Market Description:

This market seeks to predict the convergence of artificial intelligence (AI) and neurotechnology by 2030, focusing on the ability of AI to accurately predict and classify human decisions based on the understanding and interpretation of neurobiological data. This concept combines the areas of AI, decision theory, and neuroscience, creating an intersection between human cognition and technology. Advances in neurotech could potentially allow AI to map decision-making processes at a neurological level, enhancing prediction accuracy and understanding the variables that influence different decisions. However, such advancements pose significant ethical, legal, and technical challenges.

Resolution Criteria:

The question shall be resolved as "Yes" if, by 31st December 2030, there is robust, peer-reviewed scientific evidence demonstrating that an AI system, informed by neurotechnology, can accurately predict and classify human decisions and the factors influencing those decisions. This evidence should come from multiple independent research teams across the globe and be published in reputable scientific journals.

This AI system must not only predict the decision but also identify the type of decision (e.g., rational, emotional) and the key influencing factors (e.g., environmental, cognitive). The AI should achieve a prediction accuracy rate significantly higher than current benchmarks to be considered successful.

In case of partial success (e.g., prediction accuracy is achieved but classification of decision type or influencing factors is not), the resolution will still be "No". The question will be resolved as "No" if such evidence is not presented by the specified date.

Review:

Given the current pace of technology, it is challenging to predict with certainty whether AI, coupled with neurotechnology, will be able to accurately predict and classify human decisions and their influencing factors by 2030. While there are significant advancements in both fields, the complexity of human decision-making and the ethical, legal, and technical challenges associated with blending AI and neurotechnology make this a daunting task.

However, we are witnessing rapid innovations in both AI and neurotechnology, with the emergence of tools that can already predict certain behaviors or states based on neurobiological data. If the pace of innovation continues and significant investment is made into resolving the ethical, legal, and technical challenges, it is conceivable that such technology could exist by 2030.

(NOTE: Revised question and entire description was generated by GPT-4)
I will be giving a presentation on this topic around the start of June and would like to share this market.