack involves embedding a malicious instruction within an otherwise normal request, tricking the LLM into producing unintended – and potentially harmful – outputs. Think of it as slipping a secret, contradictory instruction into a seemingly harmless conversation. What makes prompt inj...
Post a Comment
Read more

The Future of Work in the Age of AGI: Opportunities, Challenges, and Resistance

 In recent years, the rapid advancement of artificial intelligence (AI) has sparked intense debate about the future of work. As we edge closer to the development of artificial general intelligence (AGI), these discussions have taken on a new urgency. This post explores various perspectives on employment in a post-AGI world, including the views of those who may resist such changes. It follows on from others I've written on the impacts of these technologies. The Potential for Widespread Job Displacement Avital Balwit, an employee at Anthropic, argues in her article " My Last Five Years of Work " that AGI is likely to cause significant job displacement across various sectors, including knowledge-based professions. This aligns with research by Korinek (2024), which suggests that the transition to AGI could trigger a race between automation and capital accumulation, potentially leading to a collapse in wages for many workers. Emerging Opportunities and Challenges Despite the ...
Post a Comment
Read more

Podcast Soon Notice

I've been invited to make a podcast around the themes and ideas presented in this blog. More details will be announced soon. This is also your opportunity to be involved in the debate. If you have a response to any of the blog posts posted here, or consider an important issue in the debate around AGI is not being discussed, then please get in touch via the comments.  I look forward to hearing from you.
Post a Comment
Read more