We study sampling as optimization in the space of measures. We focus on gradient flow-based optimization with the Langevin dynamics as a case study. We investigate the source of the bias of the unadjusted Langevin algorithm (ULA) in discrete time, and consider how to remove or reduce the bias. We point out the difficulty is that the heat flow is exactly solvable, but neither its forward nor backward method is implementable in general, except for Gaussian data. We propose the symmetrized Langevin algorithm (SLA), which should have a smaller bias than ULA, at the price of implementing a proximal gradient step in space. We show SLA is in fact consistent for Gaussian target measure, whereas ULA is not. We also illustrate various algorithms explicitly for Gaussian target measure, including gradient descent, proximal gradient, and Forward-Backward, and show they are all consistent.

We are on the verge of a technological revolution that will fundamentally alter the way we live, work, and relate to one another unlike anything humankind has experienced before. The main driver for this technological revolution is Artificial Intelligence (AI). Technological change driven by AI will change not only what we do but also who we are. It will affect our identity and all the issues associated with it: our sense of privacy, our notions of ownership, our consumption patterns, the time we devote to work and leisure, and how we develop our careers, cultivate our skills, and nurture relationships. But the development and applications of artificial intelligence can also present a dystopian threat to our collective and individual well being. From SIRI to self-driving cars, artificial intelligence (AI) is progressing rapidly. While science fiction often portrays AI as robots with human-like characteristics, AI can encompass anything from Google's search algorithms to IBM's Watson to autonomous robots and weapons systems. Artificial intelligence today is often referred to as narrow AI (or weak AI), which is designed to perform a narrow task (eg:facial recognition or only internet searches or driving a car). The other kind of Artificial Intelligence is termed general AI (AGI or strong AI) which is designed to "think," and solve problems much like humans.

It's hard to imagine truckers embracing the wider use of artificial intelligence or "AI" within the freight transportation industry. I'm still quite leery of it myself, having watched The Terminator one too many times. Yet, if a new global survey of nearly 3,000 employees across eight nations conducted by The Workforce Institute at Kronos Incorporated is correct, it seems that more and more of them believe there is "a significant opportunity" for AI to help improve "the workplace experience" and, yes, believe it or not, the logistics/transportation sector is one of the leading industries in this regard. The survey – dubbed Engaging Opportunity: Working Smarter with AI and conducted by Coleman Parkes Research – polled hourly and salaried workers across a variety of industries in Australia, Canada, France, Germany, Mexico, New Zealand, the United Kingdom, and the U.S. Not only did the survey discern there may be more "ground level support" regarding the use of AI in the workplace, it also found that company management is being largely close-mouthed regarding AI developments – and that, not AI itself, is what's sparking the most unease about the technology among workers.

A majority of employees globally say artificial intelligence (AI) could help improve their jobs and workplaces, according to new research from The Workplace Institute at Kronos. A little more than four out of five workers (82%) say AI could make their jobs more empowering and engaging, the report said. The findings suggest worker buy-in is in increasing, potentially allowing the workforce to be more accepting of new technologies used in digital transformation efforts. The survey looked at nearly 3,000 employees from Australia, Canada, France, Germany, Mexico, New Zealand, the United Kingdom, and the US. Respondents from all eight countries said they would accept AI in the workplace for a few reasons: Simplifying time-consuming processes (64% of respondents), balancing their workload (64%), increasing fairness (62%), and helping managers make better decisions (57%).

According to a global survey of nearly 3,000 employees across eight nations conducted by The Workforce Institute at Kronos Incorporated, four out of five employees surveyed see significant opportunity for artificial intelligence (AI) to create a more engaging and empowering workplace experience, yet admit a lack of transparency from their employers is a primary driver of fear and concern. The Engaging Opportunity: Working Smarter with AI survey conducted with Coleman Parkes Research explores how employees – both hourly and salaried from a variety of industries in Australia, Canada, France, Germany, Mexico, New Zealand, the United Kingdom, and the U.S. – believe emerging technologies should be used to improve the future of work. The Workforce Institute at Kronos provides research and education on critical workplace issues facing organizations around the globe. By bringing together thought leaders, The Workforce Institute at Kronos is uniquely positioned to empower organizations with the knowledge and information they need to manage their workforce effectively and provide a voice for employees on important workplace issues. A hallmark of The Workforce Institute's research is balancing the needs and desires of diverse employee populations with the needs of organizations.

Four out of five employees surveyed see significant opportunity for artificial intelligence (AI) to create a more engaging and empowering workplace experience, yet admit a lack of transparency from their employers is a primary driver of fear and concern. This is according to a global survey of nearly 3,000 employees across eight nations conducted by The Workforce Institute at Kronos Incorporated. The survey, "Engaging Opportunity: Working Smarter with AI," conducted with Coleman Parkes Research, explores how employees – both hourly and salaried from a variety of industries in Australia, Canada, France, Germany, Mexico, New Zealand, the United Kingdom, and the U.S. – believe emerging technologies should be used to improve the future of work. "Organizations are making significant investments in benefits, technology, and innovative workplaces, yet employees are working more than ever and engagement has remained stagnant for decades," said Joyce Maroney, executive director, The Workforce Institute at Kronos. " While emerging technologies always generate uncertainty, this survey shows employees worldwide share a cautious optimism that artificial intelligence is a promising tool that could pave the way for a game-changing employee experience if it is used to add fairness and eliminate low-value workplace processes and tasks, allowing employees to focus on the parts of their roles that really matter."

Actor-critic methods solve reinforcement learning problems by updating a parameterized policy known as an actor in a direction that increases an estimate of the expected return known as a critic. However, existing actor-critic methods only use values or gradients of the critic to update the policy parameter. In this paper, we propose a novel actor-critic method called the guide actor-critic (GAC). GAC firstly learns a guide actor that locally maximizes the critic and then it updates the policy parameter based on the guide actor by supervised learning. Our main theoretical contributions are two folds. First, we show that GAC updates the guide actor by performing second-order optimization in the action space where the curvature matrix is based on the Hessians of the critic. Second, we show that the deterministic policy gradient method is a special case of GAC when the Hessians are ignored. Through experiments, we show that our method is a promising reinforcement learning method for continuous controls.

Six-thousand retrenched National Australia Bank (NAB) employees start leaving from this week, largely from the bank's Melbourne head office, as software takes over increasingly complex tasks. The cuts -- one in every five members of NAB's workforce -- were announced in November, the same day the bank revealed a $5.3 billion annual net profit. Dominic Barton works for the world's top CEOs, as global managing partner of consulting firm McKinsey and Company. "For 60 per cent of jobs, 30 per cent of the activities are automatable," he said. In his view, automation and software that analyses information and makes decisions will transform the business landscape -- doing jobs that, until recently, required well-paid "knowledge workers".

In the tech world in 2017, several trends emerged as signals amid the noise, signifying much larger changes to come. As we noted in last year's More Than Noise list, things are changing--and the changes are occurring in ways that don't necessarily fit into the prevailing narrative. While many of 2017's signals have a dark tint to them, perhaps reflecting the times we live in, we have sought out some rays of light to illuminate the way forward. The following signals differ considerably, but understanding them can help guide businesses in the right direction for 2018 and beyond. When a team of psychologists, linguists, and software engineers created Woebot, an AI chatbot that helps people learn cognitive behavioral therapy techniques for managing mental health issues like anxiety and depression, they did something unusual, at least when it comes to chatbots: they submitted it for peer review. Stanford University researchers recruited a sample group of 70 college-age participants on social media to take part in a randomized control study of Woebot. The researchers found that their creation was useful for improving anxiety and depression symptoms.

In its latest Pulse of fintech report, released last week, the firm sets out 10 factors it reckons will drive financial technology this year, after $US31 billion of deals were struck in 2017. There are now 25 fintech unicorns around the world, collectively valued at $US76 billion (none are from Australia). On the KPMG list is the acceleration of artificial intelligence technology and the growing number of devices connected to the internet of things. It expects AI and IoT enablement "to continue at a rapid pace" as financial services offerings are embedded into home automation systems. A couple of its predictions relate to regulation.