Does technological change fuel political disruption? Drawing on fine-grained labor market data from Germany, this paper examines how technological change affects regional electorates. We first show that the well-known decline in manufacturing and routine jobs in regions with higher robot adoption or investment in information and communication technology (ICT) was more than compensated by parallel employment growth in the service sector and cognitive non-routine occupations. This change in the regional composition of the workforce has important political implications: Workers trained for these new sectors typically hold progressive political values and support progressive pro-system parties. Overall, this composition effect dominates the politically perilous direct effect of automation-induced substitution. As a result, technology-adopting regions are unlikely to turn into populist-authoritarian strongholds.

We call attention to an important, but overlooked finding in research reported by Longoni, Bonezzi and Morewedge (2019). Longoni et al. claim that people always prefer a human to an artificially intelligent (AI) medical provider. We show that this was only the case when the historical performance of the human and AI providers was equal.  When the AI is known to outperform the human, their data showed a clear preference for the automated provider. We provide additional statistical analyses of their data to support this claim.

The objective of this exploratory, preliminary study was to survey dairy farmers using robotic milking systems to better understand their mental health and potential connections to their cow health and welfare. Only farms using robotic milking systems in Ontario, Canada were visited for collection of data on management practices, cow welfare, and milk production and quality. Those farmers also completed an online survey that included validated psychometric scales used to assess resilience, stress, anxiety, and depression; results from 28 farms were analysed. Thirty cows per farm (or 30% for herds > 100 milking cows) were scored for body condition (five-point scale: 1 = thin to 5 = over-conditioned) and lameness (five-point scale: 1 = sound to 5 = lame); cows with a Body Condition Score ≤ 2.5 and lameness score ≥ 4 were defined as under-conditioned and severely lame, respectively. Farmer stress was positively associated with severe lameness prevalence, was greater for females vs males, and was greater for those feeding manually vs using an automated feeder. Anxiety and depression were greater for females vs males, and for those working alone, feeding manually, and with lesser milk protein percentage. Anxiety was also positively associated with the prevalence of severe lameness. Resilience was greater for those with automated feeding systems, but tended to be negatively associated with milk yield per robot and positively associated with milk somatic cell count. This is the first study to identify associations between farmer well-being and cow lameness, udder health, and milk yield. With future research, we can better understand this relationship to improve the well-being of both agricultural animals and their caretakers.

We clarify two points made in our commentary (Pezzo & Beckstead, 2020, this issue) on a recent paper by Longoni, Bonezzi, and Morewedge (2019). In both Experiments 1 and 4 from their paper, it is not possible to determine whether accuracy can compensate for algorithm aversion. Experiments 3A-C, however, do show a strong effect of accuracy such that AI that is superior to a human provider is embraced by patients. Many papers, including Longoni et al. tend to minimize the role of this compensatory process, apparently because it seems obvious to the authors (Longoni, Bonezzi, Morewedge, 2020, this issue). Such minimization, however, can lead to (mis)citations in which research that clearly demonstrates a compensatory role of AI accuracy is cited as non-compensatory.

Instrument delivery is critical part in vascular intervention surgery. Due to the soft-body structure of instruments, the relationship between manipulation commands and instrument motion is non-linear, making instrument delivery challenging and time-consuming. Reinforcement learning has the potential to learn manipulation skills and automate instrument delivery with enhanced success rates and reduced workload of physicians. However, due to the sample inefficiency when using high-dimensional images, existing reinforcement learning algorithms are limited on realistic vascular robotic systems. To alleviate this problem, this paper proposes discrete soft actor-critic with auto-encoder (DSAC-AE) that augments SAC-discrete with an auxiliary reconstruction task. The algorithm is applied with distributed sample collection and parameter update in a robot-assisted preclinical environment. Experimental results indicate that guidewire delivery can be automatically implemented after 50k sampling steps in less than 15 h, demonstrating the proposed algorithm has the great potential to learn manipulation skill for vascular robotic systems.

Workplace automation fueled by technological innovations has been generating social policy implications. Defying the prevalent argument that automation risk triggers employment insecurity and prompts individuals to favour redistribution, this study doesn’t find empirical evidence in the Chinese context. Analysing national survey data, this study reveals a very strong association between automation risk and popular preference for government responsibility in old-age support. Further analysis suggests that more generous local welfare systems generate a reinforcing effect between automation risk and individuals’ support for government involvement in old-age support. In a welfare system in which major redistributive policies are not employment-dependent, automation risk may not necessarily trigger stronger preferences for short-term immediate protection through redistributive programmes, but may stimulate individuals to project their need for social protection towards middle- or longer-term and employment-related policies. The generosity of subnational welfare systems moderates the formation of individuals’ social policy preferences through policy feedback.

Technological change has squeezed the demand for middle-skill jobs, which typically involve routine-intense tasks. This squeeze has coincided with an increase in the number of part-time working individuals who wish to work more hours. We argue that these two trends are linked. Due to the decline of middle-skill employment, medium-educated workers shift into low-skill employment, increasing the supply of labour for jobs in this segment of the labour market. This pushes those dependent on these jobs to accept part-time jobs, even if these involve fewer hours than they prefer. To empirically assess this claim, we analyse involuntary part-time employment across 16 European countries between 1999 and 2010. Our analysis confirms that a decline in middle-skill employment is associated with an increase in involuntary part-time employment at the bottom end of the labour market. This finding implies that the automation of routine-intense labour worsens employment possibilities in this segment of the labour market. However, we show that training and job creation schemes mitigate this effect. These programmes cushion competition either by providing medium-educated workers with the necessary skills to shift into high-skill jobs or by increasing employment possibilities. Thus, governments have the tools to support workers facing challenges in the knowledge economy.

We propose the techniques for automatic processing of measurement results in the context of golden (typical) device selection and noise figure measurement. These techniques are for golden (typical) device selection and noise figure measurement processing. Automation of measurement result processing and microwave element modeling speeds up a modeling routine and decreases the risk of possible errors. The techniques are validated through modeling of 0.15 μm GaAs pHEMTs with 4 × 40 μm and 4 × 75 μm total gate widths. Two test amplifiers were designed using the developed models. The amplifier modeling results agree well with measurements which confirms the validity of the proposed techniques. The proposed algorithm is potentially applicable to other circuit types (switches, digital, power amplifiers, mixers, oscillators, etc.) but may require different settings in those cases. However, in the presented work, we validated the algorithm for the linear and low-noise amplifiers only.

Artificial intelligence (AI) promises to reshape scientific inquiry and enable breakthrough discoveries in areas such as energy storage, quantum computing, and biomedicine. Scanning transmission electron microscopy (STEM), a cornerstone of the study of chemical and materials systems, stands to benefit greatly from AI-driven automation. However, present barriers to low-level instrument control, as well as generalizable and interpretable feature detection, make truly automated microscopy impractical. Here, we discuss the design of a closed-loop instrument control platform guided by emerging sparse data analytics. We hypothesize that a centralized controller, informed by machine learning combining limited a priori knowledge and task-based discrimination, could drive on-the-fly experimental decision-making. This platform may unlock practical, automated analysis of a variety of material features, enabling new high-throughput and statistical studies.

This study introduces automation into a Schumpeterian growth model to explore the effects of R&D and automation subsidies. R&D subsidy increases innovation and growth but decreases the share of automated industries and the degree of capital intensity in the aggregate production function. Automation subsidy has the opposite effects on these macroeconomic variables. Calibrating the model to US data, we find that raising R&D subsidy increases the welfare of high-skill workers but decreases the welfare of low-skill workers and capital owners, whereas increasing automation subsidy increases the welfare of high-skill workers and capital owners but decreases the welfare of low-skill workers. Therefore, whether the government should subsidize innovation or automation depends on how it evaluates the welfare gains and losses of different agents in the economy.

The rate of innovation in Information Technology (IT) has slowed down over time. The slowdown is evident both in the data on quality-adjusted prices of computers, and performance of microprocessors used in computers. The model in this paper shows that an IT–labor elasticity of substitution that is greater than 1 can explain the slowdown. With an elasticity of substitution greater than 1, however, slowing innovation can result in sustained labor productivity and output growth. Sustained growth is possible because an IT–labor elasticity of substitution greater than 1 results in a continuously increasing share of IT in production costs, which counteracts the effect of slowing innovation on labor productivity and output growth. In this environment of slowing innovation, increasing IT share and sustained growth, employment can increase or decrease, depending on the values of the IT–labor elasticity of substitution and the price elasticity of demand for IT-enabled consumption goods.

Intended to simplify the benefit system and ’make work pay’, Universal Credit (UC) is the UK’s first ‘digital by design’ benefit. Proponents of UC highlight the greater efficiency and effectiveness of digitalisation, while critics point to costly IT write-offs and the ‘digital divide’ between people with the skills and resources to access digital technologies, and those without. Less attention has been paid to automation in UC and its effects on the people subject to these rapidly developing technologies. Findings from research exploring couples’ experiences of claiming UC suggest that automated processes for assessing entitlement and calculating payment may be creating additional administrative burdens for some claimants. Rigid design parameters built into UC’s digital architecture may also restrict options for policy reform. The article calls for a broadening of thinking and research about digitalisation in welfare systems to include questions of administrative burden and the wider effects and impacts on claimants.