In recent years the advent of Artificial Intelligence has revolutionised the field of archaeology. This cutting-edge technology is reshaping the way we discover and interpret the secrets of the past, thus enabling the analysis of vast amounts of data in a fraction of time, which would once have taken human researchers years or even decades. The rise of Artificial Intelligence in archaeology not only accelerates the discovery process, but also enable us to gain new insights into history.
Archaeological research has traditionally been an extremely time-consuming process for archaeologists excavating sites in detail. With the introduction of Artificial Intelligence, however, researchers can now process and analyse data at unprecedented speed. Machine learning algorithms can sift through thousands of artefacts, thus identifying patterns and connections that humans cannot detect. This significantly reduces not only the time needed for discovery, but also the cost of exploration, thus enabling us to unravel the secrets of the past much faster.
Furthermore, Artificial Intelligence not only speeds up the process of archaeological research, but also improves the accuracy of discoveries. Machine learning algorithms can analyse data with an accuracy that far exceeds human capabilities. They can detect minute patterns and anomalies that might go unnoticed by humans, thus enabling more accurate and detailed interpretations of archaeological data. This increased accuracy helps to gain a deeper understanding of our history, thus providing new information about our ancestors and related civilisations.
Besides accelerating research and improving accuracy, Artificial Intelligence is opening up new avenues of exploration in archaeology. A case in point is predictive modelling: this is a technique that uses Artificial Intelligence to predict the location of archaeological sites based on patterns in existing data. It is revolutionising the way in which new sites are discovered. This method has already led to the detection of many previously unknown sites, thus expanding our knowledge of the past.
Moreover, Artificial Intelligence has been used to reconstruct historical environments and events. Using data from archaeological sites, Artificial Intelligence can generate realistic 3D models of ancient cities or simulate historical events, thus giving us unprecedented glimpses into the past. These virtual reconstructions not only provide a fascinating window into history, but also serve as valuable educational tools, thus enabling scholars, students and the public to have a first-hand experience of life in the past.
The rise of Artificial Intelligence in archaeology is undoubtedly a revolution. However, it is important to remember that Artificial Intelligence does not replace human researchers, but is rather a tool that will enhance our abilities, thus enabling us to explore our history more deeply and gain a better understanding.
As we continue to explore the AI potential in archaeology, it is clear that this technology will play an important role in better understanding the evolution of history, thus giving new prestige and lustre to research and paving the way for future discoveries.
As we move ever deeper into the digital age, Artificial Intelligence is revealing the future of archaeological discoveries, thus revolutionising the way we understand and interpret the past.
As mentioned above in terms of costs, archaeological excavations have traditionally been labour-intensive, time-consuming and often prone to human error. The process of studying ancestors’ dirt, fossilised excrement, petrified organic waste and droppings, etc., as well as detailing the results and interpreting the data can be time-consuming. The advent of Artificial Intelligence has greatly accelerated this process as well.
The role of Artificial Intelligence in archaeology is manifold. It enables archaeologists to more accurately identify potential excavation sites. By analysing large amounts of data, including geographical information, historical documents and previous archaeological finds, Artificial Intelligence can predict where important archaeological artefacts are likely to be found. This not only saves time and resources, but also reduces the potential damage to artefacts to be discovered.
In addition to predictive modelling, Artificial Intelligence is changing the way archaeological finds are analysed and interpreted. Machine learning algorithms can identify patterns and connections in data that are barely perceptible to humans. For example, Artificial Intelligence can analyse ancient pottery or pictograms in search of stylistic elements, thus identifying small similarities and differences – that would escape the human eye – and providing insights into cultural exchanges, human migration and social change.
Furthermore, Artificial Intelligence is revolutionising the way archaeological finds are preserved and displayed. Artificial Intelligence-based digital preservation technology can create detailed 3D views of artefacts, buildings and even entire archaeological sites. These models can be studied and explored virtually for a more immersive and interactive experience. This not only increases our understanding of the past, but also makes archaeology more accessible to the public.
Despite these advances, the application of Artificial Intelligence in archaeology faces challenges. The accuracy of Artificial Intelligence predictions and analyses is highly dependent on the quality and quantity of the data acquired. Incomplete or distorted data can lead to misleading results. Furthermore, although Artificial Intelligence can speed up the process of archaeological discovery, it cannot replace the meticulous understanding and interpretation that human archaeologists bring to the field.
We have to take ethical considerations into account. The use of Artificial Intelligence in archaeology raises questions about who has access to and control over archaeological data and research results. As Artificial Intelligence becomes increasingly pervasive in archaeology, it will be crucial to ensure that it is used responsibly and that the benefits are shared.
As a whole, Artificial Intelligence is leading to a new era in archaeology, thus opening up exciting possibilities for discovering, analysing and preserving the past.
As we continue to advance ever further into the digital age, we must address the challenges and ethical considerations associated with it. In this way, we can harness the AI power to deepen our understanding of human history and enrich our cultural heritage. The future of archaeological discovery lies not only in the ground, but also in the digital realm, where Artificial Intelligence will play an increasingly important role.
The future development of archaeology will see more refined and standardised methodological systems in science and technology. A number of trends such as archaeology and archaeological science are moving towards integration. AI technology is showing its talents and its core development elements are advancing internationally.
It has to be said, however, that the probability of Artificial Intelligence replacing archaeologists over the next ten years is only 0.7 per cent, because the work of archaeologists requires the identification of highly complex models, and it is not extremely profitable to have this work done by AI not yet specialized for this very high task. It is unlikely that companies or governments will make the necessary investment to automate archaeological tasks in a technological sense, at least in the short term. (1. continued).
Artificial Intelligence and Advances in Chemistry (II)
As previously seen, chemical representation types have developed several sub-types over recent years. Unfortunately, however, there is no clear answer as to which representation is the most efficient for a particular problem. For example, matrix representations are often the first choice for attribute prediction but, in recent years, graphs have also emerged as strong alternatives. It is also important to note that we can combine several types of representations depending on the problem.
Hence how (and which) representations can be used to explore chemical space? We have already said that string representations are suitable for generative modelling. Initially, graphical representations were not easy to model by using generative models, but more recently their combination with the Varational Autoencoder (VAE) has made them a very attractive factor.
In machine learning a variational autoencododer is an artificial neural network architecture introduced by Diederik P. Kingma e Max Welling. It is part of the families of probabilistic graphical models and variational Baysenian methods (i.e. family of methods for the approximation of integrals).
VAEs have proved particularly useful since they enable us to have a more machine-readable continuous representation. A study used VAEs to show that both string and graph representations can be encoded and decoded in a space where molecules are no longer discrete, but can be decoded into continuous vectors with real values of molecule representations. The Euclidean distance between different vectors will correspond to chemical similarity. Another model is added between the encoder and the decoder to predict the attribute to be reached at any point in space.
But while generating molecules per se is a simple task – we can take any generative model and apply it to the representation we desire – generating structures that are chemically valid and display the properties we desire is a much more challenging issue.
The initial approaches to achieve this goal imply models on existing data sets and their subsequent use for transfer to learning. The model is fine-tuned through a calibration data set to enable the generation of structures oriented towards specific properties, which can then be further calibrated using various algorithms. Many examples of this imply the use of string representations or graphs. However, difficulties are encountered with respect to the chemical validity or desired properties when these are not successfully obtained. Furthermore, the fact of relying on data sets limits the search space and introduces potentially undesirable biases.
An attempt at improvement is to use Markov Decision Process (MDP) to ensure the validity of chemical structures and optimise the MDP itself to achieve the desired properties through deep Q-learning (a model-free reinforcement learning algorithm to derive the value of an action in a particular state). In mathematics, an MDP is a discrete-time stochastic control process (a function or signal, with values given at a chosen set of times in the integer domain). It provides a mathematical framework for modelling the decision-making process in situations where outcomes are partly random and partly under the control of a decision-maker. MDPs are useful for studying optimisation problems solved by means of programming. They are used in many disciplines, including robotics, automatic control, economics and manufacturing. The MDP is named after the Russian mathematician Andrej Andreevič Markov (1856-1922).
A particular advantage of this model is that it enables users to visualise the preference of different actions: (a) to visualise the degree of preference for certain actions (1 being the highest preference, 0 the least preferred); and (b) take steps to maximise the quantitative estimation of the drug similarity to the starting molecule.
Although still in its infancy, the use of Artificial Intelligence to explore the chemical space is already showing great promise. It provides us with a new paradigm to explore the chemical space and a new way to test theories and hypotheses. Although empiricism is not as accurate as experimental research, computationally-based methods will remain an active research area for the foreseeable future and will already be part of any research group.
So far we have seen how Artificial Intelligence can help discover new chemicals more quickly by exploiting generative algorithms to search the chemical space. Although this is one of the most noteworthy use cases, there are also others. Artificial Intelligence is being applied to many other problems in chemistry, including:
1. Automated work in laboratory. Machine learning techniques can be used to speed up synthesis workflows. An approach uses self-driving laboratories to automate routine tasks, optimise resource expenditure and save time. A relatively new but noteworthy example is the use of the Ada robotic platform to automate the synthesis, processing and characterisation of materials. Ada tools are developed to provide predictions and models to automate repetitive processes, using machine learning and AI technologies to collect, understand and process data, so that resources can be dedicated to more value-added activities.
Ada is basically a laboratory that discovers and develops new organic thin-film materials without any human supervision. Its productivity is making most recent graduates uncomfortable. The entire thin-film fabrication cycle, from the mixing of chemical precursors, through deposition and thermal annealing, to the final electrical and optical characterisation, takes only twenty minutes. An additional aid is the use of a mobile chemical robot that can operate tools and perform measurements on 688 experiments over eight days.
2. Chemical reaction prediction. Classification models can be used to predict the type of reaction that will occur, or simplify the problem and predict whether a certain chemical reaction will occur.
3. Chemical data mining. Chemistry, like many other disciplines, has an extensive scientific literature for the study of trends and correlations. A notable example is the data mining of the vast amounts of information provided by the Human Genome Project to identify trends in genomic data.
4. Finally, although the new data-driven trend is developing rapidly and has had a great impact, it also entails many new challenges, including the gap between computation and experiment. Although computational methods aim to help achieve the experiment goals, the results of the former are not always transferable to the latter. For example, when using machine learning to find candidate molecules, we have to bear in mind that molecules are rarely unique in their synthetic pathways, and it is often difficult to know whether an unexplored chemical reaction will work in practice. Even if it works, there are problems with the yield, purity and isolation of the compound under study.
5. The gap between computational and experimental work becomes even wider, as computational methods use metrics that are not always transferable to the latter, such as Quantum Electrodynamics (QED), which describes all phenomena involving charged particles interacting by means of the electromagnetic force, so that its experimental verification may not be feasible. There is also the need for a better database. However, the problem of the lack of benchmarks arises. Since the entire chemical space is infinite, it is hoped to have a sufficiently large sample which may help in subsequent generalisation. Nevertheless, most of today’s databases are designed for different purposes and often use different file formats. Some of them have no validation procedures for submissions or are not designed for AI tasks. It should also be said that most of the databases available have a limited scope of chemical data: they only contain certain types of molecules. Furthermore, most tasks involving the use of Artificial Intelligence for chemical predictions have no reference platform, thus making the comparisons between many different studies impracticable.
One of the main reasons for the success of AlphaFold – which, as already seen, is an AI programme developed by DeepMind (Alphabet/Google) to predict the 3D structure of proteins – lies in the fact that it has provided all of the above as part of the critical evaluation of Protein Structure Prediction, i.e. the inference of a protein 3D structure from its amino acid sequence, e.g. the prediction of its secondary and tertiary structure from its primary structure. This evaluation demonstrates the need for organised efforts to streamline, simplify and improve other tasks involving chemical prediction.
In conclusion, as we continue to advance in the digital age, new algorithms and more powerful hardware will continue to lift the veil on previously intractable problems. The integration of Artificial Intelligence into chemical discovery is still in its infancy, but it is already a commonplace to hear the term “data-driven discovery”.
Many companies, whether pharmaceutical giants or newly founded start-ups, have adopted many of the above technologies and brought greater automation, efficiency and reproducibility to chemistry. Artificial Intelligence enables us to conduct science on an unprecedented scale and in recent years this has generated many initiatives and attracted funding that will continue to lead us further into an era of autonomous scientific discovery. (2. continued).
From rockets to spider silk, young scientists wow the jury – and each other!
The 34th annual edition of an EU contest for teenage researchers wrapped up this past week with participants from Canada, Denmark, Poland and Portugal claiming the top prize.
By Sofía Manzanaro
Inês Alves Cerqueira of Portugal just spent five days in Brussels and left with a top EU prize for young scientists.
But ask 17-year-old Cerqueira what she remembers most about the event, which featured 136 contestants from three dozen countries in Europe and beyond, and the much-coveted award gets hardly any mention.
‘I loved listening to all the projects and having conversations about science without having to worry about people judging me or anything like that,’ she said as the 34th annual EU Contest for Young Scientists (EUCYS) drew to a close in the Belgian capital.
Worries or not, Cerqueira and the other contestants aged 14 to 20 years were judged by a jury of 22 distinguished scientists and engineers from across Europe as part of the official competition. It featured 85 science projects in the running for first, second and third awards that shared a total of €62 000 in prize money.
The rewards also include scholarships and visits to institutions such as the European Space Agency, nuclear-research organisation CERN and a forum that brings together eight of the largest research bodies in Europe.
All the participants had already won first prizes in national science competitions. At EUCYS, four projects won the top prize and received €7 000 each.
Cerqueira claimed hers with two teammates: Afonso Jorge Soares Nunes and Mário Covas Onofre. The three Portuguese, who come from the northern coastal city of Porto, are exploring the potential of spider silk to treat bone diseases including osteoporosis.
The EUCYS projects, which ranged from rocket science and chronic-pain drugs to climate demographics and river pollution, were as varied as the backgrounds of the participants, who came from as far away as Canada and South Korea.
Canadian Elizabeth Chen was another first-prize winner for a project on a cancer therapy. The two other top-award recipients were Maksymilian Gozdur of Poland for an entry on judicial institutions and Martin Stengaard Sørensen of Denmark for an initiative on rocket propulsion systems.
‘EUCYS is about rewarding the enthusiasm, passion and curiosity of Europe’s next generation of bright minds finding new solutions to our most pressing challenges,’ said Marc Lemaître, the European Commission’s director-general for research and innovation.
Eagerness and spirit were on general display at the event. So was camaraderie.
Noemi Marianna Pia, Pietro Ciceri and Davide Lolla, all 17 year olds from Italy, said they felt themselves winners by having earned spots at EUCYS for a project on sustainable food and described the event as a once-in-a-lifetime chance to mix with fellow young scientists from around the world.
The three Italians want to develop plant-based alternatives to animal proteins. At their exhibition stand, they talked with contagious excitement about their research while holding dry chickpeas and soybeans.
Lolla said that, while his pleasures include tucking into a juicy steak, he feels a pressing need to reduce meat consumption to combat climate change and preserve biodiversity.
On the other side of the venue, 16-year-old Eleni Makri from Cyprus recalled how a classroom chat about summer plans sparked an idea to use seagrass on many of the island’s beaches to produce fertiliser.
Her project partner, Themis Themistocleous, eagerly joined the conversation to explain how seagrass can recover phosphate from wastewater. The process involves thermal treatment of the seagrass.
Themistocleous also expressed pride at having been chosen by Makri as her teammate for the competition.
‘There were a thousand people, but she chose me!’ he said with a wide grin as Makri playfully shook her head in response.
Science can also be the outcome of a partnership rather than its trigger. Metka Supej and Brina Poropat of Slovenia were brought together by sports, particularly rowing.
After years of training on the same team, they decided to research the impact of energy drinks on heart-rate recovery.
As they cheered for one another while preparing to say goodbye, the participants at EUCYS 2023 offered a glimpse of the combination of qualities – personal, intellectual, social and even professional – that turn young people into pioneering researchers.
Gozdur, the Polish top-prize winner, discovered his passion for judicial matters while working at a law firm. Before that, he wanted to study medicine and even dabbled in the film industry.
His EUCYS project drew on French and Polish criminal-procedure codes to examine the prospects for “restorative justice” – a central element of which is rehabilitation of the convict. The conclusion reached was that ‘penal populism is not beneficial to any party, especially to the victim’s,’ according to a description.
Now 19 years old and a law student in Warsaw, Gozdur said he would like international institutions to take up his work so that it influences ‘real-life’ legal norms in the future.
‘EUCYS showed me that my idea is actually relevant and that it may help societies,’ he said. ‘I would like to fight more for my project.’
For Sørensen, the Danish recipient of the top prize, venturing into rocket science as a teenager was no surprise. From the city of Odense, he began computer programming at the age of 10 and was inspired by his father – an electrical engineer – to look into engineering.
Now 19 years old, Sørensen is striving in his research to create cheaper rocket engines. His project, entitled “Development of small regeneratively cooled rocket propulsion systems”, demonstrated how small rocket engines can be cooled by using a fuel that is a mixture of ethanol and nitrous oxide.
Sørensen said he’s unsure what his future path will be while expressing interest in pursuing his rocket research.
‘I would like to continue working on this project,’ he said. ‘And I would like to do something that matters in the world.’
Chen, the top-award winner from Canada, has long had a passion for cancer research.
From childhood, she became involved in fundraisers for a Canadian cancer association and was puzzled about why significant donations had produced no cure. Now 17 years old and in high school, Chen is seeking a therapy that would avoid the often-considerable side effects of conventional treatments.
Her project focuses on a novel form of immunotherapy based on “CAR-T cells”, which are genetically altered so they can fight cancer more effectively.
‘I am really interested in going into university right away and then hopefully getting involved in some cancer research because that is just so interesting to me,’ said Chen, who comes from Edmonton.
The three Portuguese winners – Cerqueira, Nunes and Onofre – said they have developed a partnership as strong as their spider silk and plan to pursue their research while at university with the hope – one day – of conducting clinical studies.
Called “SPIDER-BACH2”, their project reflects an awareness that osteoporosis will become a growing health challenge worldwide as people live longer. It aims for in vitro production of bone-building cells known as osteoblasts.
‘The future is bright for us,’ said Nunes. This article was originally published in Horizon, the EU Research and Innovation Magazine.
Space Exploration: The Unification of Past, Present and Future
The enchanting realm of space exploration continues to unfold new wonders with every passing day, sparking a growing interest among individuals to embark on their own cosmic journeys. While exploring space with the aid of private companies that charge fortunes is a privilege usually reserved for billionaire adventurers, there are occasional exceptions that captivate our attention.
Just a few days ago on 8th September, Virgin Galactic’s third spaceflight set out on a brief mission that seized the spotlight due to some interesting details. Three private explorers, Ken Baxter, Timothy Nash, and Adrian Reynard, two pilots and one instructor, were onboard ‘VSS Unity’. However, the presence of two different and unique passengers added a twist to the journey: fossils of our ancient human ancestors. The fossil remains of two ancient species, two-million-years-old Australopithecus sediba and 250,000 years old Homo naledi, held in carbon fiber, emblazoned with the South African flag, were part of the Virgin Galactic’s spacecraft ‘crew’ for a one-hour ride, making them the oldest human species to visit space. Australopithecus sediba’s clavicle (collarbone) and Homo naledi’s thumb bone were chosen for the voyage. Both fossil remains were discovered in the Cradle of Humankind – home to human ancestral remains in South Africa.
The episode undoubtedly prompts questions regarding the underlying reason behind sending these fossil remains into the vast expanse of space in the first place. It profoundly underscores the immense power of symbols, speaking to us in ways words cannot. This voyage was not just a journey through space, but a soulful homage to our ancestors. Their invaluable contributions have sown the seeds of innovation and growth, propelling us to unimaginable heights. Now, as we stretch our hands towards the heavens, we remember them – and in this gesture, we symbolise our eternal gratitude and awe for the path they paved, allowing humanity to quite literally aim for the skies. As Timothy Nash said, ‘It was a moment to contemplate the enterprising spirit of our earliest ancestors, who had embarked on a journey toward exploration and innovation years ago.’
Moreover, the clavicle of the Australopithecus sediba was deliberately chosen given that it was discovered by nine-year-old Mathew Berger, son of Lee Berger, a National Geographic Society explorer, who played a major role in discovering both species and handed over the remains to Timothy Nash for the journey. This story serves as a touching testament to the boundless potential of youth, showing us that even the young can be torchbearers in the realm of science, lighting the path of discovery with their boundless curiosity. The unearthing of Homo naledi in 2013 wasn’t just about finding bones; it was a window into our past. This ancient ancestor, with its apelike shoulders and human-like feet, hands, and brain, wasn’t just a distant relative. They were artists and inventors, leaving behind symbols and tools in their cave homes as a silent testament to their legacy. This led to the discovery of more than 1,500 specimens from one of the biggest excavations in Africa’s history. It wasn’t just about digging up the past; it was about piecing together the jigsaw of our very essence, deepening our understanding of the roots and journey of our kind, especially in the heartland of South Africa. Each discovery, each bone, whispered tales of our shared journey, of beginnings, growth, and the undying spirit of exploration.
For those involved in the venture, the occasion was awe-inspiring as it connected our ancient roots to space exploration. However, not everyone is pleased. The event has sparked criticism from archaeologists and palaeoanthropologists, many of whom have called it a mere publicity stunt and raised serious concerns over such an act given that it poses risks to the care of the precious fossils. It was further argued that the act was ethically wrong, and lacked any concrete scientific justifications.
Setting aside this debate, the episode connects chronicles of our past with the boundless potential of humankind’s future. It celebrates the age-old quest for exploration shared across millennia. This journey, captivating in its essence, elevates space exploration to a sacred place where fossils, once cradled by the Earth’s soil, now dance among the stars. Just as with pivotal moments in space history, it is also a compelling cue to states that are currently lagging in this race to timely embrace the possibilities of this frontier. Countries, like Pakistan, should draw inspiration from such milestones to fervently chart their own celestial courses.
Upon their return to South Africa, the relics would be displayed in museums and other institutions, offering a chance to the public to view them and draw inspiration. As we witness the rise of commercial space travel, this unique journey provides glimpses of the multifaceted nature of space exploration – one that prompts us to reflect on our past, engage actively with the present and anticipate the future that awaits us. Something Pakistan’s national poet Allama Iqbal eloquently captured in one his verses, translated as: I see my tomorrow (future) in the mirror of my yesterday (past).
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Artificial Intelligence and Advances in Chemistry (I)