News

Booth #4619 - ATI brings the MC-10 Manual Tool Changer and the new Universal Compliance Compensator (UCC) to PACK EXPO 2018

Booth #S4314 - Introducing our AS40 conveyors with backlights. These backlights are specially designed to fit within the frame of our conveyors and, when coupled with a translucent belt, provide extra contrast for vision systems to aid in inspection applications

The company led the charge to develop robots that can safely work alongside humans

Robotics startup with deep roots in plant science aims to feed the world through intelligent farming

Brandon Alexander would like to introduce you to Angus, the farmer of the future. He's heavyset, weighing in at nearly 1,000 pounds, not to mention a bit slow. But he's strong enough to hoist 800-pound pallets of maturing vegetables and can move them from place to place on his own.

Scientists from Nanyang Technological University, Singapore (NTU Singapore) have developed a technology whereby two robots can work in unison to 3-D-print a concrete structure. This method of concurrent 3-D printing, known as swarm printing, paves the way for a team of mobile robots to print even bigger structures in the future. Developed by Assistant Professor Pham Quang Cuong and his team at NTU's Singapore Centre for 3-D Printing, this new multi-robot technology is reported in Automation in Construction. The NTU scientist was also behind the Ikea Bot project earlier this year, in which two robots assembled an Ikea chair in about nine minutes.

The human arm can perform a wide range of extremely delicate and coordinated movements, from turning a key in a lock to gently stroking a puppy's fur. The robotic "arms" on underwater research submarines, however, are hard, jerky, and lack the finesse to be able to reach and interact with creatures like jellyfish or octopuses without damaging them. Previously, the Wyss Institute for Biologically Inspired Engineering at Harvard University and collaborators developed a range of soft robotic grippers to more safely handle delicate sea life, but those gripping devices still relied on hard, robotic submarine arms that made it difficult to maneuver them into various positions in the water.

In this article, I discuss the primary reasons why autonomous vehicles are emerging, what factors go into developing self-driving cars, and how energy storage is a vital part of autonomous vehicle design.

Booth #N4740 - One exciting example of flexible and interactive technology will be the new collaborative robots. The new line, brought by Omron and Techman, is safer, simpler to program and easier to integrate with other equipment

Booth #N6149 - Schmalz Electrical Vacuum Generator CobotPump (ECBPi) - Intelligent electrical vacuum generator for handling airtight and slightly porous workpieces

Japan’s new Humanoid Robot built by the Japanese Japan’s Advanced Industrial Science and Technology Institute is able to install drywall:

 

Source : https://www.aist.go.jp/index_en.html

The post Drywall installer Robot appeared first on Roboticmagazine.

 

In this episode, Audrow Nash interviews Robert Williamson, a Professor at the Australian National University, who speaks about a mathematical approach to ethics. This approach can get us started implementing robots that behave ethically. Williamson goes through his logical derivation of a mathematical formulation of ethics and then talks about the cost of fairness. In making his derivation, he relates bureaucracy to an algorithm. He wraps up by talking about how to work ethically.

Robert Williamson

Robert (Bob) Williamson is a professor in the research school of computer science at Australian National University. Until recently he was the chief scientist of DATA61, where he continues as a distinguished researcher. He served as scientific director and (briefly) CEO of NICTA, and lead its machine learning research group. His research is focussed on machine learning. He is the lead author of the ACOLA report Technology and Australia’s Future. He obtained his PhD in electrical engineering from the university of Queensland in 1990. He is a fellow of the Australian Academy of Science.

Links

• Download mp3 (14.4 MB)
• Robert Williamson’s homepage
• Subscribe to Robots using iTunes
• Subscribe to Robots using RSS
• Support us on Patreon

 

The 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (#IROS2018) will be held for the first time in Spain in the lively capital city of Madrid from 1 to 5 October. This year’s motto is “Towards a Robotic Society”.

Check here over the week for videos and tweets.

And if you have an article published at IROS that you would like share with the world, just send an image and a short summary (a couple paragraphs) to sabine.hauert@robohub.org Read More

Where am I? Like humans, robots also need to answer that question, while they tirelessly glue, weld or apply seals to workpieces. After all, the production of precision products depends on robot control systems knowing the location of the adhesive bonding head or welding head to the nearest millimeter at all times. This means the robot needs some sort of eye. In the automotive industry and many other sectors, specialized sensors perform this function, most of which operate on the principle of laser triangulation. A laser diode projects a line of red light onto the workpiece, from which the light is reflected at a specific angle before being detected by a camera. From the position of the light striking the camera chip, the position and distance of the sensor with respect to the workpiece within the coordinate system can be calculated.

By Jessica Montgomery, Senior Policy Adviser

Advances in AI technologies are contributing to new products and services across industries – from robotic surgery to debt collection – and offer many potential benefits for economies, societies, and individuals.

With this potential, come questions about the impact of AI technologies on work and working life, and renewed public and policy debates about automation and the future of work.

Building on the insights from the Royal Society’s Machine Learning study, a new evidence synthesis by the Royal Society and the British Academy draws on research across disciplines to consider how AI might affect work. It brings together key insights from current research and policy debates – from economists, historians, sociologists, data scientists, law and management specialists, and others – about the impact of AI on work, with the aim of helping policymakers to prepare for these.

Current understandings about the impact of AI on work

While much of the public and policy debate about AI and work has tended to oscillate between fears of the ‘end of work’ and reassurances that little will change in terms of overall employment, evidence suggests that neither of these extremes is likely. However, there is consensus that AI will have a disruptive effect on work, with some jobs being lost, others being created, and others changing.

Over the last five years, there have been many projections of the numbers of jobs likely to be lost, gained, or changed by AI technologies, with varying outcomes and using various timescales for analysis.

Most recently, a consensus has begun to emerge from such studies that 10-30% of jobs in the UK are highly automatable. Many new jobs will also be created. However, there remain large uncertainties about the likely new technologies and their precise relationship to tasks. Consequently, it is difficult to make predictions about which jobs will see a fall in demand and the scale of new job creation.

Implications for individuals, communities, and societies

Despite this uncertainty, evidence from previous waves of technological change – including the Industrial Revolution and the advent of computing – can provide evidence and insights to inform policy debates today.

Studies of the history of technological change demonstrate that, in the longer term, technologies contribute to increases in population-level productivity, employment, and economic wealth. However, such studies also show that these population-level benefits take time to emerge, and there can be periods in the interim where parts of the population experience significant disbenefits. In the context of the British Industrial Revolution, for example, studies show that wages stagnated for a period despite output per worker increasing. In the same period, technological changes enabled or interacted with large population movements from land to cities, ways of working at home and in factories changed, and there were changes to the distribution of income and wealth across demographics.

Evidence from historical and contemporary studies indicates that technology-enabled changes to work tend to affect lower-paid and lower-qualified workers more than others. For example, in recent years, technology has contributed to a form of job polarisation that has favoured higher-educated workers, while reducing the number of middle-income jobs, and increasing competition for non-routine manual labour.

This type of evidence suggests there are likely to be significant transitional effects as AI technologies begin to play a bigger role in the workplace, which cause disruption for some people or places. One of the greatest challenges raised by AI is therefore a potential widening of inequality.

The role of technology in changing patterns of work and employment

The extent to which technological advances are – overall – a substitute for human workers depends on a balance of forces. Productivity growth, the number of jobs created as a result of growing demand, movement of workers to different roles, and emergence of new jobs linked to the new technological landscape all influence the overall economic impact of automation by AI technologies. Concentration of market power can also play a role in shaping labour’s income share, competition, and productivity.

So, while technology is often the catalyst for revisiting concerns about automation and work, and may play a leading role in framing public and policy debates, it is not a unique or overwhelming force. Non-technological factors – including political, economic, and cultural elements – will contribute to shaping the impact of AI on work and working life.

Policy responses and ‘no regrets’ steps

In the face of significant uncertainties about the future of work, what role can policymakers play in contributing to the careful stewardship of AI technologies?

At workshops held by the Royal Society and British Academy, participants offered various suggestions for policy responses to explore, focused around:

• Ensuring that the workers of the future are equipped with the education and skills they will need to be ‘digital citizens’ (for example, through teaching key concepts in AI at primary school-level, as recommended in the Society’s Machine Learning report);
• Addressing concerns over the changing nature of working life, for example with respect to income security and the gig economy, and in tackling potential biases from algorithmic systems at work;
• Meeting the likely demand for re-training for displaced workers through new approaches to training and development; and
• Introducing measures to share the benefits of AI across communities, including by supporting local economic growth.

While it is not yet clear how potential changes to the world of work might look, active consideration is needed now about how society can ensure that the increased use of AI is not accompanied by increased inequality. At this stage, it will be important to take ‘no regrets’ steps, which allow policy responses to adapt as new implications emerge, and which offer benefits in a range of future scenarios. One example of such a measure would be in building a skills-base that is prepared to make use of new AI technologies.

Through the varying estimates of jobs lost or created, tasks automated, or productivity increases, there remains a clear message: AI technologies will have a significant impact on work, and their effects will be felt across the economy. Who benefits from AI-enabled changes to the world of work will be influenced by the policies, structures, and institutions in place. Understanding who will be most affected, how the benefits are likely to be distributed, and where the opportunities for growth lie will be key to designing the most effective interventions to ensure that the benefits of this technology are broadly shared.