[vc_row type=”in_container” full_screen_row_position=”middle” scene_position=”center” text_color=”dark” text_align=”left” top_padding=”30″ overlay_strength=”0.3″ shape_divider_position=”bottom”][vc_column column_padding=”no-extra-padding” column_padding_position=”all” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_shadow=”none” column_border_radius=”none” width=”1/1″ tablet_text_alignment=”default” phone_text_alignment=”default” column_border_width=”none” column_border_style=”solid”][vc_column_text][nectar_dropcap color=”#3452ff”]T [/nectar_dropcap]he title of this blog post is stolen from this great article on the future of Artificial Intelligence by Michael I. Jordan from Berkeley. I highly recommend reading it not just for understanding what artificial intelligence is and where we need progress, but also how engineering principles can help cultivate the future of our infrastructure.
The aspect I appreciated most was the eloquence with which he describes the need for a new type of engineering, and carefully outlines its purpose and goals. One of the challenges we have always faced at the Center for Systems Science and Engineering is defining what we mean by systems. Not only has the word already been used different disciplines, but has taken on a broader context that has sometimes been detrimental because it feels like there is no concreteness to what the discipline aims to do. One of my colleagues has joked that they should just call “systems thinking,” which is what researchers in systems admittedly practice, as just “don’t be stupid.” Another colleague has described the faculty in systems at our university as the “island of misfit toys;” researchers who have taken an interdisciplinary angle to their work so that they don’t really belong anywhere. One of the best ways I have seen systems defined is by Andreas Hieronymi [Hieronymi, A. (2013). Understanding systems science: a visual and integrative approach. Systems research and behavioral science, 30(5), 580-595.], whose Figure 2 I reproduce below:[/vc_column_text][/vc_column][/vc_row][vc_row type=”in_container” full_screen_row_position=”middle” scene_position=”center” text_color=”dark” text_align=”left” overlay_strength=”0.3″ shape_divider_position=”bottom” shape_type=””][vc_column column_padding=”no-extra-padding” column_padding_position=”all” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_shadow=”none” column_border_radius=”none” width=”1/1″ tablet_text_alignment=”default” phone_text_alignment=”default” column_border_width=”none” column_border_style=”solid”][image_with_animation image_url=”6354″ alignment=”center” animation=”Fade In” img_link_large=”yes” border_radius=”none” box_shadow=”small_depth” max_width=”100%”][/vc_column][/vc_row][vc_row type=”in_container” full_screen_row_position=”middle” scene_position=”center” text_color=”dark” text_align=”left” overlay_strength=”0.3″ shape_divider_position=”bottom”][vc_column column_padding=”no-extra-padding” column_padding_position=”all” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_shadow=”none” column_border_radius=”none” width=”1/1″ tablet_text_alignment=”default” phone_text_alignment=”default” column_border_width=”none” column_border_style=”solid”][vc_column_text]Credit: Hieronymi, A. (2013). Understanding systems science: a visual and integrative approach. Systems research and behavioral science, 30(5), 580-595.[/vc_column_text][/vc_column][/vc_row][vc_row type=”in_container” full_screen_row_position=”middle” scene_position=”center” text_color=”dark” text_align=”left” overlay_strength=”0.3″ shape_divider_position=”bottom”][vc_column column_padding=”no-extra-padding” column_padding_position=”all” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_shadow=”none” column_border_radius=”none” width=”1/1″ tablet_text_alignment=”default” phone_text_alignment=”default” column_border_width=”none” column_border_style=”solid”][vc_column_text]But Michael I. Jordan goes a step further and clearly defines what type of engineering discipline is needed:
“Whether or not we come to understand “intelligence” any time soon, we do have a major challenge on our hands in bringing together computers and humans in ways that enhance human life. While this challenge is viewed by some as subservient to the creation of “artificial intelligence,” it can also be viewed more prosaically — but with no less reverence — as the creation of a new branch of engineering. Much like civil engineering and chemical engineering in decades past, this new discipline aims to corral the power of a few key ideas, bringing new resources and capabilities to people, and doing so safely. Whereas civil engineering and chemical engineering were built on physics and chemistry, this new engineering discipline will be built on ideas that the preceding century gave substance to — ideas such as “information,” “algorithm,” “data,” “uncertainty,” “computing,” “inference,” and “optimization.” Moreover, since much of the focus of the new discipline will be on data from and about humans, its development will require perspectives from the social sciences and humanities.” [Michael I. Jordan]
The paragraph above shows that the building blocks for such a discipline are already there, we just need a nice framework to put it in. He goes on to describe that discipline:
“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.” [Michael I. Jordan]
And the part that resonated with me the most was the call to link markets with infrastructure, it’s such an overlooked aspect of the possibilities of AI specifically, but broader interdisciplinary engineering research:
“Finally, and of particular importance, II systems must bring economic ideas such as incentives and pricing into the realm of the statistical and computational infrastructures that link humans to each other and to valued goods. Such II systems can be viewed as not merely providing a service, but as creating markets. There are domains such as music, literature and journalism that are crying out for the emergence of such markets, where data analysis links producers and consumers. And this must all be done within the context of evolving societal, ethical and legal norms.” [Michael I. Jordan]
The article lays out a really good plan for a “human-centric engineering discipline,” and also challenges us to think beyond our siloes of practice to realize our potential. More importantly, by the story of his daughter’s birth describing the disconnect between statistical research and technological innovation, the article also highlights the dangers of not developing such a human-centric engineering discipline. As scientists, we ignore the future at our own and society’s peril.[/vc_column_text][/vc_column][/vc_row]