Like most contemporary armies – the British Army must grapple with the challenges and opportunities posed by the application of machine learning. Namely, how to ensure that the Army remains an effective and relevant tool of UK foreign policy for the foreseeable future. With respect to machine learning – the primary (publicly disclosed) opportunities relate to data analytics. The British Defense establishment collects far more data than it can process effectively and any improvements in this capability will in turn facilitate the Army’s ability to “Protect the UK, prevent conflict, deal with disaster and fight the Nation’s enemies”.
This challenge is particularly acute in the United Kingdom (vs. the USA), where as an organization it must ‘do less with more’ in order to ensure Britain can maintain any semblance of a claim to ‘Great Power’ status. Beyond absolute effectiveness, the British army must eke out operational efficiencies wherever humanly possible in order to maintain relevance on the global stage. Operating with roughly 1/11th the budget, it simply cannot tolerate waste that might be acceptable in militaries of geo-political competitors. This is magnified by Britain’s historic and geographic pre-disposition to expeditionary warfare. Conflict in the British Isles is unlikely and so to be an effective instrument of foreign policy the organization must be capable of moving/sustaining resources across vast distances. They must do so without the vast resources afforded to the American armed forces or the ‘home-field advantage’ enjoyed by Eurasian powers such as China or Russia.
Perhaps the primary issue that the British military faces in both the near and medium term is lack of access to talent. It struggles to identify and pay talent which is drawn instead to the private sector. In order to mitigate this the Ministry of Defense has undertaken a number of initiatives aimed at building out these capabilities. For instance, the Defense Science and Technology Laboratory has launched a series of prizes aimed at specific challenges posed by AI. To date, prizes have been awarded for vehicle recognition programs and information classification programs related to news media. Both aim to address the vast quantities of data possessed by the MoD and transform them into a useable format. Another example of this is J-Hub an incubator designed to finance start-ups and projects with domain expertise relevant to the army. J-Hub also provides a fast track through the MoD’s procurement process.
In the medium term – much of the UK defense establishment’s progress with respect to machine analytics will be financed by the IRIS fund (est. 2016). The IRIS fund was established and endowed with £800mm to support disruptive technologies that the MoD believes will not be adequately supported by the private sector. The fund has a 10-year lifetime and complements the MoD’s £1.5bn annual science budget. The precise portions specifically allocated to data analytics are unclear but it is reasonable to assume a substantial portion will be directed to this.
Other approaches to Machine Learning?
Historically, the MOD has had difficulty competing for top talent and is perceived by many to be overly slow and bureaucratic. To some degree this is an inevitable consequence of Government bureaucracy which cannot afford to take risky ‘big-bets’ in the way that the private sector can. For example, something similar to Google’s acquisition of Deep Mind could not occur if it was underwritten by taxpayer funds. The public would not accept such expenditures for something so abstract even if it did provide the requisite human capital. Having said this, there are probably areas where additional efficiencies can be sought. For example, it seems reasonable that J-Hub and the challenge funds established by the Defense Science and Technology Laboratory could all be rolled into the IRIS Fund. This would have the effect of ensuring common strategic goals for technological innovation as well as streamlining other administrative overhead. There seems little marginal benefit to running many of these organizations independently.
Another step that could be taken by the MoD would be to deepen defense partnerships in the context of the EU. The British Army already has links established with the anglophone nations of the ‘Five-Eyes’ but more could be done enhance research with nations such as France and Germany – providing this is strategically acceptable.
The principal issue that occurs to me in this essay is: “to what extent does it seem prudent for Governments to partner with the private sector on issues critical to national security? Are they reliable?”. It’s clearly extremely difficult for nations other than the United States to maintain the vast R&D capabilities required ‘in-house’. How should smaller nation’s tackle this issue?
Burgess, M. (2017, April 2). UK military lab launches £40,000 machine learning prize. Retrieved from Wired.co.uk: https://www.wired.co.uk/article/dstl-mod-data-science-challenge-2017
Cummings, M. L. (2017). Artificial Intelligence and the Future of Warfare. London: Chatham House.
Jones, S. (2016, August 12). MoD sets up £800m fund to encourage weapons innovation. Retrieved from Financial Times: https://www.ft.com/content/56d82af4-5fd3-11e6-b38c-7b39cbb1138a
Ministry of Defense. (2018, November 07). Army: What We Do. Retrieved from https://www.army.mod.uk/what-we-do/
Ministry of Defense. (2018, March 15). jHub: What We Do. Retrieved from https://www.gov.uk/government/news/jhub-what-we-do