My student, Emmanuel Kahembwe, is part of Team Edina – consisting of students and postdoctoral researchers from the School of Informatics at the University of Edinburgh – who are one of 12 teams competing for The Alexa Prize. The grand challenge is to build a socialbot that can converse coherently and engagingly with humans on popular topics for 20 minutes.
Let us wish them all the best and I am very curious to see what comes out of this competition!
It is often the case that one of the crucial factors underpinning wide scale adoption of any computational procedure is the availability of easy to use packages containing that procedure, usable by people who have only a passing familiarity with the innards.
Topological data analysis is one such toolbox, the inner workings of which have often seemed too hard to penetrate for anyone but the most mathematically well trained scientists (a very small population, indeed). Yet, the tool has many appealing characteristics, given how centrally relevant its core questions are to hierarchical representations, multi-scale modelling and so on.
In this context, it is nice to see this web based interface for persistent homology calculations, a key tool in the TDA toolbox:
I came across the following wonderful gems of tech history quotes in one of my recent readings:
The relational model is a particular suitable structure for the truly casual user (i.e. a non-technical person who merely wishes to interrogate the database, for example a housewife who wants to make enquiries about this week’s best buys at the supermarket).
In the not too distant future the majority of computer users will probably be at this level.
(Date and Codd 1975; 95)
Casual users, especially if they were managers might want to ask a database questions that had never been asked before and had not been foreseen by any programmer.
As I was reading these, it occurred to me that many in my own area of robotics also often think in the same way. What would the proper abstractions look like in emerging areas such as robotics, mirroring the advances that have happened in the software space (e.g., contrast the above vision vs. Apple’s offerings today)? Our typical iPad-toting end user is going to speak neither SQL nor ROS, yet the goal of robotics is to let them flexibly and naturally program the machine – how can that actually be achieved?
- C.J. Date, E.F. Codd, The relational and network approaches: Comparison of the application programming interfaces, Proc. SIGFIDET (now SIGMOD) 1974.
- D Gugerli, Die Welt als Datenbank. Zur Relation von Softwareentwicklung, Abfragetechnik und Deutungsautonomie, Daten, Zurich, Berlin: Diaphanes, 2007.
This is the primary question shaping much of the public debate and discourse around the development of autonomous systems technologies, robots being the most visible and eye catching example – occasionally quite scary when you see them being used, e.g., as carriers of weapons and bombs. As a roboticist who is often deeply ensconced in the technical side of developing capabilities, I find most public articles in the popular media to be ill-informed and hyperbolic. So, I was pleasantly surprised to read this McKinsey report. This is not quite popular media, but in the past I have found even some of these consulting company reports to be formulaic. The key point being made by the authors is that just because something can be automated does not mean it should, or that it will. In reality, a variety of different factors, including economic and social will shape the path of these technologies – something all of us should pragmatically consider.
This key point is summarised in the following excerpt:
Technical feasibility is a necessary precondition for automation, but not a complete predictor that an activity will be automated. A second factor to consider is the cost of developing and deploying both the hardware and the software for automation. The cost of labor and related supply-and-demand dynamics represent a third factor: if workers are in abundant supply and significantly less expensive than automation, this could be a decisive argument against it. A fourth factor to consider is the benefits beyond labor substitution, including higher levels of output, better quality, and fewer errors. These are often larger than those of reducing labor costs. Regulatory and social-acceptance issues, such as the degree to which machines are acceptable in any particular setting, must also be weighed. A robot may, in theory, be able to replace some of the functions of a nurse, for example. But for now, the prospect that this might actually happen in a highly visible way could prove unpalatable for many patients, who expect human contact. The potential for automation to take hold in a sector or occupation reflects a subtle interplay between these factors and the trade-offs among them.
If you are in the UK, you know about the intense ongoing debate regarding the referendum on whether to leave the EU. Scientists for EU has organised a letter that represents the view of many in the UK scientific community, which you might consider adding your support to: http://scientistsforeu.uk/sign-save-science/.
The core argument being put forth is as follows:
Scientific advance and innovation are critically dependent on collaboration. To remain a world-leading science nation, we must be team players.
The EU leads the world in science output, is beating the US in science growth – and is rapidly increasing investment in research. The EU is a science superpower. Our place in this team has boosted our science networking, access to talent, shared infrastructure and UK science policy impact. The economy of scale streamlines bureaucracy and brings huge added value for all. International collaborations have 40% more impact than domestic-only research.
Strong science is key for our economy and quality of life. It creates a virtuous cycle, leveraging investment from industry, raising productivity and creating high-value jobs for our future. In fact, 20% of UK jobs currently rely on some science knowledge. Science brings better medicines, cleaner energy, public health protections, a safer environment, new technologies and solutions to global challenges.
If we leave the EU, the UK will lose its driving seat in this world-leading team. Free-flow of talent and easy collaboration would likely be replaced by uncertainty, capital flight, market barriers and costly domestic red-tape. This would stifle our science, innovation and jobs.
It is no surprise that a recent survey showed 93% of research scientists and engineers saying the EU is a “major benefit” to UK research. The surprise is that many voters are still unaware that UK science and its benefits would be demoted by a vote to leave.
We, the undersigned, urge you to seriously consider the implications for UK science when you vote in the referendum on UK membership of the EU.
Through my colleague, Prof. Andrew Ranicki, I came upon this interesting interview with another distinguished colleague, Sir Michael Atiyah: https://www.quantamagazine.org/20160303-michael-atiyahs-mathematical-dreams/. The interview contains interesting reflection upon many things including the notion of beauty in mathematics. Indeed, Atiyah has co-authored a paper based on a very interesting neuroscience study on neural correlates of beauty: http://journal.frontiersin.org/article/10.3389/fnhum.2014.00068.
The key conclusion of this paper is that,
… the experience of mathematical beauty correlates parametrically with activity in the same part of the emotional brain, namely field A1 of the medial orbito-frontal cortex (mOFC), as the experience of beauty derived from other sources.
This in itself is cool, but it made me wonder – if we have a parametric signal in our brains, after a certain level of expertise has already been acquired, which correlates with how ‘beautiful’ something is, then is this useful as a heuristic in the search for new such objects? One of the limitations of most computational intelligent systems is precisely that they perform so poorly when it comes to exploratory search in open ended domains. Does this signal then help us prune search in our heads, a bit along the lines of José Raúl Capablanca’s famous, “I see only one move ahead, but it is always the correct one”?
Our paper proposes a new way to account for “passivity” structure in Dynamic Bayesian Networks, which enables more efficient belief computations and through that improvements for systems modelled by POMDPs and so on. It was surprising to me when we started this project that despite significant earlier attention to exploiting conditional independence structure, there had not been work on these notions of using constraints (often imposed by physics of other background regularities) in making belief updates more efficient.
Please read the paper in JAIR: http://dx.doi.org/10.1613/jair.5044, abstract reproduced below:
Stefano V. Albrecht and Subramanian Ramamoorthy (2016) “Exploiting Causality for Selective Belief Filtering in Dynamic Bayesian Networks”, Volume 55, pages 1135-1178.
Dynamic Bayesian networks (DBNs) are a general model for stochastic processes with partially observed states. Belief filtering in DBNs is the task of inferring the belief state (i.e. the probability distribution over process states) based on incomplete and noisy observations. This can be a hard problem in complex processes with large state spaces. In this article, we explore the idea of accelerating the filtering task by automatically exploiting causality in the process. We consider a specific type of causal relation, called passivity, which pertains to how state variables cause changes in other variables. We present the Passivity-based Selective Belief Filtering (PSBF) method, which maintains a factored belief representation and exploits passivity to perform selective updates over the belief factors. PSBF produces exact belief states under certain assumptions and approximate belief states otherwise, where the approximation error is bounded by the degree of uncertainty in the process. We show empirically, in synthetic processes with varying sizes and degrees of passivity, that PSBF is faster than several alternative methods while achieving competitive accuracy. Furthermore, we demonstrate how passivity occurs naturally in a complex system such as a multi-robot warehouse, and how PSBF can exploit this to accelerate the filtering task.