Ethics are an Issue Don't kid yourself--introducing self-learning robots that can learn faster and better than humans will come with a huge range of issues. On our end, we can only program them to the extent of our human knowledge, which is always going to be limited. If we forget to set system safeties, we could have serious trouble on our hands in terms of public safety. On the other end, the question remains: do we really want to create a world of computers that think--and do--via their own free will, especially when they are smarter than humans? That's definitely an issue we need to reflect on before jumping too far into the reinforcement learning landscape.
There's been much talk about how artificial intelligence will benefit society, but what about the potential impacts that AI has when the system is poorly designed and creates problems? This is a question several researchers and OpenAI, a non-profit artificial intelligence research company, tackled in a recent paper. The paper was written by researchers from Google Brain, Stanford University and the University of California, Berkeley, as well as John Schulman, research scientist at OpenAI. It's titled Concrete Problems in AI Safety, and it looks at research problems around ensuring that modern machine learning systems operate as intended. Researchers have started to focus on safety research in the machine learning community, including a recent paper from DeepMind and the Future of Humanity Institute that looked at how to make sure that human interventions during the learning process would not induce a bias toward undesirable behaviors in machine learning robots.
The article supports the reality that rapid use of AI and Machine Learning in Safety functions is occuring. The ability to use AI to drive safety is incredible! Machine Learning even has the potential to drive true zero incident cultures! That means that more grandparents, parents, and children come home safely each day.
It's relatively easy to figure out whether or not a neighborhood is doing well at one moment in time. More often than not, you just have to look around. But how do you measure the progress (or deterioration) a neighborhood makes? That's where AI might help. Researchers have built a computer vision system that can determine the rate of improvement or decay in a given urban area.
The construction industry is one of the biggest in the USA but also is one of the deadliest. Construction has been and continues to be a dangerous occupation, resulting in many accidents, injuries and fatalities. Safe practices are crucial for the industry. Developments of methodologies for machine learning use in construction safety and the preparation of appropriate software will fill a large need in the industry. Analysis of construction equipment activities with a proper level of detail can help improve several aspects of construction engineering and management such as productivity assessment, safety management, idle time reduction, emission monitoring and control etc.