My TakeAway from The AI Summit - London
Tags: conference, ai
Published: 2024-06-17 06:27:50

**Intro**:
I was fortunate enough to attend the London AI Summit 24. The agenda was packed and most of the time I was just jumping between session. Here are my reflections from the London AI Summit. While there was a significant focus on Large Language Models (LLMs), these are some of the topics and discussions that I found particularly engaging, both as an attendee and a panelist:

**Gen AI**
GenAI Use Case Expansion:

- The rapid increase in GenAI use cases offers vast opportunities for innovation.
- Companies should review their GenAI strategies comprehensively to harness this potential.
- It's crucial to define incremental value from GenAI that aligns with existing digital investments for a cohesive, value-driven implementation approach.

Bias Mitigation in Large Language Models:

- Large language models are valuable for decision-making but require bias detection and mitigation to be fully effective.
- Proactively addressing biases can unlock untapped value and promote fair, ethical AI applications.


**Robots Operating Autonomously**

Spot, the robot dog by Boston Dynamics, was deployed at a former nuclear fusion reactor site. It gathered radiation data autonomously over 35 days with minimal human involvement.
**Advancements in Autonomy**:
The team is working on deploying robots in industrial settings where humans can't reach. Autonomy in robots allows for operation in hazardous environments, enhancing safety.
**Human-in-the-Loop Approach**:
Industrial companies currently prefer a human-in-the-loop approach for collaboration and control. Operators can intervene with robots when necessary, ensuring safety and reliability.
**Transition to Full Autonomy**:
The move toward full autonomy is gradual, as systems must improve in handling uncertainties.Full autonomy requires AI to respond to changes and optimize tasks in real-time.
**Technological Enhancements**:
Spot was fitted with lidar and advanced 3D mapping for reliable autonomous navigation. It carried a task-specific payload, like a device for monitoring radiation levels.
**Operational Efficiency**:
Spot was programmed to return to its charging unit when the battery was low. It followed a script for daily tasks, optimizing its battery life for efficient operation.
**Data Collection and Mapping**:
Long-term operation creates multiple maps of the environment, aiding operators.Robots provide more reliable data collection than humans, who may make errors due to fatigue.

![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/trdq6vp8nip33a1e1y29.png)

**NASA Charts AI, Robotics, 3D Printing as Path for Mars Sustainability**
**Mastering AI for Real-Time Data Analysis**: Essential for optimizing plant maintenance and predictive maintenance operations.
**Technological Challenges for Mars**: Developing vehicles for rocky terrain, equipment for lower gravity and cold temperatures, and smart robots with computer vision for ice exploration.
**Innovative Solutions**: Utilizing 3D printing for habitat construction, leveraging robots for material transport, and employing digital twins for personalized medicine.
**Safety Measures**: Creating handheld devices to alert humans of solar flares, with AI systems analyzing NASA data to predict solar events.
**Collaborative Design**: Using generative systems for spacecraft material design and AI models to gain insights from Earth data, in partnership with entities like IBM.
**Diverse Perspectives for Revolutionary Change**: Emphasizing the value of varied life experiences in fostering innovative problem-solving approaches.

**Effective LLM Agent consideration**
**Knowledge is Governance**: Recognizing that effective governance of AI begins with a thorough knowledge of its applications across the organization.
**Inventory of AI Use Cases**: Instructing teams to maintain an inventory of AI use cases to manage and govern AI responsibly.
**AI Risk Management**: Integrating AI risk management with other critical areas such as third-party collaborations and cybersecurity, acknowledging that AI does not operate in isolation.
**Monitoring Risks**: Advocating for robust support systems to monitor AI risks continuously.
**Inclusive Call to Action**: Encouraging a broad call to action to involve volunteers from across the business in the responsible AI process, tapping into diverse ideas and perspectives.
**Cross-Business Collaboration**: Highlighting responsible AI as an exercise that requires cross-business collaboration, involving legal, privacy, employment law, cybersecurity, procurement, and technology teams.
**Engaging Varied Stakeholders**: Suggesting the inclusion of sustainability teams and other varied stakeholders to ensure a comprehensive approach to responsible AI, emphasizing the importance of their involvement and stake in the process.

**Greener AI solution**:
**Green Data Centers**: Utilizing tools like the Electricity Map can help identify and prioritize the use of green data centers that are powered by renewable energy sources.
**Data Minimization**: Implementing policies to avoid data hoarding can reduce unnecessary energy consumption and storage costs.
**Scheduling ML Training**: Encouraging machine learning training during off-peak hours or when the data center is powered by cleaner energy sources can significantly reduce the carbon footprint.
**Task-Specific Models**: Opting for task-specific models can be more efficient and environmentally friendly compared to using large, generic models.
**Open Collaboration**: Fostering collaboration with the research community and open-source initiatives can lead to shared learning and more efficient design of ML solutions.
**Failure Logs**: Sharing failures and encouraging a failure log can help the community learn from mistakes and avoid repeating them, leading to more sustainable practices.
**Alignment with Corporate Strategy**: Ensuring that ML development aligns with the corporate sustainability strategy and quantifying the impact through KPIs can help in the wider adoption of green practices.


![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/tl7q46we1xw6o57skhl0.png)