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Not smart enough: The poverty of European military thinking on artificial intelligence

(AI) has become one of the buzzwords of the decade, as a potentially important part of the answer to humanity’s biggest challenges in everything from addressing climate change to fighting cancer and even halting the ageing process.

At the same time, some warnings that AI could lead to widespread unemployment, rising inequality, the development of surveillance dystopias, or even the end of humanity are worryingly convincing.

For Europe, 2019 was the year of AI strategy development, as a growing number of EU member states put together expert groups, organised public debates, and published strategies designed to grapple with the possible implications of AI.

Next year is likely to be an important one for AI in Europe, as member states and the European Union will need to show that they can fulfil their promises by translating ideas into effective policies.

But, while Europeans are doing a lot of work on the economic and societal consequences of the growing use of AI in various areas of life, they generally pay too little attention to one aspect of the issue: the use of AI in the military realm.

Strikingly, the military implications of AI are absent from many European AI strategies, as governments and officials appear uncomfortable discussing the subject (with the exception of the debate on limiting “killer robots”).

This double neglect means that there is comparatively little information available about European thinking on AI in the military or on how European armed forces plan to use AI –

This first section covers the various ways in which AI can support military systems and operations, providing an overview of possible AI applications, their advantages, and the risks they create.

The goal of the paper is to contribute to the emerging European debate on military AI, and to shine a light on current agreements and disagreements between important European players.

As efforts to strengthen European defence, and to develop “European technological sovereignty”, have become a main focus of the EU and a primary goal of the new European Commission, this topic will be a subject of debate for years to come.

with authors such as Frank Hoffmann claiming that AI may “alter the immutable nature of war”, and Kenneth Payne positing that AI changes “the psychological essence of strategic affairs”

Yet no one appears to believe that AI will have no impact on military affairs, even if authors such as Andrea Gilli point to mitigating factors and historical precedents, thereby relativising the extent of the expected change.

These systems can carry out the critical functions of a targeting cycle in a military operation, including the selection and engagement of targets, without human intervention.

While this engagement is laudable and the international discussions are important, they have contributed to focusing the public debate almost exclusively on this specific type of use of AI in the military realm.

In a military context, AI can, for example, help sift through the mountains of data collected by various sensors, such as the hundreds of thousands of hours of video footage collected by US drones.

It can examine photographs to single out changes from one picture to the next, indicating the presence of an explosive device hidden in the time between the photos were taken.

Other AI applications in this context include image and face recognition, speech recognition and translation, the geolocation of images, and pattern-of-life analysis.

This involves monitoring the functions of a system, such as an aircraft, and predicting when parts of it will need to be replaced based on various sensory inputs and data analysis.

Many experts believe that some of the most important AI-enabled changes in warfare will occur in the cyber realm, due to its relative lack of physical limitations.

Artificial intelligence is widely expected to make inroads into offensive and defensive cyber operations, as it will likely allow actors to both find and patch up cyber vulnerabilities at greater speed.

But while such efficiency gains are important, especially for cash-strapped militaries, technologies can only be truly ground-breaking if they provide new capabilities or allow for tactics that go beyond what exists already.

describes a system’s freedom to accomplish its goals.As the United Nations Institute for Disarmament Research argues, “the rapidly advancing field of AI and machine learning has significant implications for the role of autonomy in weapon systems.

Militaries are exploring giving systems increased autonomy because machines are faster than humans in analysing, and taking decisions based on, data.

Unlike their remote-controlled counterparts, autonomous systems can function without communications uplinks or downlinks to an operator, making them harder for enemy defences to detect.

Experts have argued that, in the military realm, swarms would be ideal for “overwhelming a nonlinear battlespace, ‘creating a focused, relentless, and scaled attack’

This means that swarms provide genuinely new capabilities: as political scientist Paul Scharre points out, “the result will be a paradigm shift in warfare where mass once again becomes a decisive factor on the battlefield”.

But these also apply to military AI applications more broadly, and are wide-ranging, often beginning with the ethical and legal dimensions of using autonomous systems in warfare.

Ethicists argue that, as machines are unable to appreciate the value of human life and the significance of its loss, allowing such systems to kill would violate the principles of humanity.

Political scientist Frank Sauer believes that “the society that allows such a practice and no longer troubles its collective human conscience with war-time killing risks nothing less than giving up the most basic values of civilisation and fundamental principles of humanity”.

There is an intense legal debate about whether LAWS could follow the laws of war, through, for instance, algorithms sufficiently adept at distinguishing between civilians and combatants (discrimination), accurately judging the proportionality of means and ends, and weighing the military necessity of the use of force.

For instance, when civil rights organisations tested facial recognition systems used by police forces, they identified a disproportionately high number of innocent people with darker skin as criminals.

Thus, there is a danger that such systems could lead to unchecked escalation: once one country starts to use them, others might feel they have to follow suit, leading to an armament spiral.

Indeed, the country has made AI a top-level priority, with President Emmanuel Macron discussing the topic at length in a widely read Wired interview (similar to the magazine’s 2016 interview on AI with Barack Obama, then US president).

Throughout the year, the government, ministries, and other private and public actors held public conferences, online consultations, and expert hearings on AI.

the German strategy is the product of a ministry-wide consultation under the leadership of the ministries of education and research, economy and energy, and labour and social affairs.

The UK does not have a single designated AI strategy but rather several documents that can, taken together, be analysed as such, even though, as will be shown below, they do not form a coherent whole.

will be lost (a belief that is particularly prevalent among 16-24-year-olds), while 74 percent worry that “when machines decide, the human element will be lost”.

The UK’s mission is “to make the UK a world leader in the use of data, AI and innovation to transform the prevention, early diagnosis and treatment of chronic diseases by 2030”.

the five key questions it asks concern the effects of AI on people’s everyday lives, the potential opportunities and risks of AI for the UK, and ethical AI issues.

The EU now aims to strengthen European defence capabilities through a variety of tools, and as such has earmarked up to 8 percent of the European Defence Fund’s 2021-2027 budget for disruptive defence technologies and high-risk innovation respectively.

The 34-page document outlines France’s approach to AI in the military, provides examples of AI-enabled military applications, and announces the creation of several bodies that will help the French military adopt AI.

In many regards, the military AI strategy follows the ideas of France’s national AI strategy, sharing its focus on data and talent, and adopting a similar geopolitical approach.

The document repeatedly expresses concern about dependence on other countries (particularly private companies from other states) and adopts “preserving a heart of sovereignty”

The strategy states: “with regard to new threat scenarios for internal and external security, in addition to research on civil security, the Federal Government [will] promote research to detect manipulated or automatically generated content in the context of cyber security.

The research on AI applications, in particular for the protection of external security and for military purposes, will be carried out within the scope of the departmental responsibilities.”

One could argue that the absence of defence elements from the strategy is due to the fact that, while the MoD and the Foreign Ministry were consulted in the process of writing it, they did not have a leading role in formulating the document.

A report for NATO’s parliamentary assembly argues that, given AI’s potential value to the armed forces, NATO’s leaders in science and technology –

But the report singles out Germany as lagging in this area, commenting: “it is encouraging to see that all of them are indeed investing substantial resources into defence-related AI, with the possible exception of Germany.”

Hence, in this position paper, the army acknowledges the existence and importance of AI in systems other than weapons (even though the fictional scenario that features at the beginning of the paper focuses exclusively on autonomous drones and AI-enabled weapons).

The House of Lords committee report argued that “perhaps the most emotive and high-stakes area of AI development today is its use for military purposes”, but conceded that it did not explore “this area with the thoroughness and depth that only a full inquiry into the subject could provide”.

In its response to the report, the government rejected outright the recommendation to realign the UK’s definition of autonomous weapons with that used by the rest of the world.

The report assesses that “a failure to understand AI capabilities may create vulnerabilities and cede advantages to competitors”, and that “conflicts fought increasingly by robots or autonomous systems could change the very nature of warfare”.

Transforming Defence”, published in 2018, underlines the fact that the MoD is pursuing modernisation “in areas like artificial intelligence, machine-learning, man-machine teaming and automation to deliver the disruptive effects we need in this regard”.

Activities covered by thisprogramme include algorithm development, AI, machine learning, “developingunderpinning technologies to enable next generation autonomous military-systems”, and the optimisation of human-autonomous systems teaming.

The European Commission should draft a coordinating strategy for military AI, outlining its ideas for areas of development in which common European engagement would be particularly useful (such as sharing systems to train algorithms), while setting red lines (in areas such as the development and use of LAWS).

Fujitsu Wins First Prize for Predictive Maintenance in Airbus AI Challenge

Top ranking in the Airbus AI Gym1 challenge for accurate sensor monitoring went to Fujitsu for developing a way of using unsupervised AI to detect anomalies in accelerometer data from Airbus pre-certification helicopters, ahead of 140 other teams participating in this helicopter challenge.

To enhance detection of early-warning signals in this vast amount of data Airbus established its AI Gym challenge, fostering research into a new way of accurately locating potential issues, especially data outliers.

The solution took data sequences from multiple sensors and analyzed them across a fixed time period, detecting abnormal sensor behavior using a deep learning algorithm based on Multivariate Anomaly Detection with Generative Adversarial Networks3 [MAD-GAN].

New functions include a semi-supervised mechanism to classify the type of sensor anomaly, addressing the imperative for engineers and maintenance services to find the root cause of anomalies, and to interpret multi-variate data and correlations between all test flights in a program.

Why the IoT Should Be All About the ROI

A rapidly growing number of businesses are adopting Artificial Intelligence (AI) to reduce their operational cost, improve customer experience, and/or generate new sources of revenue.

For example, in financial services, more sophisticated risk analysis, anti-money laundering, advanced claims management, credit-worthiness evaluation, and intelligent customer onboarding have become prime focus areas.

The ability to democratize AI transformation across the enterprise by adopting tools and capabilities to enable business users to quickly test algorithms also will be crucial to gaining traction.

The right foundation for AI Aside from identifying the right business use-case to leverage AI, increasingly companies are faced with “having the right technical infrastructure to support modern AI applications.” The integration of traditional software and data environments with modern ML and deep-learning applications is proving to be a formidable challenge.

In the same way that a building architecture blueprint specifies how electrical, plumbing, telecommunications, passages, staircases, and other utility and structural elements are to be built, a layered technical architecture provides the foundation that defines how data can be ingested and leveraged across traditional and AI-based solutions.

The services within this architecture layer automatically extract, unify, and organize information, leveraging semantic technologies that enable ingestion of this data into the knowledge fabric.

With a proper knowledge infrastructure, you can seamlessly combine highly scalable graph database technologies with complementary storage and search systems to deliver actionable insights.

This layer is where AI models and algorithms can be embedded into the very core of the architecture to create valuable insights with the potential to augment human thinking across disciplines and innovate operations, processes, products, and more.

The Human & Machine consumption layer provides easy-to-use interfaces across the web, mobile, and API services to enable access data to the knowledge fabric layer.

The data pipeline may include data harvesting in batch or real-time (streaming), training and evaluation tasks, monitoring model performances, applying AI models in batch and feeding the result back to the knowledge fabric.

The power of AI systems to work on complex problem solving on a 24-by-7 basis means that the enterprise technical architecture must deliver a continuous flow of data upon which “smart” decisions can be made.

Businesses that will successfully leverage the capabilities of AI, aside from ensuring the right focus around specific use-cases that can show positive results also must spend time defining the underlying technical architecture to enable these new generation of solutions.

How Artificial Intelligence and Machine Learning Assist Retailers and Consumers

Implementing AI solutions in brick-and-mortar retail is naturally more challenging than online retail, yet taking a step back, both are still surprisingly only in early stages.

With Enterprise AI, machine learning (ML) Leading analysts predict that smart personalization engines used to identify customer intent give businesses the ability to increase profits by 15 percent by 2020.

Browser history• Page clicks• Social interactions (likes, shares, etc.)• Previous purchases• How long a web page was viewed• Location• …and so much more One of the most high-impact areas of AI in retail is in price, promotion, and markdown optimization.

Price optimization involves, on the one hand, tailoring prices to customers in a way that they view them as attractive, fair, and non-arbitrary for the products they care most about, and on the other, predicting when it is (or isn’t) necessary to offer discounts.

Research from Forrester shows that using AI and ML for price optimization is a win-win for both retailers — due to its proven impact on bottom lines — and for customers, who primarily view it as a positive and fair practice as long as the prices presented are within their budget.

The more accurately retailers can forecast demand, manage inventory, and manage relationships with their suppliers, the more quickly they can prevent waste, cut costs, and more effectively invest their capital in profit-generating activities.

Inventory management is particularly salient in the grocery sector, where the ability to forecast demand reduces food waste and ensures that there is adequate supply of the items that customers want at a given time.

Predictive models, based on years of data and a variety of different data sources, offer retailers the chance to do this with exponentially greater precision, detecting nuances in consumer behavior that would escape the notice of even the most perceptive store manager.

Whether or not the store is busy, being able to free employees of the obligation of continually checking inventory allows the store to more effectively deploy its workforce to focus on other duties, such as customer service.

similar study by Zebra the following year found that 52 percent of retailers were already delivering their frontline employees data from IoT devices in real-time to enhance customer service.

While geofence technology has enabled retailers to detect the presence of customers in their stores, connected devices offer even greater precision, so that retailers can ping customers with promotions specific to the section of the store they’re in or the particular product at which they are looking.

Today, most products that build models incorporate some capabilities to boost the productivity of data scientists, including feature engineering, optimization of model parameters, or model selection and blending.AutoML has enormous implications for how data teams might end up working in the long term.

Data science projects would be led by data scientists, who can then leverage data analysts that have been around and know the ins and outs of the data (or even of the underlying models) to guide the feature engineering process.

AI can learn to collude and discriminate reasonably easily, and without someone monitoring the conclusions, any retailer that relies on an algorithm that has been taught the wrong information could find themselves in deep water with regulators pretty quickly.

Those that don’t adapt will find themselves slowly falling behind, losing business to rivals who favor newer technology and struggling to stay relevant in this rapidly changing consumer insight-driven industry.

Studies identified various organizational roadblocks causing retail AI to lag behind, but also unprecedented opportunities for companies in this sector to reap actionable business value from upscaling their AI and machine learning initiatives.

Data science, machine learning, and AI platforms are a clear win for retail and CPG: they can provide a platform for organizations to optimize four key pillars crucial to delivering a next-level retail experience — the ability to understand their customers, empower employees, deliver an intelligent supply chain and create a new model of retail practice that enhances the promotion and production of the products customers desire most, By choosing the right platform, retailers and brands can transform their business model and embark on the path to Enterprise AI to understand their customers and their businesses better, to deliver unique, differentiated, one-on-one experiences.

Airbus to Increase U.S. Jet Production

Airbus reportedly will increase the production rate for A320 aircraft from five to seven units per month at the Airbus U.S. Manufacturing Facility in Mobile, Ala., by the beginning of 2021.

The expansion will validate the federal government's decision to exempt components supplied to the Mobile plant from the list of Airbus products to be fixed with tariffs in the pending series of penalties that the U.S. has scheduled to implement following the World Trade Organization's ruling against the jet-builder in a long-standing dispute over E.U.

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