“The highly intriguing theory – supported by the extensive geological evidences including the bacteriological analysis of deep-laying hydrocarbons – about the a biotic nature of oil and its practically infinite recreation in the lower geological formations of earth was presented some 25 years ago. These findings were quickly dismissed, and the theory itself largely ignored and forgotten. The same happened with the highly elaborate plans of Nikola Tesla to exploit a natural geo-electrical phenomenon for the wireless transfers of high energy for free. Why? Infinity eliminates the premium of deeper psychologisation, as it does not necessitate any emotional attachment – something abundantly residing in nature cannot efficiently mobilize our present societies…”
Following the lines from the seminar work of prof. Anis H. Bajrektarevic on Energy, Technology and Geopolitics, let us present an interesting take on the E-cars, similar driverless technologies and its legal implications that will mark our near future.
Self-driving cars react in a split second: quicker than even the most attentive driver. Self-driving cars don’t get tired, they don’t lose concentration or become aggressive; they’re not bothered by everyday problems and thoughts; they don’t get hungry or develop headaches. Self-driving cars don’t drink alcohol or drive under the influence of drugs. In short, human error, the number one cause of road traffic accidents, could be made a thing of the past in one fell swoop if manual driving were to be banned immediately. Is that right? It would be, if there hadn’t recently been reports about two deaths, one during the test drive for a self-driving car (UBER) and one while a semi-autonomous vehicle was driving on a motorway and using its lane assist system (Tesla), both of which regrettably occurred in the USA in March 2018. In Tesla’s case it seems that the semi-autonomous driving assistant was switched off at the moment of the accident.
Around the globe, people die every day due to careless driving, with around 90% of all accidents caused by human error and just a small percentage due to a technical fault related to the vehicle. Despite human error, we have not banned driving on these grounds. Two accidents with fatal consequences involving autonomous vehicles being test-driven have attracted the full glare of the media spotlight, and call into question the technical development of a rapidly progressing industry. Are self-driving cars now just hype, or a trend that cannot be contained, despite every additional human life that is lost as a result of mistakes made by self-driving technology?
The legal side
For many, the thought that fully autonomous vehicles (a self-driving car without a driver) might exist in the future is rather unsettling. The two recent deaths in the USA resulting from (semi-) autonomous cars have, rather, may cause fear for others. From a legal perspective, it makes no difference whatsoever for the injured party whether the accident was caused by a careless human or technology that was functioning inadequately. The reason for the line drawn between the two, despite this fact, is probably that every human error represents a separate accident, whereas the failure or malfunction of technology cannot be seen as a one-off: rather, understandably and probably correctly, it is viewed as a system error or series error caused by a certain technology available at a particular point in time.
From a legal angle, a technical defect generally also represents a design defect that affects the entire run of a particular vehicle range. Deaths caused by software malfunctions cause people to quickly lose trust in other vehicles equipped with the same faulty software. Conversely, if a drunk driver injures or kills another road user, it is not assumed that the majority of other drivers (or all of them) could potentially cause accidents due to the influence of alcohol.
The desirability side
The fundamental question for all technological developments is this: do people want self-driving cars?
When we talk of self-driving (or autonomous) vehicles, we mean machines guided by computers. On-board computers are common practice in aviation, without the pilot him- or herself flying the plane – and from a statistical point of view, airplanes are the safest mode of transport. Couldn’t cars become just as safe? However, a comparison between planes and cars cannot be justified, due to the different user groups, the number of cars driven every day, and the constantly imminent risk of a collision with other road users, including pedestrians.
While driver assistance systems, such as lane assist, park assist or adaptive cruise control, can be found in many widespread models and are principally permitted and allowed in Europe, current legislation in Europe and also Austria only permits (semi-) autonomous vehicles to be used for test purposes. Additionally, in Austria these test drives can, inter alia, only take place on motorways or with minibuses in an urban environment following specially marked routes (cf. the test drives with minibuses in the towns of Salzburg and Velden). Test drives have been carried out on Austria’s roads in line with particular legal requirements for a little more than a year, and it has been necessary to have a person in the vehicle at all times. This person must be able to intervene immediately if an accident is on the horizon, to correct wrong steering by the computer or to get the vehicle back under (human) control.
Indeed, under the legislation in the US states that do permit test drives, people still (currently) need to be inside the car (even before the two accidents mentioned above, California had announced a law that would have made it no longer necessary to have a person in the vehicle). As a result, three questions arise regarding the UBER accident which occurred during a test drive in the US state of Arizona, resulting in a fatal collision with a cyclist:
- Could the person who was inside the vehicle to control it for safety reasons have activated the emergency brake and averted the collision with the cyclist who suddenly crossed the road?
- Why did the sensors built into the car not recognize the cyclist in time?
- Why did the vehicle not stick to the legal speed limit?
Currently, driving systems are being tested in Europe and the USA. In the USA, this can take place on national roads and, contrary to European legislation, also on urban streets. As long as we are still in the test phase we cannot talk of technically proven, let alone officially approved, driving systems. The technical development of self-driving cars, however, has already made it clear that legal responsibility is shifting away from the driver and towards vehicle manufacturers and software developers.
Whether, and when, self-driving cars could become an everyday phenomenon is greatly dependent on certain (future) questions:
- Are we right to expect absolute safety from self-driving cars?
- What decisions should self-driving cars make in the event that one life can only be saved at the cost of another?
- How should this dilemma be resolved?
If artificial intelligence (AI) and self-learning systems could also be included within the technology for self-driving cars, vehicles of this type might possibly become one day “humanoid robots on four wheels”, but they could not be compared to a human being with particular notions of value and morality. If every individual personally bears responsibility for their intuitive behavior in a specific accident situation, the limits of our legal system are laid bare if algorithms using huge quantities of data make decisions in advance for a subsequent accident situation: these decisions can no longer be wholly ascribed to a particular person or software developer if a self-driving car is involved. It will be our task as lawyers to offer legal support to legislators as they attempt to meet these challenges.
Artificial Intelligence and Advances in Physics in the Field of Gravitational Waves (I)
As an important branch of natural sciences, physics studies fundamental laws and phenomena such as matter, energy, mechanics and motion, thus providing an important theoretical basis for human beings to understand and explore the natural world. To be precise, physics models nature mathematically.
With the advancement of science and technology and the fast development of Artificial Intelligence, physics is facing new challenges and opportunities. The AI application is changing the research methods and development trajectory of physics, thus offering new possibilities for progress and innovation.
Artificial Intelligence can help physicists to build more accurate and complex models and to analyse and interpret experiments and data provided by observation. We must keep in mind algorithms such as machine learning, of which deep learning is a part.
The difference lies in the fact that deep learning is more advanced: a deep learning algorithm is not conditioned by the user’s experience. Just to make an example, in non-deep machine learning, to distinguish cats and dogs you have to tell “do it by ears, hair, etc…”, while in deep learning the distinguishing features are extracted by the code itself and, often or always, they are actually patterns that we humans would never be able to have!
It does this in the following way: you give it a set of training data and the expected results. The algorithm starts to do tests on this recognition until it reaches an acceptable accuracy value based on what it should come up with by using iterative mathematics (and obviously there is the human hand in the construction of the algorithm). When it has “adjusted”, you can use it on unknown pictures of cats and dogs, not used for learning, so that it classifies them to the human without the human having to do it himself/herself. Considering the above, Artificial Intelligence can discover hidden patterns and correlations from large amounts of data, thus helping physicists to understand and predict related phenomena.
Artificial Intelligence can be applied to theoretical physics and computational physics research to improve the efficiency and accuracy of computational models and methods. For example, Artificial Intelligence can help physicists develop numerical simulation methods since machine learning is not only for classification, but also for numerical prediction, which is especially useful in the financial field, as it is more efficient at speeding up experiments and calculations.
Artificial Intelligence also has broad applications in the fields of quantum physics and quantum computing. Quantum physics is a branch of science that studies the behaviour of microscopic particles and the laws of quantum mechanics, while quantum computing is an emerging field that utilises the characteristics of quantum mechanics for information processing and calculations. Artificial Intelligence can help physicists design more complex quantum systems and algorithms and promote the development and application of computer science.
The AI application in high-energy physics and particle physics experiments is also very important. High-energy physics studies the structure and interaction of microscopic particles, while particle physics studies the origin and evolution of the universe. Artificial Intelligence can help physicists analyse and process large amounts of experimental data and discover potential new particles and physical phenomena.
Al technology can improve the efficiency of physics research and accelerate the scientific research process. Physics research often requires large amounts of experimental data and complex computational models, and Artificial Intelligence can streamline the work of physicists in discovering hidden patterns and correlations in this data. Artificial Intelligence can also provide more accurate and detailed physics models, helping physicists solve even more complex scientific problems.
Traditional physics research often relies on existing theories and experiments, while Artificial Intelligence can help physicists discover new phenomena and physics laws. By bringing to light patterns and correlations from large amounts of data, Artificial Intelligence stimulates physicists to propose new hypotheses and theories, thus promoting development and innovation.
The AI application explores unknown fields and phenomena. By analysing and extracting information from large amounts of data, Artificial Intelligence expands the scope and depth of physics research.
The development of Artificial Intelligence offers new opportunities for the integration of physics with other disciplines. For example, the combination of Artificial Intelligence and biological sciences can help physicists study complex biological systems and related phenomena. The combination of Artificial Intelligence and chemistry can help physicists study molecular structure and chemical reactions.
Although AI technology has broad application prospects in physics research, it also has to face some challenges including the acquisition and processing of data as this is the main problem, especially when dealing with new issues for which databases are scarce; the creation and verification of the physical model; and the selection and optimisation of algorithms. In this regard, it must be said that the boom in deep learning has mainly been due to the increase in available data thanks to the Internet and the advancement of hardware. The networks that anyone uses can run on their laptops, albeit slowly, but this would have been unthinkable in the 1990s, when deep learning was already being thought of in a very vague way. It is not for nothing that we speak of the “democratisation of deep learning”.
Future development requires cooperation and exchanges between physicists and AI professionals to jointly resolve these challenges and better apply this new technology to physics research and applications.
As an emerging technology, Artificial Intelligence is revolutionising traditional physics. By applying Artificial Intelligence, physicists can build more accurate and complex models, analyse and explain physics experiments and observational data. Artificial Intelligence necessarily accelerates the research process in physics and promote the development and innovation of so-called traditional physics.
Artificial Intelligence, however, still has to face some challenges and problems in physics research, which require further study and exploration. In the future, AI technology will be further utilised in physics research and applications, thus providing more opportunities and challenges for development and innovation.
AI technology is also used in gravitational wave research, whose 2017 Nobel Prize in Physics was awarded to Rainer Weiss (Germany), Barry C. Barish (USA) and Kip S. Thorne (USA).
On 14 September 2015 this group of scientists detected the gravitational wave signal of a system of two black holes merging for the first time. At that moment, it triggered a revolution in the astrophysics community: the research group involved in the discovery of gravitational waves was listed as a candidate for the Nobel Prize in Physics ever since.
The two black holes are located about 1.8 billion light years from Earth. Their masses before the merger were equivalent to 31 and 25 suns in size, respectively. After the merger, the total mass was equivalent to 53 suns in size. Three suns were converted into energy and released in the form of gravitational waves.
For some time, gravitational waves have attracted the attention and curiosity not only of scientists, but also of ordinary citizens. Despite being a weak force – a child lifting a toy amply demonstrates this – gravitational interaction has always created questions: but what are gravitational waves?
To put it simply and briefly, this concept of gravitational waves comes from Einstein’s theory of general relativity. We all know that the theory of relativity always discusses the dialectical relationship between space-time and matter, and the viewpoint of gravitational waves is that matter causes ripples and bends into space-time. The curve propagates outwards from the radiation source in the form of a wave. This wave transmits energy as gravitational radiation and the speed of gravitational waves is close to that of light. An extreme case is a black hole. Its supermass causes a distortion of space-time; light cannot escape and slips into it.
Because our basic understanding of traditional physics is based on Newton’s theory of universal gravitation, it is assumed that all objects have a mutual attraction. The size of this force is proportional to the mass of each object. Einstein believed this theory to be superficial. The reason for what appears to be the effect of gravity is due to the distortion of space and time. Hence, if Newton’s law of universal gravitation is approximate, is our current knowledge based on traditional physics going astray? The question is an awkward one. Hence let us leave it to scientists to further study who is right and who is wrong.
Having said that, however, cosmic scientific research currently uses ever more AI techniques, such as the aforementioned detection and discovery of gravitational waves.
The biggest challenge in capturing gravitational waves is that the sampling rate of LIGO (Laser Interferometer Gravitational-Wave Observatory) data is extremely high, reaching a frequency higher than 16,000 times per second, with tens of thousands of sampling channels. Hence the amount of data is extremely large. It is then understood that with AI machine learning, etc. and state-of-the-art methods in the field of data processing, research efficiency can be improved. (1. continued)
Towards A Better World: Our Senses and How Artificial Intelligence is Replicating Them
Our five senses help us perceive the world around us. The sense of touch, for example, can bring loved ones closer but, on a darker note, can also frustrate amputees. What bothers them particularly about their prosthetic arms is missing feedback. Is what they are touching hot or cold, liquid or solid, a rose or its thorn; an aspect so universal for the able-bodied that it is not given a second thought.
Though it has not escaped Artificial Intelligence (AI) researchers who are trying to replicate these senses (Engineering and Technology, August 2023). They have been busy developing artificial hands with softer fingers and embedded sensors. How long will it be before the problem is solved?
Well, a US company Atom Limb is expecting to release a mind-controlled prosthetic limb in 2024. In it the movement sensors in the hand section of the prosthesis send electronic signals to the wearer’s stump, where the neurons once connected to the amputated hand are still in place and capable of transmission to the brain.
Notice how we know at once when there is something crawling on our skin. In May 2022, researchers at Stanford University’s Bao Research Group announced the invention of artificial skin that is durable, paper thin and stretchable. This has the future potential of being wired into the wearer’s nervous system to give a real touch capability — namely, sensing temperature, pressure, vibration and location. Thus when the finger moves from the handle to the cup itself, you sense the change in temperature and distance.
Another sense, that of hearing or rather lack thereof, is not infrequently a source of humor. Possibly because the sufferers are able to compensate through other means. Beethoven suffered from Paget’s disease. It caused skull bone enlargement which pressed on the eighth cranial nerve associated with auditory function. The loss was gradual from the age 28 to 44 when he was quite deaf. While he could still hear a little, he would strap an ear trumpet on his head so he could conduct the orchestra with his hands. He also carried a notebook and pencil to jot down musical brainstorms but also to converse with friends.
Hence the somewhat morbid joke of someone seeing Beethoven sitting on his grave furiously erasing some sheet of music. “Maestro! Maestro! What are you doing,” the person asks, to which he gets the reply, “I am decomposing.”
Hearing loss when it is congenital is no joke, however. It can inhibit language learning and speech. Thus the words ‘deaf and dumb’ are often placed together with ‘dumb’ of late being replaced by the kinder ‘mute’.
Here again technology comes to the rescue. Cochlear implants have been around for quite a while. Invented in 1957, the first implant procedure is credited to Stanford University. A single-channel electrode was used but was found to be of limited utility for detecting speech. It took a further 20 years to get to the modern multi-channel type.
Hearing aids now are small enough to be barely visible. They work for most people and only those with profound hearing loss consider the implant option.
Our sense of sight helps us navigate the world around us, and enjoy its beauty. For some it may be taken away gradually through macular degeneration (AMD). It is a form of retinal deterioration that affects the sight of some 200 million people in the world. As the photoreceptors in the central retina degenerate, it impairs the ability to read or even recognize people.
The good news is that a prosthetic replacement is now being developed to replace the lost photoreceptors with photovoltaic pixels. These convert light into electricity which stimulates the neurons in the retina. While the present version leaves the recipient somewhat shortsighted, a newer one currently being tested in rats will restore 20/20 vision.
For the future, there is Science Eye, a device employing optogenetics. It uses gene therapy to restore optic nerve cells while an ultra-dense micro-LED display panel is inserted directly over the retina.
There are others in the field including Cortigent which is making headway with a system that does not require genetically modifying retinal cells because it is a direct cortical (brian layer) stimulator. Cortigent is in the process of designing a study to get their stimulator implant approved. They have already spent five years studying the safety and reliability of their devices.
Then there are our senses of smell and taste, to some extent linked. There is a good reason food seems bland and tasteless when a person has a bad cold — the sense of smell is absent. Thus when chefs talk about flavor, they imply both taste and smell.
Taste receptors in the mouth sense sweet, sour, salt, bitter and savory — the latter also known as umami. But try sucking a lemon flavored candy while pinching your nose. You will taste the sweetness, but not the lemon flavor. The tongue is, of course, also sensitive to cold and heat.
A promising approach to treatment for loss of smell is to train the olfactory nerve through inhaling a set of odors (originally rose, lemon, clove and eucalyptus) twice daily for three months. It was found to help the nerve to regenerate.
Taste has been with humans forever. Long before scientists and their experiments, humans knew to avoid plants that tasted bitter — it signified something harmful. Yet there are people unfortunate enough to be without this sense.
Having all the senses is so commonplace that we rarely ponder their absence. So let the next gustatory and olfactory experience, or the music we hear, or the walk we take in a park where we can also smell the flowers, be all the more meaningful for valuing our senses. Harnessing them and adding that subconscious sense of perception to enhance our understanding of the world as it is, and we need only imagination to observe the world as it could be … to be ready to take the first step on the journey to a better one, a world at peace.
Development of Metaverse in China: Strategies, Potential, and Challenges Part I
In the rapid era of digitalization, the metaverse has become a hot topic among developed countries. While many nations focus on the entertainment aspect of the metaverse, China appears to have a different perspective. Adopting a more industry-oriented approach, can the Bamboo Curtain country lead the next metaverse revolution?
Why Does China Opt for an Industrial Approach?
There are several reasons why an industrial approach might be China’s key to success in the metaverse:
The metaverse, with its virtual simulation technology, has immense potential to revolutionize various industries like manufacturing, urban planning, and healthcare. In manufacturing, the metaverse can facilitate product design optimization, in-depth employee training, and manufacturing process optimization through virtual prototypes and real-time simulations, enabling instant collaboration between designers and engineers, thus reducing time and costs associated with physical prototypes.
Regarding urban planning, the metaverse can be applied for the visualization of city layouts and infrastructure in a 3D virtual environment, allowing urban planners to make better-informed decisions about urban development. Moreover, it enables public participation in urban development projects, offering citizens the chance to explore and provide input on proposed designs, and promoting sustainable development through the environmental impact analysis of urban designs.
In the healthcare sector, the metaverse can be used for medical training, patient rehabilitation, and remote consultations. Medical students can practice medical procedures in a risk-free virtual environment, while patients can undergo intensive virtual medical consultations and rehabilitation therapy. This technology can enhance the skills and confidence of prospective doctors and expedite patient recovery processes.
Overall, the metaverse offers innovative and interactive solutions that can address the specific needs of various industrial sectors, allowing enhanced learning, better design, optimized processes, and innovative solutions, which will ultimately contribute to the progress of these industries.
Metaverse technology, with its capabilities in virtual and augmented reality, opens new doors in industrial efficiency and innovation. It enables industries to develop prototypes in virtual environments, accelerating product development cycles and reducing costs associated with physical models. For instance, in the automotive industry, designers and engineers can collaborate in a 3D environment to test and modify car designs in real-time, allowing a faster response to market needs.
Additionally, the metaverse plays a crucial role in employee training and development. In the manufacturing sector, virtual simulations can be utilized for operational machine and production process training, reducing risks and enhancing employee skills while saving training time and costs. This also contributes to increased safety and reduction of incidents in the workplace.
The metaverse also supports industrial process optimization through real-time simulations and data analysis. Companies can visualize and optimize workflows, plant layouts, and production schedules to enhance productivity and reduce operational costs. This enables quicker and more accurate identification and resolution of inefficiencies and obstacles.
In conclusion, the implementation of the metaverse in industries promises a revolution in product design, employee training, and daily operations. The ability to integrate and optimize various operational aspects in a virtual environment offers opportunities for sustainable innovation and heightened competitiveness in the modern industrial era.
History shows that the Chinese government often provides full support to strategic sectors. With financial support and progressive regulations, the metaverse industry in China has the potential to grow rapidly. China is actively exploring the metaverse, with Beijing planning to create a ‘Digital Identity System’ for the metaverse and Web 3.0, following Shanghai’s footsteps. Even though known for being cautious in adopting advanced technologies, China sees significant potential in the metaverse and strives to establish a regulatory framework to develop virtual reality and the metaverse as part of the digital economy, as detailed in the Virtual Reality Development Action Plan released in November 2022. The proposed digital identity system aims to control user anonymity and identify individual characteristics in the metaverse, allowing regulated and controlled use of this technology. The regulatory proposal discussions are underway at the International Telecommunication Union (ITU), with the involvement of technology experts and Chinese telecom operators like China Mobile. This demonstrates China’s commitment to developing the metaverse in a safe and orderly manner, aligning with the high interest of its citizens, where 78% of Chinese citizens have expressed interest in the metaverse.
Differentiation from Competitors:
In the midst of tight global competition in the metaverse, China’s focus on industrial applications could be a key differentiator. While many other countries might be focusing more on entertainment aspects, China can lead in industrial applications of the metaverse. China chooses to focus on the industrial applications of the metaverse as part of its strategy to become a global leader in technology and innovation, aligning with the country’s ambitions to build competitive advantage in high technology and strengthen its trading position on the international stage. With its broad and diverse industrial sector, from manufacturing to healthcare, the integration of the metaverse allows for significant innovation and economic growth across various fields, enabling the development of specific and value-added solutions for industrial needs. Additionally, by focusing on industrial applications, China can also address challenges and risks associated with the metaverse, such as privacy and data security issues. The development of regulatory frameworks and technical standards for the industrial metaverse will ensure that this technology is developed and used safely, responsibly, and in line with national priorities and sustainable development goals.
Domestic Technology Development
China has been making substantial investments in cutting-edge technologies such as AI, 5G, and semiconductors, integrating these technologies with the metaverse to build a strong and competitive ecosystem. The adoption of 5G technology is key to the implementation of the metaverse, and I once read a book about the Metaverse written by experts in China, stating that mass adoption of the Metaverse will occur when 60% of the population has adopted 5G. Therefore, China currently seems to be focusing on developing 5G connectivity, hoping that the industrial sector will be the first adopter of this technology due to its higher purchasing power and more specific and limited scope. In the B2B context, this is considered a realistic step before introducing this technology to the end consumer. According to data from the GSMA The Mobile Economy Report China 2023, 5G penetration in China in 2023 is 45% of the population and is expected to reach 70% by 2027. Observing this data, it can be hypothesized that the evolution of Metaverse utilization in China will experience significant progress around the year 2027. Hence, in the coming years, we can observe how the integration between AI, 5G, semiconductors, and the metaverse can form a more synergistic and competitive technology ecosystem, where the industrial sector will play a key role in early adoption, before the metaverse truly enters and is accepted by the general consumers in China.
With an approach focused on the industry, China has the opportunity to lead the metaverse revolution in the future. However, as with any innovation, there will be challenges to face. With government support, investment in R&D, and a clear vision, China is on the right track to leverage the full potential of the metaverse for industrial interests and national economic growth.
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