Interdisciplinary collaboration plays a crucial role in advancing Artificial General Intelligence (AGI)

Summary of overall Gen AI Status
Several key breakthroughs are suggested as necessary for achieving Artificial General Intelligence (AGI)
Interdisciplinary collaboration plays a crucial role in advancing Artificial General Intelligence (AGI)
Productivity growth levels comparable to those seen during the Industrial Revolution through AGI development will likely require extensive interdisciplinary collaboration across multiple fields
Analysis, conclusion and some informed speculation

The current suite of AI models can generally be categorized as “narrow” in the sense they satisfy a narrow set of related functions, and are closer to advanced digital process automation.

Business deployment is heavily focussed on chat functions. Witness this weeks Salesforce results. Their CTO outlined their successes in offering their vision platform to multiple industries and growing rapidly in this lucrative market. but underlying their success is their offering as automated chat and CRM customer case management, recording and processing. AI enhances productivity through human displacement.

However I submit their model is hardly transformative to the business model and is closer to automation of current processes. Further there is no natural memory or development of context, values and beliefs we see in humans.

Some missing elements in the progress towards AGI needed are (readers please propose additional categories of data required to enhance AGI in the comments. I already recogise #1 is worthy of breaking out into multiple)

Combining diverse expertise
Addressing complex problems (which are not currently being addressed)
Replicating human cognition
Ethical considerations
Enhancing AI architectures
Fostering innovation
Addressing societal impacts
Overcoming limitations
Accelerating progress
Creating holistic solutions

Additional detail and analysis

Combining diverse expertise: AGI development requires insights from multiple fields including computer science, neuroscience, cognitive science, psychology, linguistics, and ethics. Each discipline brings unique perspectives and methodologies that contribute to a more comprehensive understanding of intelligence and cognition[1][2][3].
Addressing complex challenges: The development of AGI involves tackling complex problems that span multiple domains. Interdisciplinary collaboration allows researchers to approach these challenges from different angles, leading to more innovative solutions[2][4].
Replicating human cognition: Insights from cognitive science and neuroscience help in understanding human learning, problem-solving, and brain function. This knowledge is critical for developing AGI systems that can mimic human-like intelligence and adaptability[2][5].
Ethical considerations: Collaboration with ethicists and philosophers is essential to address the ethical implications and ensure responsible development of AGI[2][5].
Enhancing AI architectures: Interdisciplinary efforts can lead to improvements in AI architectures by incorporating insights from various fields. For example, neuroscience can inform the development of neural networks and algorithms that underpin AGI[5].
Fostering innovation: By bringing together experts from different backgrounds, interdisciplinary collaboration sparks new ideas and approaches that may not emerge within siloed research efforts[3][6].
Addressing societal impacts: Collaboration with social scientists and policy experts helps in understanding and preparing for the potential societal impacts of AGI[5].
Overcoming limitations: Different disciplines can help address the limitations of current AI approaches. For instance, cognitive science can provide insights into general problem-solving abilities that current narrow AI lacks[1][4].
Accelerating progress: By leveraging diverse knowledge and resources, interdisciplinary collaboration can potentially accelerate progress towards achieving AGI[3][6].
Creating holistic solutions: The integration of insights from multiple fields leads to more comprehensive and robust AGI systems that can better handle the complexities of general intelligence[2][5].

Conclusion

Interdisciplinary collaboration is essential for AGI development as it brings together the diverse expertise needed to tackle the multifaceted challenges of creating human-level artificial intelligence. This collaborative approach ensures that AGI research is grounded in a deep understanding of human cognition, addresses ethical concerns, and leverages insights from various scientific disciplines to create more sophisticated and capable AI systems.

Note to conclusion:

I recognise these solutions to the challenge introduce a new set of issues. AGI alternatives could result in a range of solutions, with multiple instances of AGI ranging from different levels of collaberation of modalities and inclusions to one gigantic AI/ AGI model offered as a service and allowing individual businesses, governments and entities to lever with their own personalisation sitting on top. The latter is not unlike the uniqueness we see in people so should be accepted in all likelhood. This is a topic for future posts and risk assessment.

Meantime we can safely state that AGI will require a multi faceted set of data, methods and capabilities to eve begin the AGI journey.