In fact, US lawmakers and advocates say the ruling and ‘relatively small fine in the light of FaceBook’s annual revenues’ show federal privacy laws are needed. Labeling it a ‘parking ticket’, they also say it is the beginning of the end for the Federal Trade Commission’s probe into Facebook’s alleged mishandling of more than 87 million users’ private data during the scandal. In addition to this, the company has reportedly been struggling under the weight of scandals related to privacy, hate speech, election interference and fake news over the last few years. Indeed, fake news does not support the informed decision making processes of the free citizen imagined by the founders of democratic thinking.

Utilized to influence not only consumer but also elector behavior, it jeopardizes the very meaning of the democratic political franchise. Or in other words, the vote many governments around the world rely on when aiming to assert the will of their population to benefit the greater good of each Nation, its values and constitution.

So what is the solution? Perhaps the first step is redefining privacy as a sphere that does not include only the public or confidential information about each citizen and his or her unequivocal identity from illicit access or fraudulent use, but that first and foremost protects his or her ideas and emotions from being tampered with against his or her interest and according to the agenda of clients of the IT industry itself.

The global effort to protect our privacy and dignity as human beings in the era of artificial intelligence has just started and pressure is mounting. Fake news is among the most difficult challenges – aside from real stories often being called fake by some politicians, actual fake news is used to coerce people into making decisions. Consequently, governments have been putting increasing pressure on sites like Twitter and Facebook to take more responsibility for the content shared on them. But the privacy debate is a double-edged sword with freedom of information being challenged by protection of basic citizen data rights.

In this regard AI while an enabler, is also being seen as a combatant and Twitter has already begun to put safeguards in place. With its recent acquisition of Fabula AI, it reportedly has the ability to analyze large and complex data sets for signs of network manipulation and can identify patterns that other machine-learning techniques cannot. Armed with this, Twitter states it can determine how trustworthy a claim is and hopefully make it visible to others.

It will be interesting to watch what steps the other big processors, handlers and conveyors of data, news and information take in this regard. All this remains to be seen, but is likely – and perhaps regulation or even legislation to make it happen is closer than we think. In any event, all players everywhere need to keep working at it.

Devices too are evolving. The smart phone, now commonplace and in the hands of almost every person in the developed world, is being enhanced by tools such as Alexa and other digital assistants. In the UK, for example, people use Alexa to report crime. Several governments are using chatbots as ‘artificial conversational’ systems for communicating their services. One of many is in Mississippi, where ‘Ask Missi’ allows citizens to access government information. Furthermore, digital assistants and cognitive computing facilitated by mobile, are being deployed in both the public service sector and the manufacturing industry. In business, identity and trust are key enablers for policy and governance as demonstrated by the introduction of the European General Data Protection Regulation (GDPR) and the giant steps being taken to protect against cybercrime and data breaches.

Similarly, what has been coined as the Internet of Everything, machine learning and AI have become the focus of businesses and institutions in their drive to ally employees and customers with better productivity, increased data monetization and profits. An example of this level of interconnectivity can be seen in the automotive industry with the introduction of Connected Autonomous Shared and Electrified (CASE) strategies. Online networks with 5G performance targeting high data rates are powering vehicles, drones, robots and sensors, while autonomous vehicles themselves are no longer the stuff of science fiction.

While this new digital world and the phenomenal and rapid growth of new technologies continues apace, the building blocks of ICT and identification still hold true and form the very basis of the innovations the world of data and ID is experiencing today. So these core technologies – Cards, Biometrics, RFID and Data in the Internet of Things are as relevant as they were 20 years ago. In fact, biometrics has, as predicted, taken the high ground in all sectors from finance to government, with smart cards still at the heart of ID and security, whether as a hard token or as the key component for integrated mobile logical and physical access.

In this issue, we once more probe into these new and emerging technological innovations and provide a focus on technology suppliers who play a major role in their development and implementation.

by Sophie B. de la Giroday

Integration of laser sources and optical technologies for plastic cards and passports with polycarbonate data-pages is seeing further innovation for mid-high volume card personalization using laser technology. The new concept from desktop and central issuance laser systems supplier, Ixla, is aimed at the centralized card and ePassport personalization and issuance segment. Equipped with laser technology, the compact IXLA XP24 JET System provides a footprint for ergonomic working and optimum user friendliness personalizing up 800 cards per hour. Using the latest technology and knowledge standards, the system’s modular design offers straightforward system integration for onsite upgrade capabilities and maintenance. The system comes with the combined technology of laser and ink-jet printers, as well as automatic feeding of multiple booklets. It supports all security criteria with a contactless chip module, MLI and machine readable features. IXLA owns and updates the internal technological modules, the transport electro-mechanics, the software and the hardware for the laser engraving application, representing a strong technological alliance.

NFC payment ring powered by contactless

Whether at the beach, on the daily jog, or at the gym: from now on, everyone can carry money discreetly on the body, according to the developers of a newly introduced EMVCo compliant payment ring. The product, from NFC Ring, is based on a contactless security chip from Infineon Technologies. The tiny, water-proof smart wearable works like a contactless payment card. Users can pay by simply holding their finger with the ring closely to any EMVCo contactless-enabled payment terminal. The ring uses NFC (Near Field Communication) technology to communicate data over short distances of a few centimeters. NFC Ring’s contactless system developers have built the payment finger ring with a security chip and passive contactless antenna. The small surface area and dimension as well as the shape of the ring were not the only hurdle they had to overcome. The absence of a battery with smart wearables and contactless data transmission without taking the wearable off the finger make contactless transactions more difficult, too. However, the developers say they have overcome all these challenges, due to the energy-efficient payment security chip, which communicates with the terminal via its tiny passive antenna, triggers the secured payment and does cryptographic processing. It also complies with timing requirements of a few milliseconds whilst working at a distance of up to 4cm from a reader.

Thin inlay brings security to passport polycarbonate datapage

Smart Packaging Solutions (SPS) has introduced one of the thinnest inlays that expand into the hinge of a passport bringing new security features by embedding a specific image for each government while making the assembly easier for passport manufacturers. The hinge inlay can embark a Customized Hinge Image (CHI™), a technology consisting in defining a specific hinge design for each passport issuer. For instance, a national symbol, an acronym or a specific mark can be part of the hinge design making it extremely difficult for a fraudster to reproduce. When the passport is controlled, a CHI can easily be verified by transparency, just by exposing the card in front of a light, like a watermark in a paper. In addition, as the hinge inlay is extremely thin, under 40 µm, its lamination with the other layers in a passport datapage gives a thinner datapage. For instance, thanks to SPS hinge inlay thinness, the whole datapage of a non-electronic passport is only 400 µm while the datapage of an electronic passport is between 650 and 800 µm depending on the security features.In case of an ePassport, the hinge inlay already includes the antenna, which is coupled with the chip by induction through SPS EBooster technology.

Advances in Artificial Intelligence (AI) technology and related fields have opened up new markets and new opportunities for progress in critical areas such as health, education, energy, economic inclusion, social welfare, and the environment. The US Government is already assessing the effects of AI-driven automation on the country’s job market and economy by recommending policy responses. 

In recent years, machines have surpassed humans in the performance of certain tasks related to intelligence, such as aspects of image recognition. Experts forecast that rapid progress in the field of specialized artificial intelligence will continue. Although it is unlikely that machines will exhibit broadly-applicable intelligence comparable to or exceeding that of humans in the next 20 years, it is to be expected that machines will continue to reach and exceed human performance on more and more tasks.

Accelerating artificial intelligence (AI) capabilities will enable automation of some tasks that have long required human labor. These transformations will open up new opportunities for individuals, the economy, and society, but they have the potential to disrupt the current livelihoods of millions of Americans. Whether AI leads to unemployment and increases in inequality over the long-run depends not only on the technology itself but also on the institutions and policies that are in place.

Economics of automation

Technological progress is the main driver of growth of GDP per capita, allowing output to increase faster than labor and capital. One of the main ways that technology increases productivity is by decreasing the number of labor hours needed to create a unit of output. Labor productivity increases generally translate into increases in average wages, giving workers the opportunity to cut back on work hours and to afford more goods and services. Living standards and leisure hours could both increase, although to the degree that inequality increases as it has in recent decades—it offsets some of those gains.

AI should be welcomed for its potential economic benefits. Those economic benefits, however, will not necessarily be evenly distributed across society. For example, the 19th century was characterized by technological change that raised the productivity of lower-skilled workers relative to that of higher-skilled workers. Highly-skilled artisans who controlled and executed full production processes saw their livelihoods threatened by the rise of mass production technologies. Ultimately, many skilled crafts were replaced by the combination of machines and lower-skilled labor. Output per hour rose while inequality declined, driving up average living standards, but the labor of some high-skill workers was no longer as valuable in the market.

In contrast, technological change tended to work in a different direction throughout the late 20th century. The advent of computers and the Internet raised the relative productivity of higher-skilled workers. Routine-intensive occupations that focused on predictable, easily-programmable tasks—such as switchboard operators, filing clerks, travel agents, and assembly line workers—were particularly vulnerable to replacement by new technologies. Some occupations were virtually eliminated and demand for others reduced. Research suggests that technological innovation over this period increased the productivity of those engaged in abstract thinking, creative tasks, and problem-solving and was therefore at least partially responsible for the substantial growth in jobs employing such traits. Shifting demand towards more skilled labor raised the relative pay of this group, contributing to rising inequality. At the same time, a slowdown in the rate of improvement in education, and institutional changes such as the reduction in unionization and decline in the minimum wage, also contributed to inequality—underscoring that technological changes do not uniquely determine outcomes.

Current job market

Today, it may be challenging to predict exactly which jobs will be most immediately affected by AI-driven automation. Because AI is not a single technology, but rather a collection of technologies that are applied to specific tasks, the effects of AI will be felt unevenly through the economy. Some tasks will be more easily automated than others, and some jobs will be affected more than others—both negatively and positively. Some jobs may be automated away, while for others, AI-driven automation will make many workers more productive and increase demand for certain skills. Finally, new jobs are likely to be directly created in areas such as the development and supervision of AI as well as indirectly created in a range of areas throughout the economy as higher incomes lead to expanded demand.

Recent research suggests that the effects of AI on the labor market in the near term will continue the trend that computerization and communication innovations have driven in recent decades. Researchers’ estimates on the scale of threatened jobs over the next decade or two range from 9 to 47 percent. For context, every 3 months about 6 percent of jobs in the economy are destroyed by shrinking or closing businesses, while a slightly larger percentage of jobs are added—resulting in rising employment and a roughly constant unemployment rate. The economy has repeatedly proven itself capable of handling this scale of change, although it would depend on how rapidly the changes happen and how concentrated the losses are in specific occupations that are hard to shift from.

Research consistently finds that the jobs that are threatened by automation are highly concentrated among lower-paid, lower-skilled, and less-educated workers. This means that automation will continue to put downward pressure on demand for this group, putting downward pressure on wages and upward pressure on inequality. In the longer-run, there may be different or larger effects. One possibility is superstar-biased technological change, where the benefits of technology accrue to an even smaller portion of society than just highly-skilled workers. The winner-take-most nature of information technology markets means that only a few may come to dominate markets. If labor productivity increases do not translate into wage increases, then the large economic gains brought about by AI could accrue to a select few. Instead of broadly shared prosperity for workers and consumers, this might push towards reduced competition and increased wealth inequality.

Historically and across countries, however, there has been a strong relationship between productivity and wages—and with more AI the most plausible outcome will be a combination of higher wages and more opportunities for leisure for a wide range of workers. But the degree that this materializes depends not just on the nature of technological change but importantly on the policy and institutional choices that are made about how to prepare workers for AI and to handle its impacts on the labor market.

Policy responses

Technology is not destiny; economic incentives and public policy can play a significant role in shaping the direction and effects of technological change. Given appropriate attention and the right policy and institutional responses, advanced automation can be compatible with productivity, high levels of employment, and more broadly shared prosperity. In the past, the U.S. economy has adapted to new production patterns and maintained high levels of employment alongside rising productivity as more productive workers have had more incentive to work and more highly paid workers have spent more, supporting this work. But, some shocks have left a growing share of workers out of the labor force.

In its report, the US Government advocates strategies to educate and prepare new workers to enter the workforce, cushion workers who lose jobs, keep them attached to the labor force, and combat inequality. Most of these strategies would be important regardless of AI-driven automation, but all take on even greater importance to the degree that AI is making major changes to the economy.

Strategy 1: Invest and develop

If care is taken to responsibly maximize its development, AI will make important, positive contributions to aggregate productivity growth, and advances in AI technology hold incredible potential to help the United States stay on the cutting edge of innovation. Government has an important role to play in advancing the AI field by investing in research and development. Among the areas for advancement in AI are cyber-defense and the detection of fraudulent transactions and messages. In addition, the rapid growth of AI has also dramatically increased the need for people with relevant skills from all backgrounds to support and advance the field. Prioritizing diversity and inclusion in STEM fields and in the AI community specifically, in addition to other possible policy responses, is a key part in addressing potential barriers stemming from algorithmic bias. Competition from new and existing firms, and the development of sound pro-competition policies, will increasingly play an important role in the creation and adoption of new technologies and innovations related to AI.

Strategy 2: Educate and train

As AI changes the nature of work and the skills demanded by the labor market, American workers will need to be prepared with the education and training that can help them continue to succeed. Delivering this education and training will require significant investments. This starts with providing all children with access to high-quality early education so that all families can prepare their students for continued education, as well as investing in graduating all students from high school college- and career-ready, and ensuring that all Americans have access to affordable post-secondary education. Assisting U.S. workers in successfully navigating job transitions will also become increasingly important; this includes expanding the availability of job-driven training and opportunities for lifelong learning, as well as providing workers with improved guidance to navigate job transitions.

Strategy 3: Empower and grow

Policymakers should ensure that workers and job seekers are both able to pursue the job opportunities for which they are best qualified and best positioned to ensure they receive an appropriate return for their work in the form of rising wages. This includes steps to modernize the social safety net, including exploring strengthening critical supports such as unemployment insurance, Medicaid, Supplemental Nutrition Assistance Program (SNAP), and Temporary Assistance for Needy Families (TANF), and putting in place new programs such as wage insurance and emergency aid for families in crisis. Worker empowerment also includes bolstering critical safeguards for workers and families in need, building a 21st century retirement system, and expanding healthcare access. Increasing wages, competition, and worker bargaining power, as well as modernizing tax policy and pursuing strategies to address differential geographic impact, will be important aspects of supporting workers and addressing concerns related to displacement amid shifts in the labor market.

If a significant proportion of Americans are affected in the short- and medium-term by AI-driven job displacements, policymakers will need to consider more robust interventions, such as further strengthening the unemployment insurance system and countervailing job creation strategies, to smooth the transition.

Responding to the economic effects of AI-driven automation will be a significant policy challenge for the next Administration and its successors. AI has already begun to transform the American workplace, change the types of jobs available, and reshape the skills that workers need in order to thrive. All Americans should have the opportunity to participate in addressing these challenges, whether as students, workers, managers, technical leaders, or simply as citizens with a voice in the policy debate.

AI raises many new policy questions, which should be continued topics for discussion and consideration by future Administrations, Congress, the private sector, academia, and the public. Continued engagement among government, industry, technical and policy experts, and the public should play an important role in moving the Nation toward policies that create broadly shared prosperity, unlock the creative potential of American companies and workers, and ensure America’s continued leadership in the creation and use of AI.

by Executive Office of the President, United States Government

Investment in new technology and manufacturing methodologies is being driven by the Industrial Internet of Things (IIoT) as companies rush to capitalize on a predicted 14 trillion dollar economic gain in a wave of new connected devices, which themselves require new, strong security solutions. 

Many small endpoint devices running real-time operating systems often perform critical functions in our factories, electric grid, transportation infrastructure and other essential elements of modern society. The viability of the Industrial Internet of Things (IIoT) therefore depends on the security of the endpoints, the network and all of its subsystems. Unfortunately though, traditional security solutions do not scale down to support the RTOS-based devices that make up the bulk of the IIoT. New solutions and approaches are required. 

The IIoT is being hailed by some as the next great industrial revolution. By some estimates, there are more than 60 million machines in factories worldwide and 90 percent are not connected. It is no surprise that companies are looking at this opportunity to create new connected IIoT, not just to reduce costs of operation or eliminate downtime, but to add new solutions and services for new revenue streams. An important area in which the IIoT creates value is the creation of a network of device endpoints: smart, connected sensors and controllers that not only talk to each other, but also monitor and manage a wide range of machines and industrial systems. By combining this connectivity and functionality with analytics, information technologies and operational technologies, owners of industrial plants will obtain major benefits. For example, factories can be designed to adapt in real time to changes during production, or to anticipate and avoid events that might degrade operations.

Additionally, predictive maintenance programs can be implemented to eliminate the downtime or catastrophic consequences caused by unanticipated failures of critical system components. By achieving even small percentage gains in plant operations or reductions in unplanned downtimes, these types of upgrades will dramatically improve the profitability of manufacturing operations. To take full advantage of the improvement opportunities offered by the IIoT, an entire system—from sensors, actuators and motors, up through the controllers—should be connected to information and operational technology systems, and beyond into the cloud. Expanded connectivity will boost efficiencies in operations and integrate the supply chain more tightly and in innovative ways. It will also enable entirely new business models and revenue streams.

Substantial challenges

Although connectivity is the key to unlocking the full potential value of the IIoT, it brings a risk of cyberattacks. When systems are connected to the Internet and larger corporate networks, cyberattacks become possible, even likely, from external and internal sources—whether accidental or malicious. The benefits of the IIoT therefore cannot be achieved without multiple layers of security that successfully protect all the networked systems and devices. Secure communication, secure network monitoring and securing code execution at the device level are essential, not optional. It is critical for system engineers to address security issues at every layer.

Although traditional IT-endpoint security and network-monitoring solutions protect IT business applications, such solutions won’t work for the embedded devices closest to the physical systems. These operational assets must be protected against cyberattacks by integrating security directly into the endpoint devices themselves.  Minimizing vulnerabilities requires both specialized security hardware as well as software. To support enterprise security standards, embedded devices must incorporate the following key features: secure boot code, secure application updates, tightly controlled authentications, and secure communication protocols.

Categorizing vulnerability

Security in its simplest form entails ensuring that authorized operations and actions are allowed, while unauthorized actions are blocked. Most cyberattacks against embedded devices exploit one of the following categories of vulnerabilities.

Insecure by design: devices that use hard-coded passwords, transmit login credentials in the clear, allow remote accesses without authentication, or have other obvious unprotected interfaces.

Security with significant loopholes: devices with built-in backdoors, which use weak or default passwords, permit plaintext storage or transmission of encryption keys, or have similar vulnerabilities.

Good, but partial security: devices that provide strong security against certain types of attacks, but leave other interfaces unprotected. Prime examples are systems that implement TLS only for some but not all communication; and security protocols in which the setup-phase encryption key is exchanged without being encrypted, making them vulnerable to eavesdropping attacks.

Other examples include systems that implement secure communication, but don’t incorporate secure boot capability; and systems that have a secure operating system, but fail to secure the application layer. Features that are vulnerable to exploitation: Devices that have weak encryption, exploitable buffer overflows or zero-day vulnerabilities, or cannot withstand brute-forcing of their authentication mechanisms. The hackers depicted in movies and on TV typically take advantage of these types of deficiencies. Unfortunately, although it’s vitally important to secure networked devices against such vulnerabilities, the reality is that most currently deployed embedded devices cannot withstand even very basic forms of cyberattacks. Ensuring the security of IIoT devices requires addressing all of the issues described above. Ideally, robust design solutions will include adaptable security policy management and the ability to securely update firmware to protect against new types of attacks as they emerge.

System and device complexity

The Industrial Internet of Things encompasses a wildly diverse range of connected devices and systems: from small to large, simple to complex. They span from commercial gadgets to sophisticated systems found in military, utility and processing/manufacturing systems.

Embedded devices are very different from standard PCs or other IT products, but they constitute important and growing elements of the expanding web-connected network. Many of them use specialized real-time operating systems such as ThreadX, μC/OS-III or Nucleus, or a stripped-down version of Linux. Installing new software on most embedded devices deployed in the field either requires a specialized upgrade process or simply can’t be done. Further, in most cases, these ubiquitous devices are optimized to minimize processing cycles and memory usage. Therefore, they don’t have the extra processing resources required to support traditional security mechanisms. As a result, standard PC security solutions cannot solve the challenges of making embedded devices safe from cyberattacks.

In fact, given the specialized nature of embedded systems, PC security solutions won’t even run on most embedded devices. The driving principle for enterprise security is to provide multiple layers of protection. Firewalls, authentication/encryption schemes, security protocols, and intrusion-detection/intrusion prevention mechanisms are well established, widely adopted enterprise security solutions. Nevertheless, firewalls and intrusion detection features are virtually absent in embedded systems, which typically rely on simple password authentication and security protocols. Typically, makers of embedded devices have assumed that their products aren’t attractive targets to hackers. Other common perceptions have been that networked embedded devices aren’t vulnerable to attacks and that authentication and encryption can adequately protect against cyberattacks.

These assumptions are no longer valid. Today the number and sophistication of attacks against embedded devices is rising to worrisome levels. This trend has impacted many new product designs. Whereas cybersecurity has long been a critical focus for large enterprises, it’s now a strong focus for most system engineers building sensing and control devices. Fortunately, rather than reinventing the wheel, product developers can apply the security principles used to implement enterprise security. To ensure security for embedded devices, given their specialized nature, the following concerns must be addressed:

Preservation of functionality

Embedded control devices are at the heart of the transportation infrastructures, utility grids, communication systems and other elements essential to modern society. Successful cyberattacks on them can have catastrophic consequences. Thus, security solutions must protect both the data stored on networked embedded devices and safeguard the operations they perform.

Attack replication

After embedded devices are developed, they are mass-produced. If a hacker can find a way to successfully attack one of these devices, that attack can be replicated across all devices of the same type. Thus, a single-point breach can become a mass-failure mechanism.

Assumed security 

Many system engineers have long assumed that embedded devices are not targets for hackers; i.e., they have relied on security by obscurity. That assumption is totally false today. Security should now be considered a top priority for most embedded designs.

Upgrade difficulties

Most embedded devices are not easily patched. After they are deployed, they run factory-installed software for as long as they remain operational, even if that code has security vulnerabilities.

Secure Foundations 

Cryptographic methods provide the foundation for implementation of many of the features used to secure embedded devices including secure data storage, secure communication protocols, and secure boot. These techniques require cryptographic keys, which must be kept secret. System engineers often implement secure key storage in hardware using a hardware security module (HSM). Most HSMs also offer both crypto-acceleration to offload computation-intensive operations from the main CPU and True Random Number Generation (TRNG). Additionally, some HSMs provide protected code execution that allows security-critical operations to run in a separate memory space that user-space code cannot access. This prevents programs in the user space from tampering with the operation of security-critical features or stealing keys.

Regardless of whether encryption is implemented in hardware or software, most security protocols employ both symmetric and asymmetric types. IPsec does this unless pre-shared keys are used, and TLS uses asymmetric encryption to securely exchange a secret key at the start of a session. Subsequently, IPsec and TLS both transition to symmetric encryption using the secret key established during the key-exchange process.

by  Renesas; & Icon Labs 

Sustainability can be defined as the attribute of a system capable of generating an effect without undermining or detracting from the opportunity to reiterate its generation at the same conditions in the future. But as artificial intelligence begins to pervade in many walks of life, will it support or clash with the aspiration of sustainable development?

Helping our global economy thrive as a system pursuing sustainability in the context of limited resources implies solving a dilemma we come to face early on in life: how is it possible to have one’s cake and eat it? What is the probability of winning the race against time in the replacement of soon to be exhausted resources with viable alternatives? How can far fetching consequences of disruptive innovation be factored in and addressed early enough to secure that negative implications do not outweigh the advantages reaped by turning to new routes and methodologies? Paradoxical aspects may position sustainable development in the domain of utopic aspirations, at least to some extent, but working for sustainability remains a necessary practice. While a growing population is set to leverage the wealth created by the world’s economy and of our planet’s resources, individuals included in today’s digital society claim the right to a greater slice of the pie and collectively aspire to universal democratization of lifestyle standards promoted through global communication channels.

Looking at the future

From a pragmatic standpoint, the pursuit of sustainability implies, among other things, the ability to effectively imagine the future, picturing prospective scenarios and calculating the probability of influence deriving from critical variables. In the context of sustainable development, this exercise of looking ahead to forecast what will or may happen over a longer period of time is fundamental, whatever the system and the purpose it is designed for. It equally applies when managing natural resources, governing societal prosperity and planning for the wellbeing of a community and its individuals, from a collective or subjective standpoint. It implies finding answers to a large number of specific forward-thinking questions, for example:

1. What are the chances that the availability of a given resource such as rare metals, carbon-based fuels or sweet water is exhausted over time, in relation to which factors? What alternatives can be exploited to slow down the consumption of a diminishing resource or to make it totally replaceable system-wide? What geo-political scenario will potentially add or subtract from the necessity to accelerate change? What is the factual contribution to be expected in relation to newly introduced alternatives – and by when, and to what extent is the true outlook just industrial propaganda?

2. At what speed will the effects of one-child only policies decelerate demographic explosion in emerging countries? Conversely, at which extent and pace can they affect accuracy of census and lead to breeding an undercover generation of paperless citizens? At what extent and pace can the imbalanced gender distribution resulting from parental disposing of female offspring determine a sensible influence on the mix in sexual orientation of a population, once its male element becomes severely preponderant?

3. How fast will improvements in Internet connectivity support education programs in communities still slowed down by the digital divide? Which hindering effects will hold back the enrolment and attendance of female students in online courses delivered for homeschooling?

4. How much money can students borrow and invest for their education without burdening their personal credit line to such an extent that it will hinder access to a mortgage or even crush their individual financial outlook once graduation leads to the most probable job offering they can aspire to as next step in their career? What influence on their personal outlook has the probability of their country exiting the Eurozone while they still attend university?

When aspiring to plan for sustainable development in our global, hyper-connected society, exercising a smart, forward-thinking capability is essential. In the era of big data, this ability is to be empowered, well beyond a generic change of gears to the digital and computer-aided level we have been familiar with for decades. A new horizon of prerequisites to be met includes, in fact, the capability of predicting the effects of behavior, the intelligence to undertake complex modeling efforts and draft scenarios, with the possibility of factoring in influence of any new significant variable that may arise. Turning to artificial intelligence is a natural move. It is the complexity of the world we live in and the speed of technological evolution that commands it.

Only with a modern approach in terms of methodologies and technologies it becomes possible to draft the hypothetical prospective scenarios we strive to address as we pursue sustainable development. Imagining the future acquires a whole new dimension as a machine-assisted ability. To generate defendable and actionable plans, a modern vision needs to remain rooted in a fact-based approach and a scientific methodology. If we achieve to excel in this forward-thinking ability by developing the means to speculate in a scientific, fast and adaptive way, the chances of increasing sustainability of any system could already be improved.

When machines take over

There is also a broader subject area we should keep in mind. It is being said that machines will substitute human labor by and large. Will this change the essence of sustainability in a machine-driven economy? How should we picture sustainable development within a society where human labor will have much lesser relevance and to a large effect replaceable value? Which new formulas will be needed to generate and distribute wealth and profits. Will this further weaken today’s ties between labor and profit, work and remuneration? Will this correlation be (or need to be) replaced with alternative formulas of our society’s overall wealth management? This latter aspect of how artificial intelligence transforms our society and our aspirations to sustainable development is most fascinating and it is interesting to examine how governments and international bodies working in both arenas are endeavoring to find common ground

AI appeal from a government’s standpoint

Artificial intelligence is a strategic growth area that should be focused on when thinking of a comprehensive strategy for societal development from a government perspective. An interesting result of growing the AI sector is that it helps individual countries to create jobs for extremely qualified professionals. It implies developing and mastering a series of technologies that are applicable in additional strategic fields. Thus, having a market sector such as ecommerce drive its utilization allows a government to grow and cultivate competencies, experiences, solutions and technologies within their country. No government will be willing to share these with third parties once their effectiveness is proven in highly sensitive fields of application.

In the UK, a Parliamentary Science and Technology Committee recently reviewed advances in robotics and AI and its potential to fundamentally reshape the way we live and work. But it held that the Government does not yet have a strategy for developing the new skills citizens will need to flourish in a world where artificial intelligence is more prevalent, or responding to the social and ethical dilemmas it poses. The committee concluded that artificial intelligence has some way to go before the world sees systems and robots as portrayed in the creative arts such as Star Wars. At present, ‘AI machines’ have narrow and specific roles, such as in voice-recognition or playing the board game ‘Go’. But science fiction is slowly becoming science fact, and robotics and AI look destined to play an increasing role in people’s lives over the coming decades. But is the resulting view that it is too soon to set down sector-wide regulations for this nascent field a blinkered one? This is apart from the recognition that it is vital that careful scrutiny of the ethical, legal and societal ramifications of artificially intelligent systems begins now.

Transformational impacts

AI systems are starting to have transformational impacts on everyday life: from driverless cars and supercomputers that can assist doctors with medical diagnoses, to intelligent tutoring systems that can tailor lessons to meet a student’s individual cognitive needs. Such breakthroughs raise a host of questions for society, including ethical issues about the transparency of AI decision-making as well as privacy and safety. The Committee is calling for a Commission on Artificial Intelligence to be established and believes the UK is well-placed to provide this type of intellectual leadership.

Much of the significant progress in this field—such as improved automated voice recognition software, predictive text keyboards on smart phones and autonomous vehicles—has been driven by UK-based technology start-ups, but, the committee’s view remains that Government leadership in AI is lacking. Similarly, improvements in productivity and efficiency, driven by robotics and AI, are widely predicted. Yet there are conflicting views about what this would mean for jobs in the UK. Some expect rising unemployment as labor is substituted for AI-enabled machines. Others foresee a transformation in the types of employment available, with the creation of new jobs compensating for those lost and AI augmenting existing roles, enabling humans to achieve more than they could on their own.

This opinion is being mirrored somewhat in the US with a recent White House statement that: “AI-driven automation will continue to create wealth and expand the American economy in the coming years, but, while many will benefit, that growth will not be costless and will be accompanied by changes in the skills that workers need to succeed in the economy, and structural changes in the economy. Aggressive policy action will be needed to help Americans who are disadvantaged by these changes and to ensure that the enormous benefits of AI and automation are developed by and available to all.”

Global techno giants step forward

Some major technology companies — including Google and Amazon — have recently come together to form the ‘Partnership on AI’. Is now the time to identify principles for governing the development and application of AI, and to foster public debate. iGiant, Apple has also come out of the woodwork with a report on Simulated and Unsupervised (S+U) Learning, focusing on AI in the world of graphics. With recent progress in graphics, according to Apple, it has become more tractable to train models on synthetic images, potentially avoiding the need for expensive annotations. However, learning from synthetic images may not achieve the desired performance due to a gap between synthetic and real image distributions. To reduce this gap, the company proposes simulated and unsupervised (S+U) learning, where the task is to learn a model to improve the realism of a simulator’s output using unlabeled real data, while preserving the annotation information from the simulator. This sees the development of a method for S+U learning that uses an adversarial network similar to Generative Adversarial Networks (GANs), but with synthetic images as inputs instead of random vectors. Several key modifications are made to the standard GAN algorithm to preserve annotations, avoid artifacts and stabilize training: (i) a ‘self-regularization’ term, (ii) a local adversarial loss, and (iii) updating the discriminator using a history of refined images. This enables generation of highly realistic images, showing a significant improvement over using synthetic images, and achieve state-of-the-art results on the MPIIGaze dataset without any labeled real data.

Sustainable strategies

In other quarters, Earth 2017 is looking into reports on the growth of the world’s megacities and the impact AI can have, using Beijing in China as an example. Beijing has 20 million people living in its metropolitan area. A typical driving commute is 5 hours. They have one highway with 50 lanes. This 50 lane highway experienced a traffic jam that lasted for 12 days. Research confirms how mega-cities and urbanization are reshaping the 21st century. There are 25 mega-cities today with populations of at least 10 million people and 20 of these 25 mega-cities are located in developing countries. Additionally, over the next few decades, the number of mega-cities is expected to double to 50 cities. What these mega-cities have in common is unsustainable levels of pollution, landfill waste and congestion. Their success will choke on this lack of sustainability. Recognizing this as opportunity, companies from Ford to Uber to Google are driving an urban technology revolution.

AI, clean tech and IoT

According to Earth 2017, car manufacturer, Ford, has a new vision for our future in cities. It is based on mobility, connectivity and artificial intelligence. Ford is not alone. Uber, Lyft, GM, Apple and Google are all focused on commercializing urban focused technologies that use less energy, use more renewable energy, are cleaner and reduce congestion. The breakout solutions being pursued by these companies are focused on two key technologies – autonomous vehicles – and electrification. The breakout solution is to use Artificial Intelligence (AI) and the Internet of Things (IoT) to create driverless autonomous machines. Autonomous vehicles are projected to cost about a third less to operate than human driven vehicles. Ford estimates that autonomous vehicles can cost $2 per vehicle mile less to operate by removing the driver and from increased operating efficiency. Reducing fuel consumption will also reduce vehicle emissions. This holds the potential of saving more money as governments increasing explore taxing vehicle air emissions.

Additionally, electric vehicles eliminate tailpipe emissions. This is huge for mega-cities choking on pollution. Electric vehicles’ zero emissions will improve quality of life and lower health care costs. Unlike lower emissions natural gas or hydrogen vehicles, an electric car has the potential to be fueled with zero emissions renewable energy. Today, recharging an electric car with utility supplied electricity is at a 75 cents per gallon gasoline equivalent. This cost advantage will grow as wind and solar achieve even lower prices from a global manufacturing economies of scale plus technology innovation. Electric vehicles are on a path of costing less to operate plus delivering zero emissions. Earth’s report states cities are increasingly being defined by congestion and pollution. Building more roads no longer relieves congestion. Mandating increased fossil fueled vehicle air pollution controls is still effective. Technologies that can solve problems tied to pollution, congestion and cost are now the path to urban economic growth. This is a smart and clean technology wave focused on delivering less cost congestion and less pollution.

One major implementation path anticipated is in city buildings. AI, batteries, IoT and renewable energy will combine to create Net Zero Energy buildings that cost less to operate, increase human productivity and have reduced emissions. Sustainability, clean tech and smart tech are emerging as the 21st century’s urban economic drivers. The question for every city, large or small, is how will they harvest this opportunity? According to Earth, the cities that answer that question successfully will win competitive advantage. They will realize more jobs, better jobs and healthier citizens. Their businesses will be able to compete on the global stage.

Communications

The United Nations specialized agency for information and communication technologies, the ITU, is also assessing the compatibility between AI and sustainable development. This organization too poses the question whether AI is a ‘friendly companion or threatening conqueror’. With recent advances in artificial intelligence (AI), machines are gaining the ability to learn, improve and make calculated decisions in ways that will not only enable them to perform tasks previously thought to rely on human experience and ingenuity, as well as to solve problems involving a large amount of data and lots of variables too complex for humans to perceive. Adding knowledge and reasoning to existing applications, AI technology is creating enormous opportunities for beneficial influences on society, such as improving human decision-making capabilities in regard to uncertainty and other complexities.

Existing AI applications offer invaluable assistance to medical research by analyzing huge volumes of medical images – comparing them at a level of detail beyond human capacity, and making connections between structured as well as unstructured data – to identify patterns or correlations in need of further research. So generally, is this the lucky moment for AI? It can perhaps be summed up practically when looking at how volumes of data are made available today both by people and by objects/sensors; or that the complete digitization of innumerous processes makes results achieved by analysis leveraging AI, much more robust. Also, there is an availability of computational power and the capacity to efficiently process large amounts of data at reasonable prices.

Meanwhile, technology has matured well beyond the R&D phase and investment capital targeting this sector is becoming available. An overall assessment shows AI solutions are also used across a variety of other sectors, for example, to control spam, detect frauds, improve the relevance of web search results, translate text and speech, make critical decisions in stock trading, help species conservation, predict weather patterns, make use of energy more efficient – and more. So thankfully artificial intelligence in the futuristic mix can also represent a great opportunity for world sustainability.

Tougher rules on data protection, aimed at boosting privacy and giving authorities greater powers to take action against companies that breach the rules have been voted through by The European parliament. The rules, including the General Data Protection Regulation (GDPR), form the new backbone of laws for data regulators to pursue companies with heavy fines – as much as 4% of annual turnover for global companies – for incidents such as data breaches, which have become increasingly common. The new data privacy laws comprise of the GDPR, which governs the use and privacy of EU citizens’ data, and the Data Protection Directive, which governs the use of EU citizens’ data by law enforcement. Together they aim to create strong data protection law for Europe’s 500 million citizens; streamline legislation between the 28 member states pushing a digital single market and boost police and security cooperation. It is due to replace national rules that have only allowed for small fines in cases of violation.

Germany to lift border controls

With the number of migrant arriving n Germany from Austria slowing down to a trickle, the German interior ministry has said border controls on the Austria-Germany border would be lifted by mid-May. Germany is facing a problem on another front, as more and more migrants are arriving from Italy, using the Brenner crossing, a major gateway for goods and people heading north from Italy. According to the current estimate, the Ministry said the number of migrants entering Germany through Austria had fallen to zero, in contrast to last year, when thousands arrived daily. In March, the average had dropped to roughly 140 per day. Reports say that by tightening its border control measures, Austria played a key role in blocking the ‘Balkan route’, used by hundreds of thousands of migrants to get from Greece to richer EU countries in northern Europe, especially Germany and Sweden. More than one million refugees and asylum-seekers arrived in Germany in 2015.

Frontex believe terrorists hiding among asylum-seekers

The EU’s border police, Frontex, has said that terrorists may have entered Europe by hiding among asylum seekers. Frontex noted that two of the bombers in last November’s Paris attacks made it to the continent in a smuggling boat from Turkey. As the vast majority of migrants arrive undocumented, screening activities are essential to properly verify their declaration of nationality, according to the report. Frontex’s recently released Risk Analysis for 2016, stated the Paris attacks in November 2015 clearly demonstrated that irregular migratory flows could be used by terrorists to enter the EU. Echoing observations made five months ago, the report added that two of the terrorists involved in the attacks had previously irregularly entered through Leros and had been registered by the Greek authorities. They presented fraudulent Syrian documents to speed up their registration process.

New York’s JFK airport deploys Vision-Box biometrics

U.S. Customs and Border Protection (CBP) at JFK International Airport have started using facial recognition technology to match travelers’ faces to the photo on their passport. This technology is a significant step forward in conducting biometric verification of every person requesting admission to the United States. In a critical step forward with far-reaching implications affecting the security of airline passengers around the world, U.S. Customs and Border Protection (CBP) deployed a system for biometric authentication of electronic passports. This technology allows for a highly accurate biometric matching of a traveler’s face with the facial image stored on the identification document’s e-chip, thus preventing document swapping. Vision-Box, a leading company in the area of biometric traveler facilitation, is providing the core biometric technology, the vb e-pass desktop, as part of a facial recognition solution provided to CBP by Unisys Corporation, a leading global IT firm, for this initial deployment at three JFK arrival terminals. This deployment follows the successful testing of the same system at Washington’s Dulles International Airport.

Thales opens cyber security operations as cybercrime grows

As cyberthreats grow and cyberattacks become more sophisticated, Thales continues to develop its cybersecurity operations infrastructure, incorporating the latest technologies and innovations to protect the information systems of customers. Now the company has announced the opening of a new Cyber Security Operations Centre (CSOC) in Elancourt, near Paris. The offering is built around a commitment to provide personalized support to each customer, in particular through a range of new value-added services. More than 30 major organizations already rely on Thales’s expertise to protect their information systems from cyberattacks. While information systems become more interconnected and mainstream markets embrace disruptive technologies such as cloud computing, Big Data and the Internet of Things, the number of cyberattacks is growing rapidly. In 2015, the number of recorded attacks grew by 51% in France and 38% worldwide. The question facing organizations today is no longer whether they will be attacked, but when the next attack will happen and above all whether it will be detected. Thales says its detection and rapid reaction capabilities help customers step up to this challenge.