Letter regarding the MIT Schwarzman College of Computing working groups and Idea Bank
Letter regarding the MIT Schwarzman College of Computing working groups and Idea Bank
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The following letter was sent to the MIT community on Feb. 7 by Provost Martin A. Schmidt.
To the members of the MIT community:
In October 2018, MIT announced the establishment of the MIT Stephen A. Schwarzman College of Computing. The College aims to create a shared academic structure to facilitate the connection of computing scholarship and resources to all disciplines at MIT, and to…
Dan Huttenlocher named inaugural dean of MIT Schwarzman College of Computing
Dan Huttenlocher named inaugural dean of MIT Schwarzman College of Computing
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Dan Huttenlocher SM ’84, PhD ’88, a seasoned builder and leader of new academic entities at Cornell University, has been named as the first dean of the MIT Stephen A. Schwarzman College of Computing. He will assume his new post this summer.
A member of Cornell’s computer science faculty since 1988, Huttenlocher has served since 2012 as the founding dean of Cornell Tech, a graduate school…
Exploring Quine’s Island: On a scalable research model for collaborative theoretical engineering research
Building a theory in a purely formal subject, such as that underpins the natural numbers, is a fun thing to do. When the subject admits observation and experiment, however, a theory builder’s fun should be tempered by a need to validate its descriptions and predictions where they overlap with reality.
As Quine (1951) wrote:
‘Total science, mathematical and natural and human, is [...] underdetermined by experience. The edge of the system must be kept squared with experience; the rest [...] has as its objective the simplicity of the laws.’
Quine’s observation suggests ways in which research into science, of whatever colour, could be conducted. That Quine’s scientific system model mentions no scale or point of reference means that it applies both to the smallest, most focussed to the most expansive theory. That it provides an objective measure of increasing simplicity means that it comments on theory development.
Quine’s island
We will see Quine’s idea of science in terms of a metaphor: as an island of theory surrounded by a sea of experience. We live on the island as theoreticians; we travel the seas of experience as practitioners. To put theory to practice is at once to exercise and validate it. Few who build theory have experience sufficient to validate it; few with experience have built useful theories. The greatest works are those combined from experience and theory.
Next to the sea, the land is kept flat and low, washed by experience; the island is easy to approach it is easy to approach, close by the shore the slopes are gradual; inland is thrown theory hill and vale. Experience is the force by which a changing landscape is tamed.
The sea is not the shore, nor is the shore the sea. As we peer into the waves, the sea reflects and refracts images of the pebbles on the shore. Sea washed pebbles sparkle; pick them up and they quickly go dull. One cannot take the sea in one’s hand, or otherwise use it to reflect and refract away from the shore; neither can one take a huge hill or deep valley and show how it sparkles underwater. The two are separate and must remain separated. [NB: This is our ‘Turski disconnect’, more on that later...!]
To experiment is to look though the water at the island below it. Experiment is point-like: a time, a place, an assignment of values to other variables. Looking through the water is to combine reality and theory and is the purview of observation. It is a blur of compromise: a weighed pebble is never 100 grammes, its fall back to the water never at 9.81 metres/second2.
For some islands a short, sandy shoreline is all around. Being combed in an hour or a day, a single explorer can walk this sea-washed shore alone. Some coasts are ragged with coves and craggy cliffs. A single explorer on the highest peak has a lovely but partial and lonely view; to explore such a will take numbers and planning. Of those who beach themselves looking for adventure, some will explore only the shoreline looking for pretty shells to take away. Others head for the hills to track fauna and tag flora.
A very 20first century problem
We have built an island, and laid its shoreline low hoping that it will reach the water. There is no treasure map, but early explorers show our island an interesting place to visit from the sea. Now, a group of professional seafarers are committing their skill and richest of contexts to explore the island with us. How best to accept their contribution.
POE is 20first century problem solving. It comes at a time when the world faces problems of environment, governance, health and education. It also comes at a time when the universal luxuries - amongst them, research - are unaffordable to short-sighted society and to the organisation.
Our principled Problem Oriented theory is only five years’ old, and yet we already know that organisations can benefit from applying our techniques: completed research by early research students1 shows rebalancing the risk/resource trade-off of validation is worthwhile in producing more effective processes. Independent research in progress may confirm their conclusions.
How might an organisation benefit from Problem Orientation, how does it bring our principles into the workplace when – under traditional research models – problem orientation might be 10-15 years away from academic maturity as a completely explored theory.
The essential problem is how can POE deliver value now, at low cost, and to all who can benefit from it whilst still growing as a research vehicle?
The solution is most easily understandable if we re-express this question as tangled problems:
POE needs validation of value delivered in organisations, but does not have resources to do large scale validation studies;
organisations urgently need process understanding, but cannot afford expensive thinking brought by consultants;
the best employees properly focussed, can bring process understanding.
The solution we have adopted as our research model is to recruit self- or organisation-funded research students
from organisations that need improved process understanding.
Exploring Quine’s Island
A group of consultant professionals wish to commit their time, skill and the richness of their context to:
gain a research degree with the Open University;
deepen their knowledge of problem solving in their particular area of expertise;
industrially validate the theoretical underpinnings of POE.
On Quine’s Island, these explorers land from their rich sea of experience, bringing new process problems and thinking models, to follow already worn paths, and to see them differently, and to forge new paths to new locations on the island. They come with the potential to build new structures and to change the landscape for good. Stepping from the boat onto the shore is all that that is needed to begin, and Lucia and Jon are there to welcome them ashore.
POE applies to problems of a particular form, including those for which a solution must be designed. POE for processes is a theory upon which processes leading to deigns can be built. It has three simple principles:
it is less risky but more resource intensive to validate a problem than to move to a solution without validation;
it is less risky but more resource intensive to validate a (partial or candidate) solution before moving on;
each stake-holder/organisation has a particular appetite for risk and resource profile.
These three principles combine with others into a theoretical framework underpinning POE for processes.
Organisations face process problems at strategic, tactical and operational levels in many areas, including Governance and IT Governance, Enterprise Architectures, In- and outsourcing, etc. Many professionals see processes performing sub-optimally, but do not have a toolkit for refactoring them. POE for processes provides a toolkit.
The areas of need are expansive, and – witness the number of frameworks that purport to contribute solutions to organisational problems – difficult for any one researcher to approach, or indeed any groups of researchers that do not embark on the project at the same time. Currently, funding in short in the UK, and it is no longer feasible for many creative researchers to build something from scratch, the trend has been too long to contribute to those whose ideas are 20 years old.
POE Island is large and largely unknown, it’s shore is long. Initial exploration shows POE island to be extremely rich in natural resources. Exploration is an exciting thing open to all that will venture to it...
Kenneth and Will Hopper’s book “The Puritan Gift: Triumph, Collapse and Revival of an American Dream” is an entertaining and thought provoking read that places the ills of global financial chaos squarely at the door of ‘(so-called) Experts’, modern followers of Frederick Winslow Taylor (1856–1915). Taylor was the world’s first management consultant.
As inventor of Scientific Management, to Taylor an organisation was a collection of numbers with its management reducing to number crunching; to the neo-Taylorist ‘(so-called) Expert’ management is a standalone profession separate from its domain of application. The Hoppers level the unflattering, but not unjustified, criticism of the ‘(so-called) Expert’ as living in a world of ‘semi-fantasy, making life-and-death decisions about organisations that [are seen] only through a statistical glass darkly.’ In contrast, the Hoppers’ eponymous Puritan is the ‘generalist manager,’ endowed with good interpersonal skills and domain knowledge gained by spending a lifetime in an organisation or industry, or by working closely with people who have done so.
The Hoppers trace the generalist manager from the European Puritan forebears that founded modern America, and American growth and prosperity. The Hoppers, and other modern critics, ascribe the rise of the Taylorist to Business Schools, their ‘Temples of the Cult of the (so-called) Expert’. Their critique is that MBAs place insufficient emphasis on understanding the domain of application in the development of management skills, and on ethics and on other social considerations, so as to be detached from reality. Although not be a criticism that can be levelled at all business schools (for instance, Sloan’s students have, on average, 5 years of full-time management-relevant experience before joining their MBA), it’s true that many, if not most, new MBA graduates will enter their new management career with little more to trust than their studies.
However, the god was false. Nassim Nicholas Taleb in “The Black Swan” is as complimentary about experts as are the Hoppers, saying that ‘certain professionals, while believing they are experts, are in fact not. [...They’re only] much better [...] at smoking you with complicated mathematical models. Now, whereas managers have always had sophisticated thought tools to help them manage, Taleb’s mathematical complexity is possible only when backed by computation. Taleb lays much of the vulnerability of banks and trading firms at the door of their defective models, models made possible by Computing and accepted in haste as genuine.
The most cursory of scans reveals how computation-dependent is business and finance, all the way from the lowly desktop PC that pushes emails around an organisation to the complex derivatives that have leveraged a shrinking real-asset basis to generate great wealth for the few. The true impact of that dependence is revealed only with the recent crash: IT has been and is at the centre of the radical transformation of the organisation into one with global impact. The problems of scaling the organisation—slow communication and poor distributed information management–have been overcome by IT and the Internet leading to removal of all terrestrial limits to growth. Banks, for instance, have grown to be global organisations mixing dour retail business with risky pleasure in the world’s financial markets, with no safety net to protect the exposed governments and tax payers below. With IT and the Internet, banks have become ‘too big to fail.’
By removing the limits of growth, IT is implicated in the moral hazard of being too big to fail. The organisational pull for IT outdid IT leadership’s ability to say ‘No!’. It can, therefore, be argued that the failure of IT leaders to stand up and take responsibility for guiding their organisation’s use of information has been a key factor in recent high-profile failures; by permitting the creation and use of false metrics and measures they have allowed and enabled other leaders in the business to, often wilfully, delude themselves by creating their own idea of truth and consequently scientifically mismanaging their businesses into catastrophe.
After the crash, what can be learned? Today’s three key enablers of the organisation are finance, talent and information. Should IT aspire to the established position of CIO (Chief Information Officer) within business as a board level appointment, sitting alongside the HR Director and CFO (Chief Financial Officer)? To do so, rather than just a compliant ‘yes’, we must expect from our CIO the same ‘No!’ as that the CFO gives when they see bad finance.
That’s a great deal of responsibility and, for our aspiration to become reality, we must understand the metaphorical DNA of the few CIOs that currently occupy that responsible position. This CIO DNA includes their background, their behaviours and their actions in support of their organisation, but it must also include their understanding of their environment— that which determines their fitness-for-purpose—and so, to be IT leaders, we really need to understand the business effects of the use of information and of information systems.
Science, Technology and Engineering in Computing: thoughts on nomenclature and scope
As everyone now agrees that the teaching of computing in schools is necessary, it’s time to move on to discussing what should be taught as part of it. Everyone has different views.
Firstly, define your terms
The definitions follow and adapt those of GFC Rogers ‘The Nature of Engineering: A Philosophy of Technology’, Macmillan, 1983. Sadly, out of print.
Engineering refers to the practice of organising the design and construction of any artifice that transforms the physical world around us to meet some identified need.
An Engineer is a professional with theoretical knowledge and practical experience sufficient to take responsibility for technical projects and to be a driving force for technical innovation. As a professional, an engineer sustains themselves through their practice, and so will have knowledge of technical matters together with adequate knowledge of business, economics, accountancy and law, as well as some organisational ability. The technical matters of which an engineer should know about form the technology of his sphere of activity.
Technology refers to an area of study and practice concerned with augmenting embodied capability through the application of existing theory and the development of practical knowledge not yet susceptible to theoretical analysis.
A Technologist is a professional with theoretical and practical knowledge sufficient to solve problems within their technology. As a professional, a technologist sustains themselves through technical projects and technical innovation, and through contributions to practice.
Science refers to an area of study concerned with the understanding of the world around us.
A Scientist is a professional with theoretical knowledge and practical methodology sufficient to understand and describe the world they see, driven by their curiosity. As a professional, a scientist sustains themselves through contributions to knowledge.
Of course, in practice, scientists sometimes act as technologists who sometimes act as engineers who sometimes act as scientists, and vice versa.
Scientists, Technologists, Engineers in Computing
The Computer Scientist has a remit to look into everything computational. Their investigations can wander from topic to topic, from detail to detail with guide the unguessed, unexpected thrown-up question that catch a scientist’s fancy.
The Computer Technologist or Information Technologist, on the other hand, is guided not by the heat of the chase but by the needs of others whose capabilities their technologies augment. Those needs change as the social and economic situation changes, as the technologies produced will come into and go out of fashion, and as neighbouring enabling technologies are developed and perfected.
Computer Engineers and Software Engineers organise the design and construction of Computing and Software Solutions which transform the world around us to meet an identified need.
Engineering is, however, a discipline of creation through combination: an identified need will not be met until the kith and kin technologies come together in the right combination, adapted and combined based on the understanding and know-how provided science and technology.
Because of this, Computer and Information Engineering as engineering disciplines cannot be circumscribed in the way that Computer Science and Computer and Information Technology are: because they will be involved in combining, Computer and Information Engineers should have an adequate working knowledge of neighbouring technologies that combine.
Of course, Digital Literacy is an important topic that’s taught well in the UK and elsewhere. But the other half of the sky – the means of which Computing is an end; the important problems it solves and the nature of its solutions; its science, its technology, its engineering and its real-world relevance – is still needed to inspire a new generation of thinkers and doers. For the future state and sustainability of our discipline, a non-teleological view of Computing completes the whole picture and so is of fundamental importance. A non-teleological view is difficult to reach without a detailed understanding of what the subject is; we posit that it comes from and understanding of, what we will term, the core generative problems that have driven Computing as a discipline.
Thus, establishing the core generative problems for Computing is critical to getting our early teaching right as, without them, a student has no chance of understanding the real-world relevance of what they are taught, nor — more importantly for the sustainability of our discipline – any chance of dreams of problem solving by which to gauge whether their interest lies therein. From a pedagogical perspective, without the understanding derived from them, we drift into what Howard Gardner, author of The Unschooled Mind, calls the ‘correct-answer compromise.’ This is, to paraphrase, an unwritten contract between student and teacher:
The student’s part: my school life removes me from the reality in which I live and as I cannot understand the relevance of much of what you’re teaching me; I will need other criteria for success. Therefore, I’ll say I’m suceeding if, no matter which test you, as a teacher, give me I can choose the right answer.
The teacher’s part: I do not know the full context of Computing, and therefore cannot teach you the problem-solving and critical-thinking skills that define Computing and have changed the world. However, you’re going to need to be assessed on your learning: I’ll teach you the correct answers as determined by those that set the exams so you can pass whatever tests they throw at you, and so move on to where you’ll learn what Computing is really about.
We intend no critique of the student or the teacher: the teacher unversed in Computing’s core generative problems can do little better than take a teleological approach; the student uninspired by dreams of the problems they can solve with Computing’s knowledge will have no entrance to their detailed study. Neither is culpable. Add to this the darker forces that make Gardner’s correct-answer compromise the most practical path: a (UK) system weighted towards the recording of exam performance with league tables based thereon determining how desirable a school is in a market of supply (schools) and demand (parents, not students).
For some subjects, French, for instance, one can argue that the core generative problem is to speak and read French so to be able to access French culture. Others are less apparent: for Sport, it might be how to develop the individual and team to work together towards a goal (or a try, a run or a basket!) in an aggressive environment. Religious Studies – when more than religion – reveal how different cultures think and act. Other still are well-known and serve teaching well: Chemistry is concerned with ‘atoms and their interactions with other atoms, and particularly with the properties of chemical bonds’, the core solution being to allow the controlled investigation of their properties that is the scientific method. Similarly for Physics, which produces predictive theories for matter and energy. Mathematics is about analysis and abstract thinking; many of its core generative problems take the form of puzzles: the prime numbers, for instance, or Fermat’s Last Theorem. Of course, as with all these examples, the subject is much more than its response to its core generative problems, but they are a defining characteristic; they provide the motivation for the subject, show how it has evolved, and determine its real-world value and relevance.
Computing is a young discipline. So young, in fact, that we are still exploring its core generative problems, let alone determining their solutions. That means two things: we must be careful to understand that no single author (not least this one) has a complete, correct view of what they are and there is a critical need for a debate over their nature.
With this in mind, there are three core generative problems I’d like to focus on:
External Agency Control Problem: This problem is to have a device that works to perform repeatedly and reliably the same operations over and over again, where the goal of those operations is determined by some external agent. This is the problem that Charles Babbage overcame in his computations for the admiralty; it was also the problem that Tommy Flowers encountered in building Colossus, the first programmable electronic computer, to solve encrypted German messages at Bletchley Park. Amongst Babbage’s contributions were physical architectures by which cards can control cogs; Flowers contributed electronic architectures by which electrons can be cajoled into following certain paths under the control of programs. Flowers work at Bletchley was, of course, coincident with Turing’s, who had already provided the new thought tools needed to allow us to think about the core generative problem of agency-influence.
Dealing with Complexity: The second core problem is how to grow a few lines of code to many lines — many millions of lines — so that useful work within a complex environment of a Computing device can be achieved. This motivated Royce’s contributions of process architectures by which many people could work together collaboratively on the most complex artefacts ever created. The thought tools were provided by others: software architectures, software patterns and other structuring mechanisms were one response to external complexity, another was databases. Complexity also motivates the characteristics that the software must have to justify its inclusion in those environments, characteristics such as reliability, robustness, usability, etc., and so the sub-disciplines of formal methods, testing, theories of concurrency and the like.
Dealing with Volatility: The last in this short list is the problem of building and working with delivered complex computational products in an environment that is highly volatile, i.e., in the face of stake-holders that change their minds as to their requirements or change their context and do so without warning. This motivates ever more complex and adaptive process and software architectures, which are still being determined and investigated. Volatility motivates process agility; it also motivates many of the subjects that concern us in this journal: neural networks, autonomous systems, ontologies and the like.
The greatest Computing minds have worked to solve these problems; perhaps still greater minds are yet to come. In Turing’s year, and in his honour, we should debate the nature of Computing’s core generative problems, some of which he defined and provided the first solutions. Through this debate, we will add to Turing’s legacy our deep thought in Computing education so that those that will follow him are inspired to reach their potential.
