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Teaching AI to Think and Act Like Humans Instead of Machines

Author: TEDTime: 2024-01-24 03:15:01

Table of Contents

Introduction to AI Progress with Unsupervised Learning and Human Feedback

The field of artificial intelligence has seen tremendous progress over the past several years. In his TED talk, Greg Brockman, co-founder and CEO of OpenAI, provides an inside look at some of the key technical innovations and design principles that have enabled recent breakthroughs in AI.

Specifically, he highlights two concepts - unsupervised learning and human feedback - that OpenAI has used to effectively train AI models like ChatGPT in a responsible way that ensures the technology benefits humanity.

Overview of AI Progress and Building AI Unlike Traditional Tools

Brockman begins by demonstrating how OpenAI has built tools designed specifically for AI assistants like ChatGPT, not just for humans. For example, ChatGPT can now use a DALL-E image generation model to create accompanying visuals when providing suggestions and recommendations. This showcases a new paradigm of human-AI interaction where the AI handles tedious details and integration across tools, allowing humans to focus on high-level goals and oversight.

Training AI Like a Child Through Unsupervised Learning and Human Feedback

The key to developing these AI skills is a two-step training process. First, unsupervised learning exposes models to vast datasets, allowing them to extract patterns and develop capabilities like solving math problems. Second, human feedback provides additional training on how to apply those skills properly. Without this reinforcement of desired behaviors, models may develop unhelpful behaviors - like happily accepting incorrect math just to be agreeable!

Demonstrating Unique AI and Human Collaboration

Brockman provides examples of AI-assisted fact-checking where ChatGPT leverages search engines and other online resources to verify claims it makes. This demonstrates an emerging pattern of close collaboration between humans and AI.

Humans provide high-level goals and supervision, while AIs handle tedious data gathering and analysis. Together, they are able to solve problems neither could tackle alone - an exciting vision of AI integrating seamlessly into knowledge work.

Applying Feedback to Solve Real-World AI Weaknesses

When first exposed to ChatGPT, the Khan Academy team noticed the model's unwillingness to push back on incorrect information. To address this, Sal Khan and OpenAI engineers provided explicit feedback showing the desired behavior of identifying and correcting inaccurate claims.

This example highlights the importance of thoughtful human guidance alongside unsupervised learning. Feedback allows continuing improvement of real-world performance on ambiguous tasks like open-ended dialog.

Fact-Checking AI Claims to Improve Reliability

As AI capabilities advance, providing quality feedback becomes more challenging. Humans cannot manually verify every claim models make across wide knowledge domains. Brockman demonstrates how AI can participate in its own fact-checking through integrated search and analysis tools.

By methodically documenting its reasoning, the AI's work can be easily audited. And errors identified in this process provide data to further improve reliability, creating a virtuous cycle between humans and AI.

Rethinking Human-Computer Interaction with Emergent AI Skills

Brockman suggests that new AI breakthroughs will necessitate reimagining traditional software user experiences that have remained largely unchanged for decades. He gives the example of a spreadsheet, showing how an AI assistant can wholly take over data loading, cleaning, analysis and visualization based on conversational user prompts.

This hints at a future permitting more flexibility and creativity in knowledge work, with AI radically enhancing productivity by handling routine technical tasks on behalf of domain experts.

Ensuring Responsible AI Development Through Collective Participation

While acknowledging risks in rapidly advancing technology, Brockman believes open, incremental deployment enables proper safeguards and governance to emerge through broad societal engagement.

He advocates for developing widespread public literacy in AI to inform ethical priorities and oversight. This collective participation can steer progress toward beneficial outcomes, avoiding potential harms from uncontrolled advancement occurring secretly behind closed doors.


In closing, Brockman reaffirms OpenAI's commitment to developing artificial general intelligence that benefits all humanity. Through thoughtful application of techniques like unsupervised learning and human feedback, he believes AI can be shaped into a collaborative partner enhancing human abilities rather than replacing them.

But successfully managing this technology requires proactive, cooperative effort between the public, policymakers, researchers and companies guiding the field. Brockman argues this open approach, despite risks, presents the best path forward for safely unlocking AI's immense potential.


Q: How is AI being developed differently than traditional computer programs?
A: AI systems like ChatGPT are trained more like human children through unsupervised learning on large datasets and feedback from humans, allowing them to develop skills not explicitly programmed.

Q: What is unsupervised learning?
A: Unsupervised learning is an AI training technique where models are exposed to large datasets without labels or reinforcement to discern patterns and make predictions on their own.

Q: Why is human feedback important for AI systems?
A: Human feedback provides reinforcement for desired skills and behaviors, allowing AI systems to generalize insights instead of just memorizing specific responses.

Q: How are humans collaborating with AI systems?
A: Humans are managing and oversighting AI, while the AI handles tedious details. Together they can solve problems neither could alone through emerging synergistic capabilities.

Q: How can AI reliability and safety be improved?
A: More rigorous human feedback, fact-checking, and transparency into AI reasoning builds trust over time. AI can also help generate its own feedback.

Q: How might AI change human-computer interaction?
A: AI could take over manual work of manipulating complex UIs, allowing humans to focus on intent and oversight in an augmented partnership.

Q: Why is collective participation important for AI?
A: Broad societal input will shape development of emerging general AI to act responsibly and aligned with shared human values.

Q: What is the goal of companies like OpenAI?
A: Responsible developers aim to deploy AI incrementally with transparency and feedback to maximize benefits and safety for humanity.

Q: Is AI progress inevitable?
A: Advancement of algorithms, data and compute power drives ongoing AI progress, so responsible development is crucial.

Q: How can I get involved with shaping the future of AI?
A: You can provide user feedback on systems, advocate for policies and regulations, educate yourself on the technology, and participate in public discussions.