5. Virtually here

- information technology (includes digitalisation, Internet, social media, world wide web, automation, artificial intelligence, mobile phones, etc) is reshaping everything we do, ie greater connectivity, more goods/services sold online, etc

For  many years around 75% of future jobs in the most rapidly growing industries will require skills in Science, Engineering & Mathematics (STEM) (MGS 2016)

- the virtual world is becoming the real world, eg shopping, education, communications, work, leisure, etc. More devices like phones, laptops, traffic lights, air-conditioners, supercomputers, iPods, etc are being plugged into one enormous and rapidly expanding network of gadgets. It's the emergence of a network, all plugged into each other that has the impact, ie meta-level functionality, like cloud technologies. There are some suggestions that they will get smarter than us and we will merge with the machine!!!!
- increasingly changing lifestyles and redesigned labour markets like

i) robotics will replace more professions, jobs, etc, eg artificial intelligence, automation, etc

ii) "bricks and mortar" stores being replaced by online retailers which includes overseas sites, etc. It is estimated that the global e-commerce markets is worth around US$ 1 trillion and is growing (2015). It is thought that for the traditional store to survive will have to have an "experience factor", ie a place to interact with trained and knowledgeable people who could help the consumer navigate his/her way through the many options and buy a product that meets budget and needs. It will provide an experience with emphasis on services, not the products.

iii) more sophisticated, responsive, background supply chains, eg consider logistics: use of drone-like delivery vehicles; in Australia parcel delivery is more profitable than mail; increase in land, sea and air deliveries

iv) information technology will allow us to live and work away from the traditional office. This has caused a rethink in our design of office buildings and cities, with a move away from open plan office layouts to activity-based layouts. More people are working from home, eg virtual offices. More people are working as freelancers who sell their services to the global market from any location. The office of the future will be more fluid, dynamic, connected.

v) democratisation of knowledge with more knowledge free and accessible. 
- create exponential growth linked with nanotechnology, quantum computers, big data, bio-mimicry, P2P, etc. This involves understanding the second half of the chess board analogy, ie doubling output when going on each space of the chess board, and that we are limited only by our imagination about what the future holds

vi) Education
Growth of personalised learning around life-long learning with a strong literacy and numeracy skills basis plus a creativity-problem-solving-collaborative-growth focus, ie tailor learning to individual needs, and less use of traditional classrooms/lecture theatres, ie more online and use of open, flexible space.

"...you walk in and there are hundred kids, and all the classrooms have been opened up. There are groups working together, there are groups working in little autonomous pods, there are some specialist one-on-one, and there might be five or six teachers in with those 100 kids. The place is just alive with learning..."
Mark Scott as quoted by Tim Dodd, 2017

Impact of technology on work

"...The way the Industrial Revolution and the introduction of machines changed manual labour, this revolution is going to change a lot of the labour that was done by thinking and people engaging with their minds. Many white-collar jobs could disappear, or change beyond recognition, under the impact of automation......will need to master new technologies and thrive in industries that haven't been invented yet..."
Mark Scott as quoted by Tim Dodd, 2017

Cognitive Computing
It is like the brain and nervous system sitting on top of the Internet of Things. It helps us to make sense of the 9 b.  connected devices operating in the world today which generate 2.5 quintillion bytes of new data daily.

Leveraging cognitive computing will augment human intelligence to deliver exceptional service to customers, etc.

"...new generational computer hardware - massive parallel processing networks, leverage graphic processing, corralled in server farms - along with real-time diverse data collections and highly advanced learning algorithms, that form deep neural networks, are resetting the state of the possible.  The past eight years has seen a 10,000 fold improvement in processing speed and the dawn of quantum commuters will deliver another step change..."
Beverley Head, 2016

NB As humans we experience things, try things, learn, create implicit mental models, ie we see, we hear, we sense the world around us - we know that if we push the door, it opens.  Cognitive platforms similarly ingest information, make sense of it, learn from it, optimise themselves based on that learning and then set up something useful, ie smart machines will find patterns in data and extrapolate generalisations from these patterns.

With powerful and cost-effective computer systems, more information can be processed at high speed.  Link this with Big Data collection, social networks, sensors, etc plus the advances in machine learning and deep neural networks, etc and it is possible to use cognitive platforms to generate more useful insights. 

Smart content or machine learning has
"...software has enabled them to provide greater high-value content to humans. They are getting better at understanding the idiosyncrasies of people's voices, accents and colloquial speech. A person can carry out a conversation with these devices giving the appearance of intelligence and consciousness..."

Timothy Carone, 2017/18

The more content these machines collect, the more complex answers they can handle. This is called smart content, ie
"...derived from virtual assistants, sensor collecting data from millions of conversations, thousands of Internet sources, and sensors collecting data on physical surroundings. The machine learning software determines the meaning of the data and creates the smart content verbalised..."
Timothy Carone, 2017/18

Some examples on the use of smart content
- call centres (interact with customers)
- reporting (collecting facts and writing articles)
- financial portfolios (making investment decisions)
- automobiles (this on-board software makes decisions thousands of times a second to ensure a smooth and safe ride)
- aeroplanes (auto pilots)
- emergency hospital rooms (making decisions on treatment is faster than ER staff),
- lawyers (conducting discovery, investigating precedents, etc faster and more accurate than humans),
- accountants (accurately analysing financial information, data, etc), etc

NB There is more concern about how smart content will handle philosophical, ethical, religious, etc concepts that will require insights and education, ie smart content
"...is more reflective of scientism and reductionist thinking because the smart content it verbalises is shaped by moral relativists..."
Timothy Carone, 2017/18

It is the technology behind autonomous vehicles, natural language computer interfaces, smart homes and factories, etc.

These cognitive computing platforms range from dealing with the routine automatically, like the automation of contact centres to handling routine enquiries to supporting specialists in more complex challenges such as training young surgeons via haptic devices that give tactile feedback before the surgery starts.

Some more examples include
- providing computer-based support for elderly people living alone or recovering from an operation (Annacares)
- search-based marketing tool which matches brands to non-branded search items by understanding what people are really looking for (YourAmiga.com)
- using information in project reports to develop insights to give guidance in investment in design, fabrication and construction ( Woodside - oil and gas company)
- to assess life-insurance wrists (Swiss Re)

"...today's cognitive platforms, such as IBM's Watson and Google's Deep Mind, can...... ingest large bodies of information, including unstructured information such as images, video and speech, detect patterns that people cannot that people cannot......appear to understand large bodies of medical literature, case study law or content to provide deep advice for experts; make self driving cars possible; manage productive dialogue with customers; and - importantly - make mistakes..."
Gartner as quoted by Beverley Head, 2016

In 1997 chess playing program, Deep Blue, managed to defeat the reigning human world champion, Gary Kasparov.

"... Deep Blue could evaluate 20 million positions per second. It never got tired, never blundered in a calculation and never forgot what it had been thinking a moment earlier..."
Steven Strogatz, 2019

Since then machine learning has progressed where the machines are learning from experience and can update their neural networks. For example, Alpha Zero researched the principles of chess on its own and quickly became the best player ever. At this stage the machine can understand the important principles about chess but can't communicate them to us. At the same time, machine learning can help humans understand problems in science and medicine like cancer, consciousness, immune system, genome, etc

IBM's Watson was the first commercially available cognitive computing platform that demonstrated its capacity in 2011 when it read 30 m. pages per second to win the US game show Jeopardy. Watson was launched commercially as a cloud-based service in early 2014.

"...Cognitive computing is about amplifying human cognition.  They don't do your thinking for you, they do your research for you so that you can do your job better, that could be as mundane as asking a question or as profound as recommending the right treatment..."
Bob High as quoted by Beverley Head, 2016

For example, Watson Oncology provides support to doctors as they diagnose and treat patients, analysing vast amounts of research and published papers, and leverage micro segmentation to tailor treatment recommendation for individual patients.

Leverage of these cognitive platforms will help organisations expand. There are many industries like life-insurance, banks, government, education, etc that have much unstructured data, will benefit from these corporate IT platforms.  For example, a pharmaceutical company could leverage its data to become a consumer health management business.

NB More data, more accurate and smarter the cognitive platform gets. The learning capabilities of cognitive platforms mean that they are effectively writing code using their own neural networks and without human intervention.  It is hoped that humans and machines will work together and be able to outperform machines or humans working independently.  There will be governance issues around this, ie to prevent misuse and the need for human conscience

Recently the development of neuromorphic chips is a further but limited gain, ie

"...cognitive computing is based on deep learning neural nets which have some degree of similarity with how the human brain functions.  But the neural nets are very basic compared with human neural systems: we currently use one sort of neuron, one type of synapse; the human brain has it least 150 different types of neuron..."
Bob High as quoted by Beverley Head, 2016

These systems are tuned for inductive reasoning, ie where the answer to a question exists somewhere in the knowledge base and can be served up with a degree of probability.  This could be extended to more deductive reasoning, especially for health care and finance.  Then the system could be tuned to abductive reasoning.  In other words, all forms of reasoning intended to help humans do their jobs.

More on cognitive computing

"...cognitive computing will usher in a new wave of change, giving organisations the power to gain insights and make decisions from vast amounts of data......use natural language recognition and machine learning to discover new insights on vast amounts of unstructured data, at a speed never before possible..."

Beverley Head 2016

For example, banks are deploying cognitive solutions to help employees identify customers' needs faster and in a more personalised way. Additional uses are in fraud analysis and investigation, automated threat intelligence and prevention, and smarter financial advice.

"...intelligent, increasingly autonomous systems now have the capacity to reshape business, health, education, government and society by augmenting human intelligence..."

Toby Walsh as quoted by Beverley Head 2016

Some examples include

- legal practices (an intelligent platform will be able to effectively handle discovery rather than junior clerks poring over thousands of corporate documents.)

Artificial intelligence has 2 elements, ie

i) learning from the past, eg machine learning and big data

ii) decision-making based on improving efficiency and effectiveness by using what has been learnt from the past, eg rostering of staff, scheduling of production, reducing overtime, etc.)

- personalising healthcare (in the developing world people are dying from diseases that we know how to diagnose and to prevent/treat cheaply. We already have the technical ability, via smart phones, to diagnose and fix these problems)

- personalising education (systems are being developed that will follow you for life, ie knowing what you know, what you need to know and identifying gaps in your knowledge. Also, the system will know how you learn and how you best understand)

- job selection/career development (linking psychometric testing with job opportunities to find the right people)

Banks are deploying cognitive solutions to help employees identify customers' needs faster and in a more personalised way. Additional uses are in fraud analysis and investigation, automated threat intelligence and prevention, and smarter financial advice.

IBM is now in the business of cognitive computing, ie a combination of digital business and digital intelligence. The cognitive element is augmenting our intelligence, ie man and machine working together to improve decision-making. IBM's cognitive computing platform (Watson) was launched commercially as a cloud-based platform in 2014. It allows, via application programming interface (APIs), organisations to embed cognitive services into existing business processes. This will unlock and an organisation's latent knowledge and link it with natural language processing. It should help strip away the unconscious bias we as humans have; it is providing an analytical perspective and insight. On the other hand, humans need to provide the moral compass to balance the objectivity of the machines.

The combination of cognitive computing, block chain and the Internet has the potential to be very powerful, eg "...in logistics......blockchain. is a process is already seen as significant. Add to that, data delivered by IoT sensors that feed into the block and cognitive computing insight engine and the process is radically improved..."

Dee McGrath (IBM) as quoted by Beverley Head 2016


"...Blockchain constitutes a suite of 5 technologies: cryptography, a database that can be added but not altered, peer-to-peer networking, an application of game theory, and an algorithm of ensuring a consensus about what information is held on the ledger..."
Chris Beck et al, 2017

Individually these technologies are not as powerful as when they are joined together. It will allow the creation of new forms of business structures and new ways to work together.
It will drastically reduce the costs of tracking, recording, and verifying transactions. It has uses in many industries like property registers, intellectual property, security logistics, health care records, etc..

One part of it is constant, ie
"...the blockchain's nested levels of encryption are built to ensure that once something is placed on the blockchain it is permanent, immutable and accessible only to those who own it. Blockchains work only because the users have absolute confidence that the system is secure..."
Chris Beck et al, 2017

An example is with in the health industry with melanoma (skin cancer). If melanoma is discovered early, it can be treated. Using cognitive technology to support early diagnosis; building on the work done with MoleMap (collected 40,000 visual analytics around melanoma). Cognitive technology (IBM's Watson) was able to demonstrate a 91% accuracy in detection of melanoma

"...building algorithms that recognise the morphology of a mole or lesion and then help clinicians interpret the morphology by examining its symmetry, border, colour and dermoscopy pattern, in order to then predict the level of confidence about whether melanoma is present..."

Beverley Head 2016

"...we are teaching Watson how to see. We are teaching Watson the science of melanoma and to become that trusted adviser. We are reshaping what's possible: our thinking on cancer for example......the system has ingested 200 medical journals and 300 textbooks and has been trained by clinicians to read x-rays and EMR images..."

Joanna Batstone (IBM) as quoted by Beverley Head 2016

Courtney Best estimates suggest that 95% of all the world's data was created in the last 3 years but 80% is not readily available. Computers can store this information and be programmed to recognise its relevance (Beverley Head 2016).

The biggest benefits will be in prevention of health issues. Once this is integrated with genomics, this will personalise medicine and have the possibility of heading off chronic disease and multiple morbidities.

Another example is in the oil and gas industry. Woodside (Australia's largest independent oil and gas company) has so much data that making sense of it is hard. It is using a cognitive platform which has suggested much of the firm's data. For example, 1 of its 7 platforms draws on 28,000 project documents (each at least 100 pages long) that represents 30 years of projects. A technical question can be answered very quickly. This is enabling graduates and young professionals to gain knowledge that would normally take decades to develop.

"...Machine learning capabilities mean that over three months the platform has lifted its performance from 1 in 20 success rate to 1 in 4. And the platform will continue to grow as Woodside adds a further 12,000 documents......plus data from 200,000 sensors..."

Shaun Gregory and Russ Potapinski (Woodside) as quoted by Beverley Head 2016


"...from cognitive we get continual insights and mimic human thinking from a program that we can learn from mistakes, apply inference and use natural language. With block chain the entire premise is that each block has provenance in historical context, and that insures transactional trust..."

Nitin Gaur (IBM) as quoted by Beverley Head 2016

"...technology is part of the cure and part of the problem. In the last revolution, the industrial revolution, we had to change our society in a big way: the introduced unions, child labour laws, universal education to help get people jobs. We potentially face equally challenging times......with education as a lifelong activity..."

Toby Walsh as quoted by Beverley Head 2016

 - increasing focus on human inter-action despite technology allowing people to work on their own, ie it encourages linkages and communications for a range of activities like friendship, family, romance, hobbies, emotional support, learning, activism. etc. The "6 degrees of separation" has change to around 4, ie

"...the online world is bringing people closer together, increasing the speed and distribution of information flows. This will continue to have a profound impact on how people obtain, trust and use information into the coming decades..."

Stefan Hajkowicz, 2015

- need to focus more on advancing techniques rather than cost competitiveness. Advanced techniques include robotics, automation, materials and composites, digital design, 3-D manufacturing, data analysis, bio-manufacturing, micro and precision manufacturing, virtual reality systems, etc. New skills are required in the areas of cyber security, data science, artificial intelligence and cognitive business (Ben Potter, 2017)

- need to move away from incremental new-to-the-firm innovation to big step innovation which is new-to-the market.

- need to find ways to translate ideas into businesses.

"...There may be a perception that it's about the genius having the individual breakthrough, rather than the systematic application and iteration and bouncing back from inevitable failures..."

Charlie Day (CEO innovation and science Australia) as quoted by Joanna Gray 2016c

- In the software industry, relevance and impact are more important than longevity; the industry does not respect tradition, only innovation.

Human challenges around technology

What does it mean to be human when interacting with and perhaps being dominated by machines and as the machines are becoming more human like?

When deciding the suitability of AI, need to understand its capabilities, limitations and the appropriateness of its application. To understand its applications there is a need for a 

"...basic understanding of machine learning and inputs and underpinned the development of everything from virtual assistants to employment tools, credit assessments, safety tools and content analysis......(need) to build an open, trustworthy data eco-system..." 

Sally Patten 2019 

To understand its capability and limitations you need to interrogate the basics, ie 

"...If the data going in is not complete, inaccurate or reflects biases, the decision ( that comes out the other end) will not be right..." 

Emma Martinho-Truswell as quoted by 
Sally Patten 2019 

Other limitations include 

damaging personal relationships with stakeholders, ie depersonalising relationships

- not suitable for the last stages of hiring staff, settling employment disputes and granting of sick leave
, eg 

"...last October Amazon scrapped a sexist tool that used AI to decide the best candidates to fill the jobs. Members of the team working on the system said it effectively taught itself that male candidates were preferable. In 2016 Microsoft shut down its artificial intelligence chatter bot because it was unable to recognise when it was making offensive or racist statements..." 

Sally Patten 2019 

- not innovative as 

"...AI is trained on a series of examples. AI will not come up with a novel solution..." 

Emma Martinho-Truswell as quoted by Sally Patten 2019 

"...issues in the use of artificial intelligence in our offerings may result in reputational harm or liability...... AI presents recent challenges that could affect its adoption, and therefore our business..." 

Microsoft report as quoted by Sally Patten 2019 

One of the challenges is capture, curation, circulation and sense making of data

Impact of technology

An example - the cost of core banking since early 2000's has dropped by 100 fold (James Eyer, 2017). The key to success is knowing how to apply the right methodology to the right people and the right problem. Linked to this is the need to put the mechanisms in place that will encourage the changes of behaviours that are required to handle the future, eg failure needs to be rewarded, not punished. Too often experimenting that is not successful is seen as a failure and has a negative impact on careers. Rather, it should be seen as an important part of learning.

Organisations need to realise that they are now Internet companies.

In fact, no industry is safe owing to technological changes around the internet, artificial intelligence, digitalisation, software usage, access to data, etc. Social media, smart phones, internet connected gadgets, blockchain, quantum computers, etc are challenging almost all industries. 

Most of the disrupting is being done by large tech firms like Apple, Alphabet (Google), Microsoft, Amazon, Facebook, Netflix, Spotify, etc. The first 4 are the world's most valuable public companies by market capitalisation; Facebook is in the top 10. Combined these 5 are worth almost US$ 3 trillion (mid 2017). For example, Google

"...controls around 90% of Internet search market...... Amazon, which has decimated bookstores and is hurting shopping malls, is easily America's biggest online retailer. Facebook dominates social media. Apple sold 78 m. smart phones last year......The tech industry is not only highly disruptive against non-tech players, but former Internet highflyers have also fallen victim to creative destruction. MySpace, an early social media network was obliterated by Facebook. Yahoo! dominated Internet search market in the 1990s before Google took over. Yahoo!......was recently acquired by Verizon Communications for US$ 5.5 million, a shadow of its near US$ 140 billion......in the dot com bubble era in 2000. Fellow Internet pioneer America on-line (AOL), the software service company that allowed computer users to access the Internet community, suffered a similar fate after broadband destroyed its dial-up Internet access model...... when the US government moved to regulate IBM as a monopoly due to its dominance in desktop computing and software, Microsoft and Intel emerged as disrupters..."

John Kehoe, 2017

These successful Internet companies take control of their value chain

Amazon online sales is decimating the shopping malls and changing retail industry, eg dominant US retail department chain (Sears) that sold almost everything like life-insurance, clothing, bicycles, tools, music records, electronic goods, farm machinery, etc) announced the closing of 150 stores (2017); its share price was then around US$ 7 compared to a peak of US$ 192 around a decade ago. Furthermore, Amazon is becoming a sprawling conglomerate across e-commerce, cloud computing, online subscription television, small business loans, artificial intelligence and in-house devices.

Evidence of the Amazon behemoth status totally overwhelmed e-Bay. At one time eBay was a leading in Internet retailer with a market capitalisation greater than Amazon. By mid 2017 Amazon is 10 times the value of eBay.

These large tech companies compete against each other such as in online music, videos, driverless cars, drones, etc. To survive they need to keep innovating to reduce the chance of being disrupted themselves.

The data that these companies collect poses a threat to privacy and could block the emergence of competitors. These databases and algorithms that are controlled by the likes of Amazon, Google and Facebook are thought to be very valuable and necessary for success as it gives them an understanding of people's behaviours, consumers' preferences and personal characteristics.

Many people are concerned about the rising power of technologies and the firms that own and operate it. On the other hand, humans have been through this before like arrival of electricity and television. There were similar concerns about the impact on society. For example TV in the mid 20th century

"...Was it going to make us into Americans? Is it good for our kids? The technology is 70 years old and we still bedding it down inside our culture. We are still rearrange our furniture around screens. And it took regulatory moves, second-wave kids, all kinds of stuff to get that sorted...... society usually takes a bit longer to catch up..."

Genevieve Bell as quoted by John McDuling, 2017


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