Go back

Research Insights: Key Lessons Learned from 2023

Edit page

Research Insights: Key Lessons Learned from 2023

By the Alohomora Labs Research Team

Introduction

As we begin 2024, it’s valuable to reflect on the lessons learned from our research activities in 2023. This post shares insights from our successes, challenges, and everything in between, with the hope that these learnings will benefit both our team and the broader research community.

Technical Insights

1. The Importance of Proper Experiment Design

Lesson: Start with Clear Hypotheses

What We Learned: Early in 2023, we often dove into experiments without clearly defined hypotheses. This led to scattered results and difficulty interpreting outcomes.

Impact: Projects without clear hypotheses took 40% longer to complete and had 60% lower success rates.

Solution: We implemented a mandatory hypothesis statement for all experiments:

Hypothesis: [Clear statement of what we expect to find]
Success Criteria: [Measurable outcomes that would validate the hypothesis]
Failure Criteria: [What would indicate the hypothesis is incorrect]

Lesson: Document Everything, Even Failures

What We Learned: We lost valuable insights by not documenting failed experiments thoroughly.

Impact: We repeated similar mistakes and missed opportunities to learn from failures.

Solution: Created a “Research Log” template that includes:

2. Data Quality Over Quantity

Lesson: Clean Data Trumps Big Data

What We Learned: Large datasets with poor quality often performed worse than smaller, well-curated datasets.

Example: Our transformer optimization project initially used a 10M token dataset with mixed quality. Switching to a 2M token, carefully curated dataset improved model performance by 15%.

Best Practices Developed:

3. The Power of Incremental Progress

Lesson: Small Wins Compound

What We Learned: Focusing on incremental improvements rather than breakthrough innovations often led to better long-term outcomes.

Example: Our model compression work started with simple pruning techniques, gradually adding more sophisticated methods. This approach was more successful than trying to implement complex compression algorithms from the start.

Strategy: Break large research goals into smaller, achievable milestones with clear success metrics.

Team and Collaboration Insights

1. Cross-Disciplinary Collaboration

Lesson: Diverse Perspectives Drive Innovation

What We Learned: Our most successful projects involved researchers from different backgrounds (ML, software engineering, domain experts).

Example: Our MLOps best practices project benefited greatly from having both ML researchers and infrastructure engineers on the team.

Implementation:

2. Communication is Critical

Lesson: Over-communication is Better Than Under-communication

What We Learned: Research projects often failed due to misaligned expectations or unclear communication.

Solutions Implemented:

3. Mentorship and Knowledge Transfer

Lesson: Senior-Junior Pairing Accelerates Learning

What We Learned: Pairing senior researchers with junior team members led to faster skill development and better project outcomes.

Program Structure:

Strategic Insights

1. Research Direction Selection

Lesson: Balance Between Novelty and Impact

What We Learned: Projects that were too novel often lacked practical applications, while purely applied research sometimes lacked innovation.

Framework Developed:

Novelty Score (1-10): How new is this approach?
Impact Score (1-10): How much would success matter?
Feasibility Score (1-10): How likely are we to succeed?
Overall Priority = (Novelty + Impact + Feasibility) / 3

2. Resource Allocation

Lesson: Time is More Valuable Than Money

What We Learned: Having the right people with enough time was more important than having unlimited computational resources.

Strategy:

3. External Collaboration

Lesson: Open Collaboration Accelerates Progress

What We Learned: Collaborating with external researchers and institutions led to faster progress and better outcomes.

Examples:

Process Improvements

1. Research Planning

Lesson: Quarterly Planning Beats Annual Planning

What We Learned: Annual research plans became outdated quickly. Quarterly planning allowed for better adaptation to new developments.

Process:

2. Documentation and Knowledge Management

Lesson: Good Documentation Saves Time

What We Learned: Poor documentation led to knowledge loss and repeated work.

Improvements:

3. Evaluation and Metrics

Lesson: Measure What Matters

What We Learned: We often measured outputs (papers, models) rather than outcomes (impact, adoption).

New Metrics:

Looking Forward to 2024

Key Focus Areas

  1. AI Safety and Alignment: Increased focus on responsible AI development
  2. Efficiency Research: Making AI more sustainable and accessible
  3. Multimodal Learning: Exploring vision-language integration
  4. Open Source Contributions: Giving back to the research community

Goals for 2024

  1. Publish 12+ high-impact papers
  2. Contribute to 5+ open-source projects
  3. Host 4+ external research collaborations
  4. Improve team satisfaction scores by 20%

Conclusion

2023 was a year of significant growth and learning for our research team. The key insight is that successful research requires not just technical excellence, but also strong processes, clear communication, and a supportive team environment.

The lessons learned will guide our approach in 2024 and beyond, helping us become more effective researchers and better contributors to the AI community.

We’d love to hear about your research lessons learned in 2023. Share your insights in the comments or reach out to us at research@alohomora-labs.com.

Resources

Edit page
Share this post on:
Share this post via WhatsAppShare this post on FacebookShare this post on XShare this post via TelegramShare this post on PinterestShare this post via email
Previous Post
Weekly Lab Talk: Advances in Transformer Architecture Optimization