Center for the Study of Automated Systems, Robotics, Virtual Reality and Artificial Intelligence

I was privileged to invite Alvin Toffler to the UN where he made presentations to staff members, I am a great ‘fan’ and had read all his books. I often quoted his book The Third Wave. I know that there was going to be a fourth wave, a fifth wave. At BAASANA academic conferences we have talked about these. Besides the research papers are available at the web site of BAASANA. At one of the academic conferences of CASA I ventured onto the seventh wave i.e. the confluence of robotics, automated systems, virtual reality and artificial intelligence RASVRAI:

https://www.g-casa.com/conferences/warsaw/ppt_pdf/Owarish.pdf

Businesses relying on automation to deal with human resources issues

Businesses Are Increasingly Relying on Automation to Compensate for Labor Shortages (msn.com)

5G and robotics

https://www.mouser.com/empowering-innovation/5G?utm_source=Taboola&utm_medium=display&utm_campaign=2021&tblci=GiB1yWYtRvgE-mlpWS0Fww8d0XvKp_4QrKj-yt1E-qHq-iCR71Ao9LCalfX8lpiJAQ#article3

Virtual Human at CES  2021

https://www.theverge.com/2021/1/11/22224601/lg-ces-virtual-influencer-dj-reah-keem-uvc-robot

Robotics, Automated Systems, Virtual Reality and Artificial Intelligence

by Frank Owarish Ph.D., Computer Science and Sam Owarish, Ph.D., Mechanical Engineering

Introduction and overview 

Robotics, automated systems, virtual reality and artificial intelligence are four sets of technologies which have grown in importance in recent years have interrelationships which impact each other in terms of practical applications resulting in major transformation in the way things are done. It would be recalled that technologies in the 1970s had major transformational evolution with the convergence of computer and telecommunication technologies, at the time the first one based on digital signals and the second on analog signals and both combined through a digitalization process (Signal Analysis). The corner stone of the current ongoing technological transformation is systems theory which in a nutshell posits that practically all endeavors can be construed as a set of inputs which get transformed through processes into outputs. The following are examples of technological outputs: self-driving cars, computer integrated manufacturing, automated fight systems including ILS (instrument landing systems) and soon planes without pilots, cars largely manufactured by robots, robot financial advising, algorithmic trading (in the financial markets), robotic surgery  and so on. The processes rely on codes which are increasingly sophisticated. The paper will take a brief look at the basics of these technologies including their mechanics, past evolution and state of the art. Potential future evolution will be reviewed as well. Analysis will focus on their contributions and impacts on industry and commerce, the distribution of wealth at national and global levels, the worlds of transit, medicine, finance, academia, business, entertainment and defence. Drawbacks such as the deskilling of the work force with its financial consequences and the creation of new elites will also be appraised.  Relevant claims reported in the media will be also be examined.

Technological revolution

Technologies are constantly moving, evolving all the time but at time something big happens, for example the IOT, now RASVRAI* is happening big time, a real revolution because they are happening at the same time often impacting each other; the interrelationships are quite significant; recall Alvin Toffler’s First Wave, Second wave, Third Wave; would have qualified the current one as a mega-wave

*R=robotics; AS=automated systems; VR=virtual reality; AI=artificial intelligence

Elements of the Mega Wave

Automated system {AS}

Robotic {R}

Virtual reality {VR}, added reality/ augmented reality {AR}, mixed realty {MR}

Artificial intelligence {AI}

Basic automated system

This is typically a computer-based system which takes a series of steps and convert them into an algorithm which can run itself with minimal human intervention

Basic robotic

Basically a machine which runs an automated system with minimal to no human intervention

Basic virtual reality

This is a replication of reality, real or created, which runs on a computer system

Basic artificial intelligence

This is a state where a computerized system is able to make decisions, for example a thermostat set at a specific temperature makes adjustment by increasing or decreasing heat generated by the system so that the constant temperaure is maintained; similarly for a car in a cruise-control mode

Complex automated systems

Have been going on for decades

What’s different now is that current day automation can be said to be intelligent not just mechanical

Often working in tandem with robotics, VR and AI

Complex robotics

Have also been going on for years but  today increasingly relating to AI; often called smart robotics albeit robots that make decision

Complex virtual reality

The smart phone revolution ushered in a whole new world of virtual reality

You can visit a vacation resort online and see what it is like before making a decision

Associated terms are added reality, augmented reality, mixed reality

Complex artificial intelligence

Again AI has been happening for years, as a matter of fact started with the Turing Machine and ‘enigma’

Today AI is coming of age

Case study methodology at Harvard University

When the MBA first started decades ago the field of business administration was partly known and partly in the making; the Harvard philosophy is that in context the material is best studied through existing and generall accepted concepts and also through case studies bringing light to the multifaceted aspects of the field; this methodology is warranted for the new field emerging, shaping up

Case studies to broaden knowledge and understanding

Real life ‘cases’ albeit stories

At times, with an attempt to conceptualize

Collection makes up new body of knowledge, of course evolving in nature

AS: Commercial flights

Commercial aviation is already heavily automated. Modern aircraft are generally flown by a computer autopilot that tracks its position using motion sensors and dead reckoning, corrected as necessary by GPS. Software systems are also used to land commercial aircraft

(Source: https://www.nytimes.com/2015/04/07/science/planes-without-pilots.html)

R: Robotic surgery

Robotically-assisted heart surgery, also called closed-chest heart surgery, is a type of minimally invasive heart surgery performed by a cardiac surgeon. The surgeon uses a specially-designed computer console to control surgical instruments on thin robotic arms.

(Source: https://my.clevelandclinic.org/health/treatments/17438-robotically-assisted-heart-surgery)

VR: Virtual reality LAMPIX VR platform

https://www.kickstarter.com/projects/868083461/lampix-intelligent-interactive-tabletop-ar-is-here?

Augmented reality rocket launch

https://www.usatoday.com/story/tech/nation-now/2018/03/29/augmented-reality-rocket-launch-app-shows-you-spacex-launch-like-never-before/461696002/

Mixed reality

Nature as it truly is; nature captured and rendered via music videos:

www.natureappreciation.org

AI: Five predictions 2018 (Daniel Newman, Forbes) https://www.forbes.com/sites/danielnewman/2017/12/12/5-artificial-intelligence-predictions-for-2018/#6fb7930f1063
1. Data driven machines

It’s what most companies will be focusing on … With the steady growth of data produced by the Internet of Things (IoT), businesses will be turning to machine learning to process, trend, and analyze the information. Machine learning AI is a must-have. It’s the only way companies can make valuable sense of the flow of data—both structured and unstructured—coming in (simply be too much for any human to manage). (If you don’t yet have an AI strategy in place, don’t worry. Forrester predicts up to 80% of firms will rely on “insights-as-a-Service” in at least some capacity in 2018.)

2. More Human-like Help

More and more businesses are moving toward harnessing the power of conversational AI chatbots and other virtual assistants to manage the day-to-day flow of work. It is estimated that some 85% of customer interactions will be managed by AI by 2020. In the near-term, we’ll likely see an increased focus on bot sensitivity training, which will allow humans to offload even more work on chatbot shoulders. And I’m not just talking about business. Amazon’s Alexa recently began syncing with Outlook and Google to help families keep up with their hectic schedules. Meanwhile, a new virtual assistant from X.ai called “Amy” can be trusted to respond to messages regarding meetings, meals, and calls without ever alerting the sender she’s a bot. We may not be ready for the Jetsons’ Rosie, but we’re getting closer.

3. Siri—And Other Tech—Will Hear You Better

Ugh, the pain of voice-texting a message, only to find you need to edit nearly every word because Siri didn’t get it. My guess is we’ll finally get a handle on conversational technology in 2018, including not just emotional sensitivity, but translational technology that allows us to communicate seamlessly between languages. This is big for both business and personal life, as I recently discussed in The Many Benefits of Conversational AI. Amazon is already training Alexa to recognize speech patterns that may be indicative of suicide. Eventually, bots may be able to perform psychiatric counseling or serve as a support network for those who are isolated.

4. Smart Automation Continues

Have you noticed Amazon’s subscription order services? When you purchase day-to-day products, it actually auto-populates your order to schedule regular delivery of these items—whether it’s paper towels, laundry, detergent, or dog food—on a weekly or monthly basis. Talk about locking you in to future sales. Just as AI has already shaken up the marketing industry with its ability to personalize marketing campaigns—and even tweak them in real-time—we’ll continue to see AI bringing smart automation to an even wider range of industries—from retail product delivery to machine maintenance, energy conservation, and more.

5. AI Adoption Will Continue To Grow

OK, this may not warrant its own bullet, but I just wanted to emphasize the fact that AI isn’t going anywhere. In fact, as the technology continues to refine itself, engineers are finding even greater—and more granular—uses for it, in all aspects of our lives. Yes, robots and self-flying delivery drones are cool. But the things we’re seeing now—and into 2018—are perhaps just as amazing. They have the power to take much of the drudgery out of our daily work and personal lives—something pretty much all of us can rally behind as we close another active year of tech advancement.

Observation

Adding all the elements together equals a mega revolution transforming the way business operates

The impact will also be felt in government and non profit organizations

Chapter 1 Review of the technicalities

Robotics, automated systems, virtual reality and artificial intelligence are four sets of technologies which have grown in importance in recent years have interrelationships which impact each other in terms of practical applications resulting in major transformation in the way things are done. It would be recalled that technologies in the 1970s had major transformational evolution with the convergence of computer and telecommunication technologies, at the time the first one based on digital signals and the second on analog signals and both combined through a digitalization process (Signal Analysis). The corner stone of the current ongoing technological transformation is systems theory which in a nutshell posits that practically all endeavors can be construed as a set of inputs which get transformed through processes into outputs. The following are examples of technological outputs: self-driving cars, computer integrated manufacturing, automated fight systems including ILS (instrument landing systems) and soon planes without pilots, cars largely manufactured by robots, robot financial advising, algorithmic trading (in the financial markets), robotic surgery  and so on. The processes rely on codes which are increasingly sophisticated.

Here we summarise the basics of these technologies including their mechanics, past evolution and state of the art. Potential future evolution will be reviewed as well. Analysis will focus on their contributions and impacts on industry and commerce, the distribution of wealth at national and global levels, the worlds of transit, medicine, finance, academia, business, entertainment and defence. Some writers have commented about some drawbacks such as the deskilling of the work force, its financial and social consequences such as depending the gaps between rich and poor and the creation of new elites.

Automated systems are used in ever increasing application with various degrees of sophistication. They are generally task specific. In the home a basic example is a washing machine which runs through different cycles as chosen by the user. In industry, automated factories have been used for decades using Programmable Logic Controllers which receive input from sensors and switches in real time. Using ladder logic they control valves and actuators to carry tasks such as operating mail sorting systems, convey components on assembly lines or control industrial processes acting as a proportional–integral–derivative (PID) loops.  Large plants such as a flour mill are now run by about a few individuals.  Modifying the software in those controllers, without changing any wiring, allows the plant to work different way and make different products. In mission critical systems malfunctions can have disastrous consequences both financial and to the personnel. Therefore automated systems have to be designed to be failed-safe with duplication of components of the system in order to increase reliability.

The four technologies intersect in various degrees. Robotics can be viewed as a sub-set of automation. Artificial Intelligence software is used to power automated systems, robotics and virtual reality. The common factors in all of them is computing power, sophisticated algorithms and sophisticated sensors of many types.

Robotics is in effect an offshoot of automation. Robots use sensors and actuators to interact with the world via an algorithm that directs their actions. The main components of a robot are the manipulator (an arm with joints and links), the endeffector (which interact with the world via a grip or tool), the locomotive device (powering the movement using a pneumatic, hydraulic or electric actuator), the controller (a computer which control the movement of the manipulator and endeffector), the sensors (which measure position, velocity, force, light, sound, temperature, proximity, distance, pressure, positioning, acceleration and others depending on the application). Other relevant factors of a robot are the degrees of freedom (the number of axes the arm is able to rotate) and robot calibration by which the accuracy of the robot is determined.

A robot is structured according to the role it is designed for. Some takes a human-like form, are able to “empathy” carry out task such as assisting with the sick and elderly, acting as a receptionist and used in entertainment. There are researches in progress aiming to create robots with some human characteristics, but this task remains a complex undertaking because of the overwhelming complexity of the human body and human cognitive power. An example of such is the robot receptionist, called Pepper, which costs £26,000 and will apparently operate cheaper than a human employee in the long run and “can identify emotions and adapt its responses” accordingly. Some other state of the art humanoid robots:

• Google’s Atlas – developed by Boston Dynamics, is able to open doors, lift boxes off the floor and take human-like walk in a forest.
• Sophia – is able to speak and make multiple facial expressions.
• OceanOne – helps with the exploration of deep seas

Some of the advantages that robots have over humans:

• efficiency – can work for long hours without break
• accuracy – can carry out repetitive tasks without making mistakes and getting bored
• flexibility -can be reprogrammed for different set of tasks
• Robust – are appropriate for tasks in radioactive environments and where for repetitive strain injury is likely to humans

Robots can be expensive and required a capital investment which can be amortised over many years. Despite of all progress so far, the technology is still in its infancy and major developments are in the pipeline.  It is predicted that by 2019 there will be 2.6 million industrial robots.

To summarise the current situation:

• Some tasks have already been taken over: chef, factory operator, some surgical operations, security officer, pharmacist, combat soldier, librarian, warehouse operator, farmer, bomb disposal, housekeeping and bank teller. Currently 70% of these robots are used in the automotive, electrical, metal and machinery production.
• Some tasks are likely to be taken over: the number crunching of information for middle management, accountants and bookkeepers and medical diagnostics.
• Some tasks least likely to be taken over: all therapies requiring empathy including mental health and audiology, installers and repairers of mechanical, electronics and electrical systems, emergency services, firefighters, physicians and surgeons, primary and secondary school teachers, system analysis and special types of software programming where individuals of great talents will always excel.

The Internet of Things (IoT) is well on its way and will allow intelligent devices to be connected and share data. General Motors Co. has already linked thousands of its vehicle assembly robots for motoring in order to prevent disruptions of assembly lines.

Robotic Process Automation (RPA) can be used to automate clerical processes. It is a means of improving efficiency and reducing cost by the use of a software which replicates the actions taken by humans, reminiscent of the Time and Motion study of Frederick Winslow Taylor. It is most useful in environments which use manual, high-volume, repetitive and well-ordered procedures. There are many RPA tools available on the market and they can be configured according to the users’ requirements.

Governments worldwide are investing in robotics in order to gain some pre-eminence in the technology. The main players are the US, China, Japan, South Korea and the European Union. It is estimated that the autonomous system market will be worth several trillions US$ by 2020.

There is also a major security issue if the software operating advanced humanoid robots are hacked into. They could in theory be turned into life threatening devices which is a popular theme in science fiction.

It is also worth mentioning that the world of microrobotics (less than 1mm in size) which is very much in its infancy has enormous potential in searching and investigations areas that are difficult to access such as a collapsed building.

One of the dictionary definitions of Artificial Intelligence is “the capability of a machine to imitate intelligent human behaviour”. In 1951 Alan Turing developed the Turing test to determine whether a machine can exhibit intelligent behaviour. The test consists of a conversation between a human and a machine. If the human cannot detect that the conversation is with a machine then the latter passes the test.

The backbone of Artificial Intelligence consists of the amazing growth in computing power, increasing sophistication in algorithm and a knowledge base which uses a different technology from conventional database to store complex structured and unstructured information. In fact AI can be viewed as human intelligence transferred and enhanced by hardware and software, therefore transferred human ingenuity. The knowledge base tied to a given application must be maintained and fine-tuned as knowledge and understanding of the field grow with time.

The AI field is vast and is in constant change. However AI can broadly divided as follows:

• Artificial General Intelligence(AGI) is capable of cognitive tasks matching that of a human in intellectual prowess. The Turing test is one among many used to determine its existence.  Currently AGI is still a long way from this goal. This AI will have consciousness, self-awareness, sentience (able to perceive emotions) and sapience (able to analyse a situation and decide on a sensible course of action). AGI will have the flexibility to handle any problem.
• Weak AI, in sharp contrast to AGI, has the ability to deal with a specific problem. All current AI applications such as driverless cars, robots in any application, managing a national electric grid, analysing the stock market for trends and stock picking, are examples of Weak AI. Despite of its narrower remit, Weak AI can still malfunctions with major consequences.

There is no definitive computer language for the use in programming artificial intelligent systems, although currently the main favourites are:

Python–can be used coherently with data structures and have libraries useful for AI applications.

Java – object-oriented with high-level features useful for AI applications

Lisp – excellent prototyping capabilities useful for AI applications

Prolog – offers pattern matching and automatic backtracking useful for AI programming

C++– has ability to talk at the hardware level and therefore has fast execution time useful for time-sensitive AI applications.

Writing and debugging the software of a complex AI system will remain a difficult until the procedures to build such a system have been well thought out and made rigorous.

Currently some of the applications of Artificial Intelligence are: Optical character recognition, Handwriting recognition, Speech recognition, face recognition, Automation, Data mining, Diagnosis, investment (Algorithmic trading) and Litigation.

Some AI projects in progress by various bodies worldwide both overtly and covertly, many more can be read by search the internet:

Google Brain aims to make people’s lives better by making machines more intelligent

4CAPS (Cortical Capacity-Constrained Concurrent Activation-based Production System) -developed at Carnegie Mellon University. Its aim is to formalise the knowledge available about the processes of the mind in a computer model in order to increase understanding of the thinking process.

Braina acts as an intelligent assistant used with Microsoft Windows. It interacts with users via typed text and speech recognition and allows users to find information from the internet, take dictation, find and open files, control windows and other computing related tasks.

SEAS (Synthetic Environment for Analysis and Simulations) is used by the US government security agencies to simulate crises in the US. It is capable of running such simulations of other countries. The input to the simulation are breaking news, census data, economic indicators, and climactic events. It was first develop to help major companies in planning their investments. The latest version runs continuously modelling the real world and seek to predict events and possible course of action.

AI has potential as an aid to General Practitioners with their diagnostics given the complexity of medical knowledge and the time limits for consultations.

AI is making inroad in many areas never thought possible such as the legal world because of its power of pattern recognition and key words search. It has the ability to score millions of documents in order to determine relevancy to a case.

It is predicted Virtual Reality will be used to design robots running AI systems. This is meeting point of VR, AI and robotics. Virtual Reality has been evolving in various forms since the 19^th century. Its modern form took shape in 2010 when Palmer Luckey created a VR headset that became the Oculus Rift. Although the internet is filled with explanations the mechanics of VR work, it is worthwhile to remind ourselves of the basics. Procreates a simulation of a real environment the user can interact with in real time. VR goggles works by using a pair of screens each displaying an image side by side one for each eye. A set of lenses focuses and reshapes the picture for each eye creating a 3D stereoscopic image. Sensors (gyroscope and other sensors) in the google monitor the wearer’s head continually. Used in conjunction with headphones, hand controllers equipped with touch sensors using vibration (known as haptic communication), enhances the feeling on immersion in virtual world.

To experience VR, a goggle connected to a mobile device like a smartphone or a gaming console or a personal computer for more sophisticated applications. For the latter, the PC is equipped with a high end graphics card, a power processor and ample memory. There are also standalone VR goggles with inbuilt computer processors. VR apps can be purchased for smartphones. The development of even more powerful graphics card with their own processors is playing a very important role in rapidly changing VR technology.

VR goggles vary widely in price. While their use is not widespread yet as the ubiquitous mobile phone, yet strong growth is predicted for the coming years.

There is a growing number of tools for developing VR applications and many software companies provide a service to develop bespoke applications to help companies achieve their objectives.

Combined with VR is Augmented Reality (AR) in which real world images is superimposed on images created by a VR system in order to enhance the experience.

Starting in gaming, the future of VR is in Education, Training and design of prototypes of industrial and commercial products. Crucial for the success of VR is content creation that add value to products, cut development costs and help companies reach their goals. A few companies in the VR sector among the hundreds both in the US and worldwide are given here:

Microsoft HoloLens– uses both VR and AR and aims at both games and practical applications such as architectural engineering and CAD design.

HTC Vive uses a headset, two hand-held devices and sensors positioned so as to track the movements of the user.

WorldViz makes 3D interactive, immersive visualization and simulation products. It objective is to allow its clients to make more economical full size virtual models of design such architecture design instead of physical ones.

Firsthand Technologytargets healthcare sector.  It claims that Virtual Reality is able to reduce pain and anxiety. One of its product “trains” the user to control their heart rate. According to the theory of Cognitive Behaviour Therapy, the brain’s attention can be focus on only one thing at a time. Therefore it follows that it makes sense that if the brain is occupied with playing a game of interest then less pain and anxiety is felt.

Virtalis markets ActiveWorks which the user can use to create 3D displays and virtual visualization of data and CAD designs of large scale models through which the user can walk through.

A comprehensive of resources to build generic VR systems is given at: https://virtualreality.to/resources/

These include tools for capturing images, videos, software for editing images and videos, software for creating VR applications including those using drag and drop technic, software for creating interactive interfaces, head mounted displays, hardware to play VR contents, peripherals such as hand and motion sensors and interactive chairs.

The following website provides a comprehensive list of VR hardware and software for the architecture, engineering and construction (AEC) professionals.

https://www.viatechnik.com/resources/50-virtual-reality-technologies-in-architecture-engineering-and-construction/

Computer aided design (CAD) used by engineers and architects will being transformed from producing 2D and 3D models to VR models. They will be able to create, view and edit models and visualise them at different level of scales, even walking through the virtual models. It will be able to import a CAD model into a VR model or produce the model completely in VR.

Augmented reality (AR) is a live direct or indirect view of a physical, real-world environment whose elements are “augmented” by computer-generated or extracted real-world sensory input such as sound, video, graphics or GPS data. It is related to a more general concept called computer-mediated reality, in which a view of reality is modified (possibly even diminished rather than augmented) by a computer. Augmented reality enhances one’s current perception of reality, whereas in contrast, virtual reality replaces the real world with a simulated one. Augmentation techniques are typically performed in real time and in semantic context with environmental elements, such as overlaying supplemental information like scores over a live video feed of a sporting event.

With the help of advanced AR technology (e.g. adding computer vision and object recognition) the information about the surrounding real world of the user becomes interactive and digitally manipulable. Information about the environment and its objects is overlaid on the real world. This information can be virtual or real, e.g. seeing other real sensed or measured information such as electromagnetic radio waves overlaid in exact alignment with where they actually are in space. Augmented reality brings out the components of the digital world into a person’s perceived real world. One example is an AR Helmet for construction workers which display information about the construction sites. The first functional AR systems that provided immersive mixed reality experiences for users were invented in the early 1990s, starting with the Virtual Fixtures system developed at the U.S. Air Force’s Armstrong Labs in 1992. Augmented Reality is also transforming the world of education, where content may be accessed by scanning or viewing an image with a mobile device.

Source:  https://en.wikipedia.org/wiki/Augmented_reality

Chapter 2 Strategic considerations

The significant and spectacular progress of these technologies must be celebrated.  More is anticipated in the coming years. A study is made here to identify the factors likely to help or hinder the full potential of these technologies:

Growth in computing processing power

The continuing growth of computing power at an affordable price is of prime importance. In 1965 Gordon Moore suggested that computer power will double every 2 years. The predicted held true for 50 years. But it is becoming more and more expensive to keep packing transistors in a limited area. As an illustration of the exponential growth, in 1971 an Intel microprocessor chip has 2300 transistors while a modern Intel processor such as the Intel Skylake contains 1.75 billion transistors.

New materials are being research as alternatives to silicon for the manufacture of computer chips and alternative computer architecture whereby massive amount of data is kept in RAM for faster access by the computer processors.

An alternative being research is the Quantum Computing which makes use of the ability of subatomic particles to exits in several state simultaneously. This is an area that we shall have to watch in the future.

Deterministic and Non-deterministic robots

A deterministic robot will always behave the same way given a set of input, whereas a non-deterministic one is able to behave differently given the same set of input. This allows the robot to “think” creatively. But it must be remembered that robot’s intelligence is human creativity is transferred to it which has no will of its own through Artificial Intelligence programming and deep learning. A non-deterministic robot therefore has the ability to change its behaviour and is capable to have unintended one, but that it will lack human qualities such as courage, selflessness, self-preservation and others such like. Emotions play a very important part in the functioning of humans and some may argue that this is a frailty of the human psyche. But researchers have found that there is a link between emotions and decision making. We all make decisions without knowing all the facts and many times we take a “leap of faith”. The latest findings in neuroscience suggests that decision-making is emotional and not logical. Its research has was found that individuals in whom the part of the brain where emotions are generated is impaired to the point that they cannot express emotions cannot take decisions. Therefore this raises the challenging issue of whether emotions can be programed in a robot which shall also be able to think creatively and laterally and the issue of a “fail safe” robot.

It may also be worthwhile to remind ourselves of the laws of robotics as given by Isaac Asimov, the science fiction writer:

• A robot may not injure a human being or, through inaction, allow a human being to come to harm.
• A robot must obey the orders given it by human beings except where such orders would conflict with the First Law.
• A robot must protect its own existence as long as such protection does not conflict with the First or Second Laws.

In general most of us do not know all the answers in advance when faced with complex situations but have to think through issues on a daily basis. Some writers are claiming that robots will become “smarter” than humans within the next few decades. This claim is rather puzzling considering that these robots require humans to program and train them in the first place. It is more likely that they will do the repetitive “thinking” and original “thinking”. Robots are ideal for repetitive tasks (manual or cognitive) inhospitable and inhospitable environments without making mistake (assuming no malfunction) and without getting tired as humans are prone to. However it is highly unlikely that a robot equipped with AI and given enough “deep learning” would be able to produce works at a similar level as Shakespeare or Einstein.

Driver-less cars

In industrial applications large mining companies such as Rio Tinto and BHP Billiton are already using fleet of driverless trucks to haul billions of tons of raw materials in part of Australia where temperature can rise above 45 degrees Celsius by using GPS, radar and laser sensors for navigation. These trucks are supervised by humans hundreds of miles away. In these situations no passengers are involved, the journeys are not carried out on public highways and in case of accident or malfunctions these corporations bear all the liabilities.

Although it is estimated that by 2025 driverless technology will grow to more than $900 billion worldwide yet the case of driver-less cars the situation is more complex. Potentially safer, they suffer no lapse of attention and do not get tired. The technology is based on radar sensors, video cameras, light detection and ranging sensors, Ultrasonic sensors and a main computer scanning all this information and controlling the steering, acceleration and breaking of the car via actuators.

It is a well-established fact that algorithms are already sophisticated and increasing so and will be able to deal with all the possible scenarios involved while driving a car. However here we look at some basics.  Any driver knows that intuition, anticipation and courtesy constitute an important part of driving. Some challenging scenarios a driver-less car will have to cope with regards to impulsive human drivers, cyclists and pedestrians:

• Overcrowded roads: At peak time everybody is rushing to get to work on time in the morning and to get home in the evening. In these situations human drivers are stressed and take risks.
• Joining the motorway during a busy at time, looking for a suitable gap without running out of road. Sometimes a gap is made by a courteous driver who slows down or speeds up or changes lane depending on the traffic situation. But there can be instances where it is not possible to create a gap without putting the other vehicles in danger.
• Similarly, the driverless car must be able to create a gap to allow drivers to join the motorway before they run out of road space.
• Cars arriving at a roundabout or junctions where there is no clear right of way and each signalling their intention by their indicator lights. Resolution is normally obtained by eye contact and courtesy.
• Challenging weather conditions, flooding, rains, snow, and fog.
• Moving out of the way of emergency services where the audible warning is heard well before visual contact is made.
• Failure of critical sensors or dirty sensors registering wrong information.
• Coping with narrow, winding roads and sharp bends.
• Cars moving close to each other at high speed on multiple lanes winding motorways and winding rural roads.
• Anticipating a pedestrian using a crossing
• Anticipating and adjusting the speed at the approach of traffic lights taking all surrounding traffic into account
• A vehicle approaching the driverless car head on

The following are also relevant:

• New legislations will be required for the apportionment of liabilities in accidents.
• The facility to override the automatic system in emergencies where people’s lives are at risk.
• Road sensors and transmitters to warn driverless cars of hazards well ahead.
• A system of road beacons may also be useful to keep driverless cars in lane.
• Sensors are critical to road safety, therefore a system of self-diagnostics and failure detection and warning should be in place.

Robot financial advising, algorithmic trading, big data analytics and black swan events

Here Weak AI meets with big data analytics and deep learning. Successful stock picking is considered to be an art exercised by shrewd investors based on many years of experience. Now it would be possible to objectively assess the future performance of stocks and financial markets based on the analysis of large quantity of both qualitative and quantitative metrics.

Qualitative metrics: Management (style and track record), industry sector, recognisable brand name, news from all sources, profit warnings, new government policies, new regulations, historical effect of relevant news on share price, risk analysis, corporate governance, safety record, geopolitical risks and others.

Quantitative metrics: Market capitalisation, market share, historical and forecasted revenues, profit/loss, earning per share, dividend yield, dividend cover, return on capital employed, historical and forecasted profit & loss accounts and balance sheets, historical and current P/E ratio of companies, sectors and markets, governments and personal debt levels, changing demographics, stock brokers recommendations, contrarian factors and others.

Processing these data for the thousands of companies listed on the major equity markets worldwide Isa task for big data analytics which are capable to find hidden correlations and market trends. The robot stock pickers and advisors would essentially be AI systems using the following in order of sophistication:

• Narrow AI based on rules and decision trees.
• Machine Learning – an AI system making use of statistical technics allowing the system to become more skilled with experience.
• Deep Learning – an algorithm that allows the system to train itself by using multi-layered neural network to all the large quantity of data collected.

This system contains several dangers:

• Will it be able to detect bubble asset formation?
• A sequence of consecutive good or bad years could skewed its judgement
• It is not able to predict a black swan event which by definition is unpredictable
• Will it be able to detect fraudulent behaviour within an organisation

There is still an element of black box in this system. When it comes to investing hard cash every layer of the system will have to be clarify and their workings understood.

The system proposed here is more sophisticated than current robot financial advisers which are restricted to recommending exchange-traded funds (ETFs) because of their perceived lower the risk factor. These EFTs tracks a market index, commodity or bonds. Robot financial advisors deal with smaller investments and cheaper human advisors who deal with larger investments and also cost more.

The forecasting of by an AI system will be based on the statistical analysis of past events and will not be able to predict black swan events such as a geopolitical crisis or a major natural disaster in the same way as humans fail to.

The analysis and advice produced by these systems are the result of statistical analyses. Human understanding with all its versatility and intuitiveness is a much complex process. But humans have a limited span of attention and these AI tools will help to bridge the gaps. Hedge funds and investment managers with enough resources could take the challenge and build a system based the principles outlines above.

Human brain power vs Artificial Intelligence

Some technical writers claim that robots equipped with AI will be more “clever” than humans within a decade or two. The claim is made that robots then “will be able to understand what we say, learn from experience, make jokes, and tell stories and even flirt.” In order to determine the validity of claims an investigation of the fundamental differences between the human brain and AI is required. In theory it is indeed possible for an AI system to be more “clever” in a given field than a human because it would pool the knowledge and expertise of many humans and do searches/comparisons at a very fast rate. But this is still not equivalent to human understanding.

Computer chips are silicon based whereas the human brain is carbon based. Computer chips are operated by electrical impulses whereas the human brain operated by electrical impulses and biochemical reactions. Carbon has one advantage over silicon in its ability to bond and create long complex chains of molecules which can be broken and re-created in many configurations with relative ease, thus allowing the ever-changing and very complex biochemistry of life. These biochemical reactions and electrical impulses result in thoughts, emotions, memory and imaginations which constitute the mind. The capacity of the mind to solve complex problems and rational thinking is formidable. The mind, when assisted with tools such as scientific instruments and computers increases its ability hundred folds. At any moment countless ideas pop up in the mind and this accounts for its creativity. But the thinking process can go awry. When negative and destructive ideas keep popping up in the mind on a regular basis, the result can be distress, depression and in more serious cases psychosis.

The human brain memory capacity is around 2.5 million gigabytes and operates at molecular level, the level at which computer transistors have not reached yet. But the brain is not wired to perform fast mathematical calculations as a computer chip is and suffers from tiredness. But a brain can train itself, look at a problem from a different perspective, assess the result produces and change perspective when the current one is not producing satisfactory result.  Thus automatons cannot reprogram themselves and will always require human beings to do so for them.

IBM has developed a computer chip with 5.4 billion transistors capable of simulating 1 million neurons and 256 million neural connections (synapses). Yet the human brain contains around 86 billion neurons and 100 trillion synapses. Quantitatively there is a lot of catching up to do by computer technology. Also there are fundamental qualitative difference as follows:

• Brains are analogue; computers are digital
• The brain uses content-addressable memory
• The brain is a massively parallel machine; computers are modular and serial
• Processing speed is not fixed in the brain; there is no system clock
• Short-term memory is not like RAM
• No hardware/software distinction can be made with respect to the brain or mind
• Synapses are far more complex than electrical logic gates
• Unlike computers, processing and memory are performed by the same components in the brain
• The brain is a self-organizing system
• Brains have bodies
• The brain is much, much bigger than any [current] computer

Thus there are massive quantitative and qualitative difference between brain/mind and computer hardware/AI.  This analysis sets the bench mark to spur future development; the challenge is on.

Chapter 3 Economic and human considerations

These four technologies are powerful tools for wealth creation. They will usher the era of the fourth industrial revolution which will bring changes on a scale never seen before. There will be winners and losers on a large scale. The changes will have to be managed by governments, international organisations and not just technological companies in order to minimise their impact on the more vulnerable. Low skill jobs will be at threat. Emerging economies relying on cheap and low skill labour will also suffer as a result. With increasing computing power and ever increasing sophistication in algorithms, large number of industries will be automated including higher skill manufacturing, teaching, financial market investing, medical diagnostics, defence and security.

In the past each industrial revolution increased productivity and made back breaking tasks more human friendly resulting in rising standard of loving and better quality of life for the many after a painful adjustment period from the old to the new.

It was reported on the World Economic website that the percentages of jobs at risk in the future will be 47% in the US, 35% in the UK and 77% in China. This could result in devastating social consequences. Social stability will have to be maintained and the worldwide economy keep functioning. Goods could be made by robots for human consumption. If these humans cannot earn money to spend then the economy will go into free fall as we saw in the credit crunch following the financial crisis of 2007/2008. Therefore governments may have to pay every citizen a nominal salary in order for enable themselves and the economy.  Governments will then have to raise money through corporation tax and perhaps even by taxing automated and robotics systems. The flow of money in an economy keeps its going – remember the credit crunch and the Great Depression. Large proportions of the work force will compete to find a niche in the employment market or will have to pursuit other interest in the arts, travel and exploration. The situation is a little similar to the historic case where division of labour in the 18^th century brought great wealth accompanied with social disruption. It must never be forgotten that the characteristics of a great society is social justice, equal opportunity for all and opportunities for them to fulfil their potential. A bread and circus type of society after the Roman model is not satisfactory.

Economic systems will have to be reformed to allow people to work for less hours. Perhaps the nominal salary suggested above should have a social component the level of which will be according to the time spend in helping, mentoring and caring for others.

A new elite is in the process of being created which comprise of talented of software engineers, system analysts, mechatronics (combination of mechanical, electrical and control engineering) engineers and those who understand and can harness the new technologies though they are not creators.

Work has always been a way of obtaining a share of the wealth created by earning an income. Work also provide social interactions and a sense of purpose for many. If the wealth created is not shared, there could be an underclass of unemployed unskilled workers leading to social unrests on a large scale. Each country will have to manage this 4^th industrial revolution according to the will of its people according to the political system they have.

Issues with Artificial General Intelligence research and development

The characteristics of Artificial General Intelligence (AGI) systems have been given in section 1.3. In summary, the anticipation is for these systems to eventually have cognitive power akin to human beings. Stephen Hawking has voiced his concern about the possibility that AI will progress at faster rate than human intelligence due to the slower rate of human evolution. Other prominent personalities as Elon Musk have voiced their fear of potential dangers of AI. They are right to highlight these dangers as we are heading for an uncertainty and perhaps dangerous period of human history. Therefore a plan must be put to minimise the dangers and maximum the benefits. These personalities in collocation with others have developed 23 principles with the view that their practise will keep AI safe. Because of their importance for all stake holders, present and future, these principles are given in full here:

1. Research Goal: The goal of A.I. research should be to create not undirected intelligence, but beneficial intelligence.
2. Research Funding:Investments in A.I. should be accompanied by funding for research on ensuring its beneficial use, including thorny questions in computer science, economics, law, ethics, and social studies, such as:

• How can we make future A.I. systems highly robust, so that they do what we want without malfunctioning or getting hacked?
• How can we grow our prosperity through automation while maintaining people’s resources and purpose?
  How can we update our legal systems to be more fair and efficient, to keep pace with A.I., and to manage the risks associated with A.I.?
• What set of values should A.I. be aligned with, and what legal and ethical status should it have?

3. Science-Policy Link:There should be constructive and healthy exchange between A.I. researchers and policy-makers.
4. Research Culture:A culture of cooperation, trust, and transparency should be fostered among researchers and developers of A.I.
5. Race Avoidance:Teams developing A.I. systems should actively cooperate to avoid corner-cutting on safety standards.
6. Safety:A.I. systems should be safe and secure throughout their operational lifetime, and verifiably so where applicable and feasible.
7. Failure Transparency:If an A.I. system causes harm, it should be possible to ascertain why.
8. Judicial Transparency:Any involvement by an autonomous system in judicial decision-making should provide a satisfactory explanation auditable by a competent human authority.
9. Responsibility:Designers and builders of advanced A.I. systems are stakeholders in the moral implications of their use, misuse, and actions, with a responsibility and opportunity to shape those implications.
10. Value Alignment:Highly autonomous A.I. systems should be designed so that their goals and behaviours can be assured to align with human values throughout their operation.
11. Human Values:A.I. systems should be designed and operated so as to be compatible with ideals of human dignity, rights, freedoms, and cultural diversity.
12. Personal Privacy:People should have the right to access, manage and control the data they generate, given A.I. systems power to analyse and utilize that data.
13. Liberty and Privacy:The application of A.I. to personal data must not unreasonably curtail people’s real or perceived liberty.
14. Shared Benefit:A.I. technologies should benefit and empower as many people as possible.
15. Shared Prosperity:The economic prosperity created by A.I. should be shared broadly, to benefit all of humanity.
16. Human Control:Humans should choose how and whether to delegate decisions to A.I. systems, to accomplish human-chosen objectives.
17. Non-subversion:The power conferred by control of highly advanced A.I. systems should respect and improve, rather than subvert, the social and civic processes on which the health of society depends.
18. A.I. Arms Race:An arms race in lethal autonomous weapons should be avoided.
19. Capability Caution:There being no consensus, we should avoid strong assumptions regarding upper limits on future A.I. capabilities.
20. Importance:Advanced A.I. could represent a profound change in the history of life on Earth, and should be planned for and managed with commensurate care and resources.
21. Risks:Risks posed by A.I. systems, especially catastrophic or existential risks, must be subject to planning and mitigation efforts commensurate with their expected impact.
22. Recursive Self-Improvement:A.I. systems designed to recursively self-improve or self-replicate in a manner that could lead to rapidly increasing quality or quantity must be subject to strict safety and control measures.
23. Common Good:Super-intelligence should only be developed in the service of widely shared ethical ideals, and for the benefit of all humanity rather than one state or organization.

The dangers of using AI and robotics for warfare

Many tech experts, including Elon Musk, consider the use of AI and robotics for warfare to have the same danger level as chemical, biological and nuclear weapons. They have written an open letter to the UN asking for AI to ban for the use in warfare. Otherwise another arm race based on the use of AI could be triggered. Already many countries are in the race to become master of the technology. Switching from civil to military use would not be difficult step to take as shown in the past whereby technologies ended up in military applications. The old adage that “all is fair in war” seems to be the rule.

The other danger could the rise of neo-colonialism. In the past more technological advanced countries turned their advantage in a military one and colonised the less technological advanced. Enormous harm done as a result and this must never be allowed to happen again.

Caution is required in the face of the many speculations and noise from both popular and scholarly sources.  The emphasis on transparency must never be overlooked. Irrespective of how intelligential becomes our last defence in all walks of life will still be using our cognitive skill to evaluate the world around us on a daily basis and make the many decisions that have bearings on our daily living. The invention of wheel millions of years ago did not make human legs redundant.  The discovery of fire did not cause mankind to go up in flame. Rather the wheel and fire propelled the progress of mankind forward. In the same way automation and AI will propel mankind forward again in ways we have not yet dreamt of. However, in this process the human qualities of empathy, civil liberties, and social justice must not be forgotten in the dash for more and more technology. Unfortunately both of the wheel and fire were used to build weapons of war. Nowadays a similar scenario could be prevented by international agreements through the UN.

The keys points are:

• Unprecedented changes will happen within the next few decades due to these technologies. We could be on the brink of great prosperity and leisure provided the environment is not destroyed and the climate is not adversely and irrevocably changed.
• Some areas VR is currently used: providing medical care, training of medical students, helping with disabilities, PTSD therapy, fear and anxiety therapy, virtual conference rooms (this will benefit the environment and climate change) and design tools by engineers and architects.
• Some areas robots are currently used: as military automatons to defuse explosives, in assisting in the manufacture of cars, in surgery and underwater exploration.
• Some areas AI is now used are: video games, driverless cars, advertisement based on previous purchases, monitoring of fraud, intelligent security surveillance and intelligence devices in the home.
• Potential significant increase space exploration by robots and the finding another earth type planet among the billions of planets in existence in the vast universe
• The protection of Aliant robotics systems against hacking will be paramount for all our security.
• If managed well the changes could bring a utopian society, else they could bring a dystopias society of epic proportion with the gap be rich and poor grown to a chasm and all the negative consequences this will bring
• Rise of new elite and aristocracy who understand and produce the new technologies
• Despite the many bold claims, these technologies are primarily tools to extend human capability, replacing humans in repetitive tasks, helping humans in problem solving and decision making.
• International agreement through the UN and other international organisations for these technologies to be used for civilian use and for some defensive military use.

Robot Advising by Hao Peng

Robot Advising – How It Changes the World of Financial__ Advising

Video book Part 4 Virtual Reality, Augmented Reality, Mixed Reality