AI is non-biological intelligence – more specifically, technology that enables machines to accomplish complex goals. One typically distinguishes between weak/narrow AI, designed and trained for a particular task such as spam filters, self-driving cars or Facebook’s newsfeed, and general AI or Artificial General Intelligence (AGI), which is able to find a solution when presented with an unfamiliar task, with human-level ability or beyond.
The current quest for AGI builds on the capacity for a system to automate predictive analysis – a process generally described as machine learning. One important element of machine learning is the use of neural networks: systems that involve a large number of processors operating in parallel and arranged in tiers. The first tier receives a raw input, and each successive tier receives the output from the tier preceding it. Neural networks adapt and modify themselves autonomously, according to initial training and input of data, in ways that are typically not transparent to the engineers developing them.
If researchers one day succeed in building a human-level AGI, it will probably include expert systems, natural language processing and machine vision as well as mimicking cognitive functions that we today associate with a human mind, e.g., learning, reasoning, problem solving, and self-correction. However, the underlying mechanisms may differ considerably from those happening in the human brain just as the workings of today’s airplanes differ from those of birds.
In narrow domains, artificial intelligence (AI) systems have reached superhuman level relatively quickly – for instance, in identifying the location of a photograph or playing complex games like Jeopardy or Go. In the coming decades, there is a high probability that these systems may surpas humans in broader domains. The danger of entities more intelligent than us can be understood by considering the power we humans have drawn from being the smartest creatures on the planet. Even if the values of artificial intelligence systems can be aligned with those of their creators, they are likely to have a profound impact on socio-economic structures and geopolitical balance. But if the goals of powerful AI systems are misaligned with ours, or their architecture even mildly flawed, they might harness extreme intelligence towards purposes that turn out to be catastrophic for humanity. This is particularly concerning as most organizations developing artificial intelligence systems today focus on functionality much more than ethics.
AI is non-biological intelligence – technology that enables machines to accomplish complex goals.
Most experts agree that a superintelligent AI is likely to be designed as benevolent or neutral and is unlikely to become malevolent on its own accord. Instead, concern centers around the following two scenarios:
As these examples illustrate, the concern about advanced AI isn’t malevolence but competence. A super-intelligent AI will be extremely good at accomplishing its goals, and if those goals are not aligned with ours, we have a problem. You are probably not an evil ant-hater who stomps on ants out of malice, but if you are in charge of a hydroelectric green energy project and there is an anthill in the region to be flooded, too bad for the ants. A key goal of AI safety research is to never place humanity in the position of those ants.
It is now widely accepted that we will be able to create AI systems capable of performing most tasks as well as a human at some point. According to the median surveyed expert, there is a roughly 50% chance of such AI by 2050 – with at least a 5% chance of superintelligent AI within two years after human-level AI, and a 50% chance within thirty years. The long-term social impact of human-level AI and beyond, however, is unclear, with extreme uncertainty surrounding experts’ estimates.
The ability to align AI with human values is widely considered to be important in determining the risk factor. However, aside from the open question of which values to select, there are important unsolved technical problems regarding how to make an AI understand human goals, making an AI adopt these goals, and ensuring that it retains these goals if it recursively self-improves.
Co-founder, Future of Life Institute.
President and Co-founder, Future of Life Institute.
Director of Media and Outreach, Future of Life Institute.
Director of AI Projects, Future of Life Institute.
Co-founder, Future of Life Institute.