AI Bots for Forecasting Research
One way to test the significance of a research area - run it by a forecasting bot!
The forecasting bot typically works by aggregating expert opinions and data, applying probability models to predict the likelihood of specific outcomes, providing insights into uncertainty rather than definitive answers, and reducing human biases like confirmation bias by offering data-driven predictions that can challenge assumptions. This process can help researchers assess the probability of future trends in their fields with greater accuracy and objectivity.
Try this forecasting bot by the Center for AI Safety
Application Example
Here's an example of using a forecasting bot to understand some aspects of my research exploring "AI characters as Parasocial Learning Experiences for Kids”. According to the forecasting bot by the Center for AI Safety , here are some interesting predictions:
??There’s a 78% probability that AI characters will become a big part of children’s education and entertainment?
??There’s an 85% probability that AI characters can support child development?
??There’s a 45% probability that AI characters will create an equalizing effect for children’s education in low socioeconomic conditions
??There’s a 35% probability that children will have Parasocial relationships with AI characters?
??There’s a 30% probability that AI characters will become always-on companions with the agency to communicate without being prompted?
BUT
?? There’s a 30% probability that children’s interactions with AI characters will reduce their quality of interactions with other humans
?? There's a 35% probability that a 24 hour AI companion will have a strong negative impact on the environment
??There’s only a 5% probability that AI characters will replace human teachers in the classroom
领英推荐
??There’s only a 5% probability that AI characters will replace caregivers at home?
These predictions give me data-informed insight that while AI characters will be useful in education and entertainment for children, educators and caregivers will always remain a crucial part of child development.?
Knowing that I’m the general good direction with my research area, I will now rely on more specific and contextual studies to ask more nuanced questions and find their answers through the traditional research processes.?
Pros and Cons of Forecasting Bots in Research
The outcomes in my example may sound like “obviously, duh!” insights - but predictive algorithms consider a wide number of factors based on existing knowledge to generate a probability percentage.?
Forecasting bots can provide researchers with data-driven predictions based on expert input and statistical models. They may help challenge assumptions, reduce confirmation bias, and assist in prioritizing impactful research questions.
These tools offer probabilistic insights that clarify uncertainty and complement traditional research methods for hypothesis testing.
Like any other shiny tech, there are always CONS accompanying the pros. Predictions rely on available data, which may be incomplete or biased, potentially affecting accuracy. These tools can sometimes oversimplify complex research topics and should not be viewed as definitive answers. It's important for researchers to use forecasting bots alongside traditional methods to produce well-rounded and thorough analysis.
Using Forecasts to Compare the Pros and Cons of Research Area
Back to my example of AI companions. Based on the forecast, interacting with always-on AI companions may have dual effects:
Pros: AI companions and technologies provide significant benefits, including improved user experiences, advanced learning opportunities, and the potential for more personalized interactions. AI systems are becoming integral in education, healthcare, and customer support, where their ability to process large amounts of data quickly can yield more efficient and tailored services. Companies like Microsoft are also investing in renewable energy to reduce the environmental footprint of their AI technologies. With proper energy management strategies, AI could be supported by more sustainable resources over time(AI energy forecast).
Cons: However, the rapid expansion of AI technologies, particularly in always-on devices and data centers, raises serious environmental concerns. By 2030, AI data centers could consume up to 4.5% of global energy, with large carbon footprints resulting from increased demand. The energy consumption required by deep learning models and GPUs contributes to greenhouse gas emissions, as seen by Google's 50% increase in emissions over five years. Without a shift to renewable energy or more energy-efficient AI models, the environmental impact could continue to grow at an unsustainable rate(AI energy forecast).
Direct quote from the probability bot that seems worth highlighting:
The high energy consumption and emissions are significant concerns, but the mitigating efforts through renewable energy investments and efficiency improvements are also substantial. The environmental benefits of AI and regulatory pressures further support a lower probability of a strong negative impact. Considering priors and base rates, the rapid growth of AI technologies and their energy demands are relatively new phenomena, so historical data may not fully capture the potential impacts. However, the ongoing efforts to mitigate these impacts are promising and should be factored into the forecast.
Read more for sources of this information https://forecast.safe.ai/?id=66f34fdb9874830587108ee0
How much energy did I use/waste in generating these forecasts?
I'm part proud, part guilty.
E-commerce beyond 'E' - AI, automation & scalable B2C/B2B/D2C.
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Child Experience learner, strategist and facilitator that ?? play & mindfulness
5 个月I liked the structure of the forecasting, the transparency of design and how it helps you expand your thinking. Also the way you put it in use for your research is creatively awesome as usual!