Among political observers, there is a widespread notion that U.S. President-elect Donald Trump will inherit an economy in the best of shape. Inflation is down to historic and desirable levels, the unemployment rate stands at 4.9% and U.S. economic growth is better than expected. Moreover, observers can’t help but hear Mr. Trump’s boastful rhetoric as soon as he steps onto the bully pulpit. But as promising as the picture might seem, it will be very difficult to carry off his promise of ‘getting back our jobs’ in the long term.
Since 1980’s and up to the 2000’s, the world has undergone immense changes. The most prominent and significant being that in the realm of technology. And, the internet generates new, mind boggling marvels with each passing day – and continues to do so. Through the ‘Internet of Things’ and automation people are experiencing massive changes in the way the world works while scientists are signing letters foreboding the dangers of the rising AI. U.S. politicians and the media have typically blamed offshoring [usually to China] and international trade agreements for wrecking the domestic economy. A University of California study asserts that approximately 14 million white collar jobs are susceptible to off-shoring . Ron and Anil Hira, in their book “Outsourcing America, believe that US companies justify off-shoring by arguing that to create more jobs domestically through cost savings are “self-delusion.” (Ron Hira is a professor at the Rochester Institute of Technology and Anial Hira is a professor at Simon Fraser University.)
In other words, it is not the intervention of foreigners which leads to the scarcity of jobs but automation. There are two vocal camps on this issue: One believes that automation, instead of creating a paucity of jobs instead leads to the creation of more job opportunities. And the other camp remains certain that, despite the spread of AI and factory robots, their jobs will remain intact during the next coming years, as reported by a research paper issued by the non-partisan PEW Research Center. Experts surveyed by Pew called for a more optimistic approach: “many jobs currently performed by humans will be substantially taken over by robots or digital agents by 2025. But they have faith that human ingenuity will create new jobs, industries, and ways to make a living, just as it has been doing since the dawn of the Industrial Revolution”. However, there are dissenters as well.
Justin Reich, a fellow at Harvard University’s Berkman Center for Internet & Society, says: “Robots and AI will increasingly replace routine kinds of work − I’m not sure that jobs will disappear altogether, though that seems possible, but the jobs that are left will be lower-paying and less secure than those that exist now. The middle is moving to the bottom.”
One can see very clearly how technologies are replacing even white-collar jobs and thus breaking the presumption that only routine and repetitive jobs are at danger from automation. Take for example, the case of Enlitic: A deep-learning system that is now being tested in Australia. The software can diagnose diseases, analyze X-rays and identify cancer. Moreover, the field of medicine is not the only profession feeling the heat of automation. Jobs in the field of law are also vulnerable. There is software in existence that can rummage through dossiers of legal documents and easily pin-point the desired files.
“Automation is now “blind to the color of your collar”, declares Jerry Kaplan, author of “Humans Need Not Apply”, a book that predicts upheaval in the labor market.
The other camp, however, is trying to peddle a more positive future. Debunking the ‘lump of labor’ fallacy which states that there is a finite amount of work and automation, and hence opening a chasm between jobs and peoples, the proponents state that automating a task results in creating more tasks as more people or different processes are now required to operate that ‘automated’ job. Again quoting The Economist, “During the Industrial Revolution more and more tasks in the weaving process were automated, prompting workers to focus on the things machines could not do, such as operating a machine, and then tending multiple machines to keep them running smoothly. This caused output to grow explosively. In America during the 19th century the amount of coarse cloth a single weaver could produce in an hour increased by a factor of 50, and the amount of labor required per yard of cloth fell by 98%. This made cloth cheaper and increased demand for it, which in turn created more jobs for weavers: their numbers quadrupled between 1830 and 1900. In other words, technology gradually changed the nature of the weaver’s job, and the skills required to do it, rather than replacing it altogether,” says James Bessen, an economist at Boston University School of Law said.
“We already have cars that talk to us, a phone we can talk to, robots that lift the elderly out of bed, and apps that remind us to call Mom. An app can dial Mom’s number and even send flowers, but an app can’t do the most human of all things: emotionally connect with her,” according to Pamela Rutledge, PhD and director of the Media Psychology Research Center.
When Mr. Trump assumes office on 20th January, 2017, he says that one of his first priorities, among other things, is to repeal the Trans-Pacific Partners or otherwise known as “TPP.” And Trump intends to lure back U.S. companies by offering lower taxes (if not through sheer brute force as displayed in his negotiations with the air conditioner manufacture, Carrier). And yet, at the same point, Trump promises more government spending e.g. Infrastructure development. Economists generally agree that lower taxes and increased spending will increase U.S. debt which may potentially lead to a ruinous outcome for the US Economy. Nevertheless, Americans who voted for him count on his actions and his promises, including bringing thousands of jobs back to the US. Therefore, observers must consider the question: What is that is more dangerous? Off-shoring or Automation?
Your new digital rights across Europe during summer holidays
This summer, European citizens will enjoy more digital rights than ever before. Following the end of roaming charges across the European Union last year, holidaymakers can now travel with their online TV, film, sports, music or e-book subscriptions at no extra cost. In addition, everyone across Europe can enjoy world-class data protection rules that ensure all Europeans have better control over their personal data.
Andrus Ansip, Vice-President for the Digital Single Market said: “Europeans are already starting to feel the benefits of the Digital Single Market. This summer you will be able to bring your favourite TV programmes and sports matches with you wherever you travel in the EU. By the end of this year, you will also be able to buy festival tickets or rent cars online from all over the EU without being geo-blocked or re-routed.”
Věra Jourová, Commissioner for Justice, Consumers and Gender Equality added: “The digital world offers tremendous opportunities, but also challenges; for example, our personal data is a useful asset for many companies. With the modern data protection rules we have put in place, Europeans have gained control over their data whenever they shop, book their holidays online or just surf the internet.”
Mariya Gabriel, Commissioner for the Digital Economy and Society said: “We are improving the daily life of our citizens, be it end of roaming charges or safer online environment. By completing all our digital initiatives we will bring even more positive change to consumers and businesses alike.”
Digital rights already in daily use
Since June 2017, people have been able use their mobile phones while travelling in the EU just like they would at home, without paying extra charges. Since the EU abolished roaming charges, more than five times the amount of data has been consumed and almost two and a half times more phone calls have been made in the EU and the European Economic Area.
Since April 2018, consumers can access online content services they have subscribed to in their home country also when travelling across the EU, including among other films, series and sports broadcasts (see examples in factsheet).
Under the new data protection rules which have been in place across the EU since 25 May 2018, Europeans can safely transfer personal data between service providers such as the cloud or email; everyone now has the right to know if their data has been leaked or hacked, or how their personal data is being collected. Furthermore, with the ‘right to be forgotten’, personal data has to be deleted upon request, if there are no legitimate reasons for a company to keep it.
Finally, with the net neutrality rules applying since spring 2016, every European has access to open internet, guaranteeing their freedom without discrimination when choosing content, applications, services and information of their choice.
With some digital rights already in place, there is more to come in the upcoming months. From September, Europeans will have increasingly the right to use their national electronic identification (eID) across the whole EU to access public services.
As of December, everyone will benefit from the free flow of non-personal data, as they will have access to better and more competitive data storage and processing services in the EU, thus complementing the free movement of people, goods, services and capital. Entrepreneurs meanwhile will have the right to decide where in the EU they store and process all types of data.
As of 3 December, Europeans will be able to shop online without unjustified discrimination wherever they are in the EU. They will not have to worry about a website blocking or re-routing them just because they – or their credit card – come from a different country.
As of next year, citizens will be able to compare parcel delivery costs more easily and benefit from more affordable prices for cross-border parcel delivery.
Agreed rules on value added tax for e-commerce will allow entrepreneurs to take care of their cross-border VAT needs in one online portal and in their own language.
With the recently agreed European Electronic Communications Code, Europeans will have the right to switch internet services and telecoms providers in a simpler way. They will also have the right to receive public alerts on mobile phones in case of an emergency. The new rules will also guarantee a better and more affordable connectivity across the EU.
With the updated rules for audiovisual media, Europeans will have the right to a safe online environment that protects them from incitement to violence, hatred, terrorism, child pornography, racism and xenophobia.
The Digital Single Market strategy was proposed by the Commission in May 2015 to make the EU’s single market fit for the digital age – tearing down regulatory walls and moving from 28 national markets to a single one. This has the potential to contribute €415 billion per year to our economy and create hundreds of thousands of new jobs.
Three years later, the strategy is well on its way: 17 legislative proposals have been agreed on, while 12 proposals are still on the table. There is a strong need to complete our regulatory framework for creating the Digital Single Market. Thanks to this the value of Europe’s data economy has the potential to top €700 billion by 2020, representing 4% of the EU’s economy.
Artificial intelligence: Between myth and reality
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.
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.
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.
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.
13 Health Tech Innovators Changing the World
Thirteen early career scientists from across the Asia-Pacific who are revolutionizing healthcare technology have been named finalists for the 2018 APEC Science Prize for Innovation, Research and Education.
From new preventative treatments to enhanced threat detection to rapid recovery solutions, the cross-border research breakthroughs developed by these innovators under 40 years of age were recognized in accordance with this year’s ASPIRE Prize theme: Smart Technologies for Healthy Societies.
The winner will be announced by science and technology officials and industry representatives from the APEC Policy Partnership on Science, Innovation and Technology when they convene in Port Moresby in August. The winner will also receive USD 25,000 in prize money sponsored by Wiley and Elsevier, publishers of scholarly scientific knowledge.
Meet the 2018 ASPIRE Prize Finalists:
Dr Madhu Bhaskaran
Associate Professor, RMIT University, Melbourne, Australia
Field of research: Electronic materials engineering
Dr Bhaskaran’s work transforms the way we imagine, use and interact with electronic devices and sensors. She has developed ways to combine functional oxide materials processed at high temperatures with elastic and plastic materials. Her work has led to the development of wearable elastic electronics and sensors including gas and UV sensors and flat optical devices—all of which are stretchable, optically transparent and as thin as a nicotine patch. An example of an application includes the development of devices to detect amount of exposure to UV rays which contribute to skin cancer.
Dr Daniel Fuller
Assistant Professor and Canada Research Chair in Population Physical Activity, Memorial University of Newfoundland
Field of research: Human kinetics and public health
Dr Fuller’s research involves designing healthier cities by using mobile health technologies like wearable devices, mobile phones, machine learning and geographic information science to increase physical activity. He works closely with cities and local community organizations to evaluate the impact of existing interventions such as bicycle share programs, bridge construction and snow clearing on physical activity.
Dr Pablo González Muñoz
Assistant Professor, Pontificia Universidad Catolica De Chile
Field of research: Host-pathogen interactions, human viruses, immunology, microbiology and immune evasion
Dr González studies the virus herpes simplex-2 (HSV-2), a virus that currently affects about 500 million people for which a vaccine is not available. Dr González has worked with at least five human pathogens. Through his research, he has developed a fast, affordable and easy-to-use diagnostic kit to detect viral infections for different tissues that can be used in rural areas.
Dr Liu Guanghui
Professor, Institute of Biophysics, Chinese Academy of Sciences
Field of research: Stem cells and healthy aging
Dr Liu researches aging, organ and tissue homeostasis, and aging-associated diseases. Through his work, he has discovered therapeutic interventions to allow for the “healthy aging” of human stem cells. Dr Liu’s work continues to enhance the study and treatment of disorders related to aging.
Dr Chairul Hudaya
Assistant Professor, University of Indonesia
Field of research: Electrical power and energy materials
Dr Hudaya researches affordable smart energy storage technology for healthy societies, especially those living in remote and isolated areas. His two main projects include: 1) a portable energy storage device used with an infant incubator, serving premature infants; and 2) a smart monitoring system installed in a laptop enabling nurses to communicate in remote, off-grid areas.
Dr Choongik Kim
Associate Professor, Sogang University
Field of research: Wearable/flexible electronics, organic/polymeric materials, semiconductors
Dr Kim researches and develops novel electric materials for use in wearable devices, including activity monitoring bracelets, smart watches and GPS enabled shoes. In 2007, as published in Science, he was the first to expand upon the relationship between electric materials and electronic device performance. Dr Kim’s research is used to develop the core technology for new wearable technologies, leading to more real-time applications that can support meaningful improvements in health outcomes.
Dr Siti Hamidah Mohd Setapar
Associate Professor, Universiti Teknologi Malaysia
Field of research: Micellar nanotechnology, extraction of national products, product development from natural ingredients, cosmetic formulation and separation processes
Dr Mohd Setapar’s research focuses on micellar nanotechnology, a cutting-edge technology used in skincare and cosmetics to improve the effectiveness of the skin cleansing process and enhance the absorption capacity of cosmetic ingredients into the skin. She has commercialized a range of cosmetic and skincare products through her university spin-off company. Dr Mohd Setapar’s mission is to empower Malaysian women with safer, high-quality cosmetic products and to make available high-value cosmetics combining micellar nanotechnology with local natural extracts at lower prices.
Dr Mario Antonio Jiz II
Research Institute for Tropical Medicine
Field of research: Immunology
Dr Jiz researches schistosomiasis, a disease caused by parasitic worms that is second only to malaria as the most devastating parasitic disease. Dr Jiz develops vaccines for schistosomiasis and has patented a large scale production of a solution for the body to induce immunity against the disease.
Dr Vladislav Voitenkov
Ministry of Economic Development of the Russian Federation
Field of research: Clinical neurophysiology, infections of the nervous system, encephalitis, meningitis, myelitis and inflammatory polyneuropathy (Guillain-Barré syndrome)
Dr Voitenkov’s work focuses on understanding neurology and functional diagnostics, especially in the aging process. He studies the rare disease, inflammatory polyneuropathy (Guillain-Barré syndrome), where the body’s immune system attacks your nerves, paralyzing a person’s entire body.
Dr Daniel Shu Wei Ting
Assistant Professor, Duke-NUS Medical School, NUS
Field of research: Artificial intelligence using deep learning in screening for diabetes eye screening
Dr Daniel Shu Wei Ting’s work focuses on screening techniques for diabetic retinopathy, an eye disease for people with diabetes, which can lead to loss of vision. He has led a large research team in building the world’s first artificial intelligence system using deep learning to detect three potentially blinding conditions.
Dr Ming-Kai Pan
Physician Principle Investigator, NTUH
Field of research: Neurology—movement disorders
Dr Pan specializes in human physiology and mouse models of neurological disorders. His work is focused on discovering novel ways to measure brain physiology for movements which have implications for Parkinson’s disease, essential tremors and cerebellar ataxic disorders. Dr Pan has also invented smart technology to identify the most common movement disorders affecting 20 per cent of the elderly population.
Dr Wanpracha Art Chaovalitwongse
Professor, University of Arkansas, Fayetteville
Field of research: Data analytics, machine learning, artificial intelligence, health informatics, medical imaging analysis and medical decision making
Dr Chaovalitwongse’s research focuses on data analytics in medical and healthcare applications, especially in analyzing brain activity to predict and monitor epilepsy. Through his work, he has developed solutions for problems caused by attention deficit/hyperactivity disorder, Alzheimer’s disease, Parkinson’s disease, non-small cell lung cancer, sarcoma and esophageal cancer.
Dr Kara Spiller
Assistant Professor, Drexel University
Field of research: Biomedical engineering
Dr Spiller focuses her research on the design of “smart” biomaterials that can control the behavior of immune cells to promote tissue repair and wound healing. She has developed a point-of-care diagnostic to tailor optimal treatment for patients based on the state of their immune system according to factors such as age, genetics and nutrition.
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