Panel: 50 Years of Language Evolution: From Simula’67 to the FuturePanel
As computers and computer platforms have evolved over the past fifty years, our approaches to programming have changed. Not surprisingly, first generation languages evolved or became obsolete and new languages have emerged or have been retasked for new purposes. Domain specific languages have proliferated and emergent languages have catalyzed different programming styles – such as parallel processing, objects, functional programming, logic programming, fuzzy data etc. In this panel, our panelists will compare their experiences and offer insight on what makes a language useful (or not) in the unpredictable future.
It has been 50 years since the emergence of Simula 67 (SIMple Universal LAnguage 67) which is considered the first object oriented language. Simula 67 was developed by Ole-Johan Dahl and Kristen Nygaard in Oslo, Norway and has greatly influenced language development. This panel brings together language innovators to discuss past, present, and future language evolutions.
Panelist Positions & Bios
Steven Fraser is an independent consultant with Innoxec (Innovation Executive Services) advising on open innovation and tech transfer engagement strategies. He has held senior R&D leadership focused on scouting, incubating, and leveraging company-university collaborations at HP, Cisco, Qualcomm, Nortel, and Bell Northern Research. Prior to the turn of the century he consulted as a Visiting Scientist at the Software Engineering Institute (SEI) on team-based domain analysis techniques. Steven is a Senior Member of both the ACM and the IEEE and holds a PhD in Electrical Engineering (software validation) from McGill University in Montreal.
Lera Boroditsky. Humans communicate with one another using 7,000 or so different languages, and each language differs from the next in innumerable ways. How do natural languages evolve and change and what consequences are there for their speakers? Do people who speak different languages think differently? Do languages merely express thoughts, or do they secretly shape the very thoughts we wish to express? Are some thoughts unthinkable without language? The question of whether the languages we speak shape the ways we think has been at the center of controversy for centuries, and with good reason. At stake are basic questions all of us have about ourselves, human nature, and reality. Why do we think the way we do? Why does the world appear to us the way it does? In turn, how do humans shape and change natural languages to suit their needs? What makes an element of a language more or less likely to change quickly over time?
Lera Boroditsky is an Associate Professor of Cognitive Science at UCSD and Editor in Chief of Frontiers in Cultural Psychology. She previously served on the faculty at MIT and at Stanford. Her research is on the relationships between mind, world, and language (or how humans get so smart). She has been named one of 25 Visionaries changing the world by the Utne Reader, and is also a Searle Scholar, a McDonnell scholar, recipient of an NSF Career award, and an APA Distinguished Scientist lecturer. She once used the Indonesian exclusive “we” correctly before breakfast, and was proud of herself about it all day.
Robert Gentleman. It seems very likely that the future will be driven by data and our interpretation of it. Medicine and medical care, self-driving cars, drug discovery, and marketing are just a few of the very many areas where data are changing what we do and how we live. Domain specific languages (or tool kits) will likely play important roles and R is essentially an engine for producing DSLs that have inherent data processing, analysis and graphing capabilities.
R is an interactive language that built on the pioneering work of John Chambers and colleagues at Bell Labs who created the S Language. R has created strong communities of developers and users. Newer developments such as the Shiny web application framework (RStudio) provide exciting opportunities for developing interactive interfaces that allow users to explore and manipulate data. I believe that these sorts of tools will become increasingly important.
Robert Gentleman received his B.Sc in Math from the University of British Columbia and PhD in Statistics from the University of Washington. In 1992, at the University of Auckland, he collaborated with Ross Ihaka on a project that grew into the R language. Subsequently he was an originator of the Bioconductor Project. He is presently at 23andMe and his interests are focused on drug discovery and human genetics.
Chris Granger. The next generation of languages have to tackle the fact that the 1970’s computer that served as the basis for our mainstream languages no longer exists. “The machine” isn’t a serial disconnected processor with a little bit of ram and hard-drive space anymore. Instead it is the devices in our pockets, on our walls, and on the roads just as much as it is the massive clusters that live behind AWS and Azure. Adding layers of abstraction has served us well over the years, but when the entire context changes, it’s time to re-evaluate whether our base models are still viable. Every program is now a distributed one and has to struggle with latency, failure, and consistency. To do so, we won’t be able to just put another layer on top, we have to design for it from the very bottom. Whatever that ends up looking like, the focus has to be on simplicity, because the reality is already far too complicated for us to deal with.
Chris grew up as part of the Nintendo generation, having learned the parts of a computer at the age of two and later learning numbers and colors from a Sesame Street game on the NES. He started programming at the age of ten and took his first paid development gig at 17. Since then he’s built websites large and small, written frameworks and libraries used by thousands, taught developers around the world, and helped envision the future of development at Microsoft. These days, he’s the co-founder and CEO of Kodowa, where they built the next generation code editor Light Table and now Eve, a new vision for putting distributed computation in the hands of everyone.
Crista Lopes. Lopes’ research is related to languages and communication systems. The ultimate goal of her research is to deepen the knowledge about communication, in particular in systems that involve humans and machines. With this utopic goal in mind, she has done work in a variety of fields such as programming languages, security and applications of audio signal processing.
Sumit Gulwani. Sumit Gulwani leads a research & engineering team at Microsoft that develops program synthesis technologies for data wrangling and incorporates them into real products. His programming-by-example work led to the Flash Fill feature in Microsoft Excel used by hundreds of millions of people. He has published 110+ papers in top-tier conferences/journals across multiple computer science areas, and delivered 30+ keynotes/invited talks at various forums. Sumit was awarded the ACM SIGPLAN Robin Milner Young Researcher Award in 2014 for his pioneering contributions to end-user programming and intelligent tutoring systems. He obtained his PhD from UC-Berkeley, and was awarded the ACM SIGPLAN Outstanding Doctoral Dissertation Award. He obtained his bachelor’s degree from IIT Kanpur in 2000, and was awarded the President’s Gold Medal.
Programming Languages have traditionally evolved to make developers more productive to create traditional software. The present and future however raises new responsibilities for language evolution to cater to new advances (Intelligent software development), new audience (End users), and new purpose (Education). The upcoming AI revolution provides opportunities for intelligent “software development” but also necessitates evolution of languages for “intelligent software” development— software that can evolve its intelligence with usage and can personalize to different users/workloads. The digital revolution has resulted in widespread accessibility to computational devices with 99% of computer users being non-programmers. These users can easily communicate their intent using informal means of examples and natural language. Can languages evolve to cater to these end users? There is well-justified ever-increasing focus on computing education in K-12 curriculum across various countries. This presents a responsibility to design languages to make computing education fun and accessible.