We need answers to AI policy and strategy queries desperately because
Courtesy: New York Times
AI policy is the analysis and application of social decision-making concerning AI. The term AI strategy is typically used to check with the study of big image AI policy questions, like whether or not we should always wish AI to be narrow or cosmopolitan and which analysis issues need to be prioritized.
Below, we tend to principally embody such strategic queries under the umbrella of “Long-term AI policy”. References at the tip of the document give smart introductions to the range of problems falling below the AI policy and AI strategy umbrellas.
AI and Machine Learning policy umbrellas have always been the zone of active debate, many of which have been hosted by AI Labs from their office in Kolkata, India.
Most policy selections associated with near-term slender AI systems are unlikely to own very long-lived implications in and of themselves. However, acting on such problems could also be valuable for gaining expertise to later work on policy problems associated with a lot of powerful AI systems, and a few of identical policy frameworks and tools could also be applied to each.
We would estimate that less than 10% of labour on AI policy is specifically involved with problems associated with extremely capable AI systems which may be developed in the future, whereas there is a bigger and earlier growing body of work targeted on additional near-term problems like driverless automotive policy and drone policy.
There are trade-offs between spending time operating directly on resolution analysis issues and increasing relevant expertise. However, those inquisitive about AI policy ought to attempt to get a minimum of some expertise operating in/around sensible policy problems if possible.
This section, generalizing a little, can describe 2 typical ways in which one would possibly get into AI policy-moving from being an AI scientist into being an AI policy researcher/practitioner, and moving from being a policy researcher/practitioner in another space to specializing in AI policy.
Those planning on stepping foot into Artificial Intelligence Policy from any unique policy background should find it easy to get attention and rise to the top. They should primarily focus on the widespread applicability of AI and the various policy problems arising out of it.
Courtesy: Analytics India
Some potential ways to urge up speed in AI that are significantly helpful for those with a policy background are MOOCs on AI, in-person classes, and a master’s program in AI. In addition, it would be valuable for such folks to attend conferences like we robot and Governance of Emerging Technologies, to attain a stronger sense of AI policy. The problems they are likely to encounter could be similar to or completely different from the ones they have previously dealt with.
Many companies are quickly emerging in Kolkata to provide such courses and educate on prospects like Artificial Intelligence and Deep Learning and their policy governance.
Most policy choices related to near-term slender AI systems are unlikely to possess terribly long-lasting implications in and of themselves. However, functioning on such issues might even be valuable for gaining experience to later work on policy issues related to other powerful AI systems. Some of the identical policy frameworks and tools might even be applied to each.
Those planning on setting their boot into Artificial Intelligence Policy from any unique policy background should find it easy to rise. They should focus on the widespread policy issues arising out of Artificial Intelligence.