Information and communications technology (ICT) experts have stressed the need for robust and accurate data mining and usage in the private and public sectors of the Nigerian economy, in order to facilitate development in the country’s digital economy drive.

Data mining is a process used by companies to turn raw data into useful information, through data analytics.
President of the Association Telecoms Companies of Nigeria (ATCON), Mr. Olusola Teniola, said Nigeria must adopt technologies like Artificial Intelligence (AI) and Machine Learning (ML) to drive data analytics that would result in robust and accurate data that would guide decision-making in businesses and governance.

Teniola, told THISDAY that the fastest way to achieve digital economy goals, was for Nigeria to develop static and dynamic data that would help in data verification and identity, adding that data not verifiable could pose threat to identity management.

“Many Nigerians have several mobile devices and Nigeria needs location data to verify static data information of people as well as dynamic data of people in order to match data subscriptions with data subscribers for the purpose of identifying individuals with criminal tendencies, aside using the data for business decision making,” Teniola said.

President, Institute of Software Practitioners of Nigeria (ISPON), Dr. Yele Okeremi, told THISDAY that data sovereignty and control, remained key factors that Nigeria must consider when it comes to data generation and usage.
“Nigerians generate a lot of data every day in different ways but do not have control of their data. Most of Nigerian data are in the hands of foreigners and we need to have robust platforms where we can control the data we generate for the purpose of informed decision-making. “Access to data is key because it is only those that have access to data that can plan and make informed decision. Data generation is therefore important, but more importantly is data control, data monetisation and data kinetics for informed decision-making,” Okeremi said.

The ICT experts were responding to the views of the Statistician-General of the Federation and CEO of the National Bureau of Statistics (NBS), Dr. Yemi Kale, who spoke during a recent webinar, organised by NBS in collaboration with Softcom Limited, a technology company.

At the webinar, Kale said the twin shocks of the health and economic crisis coming amidst historically low oil prices, would provide a unique opportunity to develop frameworks that solidify better use of data to drive Nigeria’s response to the new global challenges.

The Statistician-General of the Federation highlighted the importance of data generation, data mining and the use of data for economic development.

According to him, “Data aids the decision- making process by enabling us to establish numerical benchmarks and monitor and evaluate the progress of policies or programmes, thus ensuring that our policy interventions are well designed.

“Without data, we cannot make well-informed decisions that will catalyse our socio-economic development and transform the future of generations. It is when we are able to collate, understand and interpret data correctly, as well as identify key areas in our society or our economy that require change, that the policy prescriptions and direction of our governments and businesses are more likely to respond to the real needs of our communities.”

Speaking about the usefulness of data, Kale said: “Our recent longitudinal study on the Impact of Covid-19, undertaken in collaboration with the World Bank, can help policymakers to track the dynamics of the pandemic and design targeted responses.”

He however said despite the ability to impact on the process of good governance, “our data production architecture still faces some challenges. For instance, we still need to address the resistance to data sharing internally, which is reinforced by the realisation that with transparency comes accountability, or the common misconception that sharing data means the loss of ownership or visibility as it relates to the information being shared.”

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The EU wants to see more non-personal data shared between businesses, but that could prove easier said than done. On 19 February, the European Commission presented a three-part package to boost Europe’s digital economy, including a European strategy for data.

Buried beneath the headlines about artificial intelligence is a set of policy options that Internal Market Commissioner Thierry Breton believes could herald a new age of European data success.

As former CEO of Atos, Breton is well aware of the value of business data and wanting to leverage that for the European economy seems like a “no brainer.” But lurking among the proposals are some ideas that have given the tech sector cause for consideration.

One of the suggestions to encourage data sharing across the bloc is to give public subsidies to a so-called “European cloud,” prompting cries of “protectionism” from outside the EU.

Breton is clearly worried that Europe is lagging behind the U.S. and is at the mercy of much-demonized “Big Tech”. He admitted that Europe has lost the race to monetize personal data through B2C platforms, but he sees an opportunity to pivot to B2B or industrial data-driven services.

“Many decisions that strongly affect the lives of citizens and businesses are taken by private gatekeepers, based on their exclusive access to all data generated within their ecosystems,” says the Commission Communication.

“Europe has everything it takes to lead the ‘big data’ race, and preserve its technological sovereignty, industrial leadership and economic competitiveness to the benefit of European consumers,” added Breton.

Although this data trove is supposed to be non-personal data only, Matthew Lowell from the Malta Council for Science and Technology speculated that “the concept of data spaces is particularly relevant, where the government will amalgamate private and public data to provide readily available information on specific sectors.”

Quite apart from the obvious competition problems with data sharing between companies, there are a lot of issues of privacy and data protection concerns in connection with sharing data. The line between personal and non-personal may be clear in theory, but separating personal and non-personal data for the purpose of practical application is loaded with great legal uncertainty and comes with the risk of massive sanctions.

Margareta Chesaru, Public Affairs Manager at UiPath said: “When it comes to the EU plans on data sharing, it’s crucial to ensure that no commercial confidential or personal data is exchanged without the consent of the rightful owners of such data. To make it practicable, any framework for B2B data sharing would need to consider the scope of use of such data and the corresponding usage rights, without affecting the contractual clauses.”

But there are other problems. Clear, legally approved, methods of anonymization have not yet been established. Although Europe’s data protection laws are the most advanced in the world, they do not explain in technical detail the route to achieving “anonymized” data.

Some sources are even concerned that anonymization of data is a category of data processing in itself and therefore needs the consent of the data subject. All this leads to massive restrictions for processing data in companies for training AI for example and even more for sharing data. Purpose limitation could prove a stumbling block.

Although Breton’s proposals to boost the European big data economy were welcomed in principle by most of the tech sector, all the problems listed above mean that what is sound in theory may not work in practice.

“There is enormous potential around building up a data economy. It is essential for shifting value chains into the digital age, since value creation is moving increasingly from mere product manufacturing to offering services and providing for possibilities,” explained Benjamin Ledwon, head of Bitkom’s Brussels office. “But forcing data access by law is no help in promoting a data economy. On the contrary, it will prevent enterprises from investing in data mining. Moreover it is not perceptible how a data access right can be adequately balanced with existing rights of the data-holder derived from intellectual property or know-how-protection for example.”

The value of data depends on the analytical purpose, the analyzing abilities of an enterprise, and last, but not least, on the market, pointed out Ledwon. “As an economic principle, no one is willing to share data for free. So for example the remuneration for sharing data on social networks consists in the access to this network and in using its services. It is not evident why a single user should be entitled to further remuneration.”

How many times have we heard that “data is the new oil”? Would the EU expect oil companies to give away their product by the barrel load – recent market upheaval notwithstanding?

The Commission has worked hard to free up big data from governments and other public bodies so that it can be put to use by researchers and entrepreneurs, but asking businesses whose USP relies on their “secret sauce” to do the same was always going to be a tougher ask. Until difficult, technical questions about anonymity are answered many will simply not be able to share data even if – and it remains a big IF – they wanted to.

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With the digital makeover of the economy, which has allowed for transactions carried out by individuals and companies to be traced and recorded in real-time, traditional methods of collecting economic data, such as in-person surveys and interviews, run the risk of becoming outdated.

Digital records of such transactions—whether online or brick and mortar—at a product level and the growing ability to store and analyse such information offer an opportunity to better capture production and price statistics, says a new National Bureau of Economic Research (NBER) study authored by Gabriel Ehrlich of University of Michigan and others.

Such sales and price data, obtained directly and simultaneously from a single source, will also greatly refine measurement of economic activity and productivity at a product-level, according to the authors.

Advances in research and technology have also made it possible for digitized transactions data to be innovatively used to capture economic activity, the authors suggest. For instance, the “unified price index” technique developed by Redding and Weinstein (2018) makes it possible to capture the effect of changes in product appeal on their prices. However, the authors warn that more work is required to refine these techniques before they are incorporated by statistical agencies. Such agencies may not find it easy to access and process such kind of data, they argue.

Building key national indicators from product-level transactions data would also necessitate re-engineering of the statistical architecture in most countries to change how data is collected and accessed for official statistics. For instance, the simultaneous collection of price and quantity data requires combining the data collection activities that are currently spread over multiple arms of the statistical machinery. Such a shift, however, may be inevitable as non-response rates and costs of traditional surveys escalate over time, the authors argue.

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A recent report stated that a survey conducted by an Information and Technology market research and advisory firm highlights that AI adoption in the ASEAN region is on the rise.

Current AI adoption rates stand at 14% across Southeast Asia as compared to just 8% last year, marking a clear move by companies to embed some form of AI/cognitive intelligence into their operations.

Discovery of better business insights has become the most important adoption driver according to more than half (52%) of respondents, moving from third most important in 2017, revealing a maturity in the way the region is harnessing AI to enhance their business. Other top drivers this year are enhanced process automation (51%) and improved productivity (42%).

Malaysia came in fourth with 8.1% of all its organisations being open to the adoption and integration of Artificial Intelligence into their operation. It followed behind Indonesia (24.6%), which was the country that led the pack in terms of adoption. Next were Thailand (17.1%), Singapore (9.9%).

The top use cases in Southeast Asia include algorithmic market forecasting (17%), and automated asset and infrastructure management (11%).

Experts argue that there are clear opportunities for more organisations in Southeast Asia to leverage AI to create differentiating value. This is particularly because of its pre-existing positive impact already visible across banking, manufacturing, healthcare, and government.

It is expected that investments in AI will continue to rise, as more organisations begin to understand the benefits of embedding AI into their business and how data and analytics can help uncover new insights.

Organisations that do not incorporate AI into their business operations will lose out to their AI-enabled peers who will benefit from the greater predictability, efficiency, and innovation that advanced analytics can bring.

Despite the rise in adoption, organisations in the region are trailing behind those in North Asian countries, in terms of making AI a strategic agenda.

For example, more than 80% of companies in China and South Korea believe AI capabilities will be critical for organisations’ success and competitiveness in the coming years, compared to less than 40% of companies in Singapore and Malaysia.

Lack of skills & knowledge (23%) and the high cost of solutioning (23%) are among the most frequent barriers to adoption named by survey respondents.

While the overall adoption in Southeast Asia falls behind Asia/Pacific (excluding Japan), there are signs to suggest organisations in the region will catch up quickly.

For example, 34% of organisations in Malaysia have plans to adopt AI within two years, the 2nd highest among Asia/Pacific countries.

In solidifying their strategy to turn AI into a differentiator for the business, companies find data from sales, commerce, and marketing to be the readiest, followed by that from customer service & support operations, and IT, security & risk operations.

For those already embarking on their data-to-insights journey, there are varied challenges across sectors. Organisations in the financial services space face more challenges in data federation and model building, while public sector organisations are hindered by data readiness issues.

With a 32 percentage points jump in planned adoption of AI in two years since 2017, Malaysia’s increasing AI focus can be attributed to greater smart cities initiatives and applications in public safety and intelligent transportation. A lot of these initiatives would need more time to unfold and solidify.

However, many Malaysian organisations have concerns about the cost of solutioning and the quality of the model.

Compared to North Asian economies, Malaysian organisations showed less enthusiasm in having in-house AI capabilities which can hinder their ability to understand AI solutions to strengthen their business.

Nevertheless, more than 32% of companies in Malaysia prioritised speech and image recognition interfaces to improve customer experience and enhance omni-channel know-your-customer.

Organisations in Malaysia are recognising how AI and analytics can help solve complex problems and reveal unique insights, at the scale and speed required for our growing markets.

It is important to note that Malaysian organisations must understand how AI could enhance their current staff and technology to drive improved business outcomes.

In the digital economy, AI and analytics are the drivers of organisational success and companies will need a clear path from data to innovation.

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IT teams can salvage the value of legacy systems while pivoting to a new foundation offering better customer experiences.

CIOs and their teams are routinely maintaining costly, long-running legacy systems that have gathered a large amount of data about purchases, services, and customer behavior over the years. Sure, they’re getting a bit creaky in old age; but the most important problem has existed since they came online — each one was built in a vacuum and effectively siloed away from the others.

IT teams are feeling the strain of maintaining legacy systems at a time when customers are demanding a more tailored, robust, and streamlined experience from their organizations. Today, most customers want insights and capabilities to be accessed quickly and easily, whenever and wherever they need them, while receiving real-time updates and information through multiple channels (for example, web portals, mobile applications, e-mail, SMS). Because various older systems were one-off development projects that didn’t consider future dependencies or integrations, these legacy infrastructures simply aren’t prepared to meet the demands of the 24/7 digital economy without a lot of time and money being invested.

As a result, most organizations with legacy systems are losing revenue that their current offerings can’t drive or, even worse, are watching it get directed towards their competitors. New experiences that provide valuable insights and new capabilities are the best way for an organization to honor customer expectations and remain relevant in most market spaces. But here’s the problem: The data and logic necessary for these new applications is broken up across their legacy platforms, making it very difficult to access the full spectrum of information or the full range of capabilities needed to invent something new and valuable.

Fortunately, IT teams can salvage the value of legacy systems while pivoting to a new foundation. Core back-end scalable architecture supports quick and cost-effective new development without creating unnecessary technical debt. To enable those sought-after next-generation experiences, it takes a lean, thoughtful, and flexible service-oriented architecture that brokers access to both new and legacy information and services. Building this takes extra planning, talent, and effort that many IT teams aren’t ready to commit to, but here are a few key steps to make the process easier and ultimately more successful in the end.

Devise a strategy that allows legacy systems to participate in the new world. While it’s easy to imagine a new greenfield approach that will free your teams from legacy woes, it’s not realistic. Any solution needs to include an integration strategy for legacy systems that keeps them in the mix in the short term while eventually updating or replacing them when the time is right. Try to boil the ocean and you’ll get burned.

Ask the hard questions up front. IT teams must examine their current infrastructure and data assets to identify what needs to be changed to meet their objectives, then define the right technical architecture to meet the needs of the business. While it is tempting to focus on the immediate projects at hand, IT teams must consider the long-term initiatives that will follow in the months and years ahead. Systems, hardware, data, and personnel resources all need to be factored into the plan. To do so, they must ask themselves these tough questions:

• What are the problems you’re trying to solve for your business or your customers, now and in the future?
• What are the desired business outcomes that your technology would power or enable?
• How are you going to measure the success of your initiatives?
• What software, hardware, data, people, and other resources do you have in place now? Do they have the skills you need or should you hire new personnel or bring in outside help?
• What is the technology architecture you will need to facilitate the outcomes you’re after? To the previous point, are you sure that you have the right people in place to answer this correctly?
• How can you bridge the gaps between where you are now and where you need to be?

Once you have a better grasp of where you’re at, examine where you need to be from an overall enterprise architecture standpoint.  Consider a services-based approach that will drive business outcomes over several years. Here are some of the most important elements to consider:

Microservices.  A microservices architecture provides focused and independently deployable application components that fulfill three key objectives: development agility; deployment flexibility; and precise scalability. The highly granular, purpose-built nature of a microservice also facilitates a progressive migration strategy by adding to or replacing legacy components in smaller, more manageable pieces.

Big data strategy. “Big data” is so routine now that it’s better to just call this your “Data strategy.” New technology initiatives will usher in new data dependencies that you may not be accustomed to handling in your organization. As that data is processed and stored, you’ll need a consistent and rapid way to interact with it that will scale as your organization grows and the demands on your data architecture continue to evolve.

Security. Factor security into your plans from the start. You’ll want a straightforward security model that lays across infrastructure – covering data, service, and front-end tiers. Ensure that you have full customization of roles and permissions to account for the various types of users who will be working with your applications now and in the future.

Cloud support. This is also a good time to consider infrastructure flexibility. If you don’t have a cloud strategy, take a hard look at why not. Cloud providers, such as Microsoft Azure or Amazon Web Services, can provide instant geographical distribution, high availability configurations, elastic scaling, and several other useful services beyond the basics, for less money.

Production ready. Design your infrastructure with a focus on centralized monitoring, auditing, and continuous release. Your fancy new application ecosystem is worthless if you can’t manage it. You need to easily diagnose issues as they arise, and ideally have enough monitoring in place to recognize things before they turn into issues. Your system should tie notifications to workflows to prevent troubleshooting downtime and ensure that the right people are receiving the right information at the right time.

DevOps. You need deliver new functionality and fixes early and often in a reliable way, and for that you will need DevOps. Quality analysis, automated tests, packaging, and deployment should be a well-oiled machine with high levels of automation.

Lastly, be certain that your strategy achieves short- and long-term business objectives from the start, rather than doing it piecemeal. You’ll save both time and money by avoiding costly adjustments along with way. You’ll also preserve the sanity of your IT teams and development partners in the process. When you can show immediate ROI on your first initiative, you’ll develop a tail-wind of support from the business that will allow you to realize the long-term roadmap.

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This autumn or post-monsoon, depending on which part of the world you live in, the movie Blade Runner will see a return after 35 years. If you remember the first movie, the artificial intelligence character, or the “replicant”, the antagonist, played by Rutger Hauer, saves the protagonist, played by Harrison Ford, from dying. After that, the protagonist witnesses the AI character’s programmes terminate themselves.

Ford then says: “I don’t know why he saved my life. Maybe in those last moments he loved life more than he ever had before. Not just his life, anybody’s life, my life. All he’d wanted were the same answers the rest of us want. Where did I come from? Where am I going? How long have I got? All I could do was sit there and watch him die.”

That’s AI for you in a nutshell when things become sentient. Computer programmers call it “singularity”, where self-learning software would become aware with every cycle of learning and ultimately surpass human intelligence.

Rolf Bulander, Chairman of Mobility Services, Bosch GmbH, says “New technologies such as AI and ML have already ushered in the era of making networks intelligent and are changing business models.” He adds that in Germany they have already created an ethics committee to understand the man-and-machine relationship in case of automated driving.

“Fundamental questions are being raised today; who does the software save first, a child or the parent. The setting up of the committee clearly signals that human intelligence and machine intelligence should work together,” Rolf says.

Before we have robots taking over the world, serious work is being done in every industry to get tasks done by software and remove human intervention altogether.

Machines are not sentient yet, but we are already in the realm of “Narrow AI”, where mundane tasks such as customer queries and data generationwill not need humans anymore.

The world of Narrow AI

Starting from healthcare and banking to automotive and media, business models are changing thanks to Narrow AI. The languages, mostly Python and even Java, going into AI and ML are all built out of India, which is why the country is aligning its goals to the world’s quest for a global network of artificial intelligence.

The terms ML and AI are used interchangeably these days. But what are they really?

NVIDIA, the $4.5 billion GPU company, explains in a blog that most of the work in AI today can be termed “Narrow AI”, where certain tasks are better performed by technology. So before we have a sentient being (like in Blade Runner), we are already in the era of “Narrow AI”. The bot example was one of them because they use millions of data points that are processed quickly and present immediate action.

Machine Learning, on the other hand, is termed as an approach to AI. It is nothing but algorithms that make sense of data to determine or predict something, thanks to the processing power of chips today. Before Machine Learning comes something known as Deep Learning where engineers try to understand the functions of the brain and how neurons work. So while programming, engineers look at how a machine can function like neurons firing in the brain to perform tasks.

Layak Singh, Co-founder of Artivatic, says: “Engineers today can make a machine understand text, video and images. But every engine needs to be trained to understand patterns and recognise the context.”  Artivatic is building a system for banks to understand credit history of customers and reduce loan defaults.

Startups are clearly playing a major role in innovating faster than corporates, which has led to several curious partnerships. SAP India has invested in Niki.ai, a bot that improves the ordering experience. Then there’s Ractrack.AI, where a bot improves customer engagement and provides insights; it functions as a virtual communications assistant to convert the customer into a client. Racetrack is helping companies turn leads into meaningful engagements by using AI. Another startup, LUCEP, converts all potential queries into leads with their AI engine.

Bobby Reddy, of Indus Ventures, is the Co-founder of Housingman.com, which is building a platform that uses machine learning and artificial intelligence.  “The objective is to generate insights from data and simplify customer interaction with a business and also convert them into leads,” he says.

Indian startups saw $4 billion in risk capital being deployed across 1,040 angel and VC/PE deals between January and December 2016. According to YourStory Research, disclosed funding announcements have shown a decreased value of 55 percent from the same period last year (2015) and a decrease of 20 percent from 2014. About $9 billion in VC/PE capital had been invested in 2015. The number of deals in 2016, however, has increased by 3 percent over the last year. On an average, four startup deals were announced every weekday throughout 2016. VCs predict that going forward machine learning and AI would be key themes to invest in.

The era of voice and vision

Billions of dollars have been pouring into AI across the world.

Leaders like Elon Musk, the founder of Tesla, have said this will create a global apocalypse and should be “regulated”. He expressed this fear because Russian President Vladimir Putin said the race to own AI assets will determine the ruler of the world. Musk also expresses that a system of such intelligence could “start a global war”.

One must really watch Westworld or read Issac Asimov to understand the merits and consequences of AI.

Atul Jalan, founder of Manthan, says, “We are in a great era of change because human behaviour will be out-matched by superior digital technologies. AI will eventually change everything. But humans can do better things than mundane tasks.”

In Manthan’s offices, engineers and marketing teams are jointly working together to build a voice-based AI suite called “Maya”, based on Amazon’s Alexa platform. Maya, when called upon, becomes the virtual assistant to the CEO and dynamically pulls out company sales information across regions. She can even tell what categories did not sell.

“Over time the platform can prescribe what should be done and this will challenge the entire C-Suite of leaders,” Atul says.

The impact of AI is best described in a PwC report on The Future of Work: A Journey Towards 2022 . This predicts that a student protest will erupt globally in 2020 as a consequence of the non-availability of jobs. This prognosis is based on universities not being able to keep pace with sudden changes in economy and technologies such as automation.

Amid the realignment of jobs and the accompanying chaos, companies will push the boundaries of innovation in AI. It will all be about agile IT, sensors, and data, which will change the entire Indian work scenario. Coding jobs will be scarce as all platforms will be available and automated. IT and its allied services will focus on implementing products such as SAP, Microsoft and Oracle on platforms in cloud environments.

Vishal Sikka, the outgoing CEO of Infosys, said the future of Indian IT was not in services, but in software. He was prescient as all global clients want IT to be enabled to business outcomes and building agile applications.

This year, Infosys launched Nia, its AI platform. Nia worked as a PoC in a bank which generated data volume of 596 million trade transactions on a 100-node cluster in the AWS stack. The case was peculiar and it involved pulling data rapidly to avoid fines in reporting. Usually the message insertion rate into a platform is slow; it takes more than 10 minutes. However, Nia achieved it at 130,000 records per second or 18.22 MB per second.

The report execution for 30,000 trade records was done in 35 seconds, with corresponding 120,000 trade line items, including end-to-end processing. If the bank reports delays of more than 15 minutes, it attracts a penalty by the regulator. With the Nia platform, end-to-end processing and reporting just took 35 seconds instead of 10-15 minutes. The platform helped the bank avoid non-compliance and related penalties.

Again, all this is just “Narrow AI”.

Undoubtedly Nandan Nilekani, who ushered in the Aadhar database in this country, will understand the use of AI in processing data. It has a bright future as an investment opportunity, provided CIOs take the plunge in using AI in their companies to reap benefits.

The future: reducing human error

According to Gartner, by 2018 Indian CIOs are expected to spend around a third of their IT budgets on the digital economy. Analytics, cloud services, mobility and digitalisation/digital marketing are the top four spending priorities, as per a survey of CIOs in India. Globally, spending on digitalisation is expected to jump to 28 percent of IT budgets by 2018 from 19 percent in early 2017.

No wonder Google’s AlphaGo, IBM Watson and Bosch Centre for Artificial Intelligence are approaching this subject with so much fervour. Their platforms will power homes, cars and industry.

Bosch is already working with Daimler to launch an autonomous car in the early part of the next decade. It is training a whole range of cars to use computer vision, LIDAR, Radar and Ultrasound to figure out driving autonomously. Nearly 3,000 engineers from Bosch, in Stuttgart and Bengaluru, have one thing in common: they are putting their brains – figuratively speaking – into a super brain. This “brain” can crunch 30 trillion data points per second and will process data three times faster than a human brain can. This “brain”,  powered by AI, has no reason to feel guilty about anything because it is designed to not make mistakes.

In one year since its alliance with Daimler, Bosch has seen revenues of €1 billion from the sale of these sensors with an order book value of €3.5 billion. This is significant because with Elon Musk’s Tesla paving the way for a world where software in the car will determine the travel experience, a vehicle is no longer going to be a single entity. It is going to understand everything, be it the driving methods or shopping habits of a person.

Dirk Hoheisel, member of the board of management, Robert Bosch GmbH, says, “Only AI will make this possible and reduce human error.”

He adds that an AI-led car has superior reflexes compared to human beings. It can interpret if there is a ball or a child on the road.

“We have to improve our sensors to monitor surroundings and think like humans, but only faster,” Dirk says.

The post Here’s how India is working towards the future of AI and machine learning appeared first on Artificial Intelligence.

“The development of full artificial intelligence could spell the end of the human race. Once humans developartificial intelligence, it will take off on its own and redesign itself at an ever-increasing rate. Humans, who are limited by slow biological evolution, couldn’t compete and would be superseded.” This sounds like it could be an excerpt from the screenplay to the 1984 film “The Terminator,” but it’s actually physicist Stephen Hawking in late 2014, talking to the BBC.

As part of the scientific advisory board for the Future of Life Institute based in Boston, Hawking and engineer/entrepreneur Elon Musk helped to create an open letter on artificial intelligence that highlighted both the positive and negative aspects of AI. While the letter is intended to be informative rather than alarmist, it’s hard to ignore a singular phrase rumbling just beneath the surface: “Our AI systems must do what we want them to do.”

The good news is that AI researchers are thinking seriously about the ethical issues involved, sometimes referred to as “roboethics.” That term was coined by Italian robotics pioneer Gianmarco Veruggio, who organized the EURON (European Research Robotics Network) Roboethics Atelier and coordinated the EURON Roboethics Roadmap in 2006. As an overview of ethical issues involved in robot development, the Roadmap raises philosophical questions that still resonate over a decade later.

One of these relates to the very definition of a robot itself: Is a robot merely a machine, incapable of autonomy superior to that of its designer? Or do robots represent the evolution of a new species, that will exceed both the limited intellectual capacity and flawed moral dimensions of humankind?

Heady stuff—and perhaps a bit premature, considering the current level of technological development. Yet there’s certainly no question that AI has already had a significant impact on numerous sectors of society—and, as a consequence, raised ethical concerns and questions lacking easy answers.

Consider, for instance, an emergency situation in which a self-driving car has to weigh the large probability of a small accident against the small risk of a major accident. MIT’s Moral Machine uses self-driving car scenarios such as that to illustrate the sort of moral decisions that may be made by machine intelligence. While autonomous vehicles may not be ubiquitous yet, various reports indicate the very real possibility for automakers to roll them out en masse before federal laws regulating their use are in place.

Perhaps job displacement hits even closer to home. Is it wrong to allow robots to take jobs once held by people? If the answer that immediately leaps to mind is “yes,” consider this: Would it not be equally wrong to arrest society’s ability to technologically advance? (Imagine, for instance, what the world would look like today if the Industrial Revolution had somehow been stopped in its tracks.) Then, looking further down the road, as robots become increasingly capable of steering the wheels of production, is it possible that humanity will be propelled into a “leisure society” where work is no longer necessary? Wonderful as that might sound at first blush, could that also mean a lesser need for self-sufficiency, and a softening of humanity’s edge—factors that make Hawking’s concern that much more of a credible scenario?

A recent study headed by Future of Humanity Institute at the University of Oxford included a widespread survey of AI experts, and arrived at some very date-specific predictions as to when machines would be able to assume different types of job tasks. These start within the next decade—and, to give some examples, include language translator by 2024; truck driver by 2027; retail worker by 2031; and surgeon (yes, surgeon) by 2053.

Another expert, MIT Initiative on the Digital Economy director Erik Brynjolfsson, recently told web magazine CityLab that the decline in American factory jobs is not the result of work being shipped overseas—but rather of increased factory automation. He also stated his belief that, “in 50 years, there may be very few jobs which can’t be done by machines.”

Brynjolfsson is, however, cautiously optimistic about the possibilities. With less need to work and greater resources at its disposal, he says, humanity can turn to addressing longstanding problems such as disease, environmental degradation and so on; he also believes that “smart machines can help us have a lighter footprint on the planet.” His open letter on the digital economy proposes ways in which humanity can positively shape the workforce-related effects of technological change: public policy changes, new organizational models for business, ongoing research and openness to new ways of thinking. The Future of Life Institute open letter also links to an outline of recommended research priorities for maximizing AI’s societal benefit.

Production still from “R.U.R.” Source: Public domain/Wikimedia Commons.Recommendations such as these strike a decidedly more positive chord than the fears about AI takeover stirred by a century’s worth of dystopian science fiction—dating back at least as far as the 1920 play R.U.R., a fantasy about a rebellion of robot workers leading to the extinction of the human race. Incidentally, that work, by Czech writer Karel Čapek, was responsible for introducing the word “robot” into the English language.

In closing, perhaps a somewhat more light-hearted take on the possibility of being replaced by a machine might be in order. Based on a 2013 Oxford Martin School study, the website Will Robots Take My Job? prompts a user to enter a job title, then spits back various statistics, including an “automation risk level.” Examples range from professions like forensic science technician (0.97 percent probability of automation, or “Totally Safe”) to jobs like vegetation pesticide handler (97 percent probability, phrased as “You Are Doomed”). On the bright side, the listing for each of the fields includes a link to current career opportunities.

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