We are witnessing the emergence of agentic and ubiquitous AI systems that will reshape the digital world. We will see vastly more machine content than human content: nothing will be comprehensible without a machine interpreting it for us. By machines, for machines is the new paradigm.
While considering the potential risks and implications of OpenAI releasing an AI voice that appears designed to draw people in, I found an interesting r/artificialinteligence Reddit thread of people sharing their preference for talking with AI over other humans.
A recent paper entitled The Platonic Representation Hypothesis supports the idea that widespread use of singular algorithms might actually be simplifying hte world. Large-scale AI models, such as GPT-4, are all starting to learn the same representations of human digitized reality.
A recent study from MIT has been grabbing attention with its unconventional take: don't worry about AI snatching your job, it's not cost-effective.
A recent study from MIT has been grabbing attention with its unconventional take: don't worry about AI snatching your job, it's not cost-effective. The headlines highlight that only 23% of jobs at risk from AI are actually worth automating. While that's the headline-grabber, the study delves into more nuanced insights.
In 2016, we conducted our own study into labor automation, similar to the well-known Oxford study, which made waves with its claim that 47% of US jobs might be automated, and a related study by McKinsey. (For more details on our methodology, findings, and the key insights required to grasp these studies, click here). Each study comes with its own set of limitations, stemming from the assumptions researchers must make about AI's future development, the nature of jobs, and the economic factors influencing the adoption of AI.
This study stands out as the first comprehensive academic study to construct a full-fledged model of AI automation. It delves into the proficiency required for specific tasks, the associated costs to attain such proficiency (whether by machine or human), and the economic considerations driving the decision to embrace the technology. The focus on machine vision was strategic, chosen for its wealth of data on cost-effectiveness.
There are two big takeaways from the study:
The key takeaway from this study isn't just the 77% figure: it's the critical role of scale in AI deployment. This research reveals that the high cost of AI makes it viable primarily for large corporations. The authors' findings shed light on why AI adoption is limited to less than 6% of companies, which, however, account for 18% of jobs due to their size. Surprisingly, the average worker is employed by a company where automating vision tasks doesn't present a cost-effective option. To put it into perspective, "Even a hypothetical firm as big as Walmart lacks the scale to make automating 15% of their vision tasks attractive."
The Artificiality Weekend Briefing: About AI, Not Written by AI
