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Henrik Christensen's Blog

Category Archives: AI/ML

Robots for economic growth, improved quality of life and empowerment of people

17 Thursday Sep 2020

Posted by hichristensen in AI/ML, AMRON, robotics

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The New 2020 Roadmap

Recently the robotics industry celebrated its 60-year anniversary. We have used robots for more than six decades to empower people to do things that are typically dirty, dull and/or dangerous. The industry has progressed significantly over the period from basic mechanical assist systems to fully autonomous cars, environmental monitoring and exploration of outer space. We have seen tremendous adoption of IT technology in our daily lives for a diverse set of support tasks. Through use of robots we are starting to see a new revolution, as we not only will have IT support from tablets, phones, computers but also systems that can physically interact with the world and assist with daily tasks, work, and leisure activities.

The “old” robot systems were largely mechanical support systems. Through the gradual availability of inexpensive computing, user interfaces, and sensors it is possible to build robot systems that were difficult to imagine before. The confluence of technologies is enabling a revolution in use and adoption of robot technologies for all aspects of daily life.

Thirteen years ago, the process to formulate a roadmap was initiated at the Robotics Science and Systems (RSS) conference in Atlanta. Through support from the Computing Community Consortium (CCC) a roadmap was produced by a group of 120 people from industry and academia. The roadmap was presented to the congressional caucus and government agencies by May 2009. This in turn resulted in the creation of the National Robotics Initiative (NRI), which has been an interagency effort led by the National Science Foundation. The NRI was launched 2011 and recently had its five-year anniversary. The roadmap has been updated 2013 and 2016 prior to this update.

Over the last few years we have seen tremendous progress on robot technology across manufacturing, healthcare applications, autonomous cars and unmanned aerial vehicles, but also major progress on core technologies such as sensors, communication systems, displays and basic computing. All this combined motivates an update of the roadmap. With the support of the Computing Community Consortium three workshops took place 11-12 September 2019 in Chicago, IL, 17-18 October 2019 in Los Angeles, CA and 15-16 November 2019 in Lowell, MA. The input from the workshops was coordinated and synthesized at a workshop in San Diego, CA February 2020. In total the workshops involved 79 people from industry, academia, and research institutes. The 2016 roadmap was reviewed, and progress was assessed as a basis for formulation of updates to the roadmap.

The roadmap document is a summary of the main societal opportunities identified, the associated challenges to deliver desired solutions and a presentation of efforts to be undertaken to ensure that US will continue to be a leader in robotics both in terms of research innovation, adoption of the latest technology, and adoption of appropriate policy frameworks that ensure that the technology is utilized in a responsible fashion.

Main Roadmap Findings

Over the last decade a tremendous growth in utilization of robots has been experienced. Manufacturing has in particular been impacted by the growth in collaborative robots. There is no longer a need for physical barriers between robots and humans on the factory floor. This reduces the cost of deploying robots. In the US the industrial robotics market has grown 10+% every year and the market has so far seen less than 10% penetration. We are thus far away for full automation of our factories. US is today using more robots than it has even done before.

A major growth area over the last decade has been in use of sensor technology to control robots. More digital cameras have been sold the last decade than ever before. When combined with advanced computing and machine learning methods it becomes possible to provide robust and more flexible control of robot systems.

A major limitation in the adoption of robot manipulation systems is lack of access to flexible gripping mechanisms that allow not only pick up but also dexterous manipulation of everyday objects. There is a need for new research on materials, integrated sensors and planning / control methods to allow us to get closer to the dexterity of a young child.

Not only manufacturing but also logistics is seeing major growth. E-commerce is seeing annual growth rates in excess of 40% with new methods such as Amazon Express, Uber Food, … these new commerce models all drive adoption of technology. Most recently we have seen UPS experiment with use of Unmanned Vehicles for last mile package delivery. For handling of the millions of different everyday objects there is a need of have robust manipulation and grasping technologies but also flexible delivery mechanisms using mobility platforms that may drive as fast as 30 mph inside warehouses. For these applications there is a need for new R&D in multi-robot coordination, robust computer vision for recognition and modeling and system level optimization.

Other professional services such as cleaning in offices and shops is slowly picking up, this is in particular true given the recent COVID-19 pandemic. The layout of stores is still very complex and difficult to handle for robots. Basic navigation methods are in place, but it is a major challenge to build systems that have robust long-term autonomy with no or minimal human intervention. Most of these professional systems still have poor interfaces for use by non-expert operators.

For the home market the big sales item has been vacuum and floor cleaners. Only now are we starting to see the introduction of home companion robots. This includes basic tasks such as delivery services for people with reduced mobility to educational support for children. A major wave of companion robots is about to enter the market. Almost all these systems have a rather limited set of tasks they can perform. If we are to provide adequate support for children to get true education support or for elderly people to live independently in their home there is a need for a leap in performance in terms of situational awareness, robustness and types of services offered.

A new generation of autonomous systems are also emerging for driving, flying, underwater and space usage. For autonomous driving it is important to recognize that human drivers have a performance of 100 million miles driven between fatal accidents. It is far from trivial to design autonomous systems that have a similar performance. For aerial systems the integration into civilian airspace is far from trivial but does offer a large number of opportunities to optimize airfreight, environmental monitoring, etc. For space exploration it is within reach to land on asteroids as they pass by earth or for sample retrieval from far away planets. For many of these tasks the core challenge is the flexible integration with human operators and collaborators.

The emergence of new industrial standards as for example seen with Industry 4.0 and the Industrial Internet facilitates access to cheap and pervasive communication mechanisms that allow for new architectures for distributed computing and intelligent systems. The Internet of Things movement will facilitate the introduction of increased intelligence and sensing into most robot systems and we will see a significant improvement in user experience. The design of these complex systems to be robust, scalable, and interoperable is far from trivial and there is a new for new methods for systems design and implementation from macroscopic to basic behavior.

As we see new systems introduced into our daily lives for domestic and professional use it is essential that we also consider the training of the workforce to ensure efficient utilization of these new technologies. The workforce training has to happen at all levels from K-12 over trade schools to our colleges. Such training cannot only be education at the college level. The training is not only for young people but must include the broader society. It is fundamental that these new technologies must be available to everyone.

Finally, there is a need to consider how we ensure that adequate policy frameworks are in place to allow US to be at the forefront of the design and deployment of these new technologies but it never be at the risk of safety for people in their homes and as part of their daily lives.

The Roadmap Document.

The roadmap document contains sections specific to societal drivers, mapping these drivers to main challenges to progress and the research needed to address these. Sections are also devoted to workforce development and legal, ethical and economic context of utilization of these technologies. Finally, a section discusses the value of access to major shared infrastructure to facilitate empirical research in robotics.

The Roadmap is available from http://www.hichristensen.com/pdf/roadmap-2020.pdf

The roadmap is formulated based on consultations and meeting with more than 80 people from across US and involving industry, academia and government institutions.

Why Invest in AI? All about the core technologies!

03 Monday Dec 2018

Posted by hichristensen in AI/ML, robotics

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For some, investing in artificial intelligence feels like banking on the unknown. The concepts behind AI can range from sounding futuristic to downright fictional. And yet, when you break down AI and explore the core technologies that drive it, look at what they are delivering today, and consider what they are capable of delivering tomorrow, it’s suddenly quite easy to grasp how AI is changing the world around us. It is these facts that make investing in AI a not-to-be-missed investment opportunity.

Back in 2009, I was the main editor behind the formulation of US National Strategy for Robotics. At the time, as the strategy worked its way through Congress and to the White House, the conversation about why robotics mattered was an easy one. In the key areas of application—healthcare, defense, manufacturing, and logistics—it was simple to understand the physical applications of robotics in the real world. And yet, it was clear even then that robotics was somewhat limited in its application because, but its nature, robotics is restricted to interaction with the physical world.

AI is a completely different story.

Since 2009, our world has been fully transformed by one thing: Big data. AI enables us to make glean value from masses of information by finding patterns within the data to elevate the decision-making process, often exponentially. The result is a new wave of AI-driven decision-making that is adding value in nearly every aspect of our lives. AI is helping retailers make better customer recommendations, customize their offerings, and improve product design, delivery, and desirability. AI is helping medical professionals save lives by enabling them to diagnose and treat diseases in its earliest stages—often before symptoms occur—and compare millions of test results to quickly identify effective treatments. On Wall Street, AI is helping fund managers find the best opportunities, using data to quickly analyze everything from macroeconomic and statistical models to industry and geographical trends. AI is giving lawyers and judges the ability to sift through information at lightning speed to make better, more informed decisions. In the oil and gas industry, AI is making it possible to drill deeper, improve rig safety, and even identity system failures before they create mechanical failures that can threaten workers and the fragile environment.

What is spectacular is that this new level of decision-making would not be possible without the use of artificial intelligence and these core technologies that are driving faster, more effective memory processing, communication, and interactions:

  1. Big data analytics is used to examine enormous data sets to discover patterns, correlations, market trends, customer preferences, and other often hidden information to inform the decision-making process. Using predictive models, statistical algorithms, and what-if analyses, big data analytics helps companies identify new revenue opportunities, create more effective marketing, deliver better customer service, improve operational efficiencies, and drive competitive advantage.
  2. Cloud technologies enable companies to store unlimited amounts of data and balance workloads within that data. Even more, AI and the Cloud share a fascinating symbiotic loop: the Cloud serves as the primary source of information that feeds AI networks, and AI networks continuously feed the Cloud with even more data. Already a key driver of competitive advantage and a coveted differentiator, Cloud platforms support some of the most important foundations of AI, such as cloud computing, machine learning, language processing, and more.
  3. Cognitive computing came into public awareness when IBM’s Watson computer famously beat two human champions on Jeopardy! and claimed the $1 million first-place prize. It was a brilliant marketing campaign to bring awareness of the power of cognitive computing to the public and, even more importantly, to the leaders of industry. Today, cognitive computing is used to accelerate processes such as reasoning, natural language processing, speech recognition, object recognition, and dialog generation. According to research firm IDC, worldwide spending on cognitive and AI systems is expected to increase by more than 50% by 2021, taking total spending on cognitive computing from $12B in 2017 to $57.6B by 2021.
  4. Network & Security is more important than ever in this ager of cyber attacks and data breaches, resulting in estimated double-digit growth from 2018 to 2023, leading to global revenue of $193.76B by 2023. As businesses strive to protect the personal and financial information of their customers and, indeed, their own reputations and futures, they rely on AI to support identity and access management, encryption, governance, risk and compliance, unified threat management, and security information and event management. Encryption capabilities are in particularly high demand to protect information stored on consumer devices and to use that information securely in the Cloud.
  5. Semiconductors are a key component of today’s digital capabilities, enabling every computer and electronic device. While semiconductors have been around for decades, AI is driving new semiconductor designs and capabilities. Using the power of machine learning and deep learning, industry leaders are finding new ways to speed up performance and reduce power, process data as patterns rather than individual bits. This progress is the key to delivering everything from quantum computers to fully autonomous vehicles.

When we created the ROBO Global Robotics & Automation Index over 5 years ago, focusing on the broader spectrum made sense. It still does. But as the promise of AI has evolved into the new world reality of AI, it was clear that the time had come to create an index with a singular focus on AI. The result of our efforts is THNQ: an index that offers 100% pure-play exposure to companies that are investing in and delivering AI today—and changing the world as we know it. Every day, the deeper we dive into AI, the more opportunity we see.

Of course, choosing whether to invest in RAAI or AI is not a clear either/or decision. Investors who are keen to invest in the physical application of technology—warehousing, autonomous vehicles, consumer robotics—will find ROBO to be the best fit. And there is certainly plenty of exposure to AI within the ROBO index; the overlap between the two indexes is currently about 30%. For investors who are seeking pure exposure into the fast-growing world of AI that is using the power of computing to drive better decision-making, THNQ is a unique option that enhances that exposure by honing in on AI alone and positioning investors to reap the potential rewards.

Originally published at RoboGlobal

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