Setting the Framework and Message of the Conference Works: Sustainable Development in a Healthy Humane, and Capacitated World.
The first Athens Calls Athens Conference (ACA) under the theme ‘Man and His Creations’, offers a formidable opportunity to people of different backgrounds and disciplines, inspired by the eternal ideas and principles originating from ancient Athens but still vivid and valid today, to exchange their views and suggest approaches addressing the current and emerging challenges of our planet and our society, that derive from the gigantic expansion of the Anthroposphere over the natural environment. A series of related pressures, such as: overpopulation, unsustainable production and consumption, erratic economies, ignorance/lack of education inefficient mechanisms, are inter-linked in an increasingly complex world.
The works of the conference are structures along six thematics, namely Technology, Society, Health, Environment, Economy and Humanity. All themes need to be approached in a multi-disciplinary way or rather an inter-disciplinary/trans-disciplinary way in order to seek integration and synergies. They already make immediately evident the aims, philosophy, design and the overall methodological approach to be followed throughout the works of the conference; in fact it has to do with approaching sustainability or more precisely approaching sustainable development which is considered a monodrom today and our ultimate goal in this conference.
The participatory and multi/intra-disciplinary processes offer the methodology to approach more effectively the overall goal for a more human, more safe and more healthy world, to reach some useful results from the deliberations during the work of the conference and help in orienting and framing the follow-up and future Athens Calls Athens steps.
Economy, Society and Environment are the known three pillars of Sustainable Development and the questions are ‘for what’, ‘for whom’ and ‘how’.
For the first two questions a brief answer can be: for a better human world that means a society physically and mentally healthy and secure/safe. In this sense, safety goes beyond national security and independence and it should include conditions safeguarding human rights and decent living for all people. For the ‘how’ the answer can be through a capacitated world with an empowered more equitable and responsible society, reducing vulnerability for the least privileged part.
Sustainable development can also be imagined as a TetraedronTetrahedron where Environment, Economy and Society are the three sides and Governance is its basis. Major tools of governance are the institutions, the science and technology, and education, the awareness and strengthening/promotion of the appropriate culture to permeate the entire society.
Athens Calls Athens insists on the last one mentioned above, namely Culture, in its sense as the spectrum of ideas that need reconsideration and change, but also as a unifying cement of all themes.
A culture for the 3rd Millennium with needed significant changes and re-orientation but also firmly rooted on eternal values and principles: democracy, human integrity, reason, freedom of speech, and others most of which were developed in this City (Athens).
Additive Manufacturing Technologies: the Unforeseen Revolution of the 21st Century.
The presentation started with an outline of the industrial revolutions and the time proximity of the last (the 4th) with the previous.
The manufacturing pathways followed by mankind were of two kinds: the subtractive manufacturing (cutting, drilling, fretting, CNC machining etc. and the additive manufacturing which was considered the new era in manufacturing from 1960 and further.
It was at that time that the creation of solid objects using photopolymerized resins was attempted by employing a laser at the Battelle Memorial Institute in Columbus Ohio, USA. Quite similar to this approach was the patent filed by Swanson in Denmark under the title ‘Method of producing a 3D figure by Holography’.
In the early 1970s a company called Formigraphic Engine Corp. applied the dual-laser intersection pattern, aiming at the first commercially available laser-prototyping machine; as a result they were finally able to present the creation of a 3D object in 1974.
It should be noted however, that the idea of 3D printing as a kind of additive manufacturing had existed well before, for centuries or even millennia. If we look at how ancient Greeks built the columns in their temples, or look at Francois Willeme 1806s work on ‘photo-sculpture’ we can see the beginnings of this very idea.
Early additive materials and equipment were truly developed in the 1980s. In 1981 Hideo Kodama of Nakoys Municipal Industry Research Institute invented two additive methods for fabricating 3dimensional plastic models with photo hardening thermoset polymer where the UV exposure area is controlled by a mask pattern or a scanning fiber transmitter.
In July 1984 Alain Le Mehaute, Olivier de Witte, and Jean Claude Andre filed their patent for the stereolithography process. The application of the French inventors was abandoned howeerver by the French General Electric Company (later named Alcatel) because it was considered that it lacked business perspective.
The explosion came in the end of the eighties. The technology used by most 3D printers – especially hobbyist and consumer-oriented models, is fused deposition modelling; this special application of plastic extrusion was developed by S. Scott CrumpChimp and was commercialized by his company Stratasys – which marketed its first FDM machine later in 1992.
Main technologies applied in 3D printing are SLS(Selective Laser Sintering), FDM(Fused Deposition Modeling), SLA (Stereolithography), DLP (Digital Light Processing) and DLMS (Direct Laser Metal Sintering).
To the question if 3D printing can change the world the answer is ‘yes it can’; as a revolutionary methodology it can apply to medical procedures, product prototyping, architectural engineering construction, food industries, automotive and accessories, as well as in aerospace and defense.
Finally, it is foreseen that by 2030, $400 billion of the global economy will refer to these technologies of additive manufacturing.
Understanding the Internet, Digital Age of New Technologies and Environment or New Means of Transportation: Science and Tech Achievements.
In the first industrial revolution we used water, steam power to mechanize production; in the second revolution used electric power to create mass production while the third one used electronics and information technology in order to maximize production. Now a fourth revolution is built on this third one, a digital revolution as we call it that has occurred since the middle of the last century. It is characterized by a fusion of technologies, blaring the lines among the physical, digital and biological spheres.
The cyber-physical worlds relates to the 4th industrial revolution and has to do with technology innovation, digitalization and data, automation, smart systems-integrated and distributed, o demand services, as well as disruptive technologies (Internet of things, data analytics, Artificial Intelligence, Blockchain among others) which are changing the way we are doing things, the way we interact with each other, work, etc.
Mobility in the new digitalized era has the characteristics of being smart, connected, automated, digital, multimodal, user-centric, integrated, shared, sustainable, green and safe.
Smart mobility is a pillar of smart societies, which in turn are considered societies that harness the potential of digital technology and connected devices and the use of digital networks to improve people’s lives.
Delivering mobility for people and goods is the aim, and in view of that we are progressing towards mobility management based on automation and multi-modality (that means to use all means of transportation available). Cooperation of the public and private sector is required in respecting the priorities for green, safe and accessible mobility.
Mobility a s service (MaaS or DaaS) is this idea of an integration of various forms of transport services into a single mobility service available on demand.
Automated cars are an important aspect of the connectivity and automation area. With this term we mean all real-time driving functions necessary to drive a ground-based vehicle without real-time input from a human operator. 90% of all world-wide accidents are due to human factors and errors of drivers, so this type of solution is expected to reduce this heavy impact on safety.
Transport is one of the most important pillars of the logistics chain as it was evident in this last pandemia crisis, that it was required goods to be provided on the right moment in the right
Data is the new infrastructure, the key asset for future mobility, therefore we need proper data management (data handling and analytics) and to be sure of having data availability for all stakeholders involved.
AS far as mobility expectations are concerned, by 2030 more automated vehicles of L3 & L4 functions will be on the road, eco-friendly mobility will increase its share, there will be new types of vehicles integration e.g. evolution of drones and scooters, on demand mobility services, decreasing of vehicle ownership (as buying mobility services will replace buying vehicles, a trend called collective transport), mixed traffic and green transport priorities will emerge.
Cheminformatics Applications in Drug Discovery, Materials Science and the Environment Underpinned by Artificial Intelligence and Machine Learning Methods.
The use of information technology and management has become a critical part of the drug discovery process.
Cheminformatics is the mixing of those information resources to transform data into information and information into knowledge for the intended purpose of making better decisions faster in the area of drug identification and organization. The main question is ‘why cheminfromatics?’
It is the amount of information on many millions of compounds and their properties/activities that need to be explored to guide decision making. These are Storage, organization and search experimentation data, thneed to be in chemical databases as chemical information should be well organized and searchable.
There are two chemical databases. One is Pubchem which is a dataset containing info on tens of millions of compounds including an increasing amount of bioactivity data. Searching keywords, or based on chemical structure (structure, substructure and similarity searching) and other factors.
Advanced tools are available for clustering and bioactivity analysis of sets of compounds returned from a search. The other is Chembl, which is actually a database of bioactive drug-like small molecules, containing 2D structures, calculated properties (e.g. logP, molecular weight, Lipinski parameters, etc.) and abstracted bioactivities (e.g. binding constants, pharmacology and ADMET data). The fact is that millions of compounds are currently recorded in the aforementioned databases and the questions that arise are how to select useful compounds from this huge database and how to design new compounds. Cheminformatics can help by enabling fast, cheap virtual experiments to prioritize real experiments.
In so far as the physico-chemical and biological properties are concerned, the goal of cheminformatics is to develop predictive approaches and tools ; virtual screening is inevitable to analyze i.e. α huge mountamount of protein-ligand combinations.
Drug discovery and development is a long journey – in order to actually have a drug in the market. We first need to identity) a disease (i.e. to isolate protein involved in the disease, 2-5years), then to find a drug effective against the disease protein (preclinical testing needs 1-3years); formulation and scale-up come afterwards and then human clinical trials follow (2-10years), before the final approval. Therefore we have a high-risk, costly and time-consuming process of about 12 years and $800 million to bring a new drug in the market.
In the past years we had a progress in 2D and 3D structure representation – in effort to make molecule to be machine readable. A line notation like the SMILES (Simplified Molecular Input Line Entry System) notation answered the main question of how would a machine best read and store a chemical structure. Graph theory (fingerprints) and Matrix Representation also apply. Then, we need to convert the structure to a set of useful numbers, the so called molecular descriptor. This is actually the final result of a logic and mathematical procedure which transforms chemical information encoded within a symbolic representation of a molecule into a useful number or the result of some standardized experiment.
Cheminformatics has significantly contributed to a wide range of applications within several research including drug discovery, computational toxicology, and nanomaterials risk assessment.
Towards Autonomous Cybersecurity: The Palantir Solution.
The laboratory of machines, Intelligent and Distributed Systems is a unit of the Hellenic Army Academy and constitutes a centre for research, development and education in the field of Artificial Intelligence. Research domains are machines, artificial intelligence and machine learning, cognitive cyber security, robotics, self driving vehicles & multi-agent systems, natural language processing and sentiment analysis, computer vision and big data analytics, IoT and web intelligence.
The problem is that we have a number of cyber attacks and the ways we can defend ourselves against those attacks are rather obsolete because they rely on signatures which exist in databases. The consequence of that is that we need for something to happen (i.e. an attack) so to react to it and not before which in effect can create a damage to medium or even large enterprise. In the last 5 years this damage has increased approximately 72% a yearly increase of approx.. 12%. Therefore we need to have a more intelligent solution to the problem – an area that artificial intelligence (AI) has come to cover.
Palantir which appears as the solution to this problem is a Europe funded R&D project and it uses hybrid thread intelligence combining machine learning and deep learning models along with virtualized signature-based systems. It builds on risk assessment, virtualization, closed-loop control, machine learning and remote attestation to create highly dynamic and flexible cyber-security services that run on a completely secure network environment, in the form of Security-as-a-Service (SecaaS).
The major modules of Palantir are:
threat intelligence (collects the traffic, analyses it and suggests the threat along with remediation proposed to mitigate it), security service orchestrations (materializes the security threat to security action appropriate each time), trust and attestation module, risk based analysis (personalizes the elements of the architecture).
An algorithm was employed – LDA or Latent Dirichlet Allocation – a generative probabilistic model for collections of discrete data such as text corpora. Steps involved are
1. Convert all network log into words
2. Treat all network logs (words) into the same IP
3. Start topic discovery
4. Assign a mixture of topics on each document given its content
5. Calculate the likelihood of a new word.
The processes were tested and it was found that in terms of preliminary results, Accuracy, Precision and Recall are very good and since there are no other means to determine unknown unknowns, it is considered satisfactory. The same goes for the results on the classificator , which are also satisfactory.
Blockchains and Smart Contracts for Internet of Things Applications.
Blockchain is a highly distributed ledger that can be used in both public or closed private communities.
Enterprise blockchain use cases can be:
– Financial agreements and transactions, where the use of blockchain to process or record any sale or exchange of goods, value and services
– Supply chain management; oversight and/or control of goods as they are produced and transferred between producers, logistics partners, end users and others
– Smart contracts; agreements between multiple parties where as a transaction (i.e. payment or ownership change) is triggered by the fulfillment of predefined conditions
– Records management and data sharing meaning maintenance, protection and sharing of any form of electronic data
– Smart grid – flexible and decentralized energy utility networks marked by pee-to-peer trading, hybrid infrastructure and reduced reliance on central authorities
– Identity management and authentication – electronic verification of an individual’s identity to authorize transactions across different networks
– Digital rights management; attestation to provenance and/or use rights of digital media or intellectual property
– Voting; verification to participate in voting with low risk of fraud
The M-Sec project was presented; a specific project, funded under the H2020 horizon – a EU-Japan collaboration, with the mission to develop an end to end secure IoT platform upon which stakeholders can build innovative smart city applications on top of it while enabling the creation of liquid markets with profitable business models for IoT stakeholders. The vision of the project is to lay the foundation for the adoption and creation of new IoT security standards and mechanisms coupled wit blockchains and decentralized applications. The objective is to validate the viability and the sustainability of the decentralized IoT ecosystems and to define, design and implement a novel market place; to this end, smart objects can exchange along with information, energy and services. The use of virtual currencies will be allowed along with real time supply and demand.
An Indicative news case is one operated with FujiSawa in a secure and trustworthy mobile sensing platform. This use case provides a client application that allows urban environment monitoring entities (for example local governments) to visualize spatially and temporarily dense environmental data such as air quality, temperature, humidity, garbage disposal amount, unimpaired road marks. The problem to be handled was that in order to share data between cities on the smart city platform, privacy protection and security assurance mechanisms are indispensable but existing platforms still have problems in privacy protection and security assurance.
As a conclusion it was said that latest developments show a great potential for Blockchain – IoT integration. There is an ongoing trend to go from the Internet of things to a new concept of Internet of value. This will enable new modalities for autonomous interaction of smart things
Artificial Intelligence. Learning about AI Without fearing it.
Some of our fears about Artificial Intelligence (AI) stem from the fact of that we really describe what Intelligence is. We all feel confident to acknowledge intelligent behavior even though we may be unable to describe it precisely. We can act intelligently even though quite often we fail to do so. This is a big contradiction and usually contradictions have to do with things we cannot understand and where our fears are based on.
A convenient definition for AI can be that if it is a computer- based system which demonstrates behavior we would consider as being intelligent. Then we focus on a problem and solve it as best as possible (for example win as many chess games as possible) to convince observers about the demonstration of intelligence (since we tend to agree that chess masters are smart people).
A coarse map of AI is that we tend to classify systems along two major axes: one is about systems seem to act and the other has to do with how systems tend to think. The upper right quarter is systems which act like humans and that’s where most of AI applications sit.
AI can be just one thing in the grand scheme of things. We can review printing steam engines, cars antibiotics. The future historian will decide upon whether AI (or an application that AI empowered) was important or not. One example can be robotics justice: the question can be who has access to these technologies, who benefits from them, how much does it cost, who sets priorities? These are more or less the question which will shape whether AI will be for the sake of our good or not (against us).
As an example, 3 pictures were shown, one is the chess match between Casparof and IBM’s Deep Blue, the self driving cars and the next Rembrandt (a jointly analyzed Rembrandt’s painting and synthesized a new one).
AI is not the same to all of us. For the trained scientists or the engineers AI is something thy can understand, they can explain, and they can build. For the people AI is double edged sword. It can have good applications and bad applications. It’s the press that gets to decide how to paint an applications, therefore scientists and engineers should have more ssy about how the press describes their work. What are we witnessing right now is the industry’s view. Not the view of the scientists or the engineers view and the public needs to know what these two latter think about AI too.
A question/scenario can be what is the end of the road for autonomous vehicles? An assumption can that in some years from now we’re going to be needing taxi drivers, or driving companions – and at that time the word companion can be a beneficial one, not a vicious one. This is one of our fears: the fear that we are losing control of our destiny. Therefore the press has to be trained on how to describe AI so that our fear does not grow.
A committee under the Obama administration came up with a list of suggestions about AI. The first was about the long term investment in science. The rest were about how you integrate AI into the everyday life of people. It is not technical but mainly economical, political and social.