AI News: MIT Paper
Riding the AI Wave: How will AIaaS Change the Game?
Questions Powered by AI
As AI advances, how will workplaces adapt and retrain?
How feasible is AI-as-a-Service (AIaaS) for companies given technical, economic, and privacy challenges?
Can all work tasks be feasibly and economically automated by AI?
How does a company assess both the feasibility of automating a task with AI and the potential economic benefits over human labor?
What is the relationship between corporate interest and scientific research, particularly in the context of AI?
AI, or Artificial Intelligence, is the simulation of natural intelligence processes by computer systems.
Natural Intelligence (NI) involves, but isn’t limited to, human intelligence which involves consciousness, emotional intelligence, and complex cognitive processes.
NI Quotes via :
In general, I think we should be skeptical about scientific research conducted or funded by big technology companies concerning subjects the companies have a vested interest in. Nowadays, genuine academic research and advanced corporate marketing are unfortunately becoming hard to distinguish from each other.
Just because a work task can be automated does not mean that it is desirable or feasible to do so from an economic and/or technical perspective.
A simple example of how the model works from the paper: A small bakery is considering whether it should adopt an AI system to check if ingredients it uses are of a good enough quality… In conclusion: automating this task would not be profitable.
To fill in the scientific literature gaps on AI’s labor market impacts, the researchers in Svanberg et al., centered their work around two questions:
Exposure: Is it possible to build an AI model to automate this task?
Economically-attractive: Would it be more attractive to use an AI system for this task than to have human workers continue to do it?
Instead of building and adopting AI systems in-house, companies could buy AI-as-a-service from vendors in the market. However, designing an AI system for a particular task that can be generalized to the needs of many different firms is technically challenging and expensive. Also, typically, companies would have to fine-tune a more generalized model to their specific needs. By using proprietary data to fine-tune a third-party vendor’s model, companies are exposing themselves to privacy and confidentiality risks. Overall, the researchers find it unlikely that any third-party vendor could capture more than a fraction of the total market.
Source with link to MIT paper:
The introduction of AI is revolutionizing the employment landscape. While sectors like manufacturing face job displacement risks, new career paths necessitating AI proficiency are emerging. Workplace retraining is key to ride this wave of change. Ultimately, the future economy will likely witness AI and human resources optimally complementing each other.
As AI advances, how will workplaces adapt and retrain to mitigate job displacement?
How feasible is AI-as-a-Service (AIaaS) for diverse companies given technical, economic, and privacy challenges?
Subscribe to MasterVerse.AI Substack:
***** The information herein is neither professional advice nor a replacement for professional advice and may be AI generated. Please consult a professional provider as needed. MasterVerse.AI is for informational purposes only and may include fact or fiction and provides no guarantee in any form. The reader assumes all risks for reading and is responsible for fact-checking anything and everything herein. *****