OpenAI's AGI Roadmap: A Glimpse into the Future of Artificial Intelligence

In a surprising turn of events, OpenAI's internal AGI (Artificial General Intelligence) roadmap has been leaked, offering unprecedented insight into the company's vision for AI development. This revelation is particularly noteworthy given OpenAI's longstanding commitment to developing AGI for the benefit of humanity.

OpenAI's AGI Levels: From Chatbots to Corporate Takeover

• Level 1: Chatbots (You are here) - Our current AI buddies like ChatGPT.

• Level 2: Reasoners - Think PhD-level brainpower, but without the student loans.

• Level 3: Agents - AI that can work on tasks for days. Finally, someone to binge-watch Netflix for you!

• Level 4: Innovators - The Thomas Edison of AI. Hopefully with better people skills.

• Level 5: Organisations - One AI to rule them all I mean, run an entire company.

Current Progress: Debating Our AI Status

This classification system provides a clear framework for understanding the trajectory of AI development. Interestingly, OpenAI suggests we're approaching Level 2, with reports of a research project demonstrating human-like reasoning capabilities. Some speculate this could be related to the company's secretive Q project.

However, the AI community remains divided on our current progress. While some assert we've already reached Level 2, at Apperture, we maintain a more conservative stance. Our assessment places current AI capabilities at approximately Level 1.5 - significant progress, but with substantial room for advancement.

Alternative Perspectives: Google DeepMind's Scale

It's worth noting that Google DeepMind has developed its own AGI scale, offering an alternative perspective on AI progress. Their scale defines each level based on the percentile of skilled adults in which the AI's capabilities fall. Read more

Looking Ahead: The Next Five Years in AI

Despite differing opinions on our current status, one conclusion seems evident: within the next five years, we're likely to witness the emergence of Level 3 or Level 4 AI systems. This prospect raises important questions about the readiness of various industries and economies for such advancements.

At Apperture, we're enthusiastic about the potential implications of these developments. Our focus is not only on understanding how these advancements will reshape industries and economies but also on developing strategies to effectively leverage these technologies. Moreover, we're committed to assisting other companies and enterprises in navigating this rapidly evolving landscape.

Rafa.ai: Your 24/7 Wall Street Analyst Team

Imagine having a team of top Wall Street analysts at your fingertips, ready to assist you anytime, anywhere. That's the promise of Rafa AI, a tool that's revolutionising the intersection of AI and finance. We believe this is one of the most impressive implementations of AI in trading we've seen to date.

Empowering Investors, from Novice to Pro

Let's take a closer look at some of its standout features:

1. QuantPro: Your Personal Trading Strategist

• Provides expert insights on trade strategies, entry and exit points, and long positions.

• Presents information in a visually simple and summarised format, making complex data digestible.

2. AnalystPro: Deep Dive into Equity Research

• Offers in-depth analysis on specific investment questions.

• Example: Ask "If you were Warren Buffet, would you buy Ford?" and receive comprehensive data to inform your decision.

3. Options Guru: Visual Mastery for Advanced Traders

• Delivers beautiful live visualisations of crucial data.

• Helps experts craft and refine their trading strategies with visual insights.

4. News Guru: Your Market Sentiment Compass

• Keeps you updated on company sentiments, performance, and earnings.

• Summarises publicly available news, saving you time and information overload.

Our Take: A Game-Changer for Beginners, with Pro-Level Potential

Rafa AI is a well-thought-out implementation of AI in finance, particularly impressive in its accessibility for beginners. While it offers features that cater to professional investors, its true strength lies in its ability to demystify trading for newcomers.

As impressive as Rafa AI is, it's likely just the beginning. As underlying AI models continue to evolve and improve, we anticipate seeing even more sophisticated applications in the future. The prospect of fully automated trading processes, guided by increasingly intelligent AI, is an exciting possibility on the horizon.

The Next Big Wave in AI: Riding the Tide of Proprietary Business Data

In the world of AI, we're witnessing a pivotal moment. The era of boundless public data that fuelled the AI revolution is reaching its limits. Just look at the graph: we're at the cusp of an innovation window, where public data from the consumer internet is plateauing, and the next frontier lies in proprietary data locked within enterprises of all sizes.

The Public Data Conundrum

For years, AI giants have been feasting on the vast buffet of public data. OpenAI, Anthropic, MetaAI - they've all been scouring the internet, ingesting every bit of publicly available information. This approach has given us powerful, general-purpose AI models.
But here's the rub: when it comes to specific enterprise use cases, this wealth of public data isn't just inadequate - it's becoming a liability.

The Hallucination Problem

Imagine you're trying to decipher why your company's sales dipped last quarter. You turn to your AI assistant, trained on the entirety of the internet. Sounds promising, right? Wrong. This AI, despite its vast knowledge, is just as likely to confidently expound on global economic trends as it is to provide actionable insights about your specific business. This is the hallucination problem in action - AI generating plausible-sounding but potentially irrelevant or incorrect information.

The Next AI Goldmine: Proprietary Enterprise Data

So where do we go from here? The answer lies in the treasure trove of proprietary data that companies generate every day.
This isn't just any data - it's context-rich, highly specific, and immensely valuable for creating AI solutions tailored to individual business needs.

The Commercial Challenge

But here's the catch: leveraging this data isn't as simple as feeding it into existing models. The computational cost of running models on the scale of GPT-3.5 is prohibitively expensive for most organisations. It's like using a supercomputer to solve a sudoku puzzle - overkill and financially unsustainable.

The Solution: Fine-Tuned, Task-Specific Models

At Apperture, we're tackling this challenge head-on. Our focus is on fine-tuning smaller, more specialised models using company-specific data. This approach is like crafting a precision instrument rather than wielding a sledgehammer.

The benefits are clear:

1. Higher accuracy on specific tasks

2. Reduced computational costs

3. Enhanced data privacy and security

4. Models that truly understand and adapt to unique business contexts

Conclusion

We stand at the threshold of a new chapter in enterprise AI. The future isn't about having access to more data - it's about having the right data and using it intelligently. As we move forward, the companies that can effectively harness their proprietary data to create tailored, efficient AI solutions will be the ones leading the charge in this new landscape.

The question isn't whether this shift will happen, but who will be at the forefront of this revolution. Are you ready to unlock the true potential of your company's data?