Artificial Intelligence (AI) Research Scientist
(Redirected from AI Scientist)
An Artificial Intelligence (AI) Research Scientist is a computing science researcher who specializes in the AI domain (of AI tasks, algorithms, and systems).
- AKA: Artificial Intelligence Researcher, AI Scientist, AI Researcher.
- Context:
- Role Input: research problems, datasets, computational resources
- Role Output: research papers, algorithms, models, experimental results
- Role Performance Measure: publication impact, citations, research breakthroughs
- ...
- They can (typically) perform literature reviews, identifying and summarizing relevant academic papers and articles to stay updated with current research.
- They can (typically) collaborate with multidisciplinary teams, including computer scientists, engineers, and domain experts, to advance AI technologies.
- They can (typically) design and conduct experimental research to validate hypotheses.
- They can (typically) develop novel AI algorithms and architectural approaches.
- They can (typically) implement and test AI models using deep learning frameworks.
- ...
- They can (often) work in academia, conducting AI research and publishing findings in scientific journals.
- They can (often) focus on specific AI subfields such as Machine Learning, Natural Language Processing, Computer Vision, or Robotics.
- They can (often) generate hypotheses by analyzing existing data and literature.
- They can (often) present at academic conferences and research symposiums.
- They can (often) supervise graduate students and research assistants.
- ...
- They can range from being a Junior AI Researcher to being a Senior AI Research Scientist, depending on experience level.
- They can range from being a Theoretical AI Researcher to being an Applied AI Researcher, based on research focus.
- They can range from being a Industry AI Researcher to being an Academic AI Researcher, depending on institutional context.
- ...
- They can write grant proposals to secure research funding.
- They can publish research findings in peer-reviewed journals.
- They can develop intellectual property through patents and innovations.
- They can contribute to open source AI projects and research tools.
- ...
- Examples:
- Research Specializations, such as:
- Deep Learning Researchers developing neural architectures.
- Reinforcement Learning Scientists studying agent behavior.
- NLP Researchers advancing language understanding.
- Computer Vision Scientists improving visual recognition.
- Notable AI Researchers, such as:
- Geoffrey Hinton for neural network research.
- Yann LeCun for convolutional neural networks.
- Yoshua Bengio for deep learning advances.
- ...
- Research Specializations, such as:
- Counter-Examples:
- AI Engineers, who implement rather than research AI systems.
- Data Scientists, who apply rather than develop AI methodology.
- ML Engineers, who deploy rather than innovate ML models.
- Research Software Engineers, who support rather than lead AI research.
- See: AI Research Lab, Machine Learning Research, Computer Science Research, Academic Research Career.
References
2015
- (Stajic et al., 2015) ⇒ Jelena Stajic, Richard Stone, Gilbert Chin, and Brad Wible. (2015). “Rise of the Machines.” In: Science Journal, 349 (6245). doi:10.1126/science.aaa8415
- QUOTE: AI researchers also have a grander aspiration: to create a well-rounded and thus more human-like intelligent agent.
1998
- (Moravec, 1998) ⇒ Hans Moravec. (1998). “When Will Computer Hardware Match the Human Brain.” In: Journal of evolution and technology, 1(1).
- QUOTE: This paper describes how the performance of AI machines tends to improve at the same pace that AI researchers get access to faster hardware. Based on extrapolation of past trends and on examination of technologies under development, it is predicted that the required hardware will be available in cheap machines in the 2020s.