Your AI Weekend Curated Reads: 6-21-24

Lessons Learned from Building LinkedIn’s AI Data Platform

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Welcome to 8 Bits for a Byte! This week, we dive into the latest in AI, from LinkedIn’s AI evolution to advancements in autonomous vehicles. Read on to discover insights on balancing progress with perfection and more!

Let's Get To It!

Here's what caught my eye in the world of AI this week:

Deep Byte:

1. In the AI space, everyone's buzzing about what will be the first killer Gen AI app. Google and Microsoft have integrated large language models (LLMs) into their products, but this feels more like "bolt-on" AI. If I had the definitive answer, I'd be building it now!

Benedict Evans offers an intriguing perspective in his piece, "Building AI Products." He emphasizes that successful AI products should cater to users, not expect them to adapt to the technology. Over the last 50 years, consumer computing has thrived by moving toward the user, not by making them learn command/template shortcuts. Who remembers WordPerfect? It's a perfect example of how not moving toward the user can lead to obsolescence.

AI applications must be intuitive and user-friendly to drive widespread adoption. Look for AI that enhances the user experience without requiring a steep learning curve. A company that is IMHO is doing a great job is Notion.

Notion AI (Affiliate) is revolutionizing how we organize and manage information. It leverages AI to automate tasks, suggest content, and improve workflow efficiency.

• Automation: Handles routine tasks, freeing up time for more strategic work.
• Content Generation: Assists in creating documents, notes, and plans with ease.
• Strategy: Moving Towards the User

When implementing AI in your company, prioritize tools that are easy to use and enhance the current workflow. Avoid complex solutions that require extensive training or change how employees work.

• User Training: Invest in training programs that make the transition smooth.
• Regularly collect and incorporate user feedback to refine AI tools.

??Trends

Responsible AI

There is a lot of conversation about Responsible AI below are Strategies, Examples , Critical Insights, and Next Steps to ensure Responsible AI implementation in your business:

?? Strategy 1: Establish Clear Ethical Guidelines

• Develop a comprehensive AI ethics policy that aligns with your company’s values and objectives.
• Ensure transparency in AI decision-making processes and provide clear explanations for AI-driven outcomes.

Example: Google's AI Principles are a great benchmark. They emphasize accountability, transparency, and user privacy.

?? Strategy 2: Implement Robust Data Privacy Measures

• Prioritize data privacy by incorporating techniques like anonymization and encryption.
• Regularly audit your data practices to ensure compliance with regulations such as GDPR and CCPA.

Example: IBM’s approach to data privacy ensures that all AI data processes are compliant with international standards like GDPR.

?? Strategy 3: Integrate Responsibility into Product Requirements

• Include a dedicated responsibility section in your Product Requirement Document (PRD).
• Outline ethical considerations, potential biases, and mitigation strategies for each AI feature.

Example: AI Quick Bytes ?free template for AI PRDs for AI projects include sections on potential biases and ethical implications, ensuring that every AI feature is developed responsibly.

?? Strategy 4: Foster a Culture of Accountability

• Create a cross-functional AI ethics committee to oversee AI projects and address ethical concerns.
• Train employees on ethical AI practices and encourage a culture of responsibility and continuous learning.

Example: Salesforce’s Trusted AI Principles help ensure all projects adhere to their ethical standards.

Critical Insight

Most organizations default accountability for AI to IT, or don’t assign accountability at all. Responsible governance requires the business to take accountability for their approach to AI.

• Very few organizations have a formal and structured approach to AI governance.
• AI can introduce or intensify risks that affect the entire organization, but most organizations haven’t integrated AI risks into their enterprise risk management framework.
• Most organizations don’t assign accountability for AI or it defaults to the CIO – and yet authority and true accountability remain with the business.
• Policies are published without any controls to monitor and enforce compliance.

Next Steps

Start to govern AI responsibly by following a structured approach:

• Identify key risks related to AI.
• Identify a set of responsible AI principles.
• Create an AI governance structure: identify key governing organizations, their mandate, key roles, and responsibilities.
• Design an AI governance operating model.
• Evaluate policy gaps using a policy framework.
• Use your findings to develop a roadmap and communication plan to govern AI in your organization.

AI Quality Conference: Elevate Your AI Game

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1. We previously discussed LLMOps in a previous newsletter, I thought it would be helpful to take a step back. Here is an excellent primer on CI/CD , which is crucial to understand, even if you're not implementing all the details. A solid foundation in these concepts allows you to create scalable, transferable solutions. Learning how to build, automate and delegate solutions is key to avoiding long-term support burdens and ensuring you can move on to your next challenge seamlessly.

1. Verizon’s strategy to navigate the "Wild West" of AI development responsibly.

6. Friday Funnies ??

Send me your best AI meme and I will post the Top 5 and put it to a vote by our readership! I am sure anyone can do better! Winner gets a bottle Moet Champagne! Have to get at least five memes for there to be a competition.

7. Autonomous Vehicles Are Great at Driving Straight But humans still do better at turns and in the twilight

Autonomous vehicles excel at driving straight, but humans still outperform them in complex scenarios like turns and twilight. Despite occasional fatalities, banning them isn't the solution. Voltaire's "Perfection is the enemy of the good" from Philosophy 101 resonates here; waiting for perfection means more lives lost. According to IEEE Spectrum, while autonomous vehicles face challenges, they continually improve and often surpass human drivers in safety. We had a fruitful LinkedIn discussion on this, emphasizing progress over perfection.

In business, where lives aren't at stake but brand and reputation are, does this principle still hold?

What are your thoughts?

8. General Intelligence (2024)

True expertise lies in explaining complex concepts in a way that anyone can understand. The author, J. Becker, an employee at OpenAI, shows his passion as he delves into what is required to create a generally intelligent agent, highlighting three key ideas:

1. Interaction and Observation: Embodiment—the ability to perceive and interact with the natural world.

2. Robust World Models: Mechanisms for quick, accurate inference, akin to human intuition or “System 1 thinking.”

3. Deep Introspection: Mechanisms for reasoning or “System 2 thinking.”

According to Becker, these advancements will help us achieve AGI in just a few years. A must-read to gain a better understanding of AGI as defined by Becker.