AI requires pre-existing intelligence University Job platform, Handshake is flooded with AI training jobs!
Handshake [https://www.linkedin.com/company/team-handshake/], best known as a student job platform, entered the AI training market in January. Just six months in, it expects to reach a $100M run rate from this new business line by the end of the year…
https://substack.com/home/post/p-188401002 [https://substack.com/home/post/p-188401002]
The development of intelligence, especially Artificial General Intelligence (AGI), probably requires pre-existing intelligence—be it human, artificial, or arising from evolutionary processes. Although human intelligence currently propels AI advancements, the burgeoning concept of [recursive self-improvement] indicates that AI may ultimately evolve into its own creator, producing new forms of intelligence through design rather than mere programming.
No matter how intelligent autonomous AI agents become in certain respects, at least for the foreseeable future, they will remain unconscious machines. These machines have a fundamentally different operating system (biological vs digital) with correspondingly different cognitive abilities and qualities than people and other animals (Korteling et al., 2021).
Is there a coordinated "trick" to pay or trick students to train AI, a rapid, contentious adoption of AI in higher education is forcing students to unknowingly feed data into AI systems. Students are currently caught in an "arms race" where they use AI to produce work, while teachers use AI detection tools.
Concerns regarding the potential of AI to supplant human employment are considerable, with forecasts suggesting that although numerous positions may not vanish completely, they will undergo substantial transformation, especially in administrative and, progressively, white-collar fields. Rather than complete eradication, AI is swiftly automating particular repetitive tasks within roles, necessitating a transition towards more advanced human competencies.
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Recent advancements in Artificial Intelligence (AI) programming tools, such as Codex and CoPilot, enable the generation of code across various programming languages based on natural language descriptions. At present, it is evident that these AI tools rely primarily on text matching and lack a true comprehension of the specifications or the code itself. However, they can assist in the development of programs by reducing the need for certain repetitive programming tasks. My concern lies in the potential for these tools to complicate the learning process for aspiring programmers, as they do not promote a deep understanding of the material. This is particularly troubling given that they can produce code for the straightforward pedagogical and andragogical tasks commonly employed in programming education, especially when there are already numerous online solutions available for these challenges.
Furthermore, I ponder the implications these tools may have on the job market and how they might influence admissions into computer science programs. Should these tools encroach upon a significant number of entry-level programming positions, the job market may shift its focus towards senior programmers or those with specialized skills, such as expertise in hardware, operating systems, or mathematics. This shift could potentially diminish interest in computer science as a field of study.
Are there viable strategies to ensure that students genuinely grasp the concepts they are working with, especially when they have the capability to rely on computers for code generation?
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References
Korteling, J. E. H., van de Boer-Visschedijk, G. C., Blankendaal, R. A. M., Boonekamp, R. C., & Eikelboom, A. R. (2021). Human- versus Artificial Intelligence. Frontiers in artificial intelligence, 4, 622364. https://doi.org/10.3389/frai.2021.622364
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