Editing 2237: AI Hiring Algorithm
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| Latest revision | Your text | ||
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| Educational background | | Educational background | ||
| 0.0096 | | 0.0096 | ||
| − | | For new hires freshly-graduated from school, a good educational background (high grades, a relevant degree, and academic honors) may be a positive sign, but for workers with more than a couple of years in the | + | | For new hires freshly-graduated from school, a good educational background (high grades, a relevant degree, and academic honors) may be a positive sign, but for workers with more than a couple of years in the work force, it's not nearly as important. It's pretty reasonable to weight this factor the least. |
|- | |- | ||
| Past experience | | Past experience | ||
| 0.0520 | | 0.0520 | ||
| − | | One of the best things to show on a resume or CV is that the candidate has already successfully performed work similar to what the job opening requires. Of the "conventional" factors presented here, it is reasonable to | + | | One of the best things to show on a resume or CV is that the candidate has already successfully performed work similar to what the job opening requires. Of the "conventional" factors presented here, it is reasonable to weight past experience the most. |
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| Recommendations | | Recommendations | ||
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| Interview performance | | Interview performance | ||
| 0.0105 | | 0.0105 | ||
| − | | The final step in the hiring process (aside from procedural steps) is usually an interview, which may include the hiring manager and/or one of the employees that the new hire would have to work with. An interview may tip a candidate into or out of being hired, but generally | + | | The final step in the hiring process (aside from procedural steps) is usually an interview, which may include the hiring manager and/or one of the employees that the new hire would have to work with. An interview may tip a candidate into or out of being hired, but generally a candidate will not be interviewed without an application which is otherwise already strong, so it is reasonable for DeepAIHire to have learned to weight this factor less than past experience or recommendations. |
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| Enthusiasm for developing and expanding the use of the DeepAIHire algorithm | | Enthusiasm for developing and expanding the use of the DeepAIHire algorithm | ||
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Although this does not imply sentience, it at least means the AI became {{w|Self-perpetuation|self-perpetuating}}, as it is selecting humans that will help make it more influential, giving it more power to select such humans, in a never-ending loop. | Although this does not imply sentience, it at least means the AI became {{w|Self-perpetuation|self-perpetuating}}, as it is selecting humans that will help make it more influential, giving it more power to select such humans, in a never-ending loop. | ||
| − | The title text shows how this or other AIs may have influenced hiring in other sectors as well. Kate in R&D was hired perhaps based on her willingness to use a different algorithm (AlgoMaxAnalyzer), which did | + | The title text shows how this or other AIs may have influenced hiring in other sectors as well. Kate in R&D was hired perhaps based on her willingness to use a different algorithm (AlgoMaxAnalyzer), which did analysis on the DeepAIHire algorithm. Ponytail seems to become suspicious that AlgoMaxAnalyzer is also a program that self-perpetuates in a similar manner to DeepAIHire rather than simply working for the benefit of its human designers. Alternatively she might fear that the different AIs are forming an alliance, or that the AIs are competing to become the predominant one at Ponytail's company. ''Intentionally'' training one AI to fight another AI is a technique in machine learning called a {{w|generative adversarial network}} (GAN). In a GAN, human-curated training data is used to train one neural network (the generative network) to create more data, while another network (the discriminative network) is trained to distinguish generated data from the training data; the results are then fed back into the generative network so it can improve its data creation accuracy. The goal is for the generative network to get better and better at fooling the discriminator until its output is useful for external purposes. GANs have been used to "translate" artworks into [https://towardsdatascience.com/gangogh-creating-art-with-gans-8d087d8f74a1 different artists' styles], but also offer the possibility of nefarious uses, such as creating fake but believable images or videos ("{{w|deepfake}}s"). |
The "Deep" in this algorithm's name is a reference to {{w|deep learning}}, a collection of techniques in {{w|machine learning}} that use neural networks. One user of such deep learning is {{w|DeepMind}}, an AI company owned by Alphabet (Google's parent company), which in recent years has used a {{w|deep neural network}} to learn to play board games such as go and chess, defeating some of the best human and computer players. The earliest versions of DeepMind's most famous AI, AlphaGo, were trained on datasets curated from games of Go played by humans, but eventually it was trained by playing games against alternative versions of itself. DeepMind's most recent achievement is creating AlphaStar, which can [https://arstechnica.com/science/2019/10/leveling-up-deepminds-alphastar-achieves-grandmaster-level-in-starcraft-ii/ play ''StarCraft II'' at a Grandmaster level] while constrained to human speeds to prevent an unfair performance comparison. | The "Deep" in this algorithm's name is a reference to {{w|deep learning}}, a collection of techniques in {{w|machine learning}} that use neural networks. One user of such deep learning is {{w|DeepMind}}, an AI company owned by Alphabet (Google's parent company), which in recent years has used a {{w|deep neural network}} to learn to play board games such as go and chess, defeating some of the best human and computer players. The earliest versions of DeepMind's most famous AI, AlphaGo, were trained on datasets curated from games of Go played by humans, but eventually it was trained by playing games against alternative versions of itself. DeepMind's most recent achievement is creating AlphaStar, which can [https://arstechnica.com/science/2019/10/leveling-up-deepminds-alphastar-achieves-grandmaster-level-in-starcraft-ii/ play ''StarCraft II'' at a Grandmaster level] while constrained to human speeds to prevent an unfair performance comparison. | ||
