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ARTIFICIAL INTELLIGENCE + ROBOTS #3 (Robot Evolution: The Rise of the AMR,…
ARTIFICIAL INTELLIGENCE + ROBOTS #3
Robot Evolution: The Rise of the AMR
Traditionally robots have been statically installed in structured environments [like factories] + isolated from humans
But the use of "autonomous mobile robots" that can work alongside humans in semi-structured locations is growing very rapidly
Autonomous mobile robots are a simple, efficient and cost-effective way to automate material handling + in-house transportation tasks in nearly any situation where employees would previously have been required to, for example, push carts around the facility
E.g Amazon has been doubling its number of AMRs annually, + is now operating more than 100,000
Easy integration + no need to make changes to existing factory layouts mean low initial costs
AMRs are a reliable workforce that can run for 12-15hrs without breaks
Autonomous Mobile Robots: Examples
Self-driving forklifts:
well-suited for operations whose load-handling processes provide little added value, are repetitive + involve longer distances
E.g Linde's automated forklifts have a navigation laser, front + rear scanners, a 3D camera + visual/acoustic warning indicators that enable it to safely move around a warehouse in the vicinity of human workers
Unmanned aerial vehicles:
equipped with RFID-scanning tech to offer real-time inventory visibility in the warehouse
E.g PINC makes an autonomous, customisable unmanned aircraft system [UAS] that can be deployed outside of regular working hours. Can perform automatic inventory checks + identify inventory in put-away locations. Resulting scans are uploaded to the cloud
Autonomous Vehicles - Next Generation AMRs
Driverless vehicles are AMRs by another name
Adaptive cruise control and self-parking are already a reality
By 2025, high-autonomy vehicles are likely
By 2030, fully-autonomous vehicles are expected, requiring no human intervention
The implications - not least for mobility, improved crashing, insurance, production, resource savings + logistics - are wide-ranging
Taking Humanoid Form
To automate a wider range of human activities across semi-structured and non-structured environments, robots need to be more humanoid
In particular, humanoid hardware will be able to:
Labour in a world designed for humans
Maximise non-verbal communication
Develop [and control?] advanced AI
Facilitate medical symbiosis
Automation Horizons
AI and robots are set to automate a wide range of
tasks
- not entire
jobs
The Work Most at Risk
Work that can be codified into standard steps, + which is based on clearly formatted data
This includes compliance and tax accounting, as both are rule-based + data intensive
But, all tasks based on digital exchange are a candidate for AI automation
Some people with the best "digital skills" may therefore be the least employable in the future
Stepping Ahead of the Machine
Davenport + Kirby [2015] suggest that humans will remain employed if they:
Step Up
Step Aside
Step In
Step Narrowly
OR
Step Forward
Automation or Augmentation?
Davenport + Kirby [2015] argue that AI will mainly be used to augment rather than to automate, so deepening human work, rather than diminishing it
However, their case examples illustrate more productive human-AI teams w/ fewer people employed
AI Denial
Many in business + academia appear to be in "AI Denial" - claiming that AI doesn't exist, will only impact a narrow range of jobs, or will mainly 'augment'
Their argument that only repetitive tasks will be automated - so 'freeing' workers to do other things - assumes that most people can + will be able to transition to 'higher', more rewarding activities
Latham + Humberd [2018] suggest jobs/work will be "displaced" where both the involved skills, + their form of value delivery, are deemed obsolate
New Kinds of Jobs
Wilson et al [2017] report an Accenture study of companies using or testing AI + ML
They report 3 new categories of job:
Trainers e.g "customer language tone and meaning trainer"
Explainers e.g "transparency analyst"
Sustainers e.g "automation ethicist" + "machine relations manager"
People-Centred AI
Bray + Wang [2019] suggest that the key pillars for any AI implementation need to be transparency, explainability + reversibility
They suggest this is achieved by:
Creating data advocates [human stakeholders]
Mindful monitoring [to compare deep learning outputs w/ a trusted data pool]
Bounded expectations [accepted norms]
A Future AI/Robot Economy
Historically, job losses due to new tech have been offset by the creation of new forms of employment, + have raised living standards
But, there's no guarantee this will happen again
New job creation may lag old job destruction
Even if new jobs are created, the transition to AI/Robot economy could be brutal
Will we need a
universal basic income:
a cash handout, distributed irrespective of employment status?
Richard Branson believes this will be necessary, as the amount of jobs AI is going to take away will generate income inequality