Second, and more importantly, success in these domains is neither sufficient nor necessary to solve important IA and II problems. On the sufficiency side, consider self-driving cars. For such technology to be realized, a range of engineering problems will need to be solved that may have little relationship to human competencies (or human lack-of-competencies). The overall transportation system (an II system) will likely more closely resemble the current air-traffic control system than the current collection of loosely-coupled, forward-facing, inattentive human drivers. It will be vastly more complex than the current air-traffic control system, specifically in its use of massive amounts of data and adaptive statistical modeling to inform fine-grained decisions. It is those challenges that need to be in the forefront, and in such an effort a focus on human-imitative AI may be a distraction.
Hoping that the reader will tolerate one last acronym, let us conceive broadly of a discipline of “Intelligent Infrastructure” (II), whereby a web of computation, data and physical entities exists that makes human environments more supportive, interesting and safe. Such infrastructure is beginning to make its appearance in domains such as transportation, medicine, commerce and finance, with vast implications for individual humans and societies. This emergence sometimes arises in conversations about an “Internet of Things,” but that effort generally refers to the mere problem of getting “things” onto the Internet — not to the far grander set of challenges associated with these “things” capable of analyzing those data streams to discover facts about the world, and interacting with humans and other “things” at a far higher level of abstraction than mere bits.
Since the 1960s much progress has been made, but it has arguably not come about from the pursuit of human-imitative AI. Rather, as in the case of the Apollo spaceships, these ideas have often been hidden behind the scenes, and have been the handiwork of researchers focused on specific engineering challenges. Although not visible to the general public, research and systems-building in areas such as document retrieval, text classification, fraud detection, recommendation systems, personalized search, social network analysis, planning, diagnostics and A/B testing have been a major success — these are the advances that have powered companies such as Google, Netflix, Facebook and Amazon.
…we hold meetings, discuss the challenge extensively and go back and forth about how to approach it. Since it’s difficult to make decisions early on in the process, we end up going in circles with the same meetings over and over again and make no progress whatsoever.
…g a round were not about SlideShare, its metrics or potential. It was related to me as a woman CEO. The final straw was when a prominent VC firm, gave us an offer, as long as I stepped down as CEO. Needless to say, we said no to that one, and also stopped talking to anyone who seemed uncomfortabl…
Think about it: Do you think Uber knew when it started how big the potential of the business was? I can tell you from insider knowledge there’s no way. They started as an elite black-car service and only once they saw Lyft doing well in the peer driving market did they launch UberX. And Lyft? Nah. Started as Zimride, a way for people in Palo Alto to share a ride into San Francisco and vice versa.
Our goal was not to enforce a new way of working, but to observe and codify what’s currently effective and create just enough structure to aid communication, clarity and transparency throughout the lifecycle of a project, both inside and outside of the design team.