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Artificial Intelligence: A Blessing or a Threat for Humanity?

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In August 2018, Czech Technical University in Prague simultaneously hosted several conferences on AI-related topics: human-level AI, artificial general intelligence, biologically inspired cognitive architectures, and neural-symbolic integration technology. Reports were presented by prominent experts representing global leaders in artificial intelligence: Microsoft, Facebook, DARPA, MIT and Good AI. The reports described the current status of AI developments, identified the problems facing society that have yet to be resolved, and highlighted the threats arising from the further development of this technology. In this review, we will attempt to briefly identify the main problems and threats, as well as the possible ways to counter these threats.

To begin with, let us provide definitions for some of the terms that are commonly used in conjunction with AI in various contexts: weak, or specialized, AI; autonomous AI; adaptive AI; artificial general intelligence (AGI); strong AI; human-level AI; and super-human AI.

Weak, or specialized, AI is represented by all existing solutions without exception and implies the automated solution of one specific task, be it a game of Go or face recognition with CCTV footage. Such systems are incapable of independent learning for the purpose of solving other problems: they can only be reprogrammed by humans to do so.

Autonomous AI implies a system’s ability to function for protracted periods of time without the intervention of a human operator. This could be a solar-powered UAV performing a multi-day flight from Champs-Elysees in Paris to Moscow’s Red Square or back, independently selecting its route and recharging stops while avoiding all sorts of obstacles.

Adaptive AI implies the system’s ability to adapt to new situations and obtain knowledge that it did not possess at the time of its creation. For example, a system originally tasked with conducting conversations in Russian could independently learn new languages and apply this knowledge in conversation if it found itself in a new language environment or if it deliberately studied educational materials on these new languages.

Artificial general intelligence implies adaptability of such a high level that the corresponding system could, given the appropriate training, be used in a wide variety of activities. New knowledge could either be self-taught or learned with the help of an instructor. It is in this same sense that the notion of strong AI is often used in opposition to weak or specialized AI.

Human-level AI implies a level of adaptability comparable to that of a human being, meaning that the system is capable of mastering the same skills as a human and within comparable periods of time.

Super-human AI implies even greater adaptability and learning speeds, allowing the system to masker the knowledge and skills that humans would never be able to.

Fundamental Problems Associated with Creating a Strong AI

Despite the multitude of advances in neuroscience, we still do not know exactly how natural intelligence works. For this same reason, we do not know for sure how to create artificial intelligence (AI). There are a number of known problems that need to be resolved, as well as differing opinions as to how these problems should be prioritized. For example, Ben Goertzel, who heads the OpenCog and SingularityNET, open-source international projects to create artificial intelligence, believes that all the requisite technology for creating an artificial general intelligence has already been developed, and that the only thing necessary is to combine them in a way that would ensure the necessary synergy. Other experts are more sceptical, pointing out that many of the problems that we will discuss below need to be resolved first. Also, expert estimates for when a strong AI may be created vary greatly, from ten or so years to several decades from now.

On the other hand, the emergence of a strong AI is logical in the framework of the general process of evolution as the emergence of molecules from atoms and cells from molecules, the creation of the central nervous system from specialized cells, the emergence of social structure, the development of speech and writing systems and, ultimately, the nascence of information technology. Valentin Turchin demonstrates the logic behind the increasing complexity of information structures and organizational mechanisms in the process of evolution. Unless humanity perishes first, this evolution will be inevitable and will, in the long run, rescue humankind, as only non-biological lifeforms will be able to survive the inevitable end of the Solar System and preserve our civilization’s information code in the Universe.

It is important to realize that the creation of a strong AI does not necessarily require the understanding of how the natural intelligence works, just as the development of a rocket does not necessarily require understanding how a bird flies. Such an AI will certainly be created, sooner or later, in one way or another, and perhaps even in several different ways.

Most experts identify the following fundamental problems that need to be solved before a general or strong AI can be created:

Few-shot learning: systems need to be developed that can learn with the use of a small amount of materials, in contrast to the current deep-learning systems, which require massive amounts of specifically prepared learning materials.

Strong generalization: creating problem recognition technologies allowing for recognizing objects in situations that differ from those in which they were encountered in the learning materials.

Generative learning models: developing learning technologies in which the objects to be memorized are not the features of the object to be recognised, but rather the principles of its formation. This would help in addressing the more profound characteristics of objects, providing for faster learning and stronger generalization.

Structured prediction and learning: developing learning technologies based on the representation of learning objects as multi-layered hierarchical structures, with lower-level elements defining higher level ones. This could prove an alternative solution to the problems of fast learning and strong generalization.

Solving the problem of catastrophic forgetting, which is pertinent to the majority of existing systems: a system originally trained with the use of one class of object and then additionally trained to recognize a new class of objects loses the ability to recognize objects of the original class.

Achieving an incremental learning ability, which implies a system’s ability to gradually accumulate knowledge and perfect its skills without losing the previously obtained knowledge, but rather obtaining new knowledge, with regard to systems intended for interaction in natural languages. Ideally, such a system should pass the so-called Baby Turing Test by demonstrating its ability to gradually master a language from the baby level to the adult level.

Solving the consciousness problem, i.e. coming up with a proven working model for conscious behaviour that ensures effective prediction and deliberate behaviour through the formation of an “internal worldview,” which could be used for seeking optimum behavioural strategies to achieve goals without actually interacting with the real world. This would significantly improve security and the testing of hypotheses while increasing the speed and energy efficiency of such checks, thus enabling a live or artificial system to learn independently within the “virtual reality” of its own consciousness. There are two applied sides to solving the consciousness problem. On the one hand, creating conscious AI systems would increase their efficiency dramatically. On the other hand, such systems would come with both additional risks and ethical problems, seeing as they could, at some point, be equated to the level of self-awareness of human beings, with the ensuing legal consequences.

Potential AI-Related Threats

Even the emergence of autonomous or adaptive AI systems, let alone general or strong AI, is associated with several threats of varying degrees of severity that are relevant today.

The first threat to humans may not necessarily be presented by a strong, general, human-level or super-human AI, as it would be enough to have an autonomous system capable of processing massive amounts of data at high speeds. Such a system could be used as the basis for so-called lethal autonomous weapons systems (LAWS), the simplest example being drone assassins (3D-printed in large batches or in small numbers).

Second, a threat could be posed by a state (a potential adversary) gaining access to weapons system based on more adaptive, autonomous and general AI with improved reaction times and better predictive ability.

Third, a threat for the entire world would be a situation based on the previous threat, in which several states would enter a new round of the arms race, perfecting the intelligence levels of autonomous weapon systems, as Stanislaw Lem predicted several decades ago.

Fourth, a threat to any party would be presented by any intellectual system (not necessarily a combat system, but one that could have industrial or domestic applications too) with enough autonomy and adaptivity to be capable not only of deliberate activity, but also of autonomous conscious target-setting, which could run counter to the individual and collective goals of humans. Such a system would have far more opportunities to achieve these goals due to its higher operating speeds, greater information processing performance and better predictive ability. Unfortunately, humanity has not yet fully researched or even grasped the scale of this particular threat.

Fifth, society is facing a threat in the form of the transition to a new level in the development of production relations in the capitalist (or totalitarian) society, in which a minority comes to control material production and excludes an overwhelming majority of the population from this sector thanks to ever-growing automation. This may result in greater social stratification, the reduced effectiveness of “social elevators” and an increase in the numbers of people made redundant, with adverse social consequences.

Finally, another potential threat to humanity in general is the increasing autonomy of global data processing, information distribution and decision-making systems growing, since information distribution speeds within such systems, and the scale of their interactions, could result in social phenomena that cannot be predicted based on prior experience and the existing models. For example, the social credit system currently being introduced in China is a unique experiment of truly civilizational scale that could have unpredictable consequences.

The problems of controlling artificial intelligence systems are currently associated, among other things, with the closed nature of the existing applications, which are based on “deep neural networks.” Such applications do not make it possible to validate the correctness of decisions prior to implementation, nor do they allow for an analysis of the solution provided by the machine after the fact. This phenomenon is being addressed by the new science, which explores explainable artificial intelligence (XAI). The process is aided by a renewed interest in integrating the associative (neural) and symbolic (logic-based) approaches to the problem.

Ways to Counter the Threats

It appears absolutely necessary to take the following measures in order to prevent catastrophic scenarios associated with the further development and application of AI technologies.

An international ban on LAWS, as well as the development and introduction of international measures to enforce such a ban.

Governmental backing for research into the aforementioned problems (into “explainable AI ” in particular), the integration of different approaches, and studying the principles of creating target-setting mechanisms for the purpose of developing effective programming and control tools for intellectual systems. Such programming should be based on values rather than rules, and it is targets that need to be controlled, not actions.

Democratizing access to AI technologies and methods, including through re-investing profits from the introduction of intellectual systems into the mass teaching of computing and cognitive technologies, as well as creating open-source AI solutions and devising measures to stimulate existing “closed” AI systems to open their source codes. For example, the Aigents project is aimed at creating AI personal agents for mass users that would operate autonomously and be immune to centralized manipulations.

Intergovernmental regulation of the openness of AI algorithms, operating protocols for data processing and decision-making systems, including the possibility of independent audits by international structures, national agencies and individuals. One initiative in this sense is to create the SingularityNET open-source platform and ecosystem for AI applications.

First published in our partner RIAC

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Science and society: Mind the gap

MD Staff

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International regulations are failing to keep up with the mind-boggling pace of new scientific discoveries and potential “cowboy” applications.

As we go about our daily lives we never quite know what is around the corner. Is there anything we are doing—a technology we are using–which could one day cause us harm or threaten our existence?

When scientists discovered the ozone layer was being depleted, policymakers eventually heeded the dire warnings of damage to the environment and human health, and a global agreement was reached to take remedial action.

Science is double-edged in that it can bring enormous benefits to humans, but at the same time it can create things we did not intend, with harmful consequences.

Today, scientists are using new tools like 3D printing, artificial intelligence and increasingly powerful computers, microscopes and satellites to better understand our world. They are discovering possible solutions to challenges we know about and are uncovering emerging challenges.

While innovative science holds out the possibility of solving many of the climate change and ecosystems challenges we face, we must be careful not to unleash a Frankenstein. Thus, it is important that society at large understands the global implications of new discoveries and governments agree on regulations in line with the precautionary principle.

Under this principle, stringent risk assessment and the inclusion of diverse stakeholder perspectives should be applied in the development and handling of innovative applications and products. The precautionary principle states that when human activities may lead to unacceptable harm that is scientifically plausible but uncertain, action should be taken to avoid or diminish that harm.

Part of UN Environment’s work is horizon-scanning for the latest discoveries with potentially global implications. To this end, it works with scientists and organizations across the world to highlight the most important emerging challenges for decision-makers in government, business and civil society and provide them with the knowledge and options to act quickly.

At the micro-level, there have been some amazing discoveries recently. Take seed preservation, which is vital given that the world is losing plant species at an unprecedented rate, with about one in five thought to be at risk of extinction.

The Global Strategy for Plant Conservation requires that 75 per cent of threatened plant species be conserved ex situ by 2020. But seed banking (where seeds are dried and stored in a vault at minus 20°C) is not an option for many threatened plants such as oak, chestnut and avocado trees. These trees have desiccation-sensitive seeds which are killed if dried. According to models published in the journal Nature Plants, 36 per cent of critically endangered plant species, 33 per cent of all trees and about 10 per cent of medicinal plants fall into this category.

So alternative techniques are needed. Researchers are investigating cryopreservation for these hard-to-store seeds, which include staples such as coffee and cocoa. Cryopreservation involves removing the plant embryo from the rest of the seed, then freezing it at very low temperatures in liquid nitrogen.

Meanwhile in the United States, scientists have demonstrated how they can generate small quantities of electricity from a mushroom covered in bacteria.

Researchers at Stevens Institute of Technology in the United States used 3D printing to attach clusters of energy-producing bugs to the cap of a button mushroom. They made the mushroom “bionic” by supercharging it with 3D-printed clusters of cyanobacteria (a group of photosynthetic bacteria) that generate electricity, and swirls of graphene nanoribbons that can collect the current. The mushroom, a fungus, provides an environment in which the cyanobacteria can last several days longer than on a silicone and dead mushroom as suitable controls. Such discoveries herald the possibility of harnessing bacteria in new ways for clean energy generation in the future.

Synthetic biology

One challenge identified by UN Environment and partners is the advanced genetic-engineering technology known as synthetic biology. Did you know that scientists can modify microorganisms like E. coli by rewriting their genetic code to turn them into tiny living factories that produce biofuel? Or that Baker’s yeast can also be reprogrammed to derive an antimalarial drug called artemisinin, which is normally sourced from the sweet wormwood plant?

Synthetic biology is defined by the Convention on Biological Diversity as a further development and new dimension of modern biotechnology that combines science, technology and engineering to facilitate and accelerate the understanding, design, redesign, manufacture and/or modification of genetic materials, living organisms and biological systems.

However, the intentional or accidental release of genetically engineered organisms into the environment could have significant negative impacts on both human and environmental health.

Synthetic biology has been identified as an emerging issue with potentially global implications. As such, it will feature alongside governance of geo-engineering, permafrost peatlands, maladaptation (actions that may lead to increased risk of adverse climate-related outcomes), the circular economy of nitrogen, and landscape connectivity in UN Environment’s flagship Frontiers Report due to be released in March 2019.

UN Environment

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New year, new smart home innovations for your interconnected life

MD Staff

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Smart home products made major strides in 2018. More people than ever now use connected devices in their homes, and smart home hubs are constantly adding new integrations and capabilities. Research from Statista predicts that by the end of 2018, more than 45 million smart home devices will be installed in U.S. homes, fueling an exciting new phase for the smart home industry, offering consumers new and improved smart technologies and giving rise to a totally interconnected, easy-to-control environment termed the “smart home.”

This innovation looks to continue in the new year with increasingly intuitive products that make life more enjoyable and interconnected. Here are the new smart home products ready to change the way we live in the new year, coming out of the 2019 Consumer Electronics Show (CES).

Laundry made easy

The calendar might have changed, but your laundry needs haven’t gone anywhere. Thankfully, smart home technology is making the chore a little easier with LG’s Ultimate Laundry Room.

The LG Styler is a first-of-its-kind steam clothing care system certified as asthma and allergy friendly(R) by the Asthma and Allergy Foundation of America (AAFA). The Wi-Fi enabled smart LG Styler reduces wrinkles and odor and refreshes garments with the fastest cycle on the market today — as little as 20 minutes — thanks to the gentle power of pure steam technology. Furthermore, LG TWINWash(TM) with SideKick(TM) pedestal washer, an industry-first innovation for laundry, allows users to tackle small loads that are a big deal and can’t wait or wash two loads at the same time.

With LG Styler for daily refreshes, the innovative LG SideKick(TM) mini washer for small loads that can’t wait, and LG’s award-winning top and front load washers and dryers, the LG Ultimate Laundry Room suite of products can be started, stopped or monitored from anywhere using LG’s SmartThinQ(R) app. Users will receive notifications when a cycle has finished, or they can download new cycles, check energy usage and quickly troubleshoot minor issues using Smart Diagnosis. For added convenience, these home solutions can also be controlled with simple voice commands using the Google Assistant.

Smarter home with smart displays

Laundry is just one example of how smart home technology is making life easier. By adding other connected appliances and devices, you can develop a true smart home ecosystem in which seamless integrations produce valuable efficiency. One of the best ways to anchor your ecosystem is with a smart display like the new LG XBOOM AI ThinQ WK9 Smart Display. The advanced smart display builds on the capabilities of a Google Assistant speaker with the added convenience of a touchscreen display and, in partnership with Meridian Audio, delivers high-fidelity sound, precise vocal definition and accurate bass, despite its compact size.

In addition to its audio and video capabilities, the WK9 enables control of other LG ThinQ products such as LG TVs and home appliances, plus more than 10,000 smart devices from over 1,000 brands that work with Google Assistant. By establishing a go-to hub for all your smart home devices, you can increase connectivity and create a fully integrated smart home environment.

Stay connected on-the-go

As innovation continues, smart home technology is branching outside of the home itself. With new products, you can receive notifications regarding your home from anywhere, making sure you never lose touch with the most important things in your life. The first full-screen smartwatch with mechanical hands, the LG Watch W7 allows you to connect and control your smart devices. With two mechanical hands and a micro gearbox, users can enjoy the full WearOS smartwatch experience with the essence and mechanism of a true timepiece. With mobile connectivity, your life becomes easier no matter where you are.

Smart home technology is all about making our lives easier and more comfortable. Whether you’re just doing laundry, looking to power your whole home, or even taking that control on the road, new smart home products provide a level of convenience that’s changing the way we live.

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From Steel to Smartphones, Meet the World Economic Forum’s New Factories of the Future

MD Staff

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BMW Group, Regensburg Plant, Germany

The World Economic Forum today announces the addition of seven new factories to its network of “Manufacturing Lighthouses”, state-of-the-art facilities that serve as world leaders in how to successfully adopt and integrate the cutting-edge technologies of the Fourth Industrial Revolution.

The Lighthouses join a group of nine others, which were unveiled in 2018. All were selected from an initial list of 1,000 manufacturers based on their successful implementation of Fourth Industrial Revolution technologies in ways that have driven financial and operational impact.

The wider purpose of the community is to help overcome the practical challenges being experienced by industries in advanced and emerging economies when upgrading technology. Earlier work by the Forum identified that more than 70% of businesses investing in technologies, such as big data analytics, artificial intelligence (AI) or 3D printing, fail to move beyond the pilot phase. In response to this, all Lighthouses in the network have agreed to open their doors and share their knowledge with other manufacturing businesses.

The new Lighthouses represent a range of industries and geographical locations, with four factories located in Europe, two in China and one in the Middle East. Importantly, the list also contains a medium-sized business, the Italian-based Rold. One frequent challenge highlighted by businesses is that they lack the scale and resources to implement advanced technologies cost effectively.

The new Lighthouses are:

BMW Group (Regensburg Plant, Germany): This car plant manufactured approximately 320,000 vehicles in 2018. By using the custom BMW internet of things platform, it incurred time and cost, but the result has been cut the time to deploy all new applications by 80% leading to, among other things, a significant reduction in logistics costs and 5% reduction in quality issues.

Danfoss, Commercial Compressors (Tianjin, China): This factory makes compressors for refrigerators, air conditioning units and other products. By using its full digital traceability system and digital tools such as smart sensors, visual inspection, auto monitoring system etc. to improve quality control, it has improved labour productivity by 30% and decreased customer complaints by 57% within two years.

Foxconn (Shenzhen, China): “Lights off factory” – This factory, which specializes in components for smartphones and other electrical equipment, boasts a fully automated manufacturing process with machine learning and AI driving auto optimization of equipment, smart self-maintenance and real-time status monitoring in smart production. Its Fourth Industrial Revolution-first approach has resulted in efficiency gains of 30% and lowered its stock cycle by 15%.

Rold (Cerro Maggiore, Italy): This 240-employee business makes locking mechanisms for washing machines and dishwashers. As the only SME in the Lighthouse network, its use of Fourth Industrial Revolution technologies such as smart watches, rapid prototyping and digital dashboards has helped improve turnover by between 7% and 8%.

Sandvik Coromant (Gimo, Sweden): This producer of cutting tools and solutions has created a digital thread through its production processes that has significantly raised labour productivity. One example is its ‘touchless changeover’ which allows design patterns to be changed automatically, even during unmanned shifts.

Saudi Aramco Uthmaniyah Gas Plant (Uthmaniyah, Saudi Arabia): The giant’s gas processing plant has become a leader in a number of Fourth Industrial Revolution technologies including the use of Advanced Analytics and Artificial Intelligence solutions via Saudi Aramco Fourth Industrial Revolution Center, the use of drones to inspect pipelines and machinery (cutting inspection times by 90%) and wearable technologies such as digital helmets that help workers cut the time it takes to make inspections and repairs.

Tata Steel (IJmuiden, The Netherlands): This plant of 9,000 employees is putting its people first, creating an Advanced Analytics Academy to help workers come up with solutions to reduce waste, and improve the quality and reliability of production processes. This work has resulted in a significant improvement in financial results.

The Lighthouse programme has been conducted in collaboration with McKinsey. In conjunction with the expansion of the network, the Forum today also publishes a white paper, Fourth Industrial Revolution: Beacons of Technology and Innovation in Manufacturing, which showcases findings from the project to date.

“Lighthouse factories are found in companies large and small, in all industries and regions. Rather than replacing operators with machines, lighthouse factories are transforming work to make it less repetitive, more interesting, diversified and productive. Rather than staying within the factory walls, Lighthouses build a broad innovation system with business, government and civil society. Beyond local pilots, Lighthouses create value and resilience through the supply chain, and agility and responsiveness for customers. Technology, deployed wisely in our manufacturing and production system, can create a better, cleaner world. We hope this network can be a source of inspiration to help break out of productivity stagnation and deliver the maximum positive benefit for society,” said Helena Leurent, Head of the Shaping the Future of Production System Initiative at the World Economic Forum.

“These 16 Lighthouses represent a turning point. We are now seeing the start of the second phase, as Fourth Industrial Revolution technologies are penetrating the core of all industries, and our platform of 16 Lighthouses is the clearest sign we have,” said Enno de Boer, Partner and Head of McKinsey’s Global Manufacturing Practice, which collaborated with the Forum on the project. “However, these leaders have a two-year head start ahead of companies that are still sorting out how to scale. We are running the risk that the value creation will be centered around a few ecosystems, rather than disseminated across entire industries. The race has clearly started.”

The extended network of “Manufacturing Lighthouses” will be officially presented at the World Economic Forum’s Annual Meeting 2019, taking place on 22-25 January 2019 in Davos-Klosters, Switzerland, and convening under the theme, Globalization 4.0: Shaping a Global Architecture in the Age of the Fourth Industrial Revolution.

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