Predictive analytics, when combined with artificial intelligence, can assist organizations with their risk management, as well as their planning and optimization.
Two years ago, the World Economic Forum (WEF) published its Future of Jobs report – exploring employment, skills and workforces in the Fourth Industrial Revolution. This sparked debate – and growing concern – around a changing global employment landscape as the result of disruptive technologies, studded with widening skill gaps, new jobs and job displacement.
“The Fourth Industrial Revolution, combined with other socio-economic and demographic changes, will transform labour markets in the next five years, leading to a net loss of over 5 million jobs in 15 major developed and emerging economies.”
Recently, at a London-based robotics event, attendees were asked the same question that the WEF did: did your job exist when you were in primary school? The 67% ‘no’ response was hardly a surprise – the job landscape is evolving constantly, so it’s naïve to expect to be on a career path set during our early school years.
Headlines of this type are often strongly associated with ideas of robots replacing humans in every profession – although clearly there are always roles more suited to humans and their capacity for empathy. But rather than accepting this pessimism as truth, let’s embrace the new generation of jobs that automation will offer us.
We grumble at the fact that our current careers may not have been mapped out for us at infant school, instead of celebrating the fact that we somehow emerged prepared for the jobs we have today. To ease our fear, it might help to recognise that tomorrow’s workforce is more than capable of taking the same path if we guide them wisely: the fact that government is now backing education initiatives that will support children in their future professional lives is a leap in the right direction.
Last year, a group of liberated educators took steps to evolve the curriculum so that children are prepared for a future with automation – the creation of new qualifications and courses dedicated to human centric skills such as leadership and collaboration was evidence of this.
It’s refreshing to hear that 2018 is set to see government beginning to back educational initiatives with cyber skills training; hopefully the next step is more government funding surrounding training for the jobs students will need in the age of increasing automation.
In addition, we’ll need to address the current gap of skills needed for robotics and Artifical Intelligence (AI) by investing in software development, systems design, engineering, programming and data science amongst other areas, to ensure workforces of today – and tomorrow – are skilled to take charge in the robotics world. Taking into consideration the hole in numbers in Science, Technology, Engineering and Mathematics (STEM) subjects currently in university, we have a five-year lag in students moving into this area.
It’s time for government to get smarter when it comes to incentivising students in this direction – this doesn’t necessarily require radical thinking. How about reducing tuition fees for STEM subjects as a start, and creating conversion routes from other subjects?
Until then, we must focus on educating students in a way that will help them collaborate with AI in the years to come. The essence of roles that will be filled by children currently in primary school will be their humanity. Curriculum must continue evolving so that the members of the future’s workforce leave school with skills that focus on adaptability, collaboration and resilience. Instead of focusing only on the retention of facts, it’s time to teach how to question these facts.
If we can build on current momentum and continue to bridge the gaps to encourage new ‘age of automation’ careers, headlines in 2018 and beyond might look more optimistic, pointing towards a future where robots and humans work collaboratively to deliver improved services and bright new opportunities. It’s up to us to decide how full – or empty – the glass looks when it comes to the future of jobs.
With the Fourth Industrial Revolution hailed as bringing about a digital boom on the global economy, many may think: “Are we not we already well into the digital economy era?”.
It is true that there are now countless apps and computing technologies that allow people to conveniently hail a taxi, book a hotel, or clean floors with a robot. Smart machines can also already drive cars, diagnose patients, and manage finances more effectively than humans. But in a new analysis – What to do when Machines do Everything – we found the real boom is only just beginning.
In the years to come, AI will create further value, for example around safeguarding financial health, insuring families, and enabling people to heal and govern themselves – and this is just the beginning. Systems of intelligence, which combine hardware, AI software, data, and human input will help improve countless customer experiences, business processes, products and organisations.
Jobs and businesses will undoubtedly be impacted. One of the most common concerns is that the bots will take over everything. While it is true that machines will replace some occupations, and make some current skills irrelevant as robots do more of the everyday, mundane tasks, people will also become even more vital to helping an organization innovate and grow.
Machines are getting smarter every day and doing more and more; they will soon change our lives and our work in ways that are easy to imagine but hard to predict. The debate has, thus far, been in the hands of theoreticians: it is now time for pragmatists to take over. These pragmatists – whether companies or individuals realize that machines will replace some occupations, putting pressure on wages for some jobs and making some current skills irrelevant. However, machines will also enhance the human element of work. In fact, more than 80 percent of teaching, nursing, legal and coding jobs will be made more productive, beneficial and satisfying through artificial intelligence. While machines will learn to do more things, and will perform tasks more economically, more efficiently and with fewer errors, this will augment the human experience, generating more jobs, even creating professions that do not even exist yet.
As we expect 20 percent of the more administrative portions of a job go to a machine, the future workforce will require more people to fill jobs currently in short supply: data scientists, designers, technologists, and strategists, as well as create jobs that do not even exist yet.
Materials, Machines and Models – the formula to ‘win’ the Fourth Industrial Revolution
The digital revolution is fundamentally a growth story. While the future of an automated workforce can be frightening, the artificial intelligence (AI) revolution will create a huge wave of opportunity for businesses and individuals who are prepared. Typically, every previous revolution has followed such a pattern: innovation bubble, stall, and then boom. The Fourth Industrial Revolution will be no different. Early digital economy winners have aligned the Three Ms – materials, machines and models – and use them to their advantage.
Firstly, sensors will be required on nearly every “thing” – IoT devices, RFID sensors, accelerometers, motion sensors, etc. – to create massive amounts of data that is the new raw material of the digital economy. Secondly, systems of intelligence (machines) will be required to “process” this new raw material data to improve business productivity and customer. Finally, new commercial models will emerge that monetise services and solutions based on these systems of intelligence.
However, without the right business model to support data-fuelled machines, companies will struggle to be successful. Business leaders will need to decide how to instrument everything, how to harvest all the resulting data, how to ask the right questions of the data, and to “teach” the AI systems what to look for, what is meaningful, and what is immaterial.
Five essential plays for winning with AI
Each of the Three Ms in today’s business success formula must be activated to move AHEAD. There are five distinct approaches for not only winning with AI but surviving and thriving in this time of transition – automation, halos, enhancement, abundance and discovery.
1. Automation: Outsource rote, computational work to the new machine. This is how Netflix automated away Blockbuster.
2. Halos: Maximise the data products and people generate – via their connected and on-line behaviours – to create new customer experiences and business models. GE and Nike are instrumenting their products, surrounding them with halos of data, creating more personalised customer service and products as a result.
3. Enhancement: View the computer as a colleague that can help increase job productivity and satisfaction. For example, a car’s GPS system improves driver performance by enhancing navigation, providing alerts for road hazards, and ensuring the fastest route is taken on any given journey.
4. Abundance: Use the machine to open up vast new markets by dropping the price-point of existing offers. For example, UK-based start-up, Brolly, has created an AI enabled insurance advisor to allow customers to understand, manage and buy the insurance they need.
5. Discovery: Maximise use of AI to conceive new products, new services, and new industries. Just as Edison’s light bulb led to discoveries in radio, television, and transistors, today’s new machines will lead to the next generation of invention.
The world is changing faster than ever before. Our children and grandchildren will study the advances of the Fourth Industrial Revolution, just as we studied the great technological innovations of Albert Einstein and Thomas Edison. Automation and the rise of AI are truly deep and unstoppable forces – they are the core of this incredible pace of change. The shift to the new machine and AI is inevitable but if managed wisely, it will ultimately be a positive force for companies, individuals, and society. Leaders can compete and win in the next phase of global business by driving productivity, customer intimacy, and innovation if they align the three Ms and think AHEAD.
It is time to build our own future, complete with a sense of optimism and confidence. When machines do everything, there will still be a lot for companies to do. It is time to start now or risk being left behind.
Ben Pring, Vice President, Cognizant’s Center for the Future of Work and co-author of What to do when Machines do Everything
What’s hot in AI this year? Here’s what the analysts say.
Unsupervised learning, e.g., when the machine “learns” what is a spam email without first looking at a lot of emails labeled “spam” or “not spam,” is the holy grail of the AI field according to its leading practitioners.
An interim step on the journey to unsupervised learning is a hybrid approach, with some of the data labeled, but letting the machine guess the labels for the rest of the data, using associations. Google has developed one such technique, called Graph-based learning, which uses semi-supervised learning. Using its Knowledge Graph technology, which makes relation associations between words, Google is able to leverage the associations to replace the cumbersome task of labeling all of the data. Google is already using this technology for many of its products like question answering, reminders, visual object recognition, dialogue understanding, and smart email replies. Semi-supervised learning is expected to see increasing usage for very large data sets, where data labeling is an issue, especially around vision and language.
Voice assistants for the home proliferate
VoiceLabs estimates that 33 million voice-activated devices will be installed in the U.S. by the end of 2017. Amazon (Alexa), Microsoft (Cortana), Google (Google Assistant), and Apple (Siri) are investing heavily in bringing consumers into their own devices’ ecosystem by inventing ingenious ways for lock-in. One way to win customers will be offering exclusive features or specific discounts (e.g., inclusive subscriptions to content channels for a certain time period.
Social media-based messaging services in China such as WeChat have established and popular chatbots to aid in daily tasks. Facebook is just beginning to drive integration through the use of adverts which link to chatbots, as well as sponsored adverts in Facebook Messenger. These virtual agents will grow in presence and popularity, streamlining eCommerce activities such as enabling users to book flights and hotels, or to order items directly by speaking with a bot through an app.
But they are moving rapidly from consumer applications to offering assistance business users. A survey of corporate executives found that 32% said voice recognition chatbots are the most used type of AI tech in their workplace. Gartner predicts that chatbots will power 85% of all customer service interactions by the year 2020. Slack, Skype, Oracle, Salesforce, other enterprise messaging and collaboration platforms and numerous startups offer in-house or software-as-a-service functionality, helping employees do their jobs faster and better. Like the smartphone, business users of virtual assistants will eventually want these artificial intelligences to follow them throughout the day—possibly giving rise to Bring Your Own Robot (BYOR) movement.
AI as extension of enterprise IT
The enterprise use cases that are attracting the most investment today are automated customer service agents, quality management investigation and recommendation systems, diagnosis and treatment systems, and fraud analysis and investigation. The use cases that will experience the fastest revenue growth over the next five years are public safety and emergency response, pharmaceutical research and discovery, diagnosis and treatment systems, supply and logistics, quality management investigation and recommendation systems, and fleet management. The ability to recognize and respond to data flows using algorithms and rule-based logic enables AI applications to automate a broad range of functions across many industries and augment the work employees, making them more productive.
Self-driving grows up
According to McKinsey, self-driving cars will save an estimated 300,000 lives per decade by reducing fatal traffic accidents. This is expected to save $190 billion in annual critical care and triage costs. With Google alone racking up over 1 million miles testing autonomous vehicles, focus will shift from potential benefits to the necessary regulation. Legislators and policymaker will start the long process of designing and implementing the new legislation. 2020 could be the first year to see a marketable autonomous vehicle and society must begin to prepare for that day. We will see more lobbying groups in Washington DC and more vendor and user coalitions forming, to prepare the ground for widespread use.
The many faces and uses of hardware
Alternative hardware platforms such as field-programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), and specialized processor architectures will increasingly compete for attention and investment dollars with Graphics processing units (GPUs), which have been the dominant hardware platform for AI applications, specifically deep learning systems. As AI algorithms change to account for applications like autonomous driving or personalized medicine with dynamic inputs, there is a case for having memory storage on the processor itself. The evolving nature of algorithms and workloads will determine what architecture is best suited for which application.
The emergence of the AI services market
As happened recently with big data and data science, there is emerging opportunity for services related to AI, including vendor selection, implementation, training, application and algorithm development and integration, and consulting. As the skills and experience related to machine learning and AI are in short supply, we will see expansion of the on-demand services provided by cloud vendors.