Addressing complex societal challenges, such as improving public health, fostering honesty in workplaces, or encouraging eco-friendly behaviour requires effective nudges to influence human behaviour at scale. Intervention science seeks to design such nudges within complex societal systems. While interventions primarily aim to shift the system toward a desired state, less attention is given to the sustainability of that state, which we define in terms of resilience: the system's ability to retain the desired state even under perturbations. In this work, we offer a more holistic perspective to intervention design by incorporating a nature-inspired postulate i.e., lower energy states tend to exhibit greater resilience, as a regularization mechanism within intervention optimization to ensure that the resulting state is also sustainable. Using a simple agent-based simulation where commuters are nudged to choose eco-friendly options (e.g., cycles) over individually attractive but less eco-friendly ones (e.g., cars), we demonstrate how embedding lower energy postulate into intervention design induces resilience. The system energy is defined in terms of motivators that drive its agent's behaviour. By inherently ensuring that agents are not pushed into actions that contradict their motivators, the energy-based approach helps design effective interventions that contribute to resilient behavioural states.

Context: Motivation is known to improve performance. In software development in particular, there has been considerable interest in the motivation of contributors to open source. Objective: We identify 11 motivators from the literature (enjoying programming, ownership of code, learning, self use, etc.), and evaluate their relative effect on motivation. Since motivation is an internal subjective feeling, we also analyze the validity of the answers. Method: We conducted a survey with 66 questions on motivation which was completed by 521 developers. Most of the questions used an 11 point scale. We evaluated the validity of the answers validity by comparing related questions, comparing to actual behavior on GitHub, and comparison with the same developer in a follow up survey. Results: Validity problems include moderate correlations between answers to related questions, as well as self promotion and mistakes in the answers. Despite these problems, predictive analysis, investigating how diverse motivators influence the probability of high motivation, provided valuable insights. The correlations between the different motivators are low, implying their independence. High values in all 11 motivators predict increased probability of high motivation. In addition, improvement analysis shows that an increase in most motivators predicts an increase in general motivation.

In this era of abundant digital information, customer satisfaction has become one of the prominent factors in the success of any business. Customers want a one-click solution for almost everything. They tend to get unsatisfied if they have to call about something which they could have done online. Moreover, incoming calls are a high-cost component for any business. Thus, it is essential to develop a framework capable of mining the reasons and motivators behind customer calls. This paper proposes two models. Firstly, an attention-based stacked bidirectional Long Short Term Memory Network followed by Hierarchical Clustering for extracting these reasons from transcripts of inbound calls. Secondly, a set of ensemble models based on probabilities from Support Vector Machines and Logistic Regression. It is capable of detecting factors that led to these calls. Extensive evaluation proves the effectiveness of these models.

Modeling controllable aspects of the environment enable better prioritization of interventions and has become a popular exploration strategy in reinforcement learning methods. Despite repeatedly achieving State-of-the-Art results, this approach has only been studied as a proxy to a reward-based task and has not yet been evaluated on its own. We show that solutions relying on action prediction fail to model important events. Humans, on the other hand, assign blame to their actions to decide what they controlled. Here we propose Controlled Effect Network (CEN), an unsupervised method based on counterfactual measures of blame. CEN is evaluated in a wide range of environments showing that it can identify controlled effects better than popular models based on action prediction.

The data science job market is changing rapidly. Being able to build machine learning models used to be an elitist skill that only a few distinguished scientists possessed. But nowadays, anyone with basic coding experience can follow the steps to train a simple scikit-learn or keras model. Recruiters are being flooded with applications, because the hype around the "sexiest job of the century" has barely slowed down while the tools are becoming easier to use. The expectation of what a data scientist should bring to the table has changed and companies are beginning to understand that training machine learning models is only a small part of what it takes to be successful in data science.

If you've ever led a team at work before, you know how hard it can be to keep people motivated. But one Japanese startup is using technology to make that easier than ever. The Tokyo-based company Attuned offers what it calls "predictive HR analytics" to help companies understand what makes each of their employees tick. And companies in Japan are paying thousands of dollars for the chance to get a better read on their workers. It's a simple process: When a company signs on with Attuned, its employees take a 55-question online test in which they're presented with pairs of statements, such as "Planning my day in advance gives me a sense of security," and "I prefer to be able to decide which task to focus on at any given time."

I've got a broad production level puzzle I've been trying to figure out and I've never been sure if I've been working with all of the pieces. I'll try to explain the problem and my considerations. It has worked well enough. This becomes a "tax" I need to pay, and when I just have a few relatively simple characters, it's not intolerable. Now, I put on my magical hat of farseeing 3, and I see a future where my game has dozens of characters, each with FSM scripts. The overhead tax I need to pay increases proportionately, and it gets to the point where I need to spend equal amounts of time maintaining AI scripts as I do with building out game systems, which ultimately slows down the pace of development. Here's where I get a bit conflicted.

Will A.I. eliminate my job? It's a clickbait title most of us are now familiar with. In recent years we've been met with a wave of articles and soundbites -- ranging from the realistic to apocalyptic -- speculating as to whether A.I. will replace human jobs, take over the world, or otherwise render Us insignificant. Tesla CEO Elon Musk has even gone so far as to suggest that the volume of jobs that will be lost due to automation will create the need for a universal basic income. A fear of new technology, and of the impact that that technology will have upon the job market is not new.

AI's effect on the workplace will not be limited merely to repetitive, production line-type jobs. Increasingly, it also enters the realm of highly trained knowledge workers. It will also affect those who manage workers currently employed in such jobs. AI likely will reshape jobs all the way up to the C-level offices. That doesn't mean, though, that managers and executives will no longer be needed.

Vivienne Ming is a theoretical neuroscientist, entrepreneur, and author. Named one of 10 "women to watch in technology" by Inc. Magazine, she is the co-founder and managing partner of educational technology company Socos, which focuses on using machine learning and neuroscience to improve educational outcomes and workplace development. She was previously a visiting scholar at the Redwood Center for Theoretical Neuroscience at UC Berkeley, and sits on the board for companies and nonprofits like StartOut, the Palm Center, and Cornerstone Capital. We had the opportunity to speak with Ming about artificial intelligence, workforce development, and how purpose drives performance in the run-up to her upcoming Dreamtalk on November 7 at Dreamforce. You've spoken previously about your mission to leverage AI to maximize human potential.