Andrew Mayne: Prompt Engineering, Joining OpenAI, & Shark AI | Around the Prompt #7

Andrew Mayne shares his non-traditional journey into AI, from being a magician and illusionist to becoming a science communicator at OpenAI. He discusses his early experiences with AI as a child, his interest in robotics and AI, and his fascination with chatbots. Andrew also talks about his experience using AI to deceive great white sharks and how it led him to explore the capabilities of text models like GPT-2 and GPT-3. He emphasizes the importance of prompt engineering and the need to carefully craft prompts to get desired outputs from the models. Andrew Mayne emphasizes the importance of having a clear idea of the desired output and breaking down complex tasks into manageable steps. He shares his experience in teaching magic tricks and how it helped him in prompt engineering. Mayne discusses the evolution of prompt engineering and the challenges and hype surrounding it. He also talks about his personal tech stack and the tools he uses for writing, coding, and research. Mayne expresses his excitement about the accessibility of AI models and the potential impact of AI in education. He also discusses his concerns about deep fakes and the need for trust and authentication in communication.
00:00 The Future of Prompt Engineering and Model Capabilities
34:26 Exploring the Trial and Error Process in AI Development
37:11 The Impact of Generative AI on Publishing and Education
41:23 The Proliferation of Open Source AI Models
48:15 Challenges and Concerns Related to Deep Fakes
01:02:11 The Future of AI in Employment and Human Value

[@user-rp6bh7xj7s]

Over the past year, I successfully developed an innovation in auto-regressive LLM and SLM reasoning capabilities, making the model objectively smarter, better, more efficient, and more effective, using less compute while simultaneously helping users avoid common pitfalls of prompt inputs. you wouldn't believe how hard it is to share these findings without open-source publications or a whitepaper, that will never be read.

[@GregoryBohus]

Best interview to date. Loved how informative this was with concrete examples.

[@elvissaravia]

Really nice episode! Andrew is very knowledgeable on how to use LLMs. Have learned a lot from the OpenAI documentation and really appreciate his efforts.

[@GenexisAI]

1. Opening - 00:00
2. Introduction to Andrew Maine - 00:02
3. AI Journey - 00:09
4. Childhood with Robots and AI - 00:33
5. Career as a Magician - 01:32
6. Return to AI - 02:21
7. Experiments with Sharks - 03:09
ㄴ Development of Shark Detection System - 04:22
ㄴ Development of Camouflage Suit - 06:00
8. Encounter with GPT-2 - 08:00
9. Collaboration with OpenAI - 09:22
10. Work on GPT-3 - 10:05
11. Model Prompt Optimization - 14:47
ㄴ Simplification of Prompts - 15:01
12. Ways to Improve Writing Skills - 17:01
13. Model Limitations and Optimization - 20:00
14. Methods for Evaluating Prompts - 28:54
15. Systematic Approach to Prompts - 29:21
16. Reasons for Model Errors - 31:00
17. Tips for Writing Successful Prompts - 34:35
ㄴ Clear Goal Setting - 34:35
ㄴ Step-by-Step Approach - 35:18
18. Evolution of Prompt Engineering - 36:42
19. Limitations of Academic Approaches - 38:00
20. Use Cases for AI Technology - 40:01
ㄴ Cursor IDE - 41:16
ㄴ Other Tools - 42:14
21. Using Deepgram for Voice Transcription - 43:27
22. Advancement of Conversational AI Tools - 46:03
23. Release and Development of GPT-4 - 50:18
24. Impact of Generative AI on the Publishing Industry - 53:33
ㄴ Various Ways to Consume Text Information - 54:36
25. Learning Data and AI - 58:41
26. Andrew's New Project - 59:52
27. AI Accessibility and Impact on Education - 01:02:01
28. Hopes and Concerns for the Future of AI - 01:05:01
ㄴ Positive Expectations - 01:05:15
ㄴ Concerns about Technologies like Deep Fakes - 01:06:23
29. Conclusion of the Conversation - 01:08:00

[@GenexisAI]

1. 오프닝 - 00:00
2. Andrew Maine 소개 - 00:02
3. AI 여정 - 00:09
4. 로봇과 AI에 대한 어린 시절 - 00:33
5. 마술사의 경력 - 01:32
6. 다시 AI로의 복귀 - 02:21
7. 상어와의 실험 - 03:09
ㄴ 상어 감지 시스템 개발 - 04:22
ㄴ 위장복 개발 - 06:00
8. GPT-2와의 만남 - 08:00
9. OpenAI와의 협업 - 09:22
10. GPT-3와의 작업 - 10:05
11. 모델 프롬프트 최적화 - 14:47
ㄴ 프롬프트 간결화 - 15:01
12. 작문 능력 향상 방법 - 17:01
13. 모델의 한계와 최적화 - 20:00
14. 프롬프트 평가 방법 - 28:54
15. 체계적인 프롬프트 접근법 - 29:21
16. 모델이 실수를 하는 이유 - 31:00
17. 성공적인 프롬프트 작성 팁 - 34:35
ㄴ 명확한 목표 설정 - 34:35
ㄴ 단계별 접근법 - 35:18
18. 프롬프트 엔지니어링의 진화 - 36:42
19. 학문적 접근법의 한계 - 38:00
20. AI 기술 활용 사례 - 40:01
ㄴ Cursor IDE - 41:16
ㄴ 기타 도구들 - 42:14
21. 딥그램을 활용한 음성 기록 - 43:27
22. 대화형 AI 도구의 발전 - 46:03
23. GPT-4의 출시와 발전 - 50:18
24. 생성 AI의 출판업계 영향 - 53:33
ㄴ 다양한 텍스트 정보 소비 방식 - 54:36
25. 학습 데이터와 AI - 58:41
26. Andrew의 새로운 프로젝트 - 59:52
27. AI의 접근성과 교육에 미치는 영향 - 01:02:01
28. AI의 미래에 대한 기대와 우려 - 01:05:01
ㄴ 긍정적인 기대 - 01:05:15
ㄴ Deep Fake와 같은 기술적 우려 - 01:06:23
29. 대화 마무리 - 01:08:00

[@EnricoRos]

Great episode for prompt practitioners. Thanks for exploring chat-overfit, RLHF'ing capabilities out of the model, and model'ese (vs English) ♨️