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Artificial intelligence: Between myth and reality

Jean-Gabriel Ganascia

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Are machines likely to become smarter than humans? No, says Jean-Gabriel Ganascia: this is a myth inspired by science fiction. The computer scientist walks us through the major milestones in artificial intelligence (AI), reviews the most recent technical advances, and discusses the ethical questions that require increasingly urgent answers.

A scientific discipline, AI officially began in 1956, during a summer workshop organized by four American researchers – John McCarthy, Marvin Minsky, Nathaniel Rochester and Claude Shannon – at Dartmouth College in New Hampshire, United States. Since then, the term “artificial intelligence”, probably first coined to create a striking impact, has become so popular that today everyone has heard of it. This application of computer science has continued to expand over the years, and the technologies it has spawned have contributed greatly to changing the world over the past sixty years.

However, the success of the term AI is sometimes based on a misunderstanding, when it is used to refer to an artificial entity endowed with intelligence and which, as a result, would compete with human beings. This idea, which refers to ancient myths and legends, like that of the golem [from Jewish folklore, an image endowed with life], have recently been revived by contemporary personalities including the British physicist Stephen Hawking (1942-2018), American entrepreneur Elon Musk, American futurist Ray Kurzweil, and  proponents of what we now call Strong AI or Artificial General Intelligence (AGI). We will not discuss this second meaning here, because at least for now, it can only be ascribed to a fertile imagination, inspired more by science fiction than by any tangible scientific reality confirmed by experiments and empirical observations.

For McCarthy, Minsky, and the other researchers of the Dartmouth Summer Research Project (link is external)on Artificial Intelligence, AI was initially intended to simulate each of the different faculties of intelligence – human, animal, plant, social or phylogenetic – using machines. More precisely, this scientific discipline was based on the conjecture that all cognitive functions – especially learning, reasoning, computation, perception, memorization, and even scientific discovery or artistic creativity – can be described with such precision that it would be possible to programme a computer to reproduce them. In the more than sixty years that AI has existed, there has been nothing to disprove or irrefutably prove this conjecture, which remains both open and full of potential.

Uneven progress

In the course of its short existence, AI has undergone many changes. These can be summarized in six stages.

The time of the prophets

First of all, in the euphoria of AI’s origins and early successes, the researchers had given free range to their imagination, indulging in certain reckless pronouncements for which they were heavily criticized later. For instance, in 1958, American  political scientist and economist Herbert A. Simon – who received the Nobel Prize in Economic Sciences in 1978 – had declared that, within ten years, machines would become world chess champions if they were not barred from international competitions.

The dark years

By the mid-1960s, progress seemed to be slow in coming. A 10-year-old child beat a computer at a chess game in 1965, and a report commissioned by the US Senate in 1966 described the intrinsic limitations of machine translation. AI got bad press for about a decade.

Semantic AI

The work went on nevertheless, but the research was given new direction. It focused on the psychology of memory and the mechanisms of understanding – with attempts to simulate these on computers – and on the role of knowledge in reasoning. This gave rise to techniques for the semantic representation of knowledge, which developed considerably in the mid-1970s, and also led to the development of expert systems, so called because they use the knowledge of skilled specialists to reproduce their thought processes. Expert systems raised enormous hopes in the early 1980s with a whole range of applications, including medical diagnosis.

Neo-connectionism and machine learning

Technical improvements led to the development of machine learning algorithms, which allowed  computers to accumulate knowledge and to automatically reprogramme themselves, using their own experiences.

This led to the development of industrial applications (fingerprint identification, speech recognition, etc.), where techniques from AI, computer science, artificial life and other disciplines were combined to produce hybrid systems.

From AI to human-machine interfaces

Starting in the late 1990s, AI was coupled with robotics and human-machine interfaces to produce intelligent agents that suggested the presence of feelings and emotions. This gave rise, among other things, to the calculation of emotions (affective computing), which evaluates the reactions of a subject feeling emotions and reproduces them on a machine, and especially to the development of conversational agents (chatbots).

Renaissance of AI

Since 2010, the power of machines has made it possible to exploit  enormous quantities of data (big data) with deep learning techniques, based on the use of formal neural networks. A range of very successful applications in several areas – including speech and image recognition, natural language comprehension and autonomous cars – are leading to an AI renaissance.

Applications

Many achievements using AI techniques surpass human capabilities – in 1997, a computer programme defeated the reigning world chess champion, and more recently, in 2016, other computer programmes have beaten the world’s best Go [an ancient Chinese board game] players and some top poker players. Computers are proving, or helping to prove, mathematical theorems; knowledge is being automatically constructed from huge masses of data, in terabytes (1012 bytes), or even petabytes (1015 bytes), using machine learning techniques.

As a result, machines can recognize speech and transcribe it – just like typists did in the past. Computers can accurately identify faces or fingerprints from among tens of millions, or understand texts written in natural languages. Using machine learning techniques, cars drive themselves; machines are better than dermatologists at diagnosing melanomas using photographs of skin moles  taken with mobile phone cameras; robots are fighting wars instead of humans; and factory production lines are becoming increasingly automated.

Scientists are also using AI techniques to determine the function of certain biological macromolecules, especially proteins and genomes, from the sequences of their constituents ‒ amino acids for proteins, bases for genomes. More generally, all the sciences are undergoing a major epistemological rupture with in silico experiments – named so because they are carried out by computers from massive quantities of data, using powerful processors whose cores are made of silicon. In this way, they differ from in vivo experiments, performed on living matter, and above all, from in vitro experiments, carried out in glass test-tubes.

Today, AI applications affect almost all fields of activity – particularly in the industry, banking, insurance, health and defence sectors. Several routine tasks are now automated, transforming many trades and eventually eliminating some.

What are the ethical risks?

With AI, most dimensions of intelligence ‒ except perhaps humour ‒ are subject to rational analysis and reconstruction, using computers. Moreover, machines are exceeding our cognitive faculties in most fields, raising fears of ethical risks. These risks fall into three categories – the scarcity of work, because it can be carried out by machines instead of humans; the consequences for the autonomy of the individual, particularly in terms of freedom and security; and the overtaking of humanity, which would be replaced by more “intelligent” machines.

However, if we examine the reality, we see that work (done by humans) is not disappearing – quite the contrary – but it is changing and calling for new skills. Similarly, an individual’s autonomy and  freedom are not inevitably undermined by the development of AI – so long as we remain vigilant in the face of technological intrusions into our private lives.

Finally, contrary to what some people claim, machines pose no existential threat to humanity. Their autonomy is purely technological, in that it corresponds only to material chains of causality that go from the taking of information to decision-making. On the other hand, machines have no moral autonomy, because even if they do confuse and mislead us in the process of making decisions, they do not have a will of their own and remain subjugated to the objectives that we have assigned to them.

Source: UNESCO

French computer scientist Jean-Gabriel Ganascia is a professor at Sorbonne University Paris. He is also a researcher at LIP6 the computer science laboratory at the Sorbonne, a fellow of the European Association for Artificial Intelligence a member of the Institut Universitaire de France and chairman of the ethics committee of the National Centre for Scientific Research (CNRS Paris. His current research interests include machine learning, symbolic data fusion, computational ethics, computer ethics and digital humanities.

Science & Technology

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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