Robotics and Artificial Intelligence:
The delegates had been very subdued as they gathered in the meeting room where fresh hot drinks were being served. Sabrina had not joined them, and an associate had informed Kirill that she would be a little late, claiming to have a migraine, he had just nodded with an angry acceptance. He knew that the breakfast buffets had been significantly reduced and adjusted accordingly to each individual request. The delegates would not be so full as the previous morning, but that would be compensated by their tiredness which had been very apparent as they had joined him earlier.
This morning the meeting would contain reports and presentations on the direction of new business investments which were very specifically focussed on Robotics and Artificial Intelligence, and after lunch they would discuss the very secret impact of the Zodiac Program during which he expected considerable interest and direct input from all the delegates.
Kirill waited for them to be seated before introducing the agenda for the morning and how it would be presented. Sabrina arrived as he was finishing, sitting down with no apology or interaction with anyone in the group, who in unison had chosen to ignore her childish and unprofessional conduct.
The lights were dimmed, and Kirill stood at the podium facing the attentive and subdued audience and started the presentation.
He was calm and collected and spoke quietly and eloquently, turning his head slowly and looking at each pair of eyes as he gave the usual preamble about the necessary protocols “the details contained within this presentation are not for general business circulation and will not be available to the auditors or financial consultants”.
The large screen to his right lit up and the power-point presentation with his supporting dialogue began, it would take over two hours and they would then break for a light lunch and refreshments.
Each delegate had been provided with comprehensive documentation and a brief resume which contained some of the following text, which has been condensed for ease of reference to future users of this material:
Robotics and Artificial Intelligence (AI) impact on Future Investments:
- Introducing Robotics
- An Overview of Robotics 2017
- Introducing Artificial Intelligence
- Artificial Intelligence and Robotic Systems
- What does the future hold?
1921: The word ‘robot’ first emerged in a science fiction play written by Karel Capek. It told the story of a society that produced human clones to be its slaves, only for the robots to overthrow their masters.
1950: The first industrial robotics company called Unimation was formed. It invented a ground-breaking 4,000-pound robot arm that could pick-up and drop-down items based on pre-programmed commands, making it ideal for moving heavy and hot items in factories.
1969: Victor Scheinmann developed the Stanford Arm, the first electrically powered and articulated robot arm. This was a significant breakthrough, because it operated on 6 axes, giving it greater freedom of movement than previous single or double axis machines. The Stanford Arm marked the beginning of the articulated robot revolution, which transformed assembly lines in manufacturing and spurred the launch of several commercial robotics companies including Kuka and ABB Robotics.
1997: Development of the first humanoid machines that could walk on two legs, recognise gestures and respond to questions. Three years later, KIVA Systems (now Amazon Robotics) was established to supply mobile robots that could shuttle goods and pallets within complex distribution warehouses.
2017: Robots of the 21st century have broken free of their cages. The symbiosis of AI and robotics, with sophisticated software giving physical machines the wherewithal to deal with unanticipated surroundings and events. Reinforcement learning, for example, means that robots can now mimic and learn from human co-workers. Furthermore, storing data in the cloud means robots can share learning and pool experiences with other robots in a network.
Advances in robotics can also be traced to innovations in hardware. Improvements in sensors are giving robots the visual awareness necessary to navigate unstructured environments. Better materials such as silicone and spider silk make for sharper looks, while ‘mechanical hairs’ made of piezoelectric transistors are as sensitive as human skin.
Robots are no longer confined to factories and can be seen in hospitals wards, shop floors and city streets. In factories, robots continue to evolve and new machines, dubbed ‘co-bots’ and are designed to work in tandem with human workers. Research by MIT undertaken in partnership with BMW found that robot-human teams were 85 percent more productive than either working alone.
An Overview of Robotics 2018:
Five main types of physical machine now in existence:
- Articulated robots — Stationary robots whose arms have at least three rotary joints, and which are typically found in industrial settings
- Mobile robots — Wheeled or tracked robots that can shuttle goods and people from one destination to the next.
- Humanoid robots — Robots that have a physical resemblance to humans and which seek to mimic our abilities.
- Prosthetic robots — Robots that can be worn or handled to give people greater strength, including disabled people or workers performing hazardous jobs
- Serpentine robots — Snake-like robots made up of multiple segments and joints that can move with extreme dexterity. Because of their ability to traverse difficult terrains and move through confined spaces, serpentine robots have found uses in industrial inspection and search and rescue missions.
A snapshot of statistical analysis of the impact of robotics on the workforce:
Robot to worker ratios as at Dec 2017:
- Korea 4.78 Robots per 100 workers
- Japan 3.14 Robots per 100 workers
- Germany 2.98 Robots per 100 workers
- USA 1.64 Robots per 100 workers
- Global Average 0.66 Robots per 100 workers
- China 0.36 Robots per 100 workers
As the Number of Robots Grows, The Cost of Implementation Shrinks:
- In 2013 the average return period was 3.5 years
- By 2017 it had fallen to 1.8 years
The Developing World – Percentage of Jobs at Risk:
- Ethiopia 88%
- Nepal 80%
- China 77%
- El Salvador 75%
- India 69%
- Global Average 57%
The Problem is Bigger Than Manufacturing Percentage Jobs at Risk:
- Insurance Underwriters 99%
- Farm Laborers 97%
- Construction Workers 88%
- Fast Food Cook 87%
- Truck Driver 79%
- Mail Carrier 68%
Artificial intelligence generally refers to tasks performed by computer software that would otherwise require human intelligence. And ‘software’ means a bundle of algorithms that follow a series of steps to arrive at an action or conclusion.
There are two broad types of artificial intelligence:
General AI – Refers to holistic systems that have equal or greater intelligence to humans, and which can complete all manner of tasks, from playing chess to greeting customers in a shop to creating works of art.
Narrow AI – These are systems that can perform discrete tasks within strict parameters, for example:
- Image recognition — used in self-service desks at passport control, and automatic name tagging on Facebook photos
- Information retrieval — used in search engines
- Natural language processing — used in voice recognition for AI assistants like Amazon Echo and Google Home
- Reasoning using logic or evidence — used in mortgage underwriting or determining the likelihood of fraud
These tasks can in turn be grouped into three categories of intelligence: sensing, reasoning and communicating.
Other important approaches to AI include supervised learning, reinforcement learning and transfer learning:
- Supervised learning — Algorithms can be trained at their outset in one of two ways: through supervised or unsupervised learning.
- Reinforcement learning — Whereas some algorithms are written or trained only once, reinforcement learning uses positive feedback mechanisms to continuously tweak and improve algorithms as they are used.
- Transfer learning — Transfer learning involves taking an algorithm that was developed in one domain and modifying it for use in another, without having to start from scratch and source huge reams of original and labelled data.
To clarify, the above approaches to AI are not necessarily mutually exclusive and can often be used in combination.
Artificial Intelligence and Robotic Systems:
Artificial intelligence and robotic systems can be found in every corner of our economy. Example uses include:
- Cancer detection — A deep learning algorithm developed by Stanford University is capable of diagnosing cancerous skin lesions as accurately as a dermatologist
- Construction — A robot called the Semi-Automated Mason (SAM) can lay up to 1,200 bricks a day, compared with the 300 to 500 a human bricklayer is capable of
- Fraud detection — Using machine learning algorithms to spot fraudulent behaviour in financial transactions in as little as 15 milliseconds
- Housing inspections — Technology company ASI Data Science has created algorithms to predict where unlicensed landlords operate, helping to prevent the exploitation of vulnerable tenants
- Media reports — The Associated Press recently adopted machine learning software that can produce 3,000 corporate earnings reports every quarter
- Online shopping — Many retailers use machine learning algorithms to learn customer preferences and offer personalised recommendations
- Patient care — Japan’s Tokai Rubber Industries has developed the RIBA robot, which is being used in health care to lift and move humans up to 175 pounds in weight
- Parcel delivery — Starship Technologies has developed a wheeled robot that can deliver parcels autonomously and is now being trialled with logistics companies worldwide
- Utility repairs — HiBot USA uses a combination of robotics and AI to predict the likelihood of pipe failures, based on factors such as surrounding soil type and land topography
What does the future hold?
It is impossible to predict how artificial intelligence and robotics will develop in the 21st Century, but these factors will have a significant impact on the speed of progress:
- Computing power — Since the 1970s, the number of transistors that can fit into the same space on computer chips has doubled every two years — a rule known as Moore’s Law. As computing power continues to grow, including through the recent introduction of nanometer transistors, it will create opportunities for more sophisticated AI and robotic systems.
- Data capture and storage — Data is the raw material that fuels the engines of AI and robotic systems. Thanks to the advent of the internet, the digitalisation of records and files, and the boom in social media communication, the global pool of available data that machines can train on is colossal.
- Common infrastructure — It was once the case that every research lab and tech company would develop its own proprietary hardware and software. Today with common infrastructure emerging that means robotic and AI technology need not be created from scratch.
- Research investment — A fourth driving factor is the large amount of investment flowing into research and development. In 2015, the U.S. Government’s investment in unclassified R&D in AI-related technologies was approximately $1.1 billion. The EU has set up a public private partnership to strengthen Europe’s robotics industry, with $700m of public funding. The number of higher education institutions with AI and robotics departments is also expanding. There are now 100 departments in Chinese universities that specialise in automation, while there are approximately 34 UK universities offering courses in AI. Investment is also very active in the private sector, with as many as 85 AI venture capital funds in operation.
End of Presentation
The final page of the presentation appeared on the screen and Kirill began to relax. The presentation had gone according to the schedule and there would be an open forum for questions, analysis and debate. He felt exhausted, the intense concentration over the last two hours had drained his energy and he hoped that the delegates felt the same way and would not push too many demanding or awkward questions.
He looked around the room, the audience was silent, the only noise was from the shuffling in their seats and adjustments of accessories, they clearly needed a comfort break. There were no questions forthcoming, so he chose the opportunity to close the session and invited the delegates into the refreshment area. They would break for lunch before returning for the final presentation of the seminar and that would be an update on the Zodiac Program, which the delegates more commonly referred to as the big secret!