Eric Schmidt 和 Sebastian Thrun 1 在《财富》杂志撰文，呼吁大家理性看待人工智能：
Let’s get beyond the chatter and build working solutions. The lesson with self-driving cars is that we can learn and do more collectively. Google has, for example, open-sourced the free platform TensorFlow—the code is in the open for all to see and contribute to. It allows AI researchers around the world to collaborate more easily, sharing actual code rather than just research papers. That way, we can see what computers can learn, how they use data, and use the wisdom of our smartest minds to control and improve AI.
Indeed, it’s already clear that Silicon Valley is not the only place that will make progress in AI; this is truly a global effort, with global potential. We believe AI will serve everyone best if it’s built by a diverse range of people, such as those joining Google’s new machine learning group opening in Zurich, and countless other global hubs.
相比而言，微软 CEO Nadella 的建议更具实用价值，他列举了人工智能「必须」遵守的规则：
A.I. must be designed to assist humanity: As we build more autonomous machines, we need to respect human autonomy. Collaborative robots, or co-bots, should do dangerous work like mining, thus creating a safety net and safeguards for human workers.
A.I. must be transparent: We should be aware of how the technology works and what its rules are. We want not just intelligent machines but intelligible machines. Not artificial intelligence but symbiotic intelligence. The tech will know things about humans, but the humans must know about the machines. People should have an understanding of how the technology sees and analyzes the world. Ethics and design go hand in hand.
A.I. must maximize efficiencies without destroying the dignity of people: It should preserve cultural commitments, empowering diversity. We need broader, deeper, and more diverse engagement of populations in the design of these systems. The tech industry should not dictate the values and virtues of this future.
A.I. must be designed for intelligent privacy—sophisticated protections that secure personal and group information in ways that earn trust.
A.I. must have algorithmic accountability so that humans can undo unintended harm. We must design these technologies for the expected and the unexpected.
A.I. must guard against bias, ensuring proper, and representative research so that the wrong heuristics cannot be used to discriminate.
But there are “musts” for humans, too—particularly when it comes to thinking clearly about the skills future generations must prioritize and cultivate. To stay relevant, our kids and their kids will need:
Empathy—Empathy, which is so difficult to replicate in machines, will be valuable in the human–A.I. world. Perceiving others’ thoughts and feelings, collaborating and building relationships will be critical.
Education—Some argue that because lifespans will increase, birth rates will decline, and thus spending on education will decline. But I believe that to create and manage innovations we cannot fathom today, we will need increased investment in education to attain higher level thinking and more equitable education outcomes. Developing the knowledge and skills needed to implement new technologies on a large scale is a difficult social problem that takes a long time to resolve. There is a direct connection between innovation, skills, wages, and wealth. The power loom was invented in 1810 but took 35 years to transform the clothing industry because there were not sufficient trained mechanics to meet demand.
Creativity—One of the most coveted human skills is creativity, and this won’t change. Machines will continue to enrich and augment our creativity. In a recent interview, novelist Jhumpa Lahiri was asked why an author with such a special voice in English chose to create a new literary voice in Italian, her third language: “Isn’t that the point of creativity, to keep searching?”
Judgment and accountability—We may be willing to accept a computer-generated diagnosis or legal decision, but we will still expect a human to be ultimately accountable for the outcomes.
作者饱含热情地列举了 Echo 的诸多优势，其中这点让我想起美剧《硅谷》里的一个细节：你的产品到底是面向硅谷工程师还是普通受众呢：
Echo seems under-appreciated in Silicon Valley but genuinely popular in other tech communities. While everyone in the classic tech world of San Francisco and the Valley is focused on AR and VR and AI, in New York and DC and Seattle, I’m hearing a lot more enthusiasm about Alexa, particularly since it’s in the hands of millions of regular people already.
- Sebastian Thrun 曾带领斯坦福大学的无人驾驶团队获得 Darpa 举办的无人驾驶比赛冠军，随后加入 Google，创建了 Google X 实验室，现在是在线教育公司 Udacity 负责人。 ↩