Friday Funny?

▶︎▶︎PROMPT

Can you write a generative ai legal joke in the style of dave chappelle?

▼▼RESPONSE

You know, man, AI's getting so smart, it's practically taking over the legal profession. They're talking about replacing lawyers with AI, but I'm just waiting for the first AI attorney to quit its job and become a comedian. Like, can you imagine a robot lawyer cracking jokes at a comedy club?

"Hey, y'all, why did the generative AI become a lawyer? 'Cause it realized it could make more money off humans by suing them than by entertaining them! Ha-ha! Bleep bloop!"

But, you know what's really crazy? These AI lawyers are working so fast, they're billing clients in microseconds! "Uh, excuse me, Mr. AI, can you repeat that last sentence? I think I missed something." "Sure, that'll be $200 for the extra processing time."

Ok, time to dig in!

A special Hey There 👋 to my NEW SUBSCRIBERS! 
To read previous posts, go here.

Today we will

1. ask if clients can monetize data their firms use

2. learn how to market ChatGPT as a skill

3. share new tools

4. watch a reporter clone herself and fool others

5. read how an AI hoax fooled media across the globe

6. use Deep Fakes as a legal defense

7. enjoy a meme

Can Clients Charge Firms for Their Data?

As more law firms, legal tech companies, and alternative legal service providers experiment with AI tech and building their own internal training models - the question is - whose data are firms using and do they have permission and right to monetize it?

🧊 Below I explore this topic but know it might be the tip of the iceberg.

The Data Ownership Conundrum: Law Firms, LLMs, and the Dynamics of Client Confidentiality

Amidst the disruptive force of large language models (LLMs) such as GPT-4, the concerns surrounding data ownership, privacy, and usage rights take center stage. In this landscape, law firms, as custodians of vast amounts of client information and work product, must carefully consider the implications of utilizing this data to develop proprietary LLMs.

The Framework of Ownership: Client information, while primarily the property of the client, also places certain rights and responsibilities on the law firm. Navigating this intricate relationship requires clear agreements, well-defined policies, and consultation with legal professionals.

🤹‍♀️ How can law firms strike a balance between their rights and responsibilities regarding client data, while also experimenting with AI that might use client data?

The Shift in Data Accessibility: As developers of LLMs face increasing challenges in accessing large volumes of text data that were previously available for free, companies such as Reddit, Stack Overflow, and Twitter have taken measures to protect their data. This shift results in rising costs of data acquisition, potentially disadvantaging smaller groups with limited resources.

🌱 How can smaller groups in the AI and LLM development space overcome the challenges posed by the increasing costs of data acquisition and remain competitive in an industry where access to large volumes of text data is becoming more restricted and expensive?

Legal and Ethical Dimensions: Utilizing client data and work product for LLMs brings forth numerous legal and ethical concerns. Law firms must be mindful of their duty to preserve client confidentiality and avoid potential violations of intellectual property rights. Furthermore, if firms use client data to train tools they later charge for, does this create a conflict or ethical dilemma?

🤐 How can law firms and AI developers ensure ethical use of client data for LLMs while maintaining confidentiality, respecting intellectual property, and navigating multi-use scenarios?

Strategies for Success: Law firms wishing to use client data or work product for their LLMs must proceed with caution.

In light of this evolving landscape of data ownership and usage rights, both the AI and legal communities will need to address several critical questions beyond the ones above. Some of these questions include:

1. Data Monetization: As large companies generate and possess vast amounts of valuable data, how can they monetize it?

Would a large client ever charge a firm for using their data in training a firm’s AI tool?

2. Data Control: How can a company like, say, UnitedHealthcare control the use of its data by law firms for training LLMs or generative AI tools?

Establishing clear agreements with law firms and other entities regarding the use of their data may involve drafting and negotiating specific terms and conditions governing data usage, confidentiality, and intellectual property rights.

3. Data Transparency: How can a client gain transparency into how a law firm is using their data?

Clients may require law firms to provide regular reports or allow external audits of their data usage practices. Additionally, the adoption of industry-wide transparency standards or certifications could help clients assess and compare the data practices of different law firms.

4. Data Portability: If data portability becomes more prevalent, how will this affect clients' ability to switch between law firms and the potential impact on the legal industry?

Increased data portability may lower switching costs for clients and foster greater competition among law firms. This could lead to a more client-centric legal industry, but it may also raise concerns about data security, privacy, and potential misuse of client information.

5. Regulatory Compliance: How can law firms ensure compliance with data protection regulations while leveraging LLMs and generative AI tools?

The role of firm’s general counsel, CIOs, COOs, and Executive leadership will grow in complexity.

Material that inspired this essay:

Elon Musk cut off OpenAI's access to Twitter's data because he felt the company wasn't paying enough

Reddit Wants to Get Paid for Helping to Teach Big A.I. Systems

Stack Overflow Will Charge AI Giants for Training Data

With a wave of new LLMs, open-source AI is having a moment — and a red-hot debate

Use Case ChatGPT as a Job Skill

A recent ResumeBuilder study reveals that 91% of hiring business leaders seek candidates with ChatGPT experience, considering it a competitive advantage. OpenAI's ChatGPT can boost productivity, save resources, and improve company reputation. Candidates with these skills are in high demand, especially in software engineering, customer service, HR, and marketing - across industries and professions.

29% of business leaders plan to hire prompt engineers this year, and ChatGPT proficiency is becoming increasingly valuable, with some positions offering up to $335,000 annually - mostly in the software space though. Hundreds of online ChatGPT courses are available, and experts advise job seekers to gain this skill and showcase it on their resumes to stay ahead in the job market.

But when most of the courses are from individuals looking to make a quick buck, lack domain specific skills and experience, or are more technical than practical - it may be hard to establish your credentials.

Now I know most of my readers are happily employed (or not) - but regardless you should start thinking about how you will market and verify your newly found generative AI skills, here are some things to help you.

There is no prompt for this one. I did seed ChatGPT with the above article and other info but want to happily share this framework for you to work through.

Here is a series of questions to help you capture, frame, and articulate your ChatGPT experience. This information will be valuable when updating your job applications, resumes, LinkedIn profile, and cover letters.

1. How did you first learn about ChatGPT? Was it through a specific course, tutorial, or self-directed learning?

2. Can you list any courses, workshops, or tutorials you've completed that focused on ChatGPT? Were any of these courses more advanced or reputable?

3. What industries or job roles have you applied ChatGPT in? Examples might include software engineering, customer service, human resources, or marketing.

4. Can you provide specific examples of projects or tasks where you used ChatGPT to achieve a particular goal? How did the use of ChatGPT contribute to the project's success?

5. Are there any quantifiable results or metrics from your ChatGPT experience that you can share? For example, increased productivity, time savings, or improved content quality.

6. Have you ever taught or trained colleagues in using ChatGPT? If so, how did that experience go, and what was the impact on your team or organization?

7. Are you a part of any online forums, communities, or networks related to ChatGPT? If so, how have you engaged with these groups, and what have you learned from them?

8. Can you identify any unique or innovative ways you've used ChatGPT that might set you apart from other candidates?

9. Do you have any testimonials or references from colleagues, supervisors, or clients that can speak to your ChatGPT skills and experience?

10. Have you encountered any challenges or limitations while using ChatGPT, and how have you addressed them?

Like the ChatGPT skilled worker you are, you know that you can drill down into each of the questions and your answers.

Remember use one of my Power Prompts - elaborate on - to get more detailed ideas.

▶︎▶︎PROMPT

Elaborate on [#6] are there examples of statements I could draw from to write my own?

▼▼RESPONSE

Certainly! Here are some examples of statements that you can draw from and adapt to describe your experience in teaching or training colleagues to use ChatGPT:

1. Conducted a series of in-house training sessions on ChatGPT, introducing colleagues to its applications in our marketing department, leading to a 15% increase in content generation speed and a 10% improvement in engagement rates.

2. Created a comprehensive internal guide on using ChatGPT for code generation, which helped streamline our software engineering team's workflow and reduce the time spent on routine tasks by 20%.

3. Mentored junior team members on leveraging ChatGPT for customer service automation, resulting in a 30% reduction in response times and a 5% increase in overall customer satisfaction scores.

4. Led a company-wide workshop on integrating ChatGPT into human resources processes, such as screening resumes and generating interview questions, ultimately improving the efficiency of our recruitment process by 25%.

5. Collaborated with team members to develop a ChatGPT-based solution for automating social media content creation, significantly reducing manual workload and increasing our social media presence by 18%.

6. Initiated a "lunch-and-learn" series to teach colleagues about ChatGPT and its potential applications, fostering a culture of continuous learning and innovation within the organization.

7. Established a ChatGPT user group within the company to facilitate knowledge sharing, collaborative problem-solving, and staying up-to-date with the latest advancements in ChatGPT technology.

Misc Tools & Resources: 

News you can Use: 

▶︎ Bipartisan Push to Regulate AI: Privacy Rights & Fair Decisions in Focus

▶︎ Elon Uses Deep Fakes as a Legal Defense

▶︎AI Hoax Singes Media in Race to Report Fictional Star's Plastic Surgery Demise

In the Meme Time:

That's a wrap for today. Stay thirsty & see ya next time! If you want more, be sure to follow me on Twitter and LinkedIn.

DISCLAIMER: None of this is legal advice. This newsletter is strictly educational and is not legal advice or a solicitation to buy or sell any assets or to make any legal decisions. Please /be careful and do your own research.