In the ever-evolving world of AI, there is a constant influx of new technologies and advancements. From language models to live-selling platforms, the possibilities seem endless. However, as a community, we must approach these announcements with a healthy dose of skepticism. It's crucial to demand proof and transparency before accepting any claims made by these AI systems.

In the ever-evolving world of AI, there is a constant influx of new technologies and advancements. From language models to live-selling platforms, the possibilities seem endless. However, as a community, we must approach these announcements with a healthy dose of skepticism. It's crucial to demand proof and transparency before accepting any claims made by these AI systems.

One prominent example of this skepticism is the recent announcement from @InflectionAI about their new LLM (Language Learning Model). According to their claims, this LLM surpasses the capabilities of GPT3.5. However, there's a catch - the model is closed source and currently only available through a waitlist. This raises several red flags, as it prevents independent verification of the model's performance.

In the AI community, it is essential to adopt a "show, don't tell" approach. We cannot rely solely on brochures and marketing materials to evaluate the true capabilities of these AI systems. Instead, we should demand access to APIs or other means of directly interacting with the models. This way, we can put them through their paces and see for ourselves whether they live up to the claims being made.

Furthermore, it's crucial to subject these models to vibe checks. While it may sound unconventional, assessing the "vibe" or intuition of an AI system is crucial in determining its effectiveness. It's not enough for a model to generate text that technically makes sense; it should also convey the appropriate tone, style, and context. Without these qualities, the model's output may fall short of being truly useful or reliable.

Additionally, we must be wary of convenient omissions from published benchmarks. Companies often cherry-pick the most favorable results while conveniently leaving out any shortcomings or limitations of their models. It is essential for the AI community to demand complete transparency and comprehensive benchmarks that showcase the model's performance across various tasks and domains.

In the realm of AI, it's not enough to rely on claims and promises. We must insist on tangible evidence and verifiability. Without these, we risk being misled by hype and marketing tactics. As a community, we should prioritize access to APIs or other means of interacting with AI models directly. This will allow us to assess their capabilities firsthand and make informed judgments.

Moving forward, here are three actionable pieces of advice for the AI community:

1. Demand transparency: When evaluating new AI technologies, insist on complete transparency. This includes access to APIs, comprehensive benchmarks, and thorough documentation. Without these elements, it becomes challenging to assess the true capabilities and limitations of a model.

2. Embrace skepticism: Don't be swayed by flashy claims and marketing tactics. Take everything with a grain of salt and approach new advancements with a healthy dose of skepticism. Demand evidence and verifiability before accepting any claims made by AI systems.

3. Prioritize diverse evaluation: Don't solely rely on quantitative metrics or benchmarks. Subject AI systems to vibe checks and qualitative assessments. Evaluate how well they capture context, style, and tone. This will ensure that the models are not just technically sound but also genuinely useful in real-world scenarios.

In conclusion, as the AI community continues to push the boundaries of technology, it is crucial to maintain a skeptical mindset. Insist on transparency, demand evidence, and prioritize comprehensive evaluation. By doing so, we can ensure that we are not swept away by hype and marketing ploys, but rather make informed decisions based on reliable information and verifiable results.