My remote work experience as a content management specialist for an NGO has been an exciting journey of utilizing GenAI.

The Challenge: Writing About Events Before They Happen

In my role, I am responsible for crafting articles that engage the NGO’s social media audience, covering events like conferences, speeches, and news. The challenge lies in preparing content before these events actually occur. Initially, this was a daunting task—how could I write about something that hadn’t happened yet? With only a brief outline as my guide, the task seemed impossible.

However, GenAI came to the rescue.

Training GenAI for Customized Content

To fully utilize the power of GenAI, I embarked on a training process. I give AI a role as a senior content management editor from a top-tier press company, adept at crafting press releases, tweets, and news articles. I set clear prompts: a word count, a specific tone, and a targeted audience.

(The GenAI tool I use is Kimi Chat. As an AI assistant developed by Moonshot AI, Kimi excels in language understanding and generation, especially in processing Chinese text, and has a distinct advantage in handling large volumes of text.)

Through iterative training, GenAI has become proficient at providing detailed and nuanced content. Each interaction has led to more refined outputs, reducing the need for extensive revisions. This has not only streamlined my workflow but often exceeded my expectations.

Reflecting on this experience,

• I appreciate how GenAI has not only saved time but also pushed the boundaries of what’s possible in content creation.
• I always double-check and optimize every output in order to make the content more fluid, rather than looking like it was written by AI.
• However, I constantly find myself balancing AI creativity with my own ideations. It’s essential not to rely too heavily on GenAI. Independent thinking remains crucial. GenAI can be my super assistant, but it’s my ideas that drive the final outputs.

I recently came across Artificial Intelligence Virtual Artist (AIVA) which helps create music and offers a lot of customizations. As someone who has always wanted to make music, but has never been able to learn any instruments, this offered an avenue to a lot of possibilities, and I wanted to see what AI can offer us. Hence, I made an account on AIVA and was quite fascinated by the various options it provided.

Firstly, it allowed composing a track in the following ways:

1. You could provide it with a style which could vary from lo-fi, techno, sync-wave, etc., duration and the number of compositions and it will create new music for you.
2. You could provide it with a chord progression or upload an already existing one. It also suggests a chord progression, or you can give it a prompt to generate one. (The prompt was very fascinating as I provided it with an overview, and it gave a decent chord progression.)
3. A step-by-step process where you could customize everything from styles, chord progressions, composition layers, instruments, provide prompts.
4. Upload an influence or an existing music file

I started with providing it with a simple style to create a small lo-fi playlist as this is the kind of music I listen to a lot while working, and it was able to give a quick 30 second clip which was probably not the best lo-fi track but still better than I expected.

Next, I tried giving it more details and seeing what the AI could when provided with more details. I used the chord progression function, and it allowed me to generate a chord progression using a prompt. I provided it with the prompt to use the same chord progression as used in the song, “Sound of Silence” by Simon and Garfunkel. The results were quite fascinating. While the chord progression wasn’t exactly copied, but still the music generated took me by surprise and was worth listening to.

Lastly, I experimented with other genres and more detailed experiments using Daft Punk as the style, and asking the AI to generate a fast-tempo Daft Punk styled song, and I was actually able to get quite an interesting song that was worth listening to. It allowed working with composition layers, changing song durations, and also generating multiple tracks.

Experimenting with AIVA was quite fun and if not for the limit on their free subscription, I would have experimented more. I believe that with adding more customised and user-friendly interfaces where someone with minimal music knowledge could generate music clips through mere prompts, customization around instruments and also adding your own music and mixing and matching, GenAI could really transform the creative process of music making. It would also be interesting if we could compare human produced music and AI generated music side by side and see the similarities and differences.

We are living in exciting times, and it would be interesting to see how the future evolves, and how the human and machine made music combines to evolve the creative process.

Jurisdictional occupations, especially referencing lawyers and attorneys, belong to those with the most intense reading and writing efforts during preparation. Hence, the potential of using generative AI in the jurisdictional branch is huge. For different parts of a lawyer’s occupation, generative AI is in use already, ranging from analyzing contracts or documents to actual chatbots being trained for jurisdictional contents.¹ Knowing of this huge potential, I also used generative AI when I worked at a lawyer’s office. Automating specific activities saved me a lot of time. At the start of my occupation, no specified tool for jurisdictional exercises was available, leaving me to use ChatGPT. The chatbot was especially useful for receiving a quick overview of long documents, writing drafts for counter-statements, as well as for researching cases. However, problems with the usage of ChatGPT were soon to be obvious.

On the one hand, AI chatbots don’t understand what they are being fed or what they produce. Generative AI solely produces a word depending on probabilities, which are generated based on patterns of training-sentences.² Even though that mechanism works well for a variety of tasks, errors can and will occur, especially when sentences become long and complex. This risk further increases, if the model itself was not solely trained on data related to the task. This didn’t only lead to unintelligible information, but also to fully imaginary outputs.³ On the other hand, a lot of jurisdictional issues arose. Firstly, privacy issues are one of the biggest concerns, especially as ChatGPT already exhibited a data leakage scandal.⁴ Secondly, in order to legally use technical support tools in jurisdictional jobs, those tools have to fulfill specific requirements given in national laws for restricting lawyers. For example, the tool must be independent from any source not being associated with the specific topic of the case, with which a lawyer deals in that moment.⁵

What do you think about generative AI in law? Which other problems or challenges could occur?

1. Deloitte. (2023). Generative AI – A guide for corporate legal departments. Retrieved from https://www.deloitte.com/content/dam/assets-shared/docs/services/legal/2023/dttl-legal-generative-ai-guide-jun23.pdf; Legalfly. (2024). The Most Secure Legal AI Workspace. Retrieved from https://www.legalfly.ai; Harvey. (2024). The Trusted Legal AI Platform. Retrieved from https://www.harvey.ai. ↩︎
2. Huang, Ken/Xing, Chunxiao. ChatGPT: Inside and impact on Business Automation. In: Huang, Ken/Wang, Yang/Zhu, Feng/Chen, Xi/Chunxiao, Xing (Hrsg.), Beyond AI. ChatGPT, Web3, and Business Landscape of Tomorrow, Cham 2023, S., S. 45 ff. ↩︎
3. Spiegel Netzwelt. (2023). Anwalt blamiert sich mit Fake-Fällen aus ChatGPT. Retrieved from https://www.spiegel.de/netzwelt/apps/new-york-anwalt-blamiert-sich-mit-fake-urteilen-aus-chatgpt-a-8935d1c8-b6c2-4079-8ecd-1cf4c2d33259. ↩︎
4. Mudaliar, Anuj. (2024). ChatGPT Leaks Sensitive User Data, OpenAI Suspects Hack. Retrieved from https://www.spiceworks.com/tech/artificial-intelligence/news/chatgpt-leaks-sensitive-user-data-openai-suspects-hack/. ↩︎
5. Schweizerische Eidgenossenschaft. (2024). Federal Act on the Free Movement of Lawyers. Art. 12. Retrieved from https://www.fedlex.admin.ch/eli/cc/2002/153/en. ↩︎

Last summer, I found myself stepping into a role that was both exciting and intimidating: a data analyst that needed to built a data pipeline. Armed with only basic Python skills and no prior coding experience, I had to figure out how to build an end-to-end data transformation pipeline, also known as ETL (Extract, Transform, Load). This involved interacting with APIs, manipulating large datasets, and uploading data to a Snowflake database. The learning curve felt steep, but due to ChatGPT’ I was able to finish the project with succes. The tool I used in this case was the “code copilot” GPT from ChatGPT.

I initially struggled with where to begin. Although I had a foundational understanding of data analysis, I wasn’t familiar with the technical intricacies of building a complete ETL pipeline. However, with the help of ChatGPT, I was able to gradually piece together the process. I would input small tasks and concepts I wanted to tackle, and the AI would provide explanations and snippets of code. This iterative back-and-forth helped me demystify many of the tasks.

By the end of the project, I had created a fully functional ETL pipeline that automated the collection and transformation of data. Without the assistance of generative AI, what seemed like a daunting and nearly impossible task turned into a fulfilling learning experience. It empowered me to stretch beyond my initial capabilities. Generative AI truly served as a valuable tool, transforming what could have been a steep learning curve into a collaborative and enjoyable project.

Pitfalls

While generative AI was incredibly helpful in building my data transformation pipeline, there were some notable limitations. Debugging, for instance, still required significant manual effort. ChatGPT struggled with complex bugs, and I often had to turn to StackOverflow for deeper insights and solutions to fix it myself.

Additionally, technical knowledge was crucial. While the AI helped structure code, I needed to understand APIs, Snowflake, and XML parsing to implement specific details. ChatGPT couldn’t generate the entire solution on its own. ChatGPT is great for creating the global parts of code but I needed to adjust the code most of the time to make it fit in my project.

Moreover, I realized the importance of asking precise questions. You can’t ask the AI to write code without providing clear technical requirements. It’s similar to how a product owner communicates with developers: even if you don’t know how to code, you need to speak their language to convey what you want. In the end, generative AI was a valuable tool, but success depended on my ability to guide it with the right queries.

Recently, I have found out that ChatGPT has many plugins and models that are separated from the typical chat bot. It has features that can help you with productivity, such as Canva AI, or “coding” assistants, like Khan Academy tool, but the one that caught my eye is The Negotiator.

Negotiation is an important ability in many areas of life, it helps you from getting a better salary to business deals and even day-to-day disagreements. It can be not easy to navigate through these situations confidently and effectively. The ChatGPT team has built The Negotiator, a specific AI-powered tool, to upgrade your negotiation skills and help individuals get the outcomes that they want.

What is The Negotiator?

As mentioned, The Negotiator is a tool designed to help you build your negotiation skills through realistic scenarios by helping you develop strategies and personalized feedback on your arguments. The main objective of the tool is to enable users to prepare themselves for possible situations and practice negotiations. It is achieved through natural language understanding combined with AI-driven insights that will deliver pragmatic advice based on the situation and help you refine your approach.

How The Negotiator Differs from Chatting with ChatGPT?

The Negotiator differs from ChatGPT in its purpose and function. While ChatGPT is designed to respond to individual prompts, whether it is answering questions, generating text, or assisting with specific tasks, The Negotiator operates in a much more interactive and complex way. Traditional ChatGPT provides useful but often static responses based on user input. It does not weigh the needs or perspectives of multiple parties the way The Negotiator does. ChatGPT caters more to the development of negotiation skills by offering strategic and pinpointed guidance without really engaging in general casual conversations. While ChatGPT can inform you about something, The Negotiator feels more like a real negotiation.

How Can The Negotiator Help You Improve?

The Negotiator won’t just teach you tactics; it can change the way you approach negotiations. It lets you practice in a simulated negotiation. This tool can offer you:

• Better Preparation: Preparation is key in any negotiation. The Negotiator helps you define your goals and strategy in advance, so you go into the conversation prepared.
• More Confidence: Simulating realistic scenarios reduces anxiety. You are much more confident and in control once you have gone through the various responses and strategies that may pop up during negotiations.
• Better Flexibility: The conditions of a negotiation can seriously be flipped at any time. The Negotiator trains your mind to think on your feet as you enter a discussion, allowing you to shift and pivot in strategy any moment the need arises.

The Negotiator by ChatGPT is an interesting tool that can help with improving their negotiation skills. AI-powered insight, scenario-based training, and personalized feedback will guide you through mastering the art of negotiation to better outcomes.