Eunika Mercier-Laurent
email: eunika.mercier-laurent@univ-reims.fr

The Future of AI or AI for the Future

Keywords: Artificial Intelligence, Future, Planet protection

The third hype of AI and enthusiasm for applying last techniques in all fields raise great interest and some important questions on the future directions in AI research and applications. Guiding by the principle of combing the best from human and computers capacities this chapter list some important challenges to face and related directions in AI research. Multiple interrelated crisis such as natural disasters, pandemics and other generated by humans require new approaches, combining existing techniques and set new directions for research. This chapter presents briefly the Artificial General Intelligence (AGI) concept and the challenges to face in sustainability, smart resources management, future connectivity, industry, agriculture, health, economy and education. The presented vision for the future of AI includes both researchers' dreams and emergencies.

1

Artificial General Intelligence or/and AI for Human Purpose?

The third hype of AI triggered some trends, but before all, new definitions such as narrow AI and large AI, weak and strong AI, unconscious and conscious AI [START_REF] Jajal | Distinguishing between Narrow AI, General AI and Super AI[END_REF][START_REF] Bersini | The two A.I.conscious and unconscious, Keynote at Data Driven Innovation[END_REF]. All these new definitions are the tries to split AI to communities, while in fact intelligence is a whole system. Like the first generation of AI founders, some researchers still work on trying to build a machine more intelligent than humans are. They claim to be able to build Artificial General Intelligence [3]. In his provocative video entitled: Machines playing God, Max Tegmark [START_REF] Tegmark | Life 3.0: Being Human at the Age of Artificial Intelligence[END_REF] reduces AI to deep learning. However, what he mentions as future work has been already developed since 1970s by researchers in Machine Learning such as R.S Michalski, R. Quinlan (EBG, generalization from examples) and some others [START_REF] Michalski | S Machine Learning 7. Ten secret military technologies[END_REF]. Some build humanoid robots, but is it a priority in the today world while we have increasing number of unemployed and homeless? Or for super-intelligent killer-robots and drones [7]? Do we need super-intelligence? What will be the place of humans in the artificial supraintelligent society [START_REF] Tegmark | Life 3.0: Being Human at the Age of Artificial Intelligence[END_REF]? Can such AI help facing today complex challenges, mostly generated by human activities or influence people to be respectful?

Fight Covid 19 pandemic, manage the economic crisis generated by confinement, understand the new virus and elaborate vaccine is a priority. What AI to assist us in managing the Planet and biosphere protection? According to experts, we entered to Anthropocene epoch [START_REF] Mercier-Laurent | The Innovation Biosphere -Planet and Brains in Digital Era[END_REF] and it is urgent to multiply the efforts in protecting our planet Earth. It requires minimizing our footprint by minimizing the use of energy [START_REF] Kayakutlu | Intelligence in Energy[END_REF] and water and minimizing all kind of pollution in aim preserving the air we breathe, water we drink and quality of food. All fields of human activity are concerned: growing cities, transportation, technology with race for performance, agriculture, health, industry, etc. Current situation requires evaluation and monitoring the impact of human activities.

This chapter discuss two possible futures -AGI and AI for human and planet purpose.

Four generations of AI

While Aristotle, Archimedes, Descartes and Leibnitz have laid theoretical foundations of AI, Norbert Wiener [START_REF] Wiener | The Human Use of Human Being[END_REF], Warren Mc Culloch, Walter Pitts, Donald Hebb [START_REF] Johnson | Cognizers: Neural Networks and Machines that think[END_REF] and Ludwig von Bertalanffy [START_REF]von Bertalanfy General System Theory[END_REF] have introduced cybernetics, artificial neural networks and base for evolutionary algorithms before the official birth of AI in 1956. Alain Turing proposed the famous test in 1950.

First generation of AI is linked with the beginning of computers. Some call this period Early enthusiasm . First robot, called Perceptron, chess game, LISP, the first AI programming language designed by John McCarthy inspired the work on object programming and triggered the second generation of AI, those of Knowledge-based AI that has begun in the 1960s. Object programming languages, Natural Language Processing (Prolog), various knowledge representation models, Case-based reasoning, Knowledge discovery techniques, constraint programming were born between 1970s and 1990. Many successful applications (1980)(1981)(1982)(1983)(1984)(1985)(1986)(1987)(1988)(1989)(1990)(1991)(1992)(1993)(1994) demonstrated the usefulness of AI that since has been embedded to many applications in all fields [START_REF] Rauch-Hindin | Artificial Intelligence in Business, Science and Industry[END_REF].

In the middle of the 1990s Internet became the star and AI was temporarily shelved. Some talk about AI Winter (1995Winter ( -2012)). Third generation of AI was born in 2012 from the necessity of exploring exponentially growing amount of data generated by among others electronic commerce and social networks. This generation will be not possible without previous research and applications of known AI techniques such as Artificial Neural Networks enhanced with better computer performance, improvement of robots, humanoid robots, drones and Internet of Objects (IoT). Fourth generation is coming. It will combine deep learning exploration of unstructured data and knowledge-based AI to obtain the robust AI systems able to provide decision support and AI Systems as a service [START_REF] Mercier-Laurent | Implementing Horizon Europe, 11 th Innovation Summit[END_REF].

In his video John Launchbury (DARPA) illustrates the differences between three generations of AI (he forgotten the first) by the capacities of Perceiving, Learning, Abstracting and Reasoning [START_REF] Launchbury | DARPA perspective on Artificial Intelligence[END_REF]. However, he forgot to include symbolic machine learning, initiated in the early 1970 by Ryszard S. Michalski, John R. Quinlan, Jaime G.

Carbonell, Tom M. Mitchell and some others [START_REF] Michalski | S Machine Learning 7. Ten secret military technologies[END_REF]. Fig. 1 presents his 3 rd generation (in fact 4 th ) of AI that will balance all four components.

Artificial General Intelligence

The founders of the AI fields were largely concerned with the creation of hardware and software acting as human, even more intelligent. Mark Gubrud proposed the expression of Artificial General Intelligence in military context [16]. What is the progress from General Problem Solver (Newell et al, 1959)? Sure computers performance allows quicker processing but the available systems still lack intelligence. Forbes states "AI systems that can diagnose cancers with greater accuracy than human doctors, there are many other fields where specialized artificial intelligence is replicating human-like reasoning and cognition." [START_REF] Naveen | How far we are from achieving General Artificial Intelligence?[END_REF]. Forbes journalist never tried google image search? The quality of diagnosis however depend on the accuracy of data used for learning model and on the quality of learning algorithm. If the accuracy is similar to the results of available search engines, diagnose can be erroneous, while experienced oncologist diagnoses still better than those by the best algorithm. Deep learning combined with expert system may give better results. Some work on this kind of applications was done in the late 1980s in the Faculté de Médecine, Paris. Another limitation of deep learning is the exploring of past data to predict the future. It may work in linear world, but not in today dynamic one.

One of the main actors of AGI is Open AI [18]. They focus on the development of highly autonomous systems that outperform humans and will be beneficial for humanity. They aim in building safe and beneficial AGI, but will also consider their mission fulfilled if their work aids others to achieve this outcome. Among their projects, we find the following: In 2005 Kurtzweil states, "Singularity is Near". Robots still have no intuition and are unable to hypnotize human, but is it necessary? Some researchers focus on the simulation of the brain. Is it possible to simulate something if we have only partial knowledge about it? Neuroimaging technology can deliver images. Experiments such as specific activity in MRI helps discovering some functionalities [START_REF] Bihan | Looking inside the brain: The power of neuroimaging[END_REF]. The brain is NOT just supercomputer, it is much more than that and work in interaction with the other organs. According to multidisciplinary scientists [START_REF] Rajvanshi | The Three Minds of the Bodybrain, heart and gut, Speaking Tree, India[END_REF], the brain is a system component of our body and interact in connection with the other organs. Some talk about connection between three "brains" equipped with neurons: brain, heart and gut or stomach, each plays a role in decision making.

 a neural
Artificial neural network is simple implementation of biological neurons, which are much more complex. The impact of such research may be beneficial in medicine, to cure serious diseases as Alzheimer, other brain defects and in psychiatry.

In science fiction, AGI is associated with consciousness, sentience, sapiens and selfawareness. The current research is in infancy of such capacities. Marketing is much more interested in "sentiment" analysis and eye tracking then in providing us with products that we really need. They never ask if someone did not found what he/she looked for. Is it so difficult to proceed or they are not interested in?

Considering that it is more important to empower human by combining the best of human and best of computer we focus on AI for human and planet purpose.

AI for Human and Planet Purposewhat we expect from future AI?

Another trend in AI research and applications is collaborative intelligence human-machine. "Computers are incredibly fast, accurate and stupid. Human beings are incredibly slow, inaccurate and brilliant. Together they are powerful beyond imagination". This citation attributed to Einstein proposes certainly better future than those transforming human into slave of "intelligent" systems or into "shopping machine". Since years AI researchers and practitioners invented and have been experimented various AI techniques such as natural language programming, expert systems, case based reasoning, constraint programming and multi-agent systems.

Fig. 2. Available AI techniques, source [9]

These techniques combined into hybrid systems and applied knowingly and wisely, allow solving the majority of complex problems generated by present challenges. However, it requires the deep problem understanding before and experience in applying the adequate AI techniques. Today AI is "inside" of numerous applications in all fields. The future of AI has to consider all these experiences to progress in right direction.

Between 1995 and today the AI research was awaken with business goalssale more and quicker. This engine motivated development and improvement of various deep learning algorithms [START_REF]Deep learning algorithms[END_REF]. Most believe that it is possible to solve all kind of problems using deep learning; it has become "general problem solver" of the moment. Applied on navigation data to deduce client experience, for face recognition, eye tracking, chat bots, automated translation these algorithms give satisfactory results if the training set is correctly elaborated and if the algorithm is able to improve itself. For example, DeepL translator learn also from users that can correct provided result if they know well a given language.

Nevertheless, sometimes challenge is to find what is not in data. Life and intelligence is not about data.

In parallel, research in robotics progressed thanks to the innovation in electronics and miniaturization. The disasters such as Tchernobyl and Fukushima demonstrated the need for small flying robots able to evaluate the damages and act in the places that human can not access. Similar for other risks management such as earthquake, typhoon, drones and flying robots provide great help to human. Surgery robots equipped with vision systems are also of significant help, especially in the situation when high precision is required. Disinfecting robots and vehicles demonstrated their usefulness during Covid 19 pandemics. Industry 4.0 implemented the principle of collaboration human-machine in co-bots [26] and some factories of the future, such as those of Schneider Electric in Vaudreuil. AI powers the cyber physical systems and digital twins [START_REF] Monsone | Ecosystem of Industry 4.0 -Combining Technology and Human Power[END_REF]. Intuition and imagination combined with quick access to world base of problem solving give certainly better results than asking Sophia robot. We will still need truly intelligent personal assistant able to learn with the user, not necessary Alexa, Siri or more sophisticated robot.

In the nearest future, the AI approach to problem solving and combining deep learning with knowledge-based AI may bring a significant help in many fields. Combining human intelligence and capacities with those of computers and other electronic devices Deep understanding of inter-influences and finding acceptable collaborative solutions for preserving our biosphere and decelerating the Planet decline. Competition promoted by various ranking systems and research limited to a given field only are the barriers to progress by more collaborative and multidisciplinary research.

Technological innovation makes our lives easier. However, the progress without considering the impacts of human activities led to degradation of our living conditions. Many factors affect the sustainability. One of them is quick technological progress, considered as powerful engine of economy. It brings many benefits for humanity, but contributes also to Planet Crisis. Computers, smartphones, IoT and other devices are quickly outdated. The combination of various communicating software requires "up-to-date" hardware to run correctly. Most of hardware are not eco-designed and need raw material that has become scarce. Despite the large introduction of Corporate Social Responsibility, some companies still practice planned obsolescence to generate more revenues.

Social networks and various applications generate an exponential amount of data stored in data centers that need cooling. Fortunately, some apply circular energy to reduce impact on environment, but still those in Scandinavian countries clearly contribute to the rise of temperature and melting of ice.

What we have that can be reused/improved and what need to be invented?

Challenges to face

Among the challenges for humans and for planet: fix existing disaster and make IT smarter and greener. After health, time is probably one of the most important assets. Traditional IT can be smarter with AI inside. In her invited talk to 6 th AI4KM, Helena Lindskog pointed out the importance of being "time rich" today and set up the challenge for AI is to help us having more time for innovation, for family, discover end enjoy nature and other activities [START_REF] Lindskog | Globalization -Understanding the correlations between attitudes towards globalization, time, resources and Financial Resources[END_REF]. Make the IT friendly and intuitive, greening the software, smarting data centers are among the wishes. Association of offer and demand such as job searching for me in my place, like restaurant on google map, service provider, spare pieces, repair café, 3D printer close to my place.

Imagine I switch on my computer or other device and immediately I get the results of relevant search proposed by embedded intelligence, knowing my profile.

Intelligent translator exists already, ex https://www.deepl.com/translator. The pronunciation of the translated text will be beneficial. When traveling, the real-time accurate conversation translator is of considerable help. Can such translators at long term prevent us from learning languages (and using our brain)? Will our brain become lazy? Today we have to face important challenges, such as those of UN for sustainability. AI can directly improve goal 6, 7, 9, 11 and next from Fig. 4. It may also influence improvement of others. According to Anthony Wang [START_REF] Wang | Ethics and Regulation of Artificial Intelligence, Keynote to NITC 2019[END_REF] it is over 70 various principles for AI ethics. One of the most repeated is that AI has to be beneficial for humanity. I add "and planet".

3.2

From "intelligent" assistants to helping the user Intelligent assistant can take several forms:

 personal, working for one user  for children protection  linked to a company website or a platform answering the clients questions  "street" assistant helping visitors/tourists  in shop assistant, especially in big one guiding clients to the products they need  inside of museum, expositions  for people with disabilities

All have access to available resources and are designed to provide the user with immediate and relevant information, help, advice or a solution to a given problem. They have to "know" what the users do not know.

My dream is to have personal assistant able to learn my interests, scan all available and fake checked resources and provide me with the timely relevant information and this way participate in opportunity "hunting". It has to be able of immediate finding a document in my computer related to the topic I ask, picture or drawing. Producer of Korean TV series "My holo love" imagined such an assistant, able to learn from user and improve its "knowledge". It is only visible and available for its user wearing special glasses. The only problem to solve is to preserve the user's intimacy when he/she is talking to invisible assistant [31].

The relevance of answers from automated assistant depends today on the quality of data and of learning algorithm. The both chat bots and connected assistants are concerned. However, assistants available today need the capacity of "understanding" the question and provide relevant and verified answer. Equipped with multimodal interface it should follow me in my travels and "talk" languages that I do not talk, help finding the right word or expression in languages I talk, provide help for writing in other than the languages I master. Many comparators applications are available such as for tourism or insurance, but no one provides the optimal offer for the end user, because they work for their clients.

Having the access to medical cases, an intelligent assistant can provide the basic help before going to the doctor. It can also teach the user the preventive actions.

AI in school

Deep learning facilitated the development of intelligent assistants. For the most, they are helpful; however, they do not encourage people to think. Thinking and ability to use alternative cognitive approaches makes the difference between someone who just follow the school program and those able to solve problems using knowledge and limited resources. Thinking "without borders between fields" and ability to find alternative, greener solutions. Many professions change with technology and school has to prepare being flexible.

E-learning was introduced in schools at the beginning of 21 st century. Many courses (MOOC) are now available online opening access to large knowledge at all levels and providing education to the rural zones without schools and to developing countries. Digitalization of educational activities introduced tablets and now robots in schools.

Educational games make learning more fun and attractive. What is missing it is a sort of "super professor" able to evaluate a level of the student and to propose the best suited from the web matching to the student profile and request. Still intelligent assistants can provide help explaining topic or exercise. During Covid19 confinement children had to learn at home. An i-teacher detecting difficulties of each, explaining and challenging is certainly helpful. It requires AI not only tracking activity but able to "understand" what child is doing and when he/she needs help. Challenging assistantnot switching off the brain, but stimulating thinking and suggesting various approaches.

Most of schools follow the teaching program, but few take care of the specific children talents in aim helping them to choose their professional future. We can imagine an AI -based Future Advice office combining the adequate AI techniques to evaluate talents, propose a game to test some professions with projection how it will evolve next ten or more years. Several years ago, the entertainment parc Kidzania [32] was open for children to make them try various professions. The IT and AI-related professions have to be added to the spectrum of those proposed.

AI & food

In the age of processed food, the related industry ambitions in producing more food for less price regardless of nutritional quality. Pesticides and artificial fertilizers are massively used on the pretext of a duty to nourish the planet. Doing this they destroy and impoverish the soil, pollute water and pests become more resistant. Globalization made travel pests with goods; some are very harmful for local environment and difficult to fight with, because unknown, as COVID is. According to Intel and some others, the world will need to produce 50 percent more food by 2050. Nevertheless pushed by food business, the food waste today is evaluated around 50% only in the United States [START_REF]Food waste[END_REF]. Food is lost or wasted for a variety of reasons: bad weather, processing problems, overproduction and unstable markets cause food loss long before it arrives in a grocery store, while overbuying, poor planning and confusion over labels and safety contribute to food waste at stores and in homes. Uneaten food also puts unneeded strain on the environment by wasting valuable resources like water and farmland. Reducing food waste by just 15 percent could provide enough sustenance to feed more than 25 million people, annually. Food loss occurs on farms for a variety of reasons. To hedge against pests and weather, farmers often plant more than consumers demand. Food may not be harvested because of damage by weather, pests and disease. Market conditions off the farm can lead farmers to throw out edible food because the shape is out of the norms.

If the price of produce on the market is lower than the cost of transportation and labor, sometimes farmers will leave their crops un-harvested. This practice, called dumping, happens when farmers are producing more of a product that people are willing to buy, or when demand for a product falls unexpectedly.

During the COVID-19 pandemic, for example, farmers lost a major portion of their business due to restaurant and school lunchroom closures and lack of workforce for gathering.

In the recent video entitles Sustainability European Space Agency (ESA) shows-up the satellite images demonstrating how far COVID affects the food production. They deplore the impact of border closures on the harvesting of fruits and vegetables [START_REF]European Space Agency[END_REF]. Yet, because of the economic crisis, many people are partially unemployed and this workforce is not correctly managed. AI can help manage all kind of resource, but this potential is underused today.

AI system is able to detect from satellite images the zones to harvest or infected ones. Thinking -They have images of underexplored zones that can be cultivated to produce food locally. Some use harvesting robots; however, it should be cost effective.

Various AI techniques are already used to help farmers. Drones "decide" when coffee and oranges are ripe enough for picking (Hawaï, Bresil). They can also detect pests' invasion. Hoping in the nearest future, we will have devices not only detecting, but selectively (pest recognition) absorbing them instead of using pesticides. Logistics planned with constraints programming to optimize time and trucks should be more widely used. Sensors connected to automated watering help optimizing the use of water.

Greenhouse tomatoes grow in a bed of pulped coconut husks, a nutrient-free environment that allows the growers controlling what goes into the plant. Sensors monitor the fruit's progress toward perfect ripeness, adjusting light to accelerate or slow the pace of maturation. However, this kind of farming requires considerable processing power.

We can imagine another approach combining the knowledge about soil and environment with knowledge about crop rotation, association of vegetable/fruits to avoid pests attack, chose the right period for sow and plant in function of available seeds. Such farmer advisor programmed applying green software principle can be powered with solar energy. Fig. 5. Connected Farmer, source [START_REF]Precision Farmingsowing the seeds of new agricultural revolution[END_REF] While the harvesting machines have been used for years, they are now replaced by robots. Robotic harvesting equipment, partially in response to labor gaps that have left farmers scrambling to harvest crops like fruits and berries. Harvest Croo berry picker operate on the basis of machine vision and sensor fusion to "see" where harvest fruits and berries are. They use sophisticated directed movements to pick precisely [36].

Unmanned aerial vehicles or drones being outfitted with precision sensors, in order to run the fields and get the data that's needed. These airborne surveillance engines can look for stunted crops, signs of pest or weed damage, dryness and many other variables that are part of the difficulty of farming in general. With all of this data in hand, farmers can enhance their production models and their strategies across the lay of the land to decrease risk, waste and liability [START_REF] Gonzalez-De-Santos | Unmanned Ground Vehicles for Smart Farm[END_REF].

Main challenge is still protecting plants against weeds and various kinds of pests outdoors. Another alternative is to grow in greenhouses, which is being done as well, but some of the most amazing farming technology is being deployed outside. The "See & Spray" machines are an excellent example of combining artificial intelligence and computer vision [38]. Deploying mobile technologies with AI and computer vision built in, farmers can find weeds and eradicate them, instead of blanket spraying an entire crop. That makes the food cleaner, and it saves enormous amounts of money. It's just another example of real new technologies that are having a dramatic impact on yields and everything else.

The transition from conventional agriculture practices into a sustainable mode of growing food can lead to social and economic equity and a healthy environment. In the nearest future we expect connecting the modern, AI powered systems with ancestral knowledge about how to cultivate without pesticides and with natural fertilizers. Maybe the future agriculture will be not about large farms but smart ones. Autonomous houses and farms can be monitored by AI ensuring optimized use of locally available resources and minimized impact on the planet.

AI for risks/crisis management

Irresponsible human activities have leaded to Planet disaster. The increased frequency and magnitude of natural risks and those caused by human requires new, quicker and more effective ways of managing them. Frequent fire mostly of criminal origin destroy forest, our lungs and its ecosystem. In many cases, AI demonstrated its potential to help managing these disasters [START_REF] Mercier-Laurent | Preventing and facing new crisis and risks in complex environment[END_REF][START_REF] L'heritier | Identifying Criteria Most Influencing Strategy Performance: Application to Humanitarian Logistical Strategy Planning, Chapter in Information Processing and Management of Uncertainty in Knowledge-Based Systems[END_REF]. Earth observation systems can been applied for various risk management, as well caused by human as to natural risks. Fukushima, tsunami, earthquake, flooding require quick organizing of emergency actions involving people, hospitals, vehicles and other resources aligned to given disaster. Facing Covid19 pandemic has multiple effects on health, on jobs, on economy, education, agriculture and environment. The researchers and medical staff are learning from examples and experiments. Handling such crisis require related knowledge and smart managing of existing resources, real-time planning of hospital staff, equipment and quick finding of vaccine. Existing AI techniques can help in first stage diagnosis online to refer the patient to the right doctor. AI can optimize the allocation of beds, equipment, staff in function of their competencies and specialties. AI support exploration of clinical trials and accelerate finding of vaccine. Evaluation of efficiency of health policy [START_REF]Evaluation of efficiency of health policy[END_REF] can be used as model for pandemic management and evaluation of induced risks.

In his TED Talk from 2015, Bill Gates states that pandemics is the greatest treat we will have to face in the future [START_REF] Talk | [END_REF]. He said we need global alert system, technology, expertise, collaboration medical-military, simulation and diagnostic. Alternative is the understanding of real causes of pandemics and other serious risks in aim of avoiding and preventing them. Investments are necessary to fix the problem once it happen, but informed prevention avoids human, economic and environmental losses.

AI for sustainability

Paradoxically AI needs devices, mostly designed with "planned obsolescence" principle to preserve continuous business. While "green software" has been slowly introduced, few really apply these principles. All fields are concerned and evaluation of impacts before doing should become mandatory. AI may play a greater role in simulation before doing, choosing the right raw material and design easy to update or to recycle [START_REF] Mercier-Laurent | AI for Innovation Ecosystems 44[END_REF]. IT and Information Systems should be eco-designed, which is not the case today. The management and storage of big data generated massively can be controlled by using conceptual models instead of storing all data. AI can effectively support optimization of hardware software design. Neural computer is not new, but with the new hype researchers works again on this architecture. We do not know yet how far it can contribute to sustainability because nobody is in charge of this aspect. Similarly, designers of quantum computers focus on computation power and do not consider the eco-design. Race for performance and connectivity pushes designers to 5G, which is not necessary the best choice because of the impact on living. AI-based decision support systems connected with innovative design methods such as TRIZ [44] may help designers finding alternative solutions. The trendy design thinking, known longtime before as "innovation with clients [START_REF] Amidon | Innovating with clients, Chapter in The Innovation Strategy for The Knowledge Economy[END_REF] or extreme programming [START_REF]Extreme programming[END_REF], has to integrate these principles.

Maybe researchers and designers by nature (biomimetics) take into consideration these aspects. Nevertheless, AI offers a spectrum of techniques helpful in optimization and verification of environmental and other constraints.

The case of Smart City, mainly based on technology offer a playground for water and energy (including renewable ones) optimization, smart eco-buildings, green and optimized transportation of people and goods, opportunity finding (job, service, training).

All human activity that have affected our biosphere and planet is concerned and AI can help minimizing this impact and do things smarter [START_REF] Mercier-Laurent | The Innovation Biosphere -Planet and Brains in Digital Era[END_REF]. Preserving the balance in our biosphere will be beneficial for all living today and tomorrow.

3.7

AI and the Financial Service Industry (contribution of Leon Strous)

Without any doubt AI has a huge impact on the future of the financial services industry. This is widely acknowledged and described in many articles and reports [START_REF]BAFIN2018 -Big Data trifft auf künstliche Intelligenz, Herausforderungen und Implikationen für Aufsicht und Regulierung von Finanzdienstleistungen[END_REF][START_REF] Dnb | General principles for the use of Artificial Intelligence in the financial sector[END_REF][START_REF]FinTech2019 -How Data Analytics Backed By AI And ML Is Transforming The BFSI Sector[END_REF][START_REF]Infosecurity2019 -How AI is Revolutionizing the Banking Sector[END_REF][START_REF]Maruti2020 -5 Ways AI is Transforming the Finance Industry[END_REF][START_REF]WEF2020-1 -AI has started a financial revolution -here's how, World Economic Forum (WEF)[END_REF][START_REF]WEF2020-2 -Transforming Paradigms -A Global AI in Financial Services Survey[END_REF], labelling it "revolutionizing / transforming / disrupting the industry". In an already highly digitized sector, AI adds an extra dimension in a number of areas. It offers new opportunities, both for new players and for incumbent institutions. It also brings threats, in particular to the established parties who have a legacy in their infrastructure and services. Often mentioned areas where AI is transforming the industry are:

 Fraud detection and risk management. AI is extremely helpful in detecting and identifying fraudulent transactions, learning from past spending behaviors.

 Regulatory compliance. The financial services industry is heavily regulated. AI can help an institution to keep up to date with changing regulations and to be compliant with rules like Know Your Customer (KYC), anti-money laundering regulations, etc.  Customer experience. Customers nowadays expect a more personalized offer of services. Developments like chatbots that, with the help of AI, can identify the ual customer, "understand" and interpret his or her emotions via voice and/or facial recognition and subsequently can offer "tailormade" advice found in existing database.  Managing personal finances. With the move to mobile banking and the use of wallets, AI offers opportunities to help customers make decisions on spending, saving and investing money. This can help improve the "financial health" of many people. Managing finance, AI simply accumulates all the data from your web and other footprints and creates your spending graph.  Investment advisory. Here predictive analytics and recommendation engines turn into digital advisors that can even fully automate purchase and management of investments. The result is no longer a need for financial advisors / relationship managers.  Predict stock performance. Trading and investment depend on the ability to predict the future of (stock) markets accurately. Predicting this in a consistent way seems impossible for humans. Deep learning algorithms could perhaps achieve this as a result of using massive amounts of market data complemented with real-time economic and political data.

An example of the trading issue is high frequency trading (HFT). While not new in itself, since a little over a decade the execution time of transactions has moved from seconds to milli-and microseconds. HFT is a form of algorithmic trading which can arguably be labelled as an AI implementation of trading. The profits of HFT however seem to have passed their peak performance. Besides limitations caused by the infrastructure (hardware, networks) also the limitations of the algorithms play a role. Perhaps deep learning could give HFT a new boost. In [START_REF] Akioyamen | Neural Networks & Deep Learning -The Revival of HFT?[END_REF][START_REF] Shobhit | The World of High-Frequency Algorithmic Trading[END_REF], as in many other articles, benefits but also risks are listed. Algorithms are not infallible, understanding how the algorithms and neural networks predict specific outcomes is difficult if not impossible (black box), high investments needed which could result in only a few players surviving (compare the world of the big tech firms). All these developments will have a major effect on the workforce, not only in the number of employees but also in the types of jobs and skills needed for them. Another major impact is a change in the players in the financial landscape. The incumbents (current banks, insurance companies, pension funds, investment companies, etc) already face strong competition from the so-called fintechs in some areas. But also the bigtechs (Amazon, Google, Facebook, Alibaba, …) are entering the financial services industry. We seem to move towards a platform economy where the bigtechs have an advantage with their strong customer base to easily offer additional (financial) services to their customers, knowing what their customers want thanks to AI and ML. Benefits of AI in the financial services industry are clear but so are the risks. Not only for a single financial institution but also for customers, for the trust in the financial system and for the financial stability in a country or even on a global scale. That is why in [START_REF] Dnb | General principles for the use of Artificial Intelligence in the financial sector[END_REF] it is stated that "Financial firms using artificial intelligence (AI) should adhere to principles of sound and controlled business operations. responsible use of AI in financial services means that firms should pay due attention to the soundness, accountability, fairness, ethics, skills and transparency aspects of the applications they develop."

4

What perspectives for researchwhat humanity expects from Future AI?

Throughout this chapter we have already mentioned some needs, expectations and wishes for the future AI. In term of disruptive innovation, the current AI represents a little progress since its beginning. The AI techniques that we have today has been invented before and are recently extended to more powerful and to new applications. The wise combination of them in the hybrid systems allows facing quite all today challenges. What AI we need today and tomorrow? Michael Zeldich (Artificial Labour Leasing) designs subjective robots. Such robots used for example for house cleaning learn with their users and are limited to perform what user asks for. He believes that our duty is to create an artificial society able to resist the planet destruction and preserve our civilization.

Before we invent something else or all die, there are two main options for AI: to continue to progress with AGI or work on the challenges we have to face and then derive new theories and invent new approaches and techniques. The first requires deep understanding how human body works, how the organs are connected, how we interact with our environment. What future humans can expect from the AGI world? According to WEF [START_REF]WEF2020-2 -Transforming Paradigms -A Global AI in Financial Services Survey[END_REF] "Nevertheless, it is evident that more research needs to be done in order to better understand the opportunities and challenges brought about by the eventual mass adoption of AI in Financial Services. For

instance, how can finance firms open up the 'black box' of AI and facilitate more explainable and transparent applications? As AI is becoming increasingly autonomous, what will the roles of humans be and how would an effective human-in-the-loop AI system manifest itself? What are some socioeconomic repercussions and ethical implications of AI-induced biases and risks? How can regulators and policymakers harness technology solutions to effectively regulate and supervise AI in finance?"

These remarks about research trends, regulators and policymakers do not only apply to the financial industry but also to many if not all other sectors where AI is having or will have a major impact. So research is also needed to help regulators and policymakers, especially by providing them with simulators.

The issue of confidentiality of personal data is not easy to deal with. The same applies to our navigation and tracking data. Many websites refuse the access to information if the user does not accept them. However, it is much more relevant to obtain the right information from the user by asking instead bombing him/her with cookies and all sort of adds. AI can do much more than just analyze the data. Nevertheless, it requires a different way of thinking. AI research should be multi-and interdisciplinary because intelligence is. We still need to progress in comprehension of our brain/body capacities.

Future AI research should balance needs and ambitions. The Covid 19 crisis clearly demonstrated that collaboration may lead to better and quicker results. World experience in AI applications for solving complex problems should be available allowing finding immediately the solution for the problem someone has to solve.

Information processing, still conceived using traditional methods such as categorization, "data thinking", processes should evolve not by adding AI layer, but "AI thinking". We need systems able to adapt the configuration automatically working with the user, able to find immediately the file, image or video the user look for.

Hardware and software should be eco-designed and easy to recompose/recycle. AI based simulators help finding right components, minimize and "smartize" the software for the minimal energy use. MIT offers Climat Interactive simulator [START_REF]MIT Sloan simulator Climat Interactive[END_REF].

We need smart AI-powered search engines. For example, the EU database Cordis is a real "raw diamond mine" containing information about the funded research projects and their results that may inspire researchers, industrial people and investors but it need to be equipped with smart, business free, search engine for immediate finding what visitor is looking for. It can be also useful for exploring the available results for quicker progress, for quick and relevant access to references in a given field.

We need decision support systems rather than fully automated decision making, that are not 100% reliable in case of missing data in critical situations.

Many challenging research problems can be found in sectorial applications such as in agriculture, health, education, banking and others. In the nearest future we expect connecting the modern, AI powered systems with ancestral knowledge about smart farming.

Health offers a great opportunity for AI research, especially in understanding and prevention of new viruses and serious and other diseases.

Education is among the pillars of modern society. Existing MOOC, e-learning and other online teaching and training systems may be improved by interactive e-teacher, however we still need a talent detector able to encourage learners to think and direct them to the area they are gifted for.

Planet protection, smart sustainability, innovation management offer also interesting challenges.

Fig. 1 .

 1 Fig.1. DARPA Third wave of AI, source[START_REF] Launchbury | DARPA perspective on Artificial Intelligence[END_REF]

Fig. 3 .

 3 Fig. 3. Schneider Factory of the Future, Vaudreuil, source Schneider Electric [28]

Fig. 4 .

 4 Fig. 4. Sustainable Development Goals, source un.org

  network based generator of music; similar work has been done by Sony CSL [19]  Components of robots, robot hand able to solve Rubik's cube, robots learning dexterity  exploring multi-agents learning capabilities  automated text generation  learning sentiments They explore mainly various deep learning algorithms. This research lacks of overall project combining all these elements in an artificial intelligent system. Exploring multiagent learning capability is a topic of research since the beginning of multi-agent systems in the late 1980s. Automated text generation has been studied and practiced since the invention of Prolog in 1970 by Alain Colmerauer team, Marseille. Military research is more advanced, but most of the projects are confidential. We can only guess what is inside of Kalashnikov robots, UAV and other military advances equipments [20]. Before designing AGI systems, it is vital to understand what intelligence and how such systems can effectively collaborate with humans.

Detecting and tracking cyber and other criminals, identity thieves or global security are just a few fields of exploration for AI.

Multidisciplinary research without "borders" between fields and full exploring of all machine learning techniques, including symbolic, may help in elaborating of general solvers. Of course, this requires evolution of research evaluation criteria.

In this context, is it necessary to build a machine able of consciousness, sentience, sapiens and self-awareness?

Instead of splitting AI lets connecting the research fields for more spectacular results and for human purpose.

We expect a global wisening of AI and IT researchers.


Julien Chabé

Gabriel Lemarié

Beno Ît Grémaud

Dominique Delande

Pascal Szriftgiser

Jean Claude Garreau

Experimental observation of the Anderson transition with atomic matter waves

Keywords: numbers: 03.75.-b, 72.15.Rn, 05.45.Mt, 64.70.Tg

We realize experimentally an atom-optics quantum chaotic system, the quasiperiodic kicked rotor, which is equivalent to a 3D disordered system, that allow us to demonstrate the Anderson metalinsulator transition. Sensitive measurements of the atomic wavefunction dynamics and the use of finite-size scaling techniques make it possible to extract both the critical parameters and the critical exponent of the transition, which is in good agreement with the value obtained in numerical simulations of the 3D Anderson model.

The metal-insulator Anderson transition plays a central role in the study of quantum disordered systems. An insulator is associated with localized states of the system, while a metal generally displays diffusive transport associated with delocalized states. The Anderson model [1] describes such a metal-insulator transition, due to quantum interference effects driven by the amount of disorder in the system. Starting from the "tight-binding" description of an electron in a crystal lattice, Anderson postulated in 1958 that the dominant effect of impurities in the lattice is to randomize the diagonal, on-site, term of the Hamiltonian, and showed that this generally leads to a localization of the wavefunction, in sharp contrast with the Bloch-wave solution for a perfect crystal. This model has progressively been extended from its original solidstate physics scope [1,2,3,4] to a whole class of systems in which waves propagate in a disordered medium, as for example quantum-chaotic systems [START_REF] Casati | Stochastic behavior of a quantum pendulum under periodic perturbation[END_REF][START_REF] Grempel | [END_REF] and electromagnetic radiation [7,8,9]. However mathematically simple, the model predicts a wealth of interesting phenomena. In 1D, the wavefunction is always localized as recently observed in experiments using atomic matter waves in a disordered optical potential [10,11]; in 3D it predicts a phase transition between a localized (insulator) and a delocalized (metal) phase at a well defined mobility edge, the density of impurities or the energy being the control parameter.

Despite the wide interest on the Anderson transition, few experimental results are available. In a crystal, it is very difficult to obtain the conditions for a clean observation of the Anderson localization. Firstly, one has no direct access to the electronic wavefunction and must rely on modifications of bulk properties like conductivity [2,12]. Secondly, it is difficult to reduce decoherence sources to a low enough level. We thus engineered a matter-wave system that is described by an Andersonlike model, which allows us to probe the physics of disordered systems in much better conditions than in con-densed matter physics [13], namely: Almost no interaction between particles, weak absorption in the medium, no coupling with a thermal reservoir which could destroy localization and possibility of measuring the final quantum state of the system after a given interaction time. The system, the quasi-periodic kicked rotor [START_REF] Casati | Stochastic behavior of a quantum pendulum under periodic perturbation[END_REF][START_REF] Grempel | [END_REF]14,15], consists of cold cesium atoms exposed to a pulsed, offresonant, laser standing wave. The dynamics is thus effectively one-dimensional along the axis of the laser beam, as transverse directions are uncoupled. The atoms interact periodically with the spatially sinusoidal potential whose amplitude is modulated at (incommensurable) frequencies ω 2 and ω 3 , and the corresponding Hamiltonian is:

H = p 2 2 + K cos x [1 + ε cos (ω 2 t) cos (ω 3 t)] N -1 n=0 δ(t -n) , (1)
where x is the particle position (along the laser axis), p is its momentum, and K is the pulse intensity. The pulses are short enough (compared to the center of mass dynamics) to be consider as instantaneous kicks. We have chosen normalized variables such that x is measured in units of the spatial period of the potential divided by 2π, the particle's mass is unity and time is measured in units of pulse period T 1 .

By taking ε = 0 in Eq. ( 1) one obtains the standard (strictly time-periodic) kicked rotor, a system known to display the phenomenon of dynamical localization [START_REF] Casati | Stochastic behavior of a quantum pendulum under periodic perturbation[END_REF], which manifests itself by an exponential localization of the wavefunction in momentum space. Dynamical localization exists only when the classical dynamics of the kicked rotor is a chaotic diffusion. In the quantum case, diffusion is inhibited after some localization time by quantum interferences. Dynamical localization has been shown to be a direct analogue of Anderson localization in one dimension [START_REF] Grempel | [END_REF], with the following correspondences: Localization takes place in real space for the Anderson model and in momentum space for the kicked rotor; the "stochasticity parameter" K [see Eq. ( 1)] corresponds to the ratio of hopping to diagonal energy in the Anderson model. The strictly random disorder in the Anderson model correspond to a pseudo-random potential in the quasi-periodic kicked rotor. Thus, the experimental observation of dynamical localization in the kicked rotor [14] is actually the first observation of Anderson 1D localization with atomic matter waves.

In order to observe the Anderson transition, one must generalize the kicked rotor to obtain a system analogous to the 3D Anderson model. This can be done by introducing further temporal dependencies that make the Hamiltonian quasi-periodic in time, that is to take ε = 0 in Eq. ( 1) with ω 2 and ω 3 incommensurate numbers. The resulting system has been shown to be substantially equivalent to the 3D Anderson model [15], the additional spatial coordinates in the Anderson model corresponding to the additional time dependencies in the quasi-periodic kicked rotor. The parameters controlling the dynamics are ω 2 and ω 3 and, more importantly, the kick strength K and the modulation amplitude ε. In the (K, ε) plane, the ε = 0 line corresponds to dynamically localized dynamics (the periodic kicked rotor equivalent to the 1D Anderson model) and the Anderson transition takes place along a critical line in the plane ε > 0, as shown in Fig. 1.

Our atom-optics realization of the kicked rotor has been described in detail elsewhere [16,17,18]. Basically, we cool cesium atoms in a standard magneto-optical trap, and, after a Sisyphus-molasses phase, we obtain a cloud of 10 7 atoms at a temperature of 3.2 µK. This prepares a sample of atoms in a thermal state whose momentum distribution is much narrower than the expected localization length. The atoms then interact with the optical potential generated by a horizontal standing wave. An acousto-optical modulator driven by an arbitrary-form synthesizer modulates the amplitude of the optical potential. One generates in this way 0.9 µs-long pulses at T 1 = 27.778 µs (corresponding to an effective Planck constant k = 2.89), to which is superimposed a modulation of the form Eq. ( 1), with ω 2 /2π = √ 5 and ω 3 /2π = √ 13. The standing wave, of typical power 160 mW, is far offresonant (7.3 GHz to red of the atomic transition, corresponding to 1.4 × 10 3 natural widths), in order to reduce spontaneous emission. Stimulated Raman transitions allow sensitive measurements of the atomic momentum distribution [17].

The experimental values of the parameters are chosen according to the following considerations. Firstly, in order to prevent classical (KAM barrier) localization effects one must have K > 2 [19]. For all the data presented in this paper, we numerically checked that the classical dynamics is fully diffusive, which excludes that the observed transition could have a classical origin. Secondly, in order to confine the transition to a relatively narrow range of parameters one must cross the critical curve (Fig. 1) "at a right angle"; we thus vary simultaneously the K and ε along a line going from K = 4, ε = 0.1 in the localized region to K = 9, ε = 0.8 in the diffusive region; the critical line is then crossed at K = K c = 6.6. Thirdly, short enough pulses must be used, so that they can be considered as delta pulses. Numerical simulations taking into account the finite pulse duration (0.9 µs) show that less than 1% of the atoms are sensitive to the duration of the pulses. Finally, decoherence processes must be kept as small as possible. We have identified two dominant decoherence sources in our experiment: i) Spontaneous emission, which is not included in the Hamiltonian Eq. ( 1) and ii) the deviation of the standing wave from strict horizontality, which mixes quasimomentum classes and produces a stray momentum diffusion. The fact that numerical simulations of the "pure" quasi-periodic kicked rotor shown hereafter agree very well with the experimental results (in particular for the position of the Anderson transition and for the critical exponent) proves that spurious effects are well under control. Moreover, we have checked that inclusion of these effects in the numerical calculations only leads to small changes for t ≤ 150 kicks.

In order to observe the Anderson transition we apply a sequence of kicks to the atomic cloud and measure its dynamics. In the localized regime, the evolution of its momentum distribution is "frozen" after the localization time (∼12 kicks) into an exponential curve exp (-|p|/p loc ) (where p loc is the localization length). In the diffusive regime, the initial Gaussian shape is preserved and the distribution gets broader as kicks are ap- Temporal dynamics of the quasi-periodically kicked rotor. We measure the population Π0(t) of the zeromomentum class as a function of time and plotted the quantity Π -2 0 (t) (proportional to p 2 (t)). Clearly, it tends to a constant in the localized regime (blue curve) and increases linearly with time in the diffusive regime (red curve) . Close to the critical point (purple curve), it displays anomalous diffusion Π -2 0 (t) ∼ t 2/3 , as expected from theoretical arguments [24].

plied, corresponding to a linear increase of the average kinetic energy. The insets in Fig. 1 show the experimentally observed momentum distributions, an exponentially localized distribution for K < K c (blue curve), characteristic of dynamical localization, and a broad, Gaussianshaped distribution for K > K c (red curve), characteristic of the diffusive regime. Instead of measuring the full momentum distribution, it is sufficient, and much easier, to measure the population Π 0 (t) of the zero velocity class [21], as Π -2 0 (t) is proportional to p 2 (t) (by virtue of the constancy of the total number of atoms). We performed several experimental runs corresponding to points in the (K, ǫ) plane. In each run the value of Π 0 (t) was recorded as the kicks were applied. We also recorded the background signal obtained by not applying the Raman detection sequence, and the total number of atoms in the cold-atom cloud. These signals are used to correct the experimental data from background signals and long-term drifts of the cloud population. Figure 2 shows the experimentally observed Π -2 0 (t) and clearly shows the transition from the localized to the diffusive regime, with an intermediate regime of anomalous diffusion.

When one approaches the critical point from the insulator side, the localization length diverges, whereas, on the metallic side, the diffusion constant vanishes. However, a strict divergence can be observed only in macroscopically large samples; in small samples the divergence is smoothed. This fact plagued the numerical studies of the solid-state Anderson transition, as only a finite (small) lattice can be dealt with in a computer. In our system, a singular behavior would show up only for prohibitively large numbers of kicks, which are, in practice, limited to 150. To overcome this limitation, a technique named "finite-size scaling" [12,22,23] was introduced, whose basic idea is to infer the scaling law allowing proper extrapolation of the measured localization length to an infinite sample. We will next show that our data obey the scaling laws predicted by renormalization theory, and this will allow us to extract the critical exponent associated with the singular behavior at the transition.

We have adapted the standard finite-size scaling approach used in numerical studies of the Anderson transition [3] assuming that, for finite interaction time, the quantity Λ(t) = Π -2 0 (t)t -2/3 is an arbitrary function f ξt -1/3 [20,24,[START_REF] Stauffer | Introduction to Percolation Theory[END_REF], with a scaling parameter ξ which depends only on K. Using the results obtained for various values of t and K one can reconstruct both the function f and the scaling parameter ξ(K) (no assumption on the form of f is made). The result is shown in Fig. 3(a) and (b) for numerical simulations and in Fig. 3(c) and(d) for the experimental results. In both cases, the scaling hypothesis is justified by the fact that all points in Fig. 3(a) and 3(c) lie on a single curve (within the experimental error). The remarkable feature is the existence of two branches: the upper one corresponds to diffusive motion while the lower one corresponds to the localized regime, the critical point being at the tip joining the two branches. The scaling parameter ξ(K) is plotted in Figs. 3(b) and 3(d): It represents the localization length in the localized regime and scales as the inverse of the diffusion constant in the diffusive regime. Clearly, it increases rapidly in the vicinity of the critical value K c , on both sides of the transition, and it is found to behave as ξ ∼ |K -K c | -ν when K → K c . This divergence at the critical point is a key property of the Anderson phase transition. However, phase-breaking mechanisms induced by decoherence processes smooth the algebraic divergence. We model such effects by introducing a small cutoff of the divergence due to the residual decoherence:

1 ξ(K) = α|K -K c | ν + β . (2)
The experimental observations and the numerical data have been fitted with this formula [dashed curves in Figs.

3(b) and 3(d)]. We found K c = 6.4 (very close to the value K c = 6.6 obtained from the numerical simulation), and a critical exponent ν = 1.4 ± 0.3 [START_REF]As is well known from numerical experiments on the Anderson model, the finite-size scaling method slightly underestimates the critical exponents[END_REF], which is consistent with the numerical value within the error bars. The good agreement between the numerical simulations and the experimental results proves that spurious effects (such as decoherence) are under control. We emphasize that there are no adjustable parameters in our procedure, all parameters are determined using the atoms themselves as probes. Once the existence of the scaling law established, it is more convenient to use a global analysis of the numerical data at various values of K (see refs. Mackinnon [12], Slevin and Ohtsuki [23]). This, together with the fact that numerical simulations are not limited to 150 kicks and can be ran up to several thousands kicks, make it possible to obtain a more precise numerical value for the critical exponent ν = 1.60 ± 0.05. Note, finally, that although the data displayed here concerns a particular set of parameters we also verified experimentally the presence of the transition for other parameters.

In conclusion, we have presented the first experimental evidence of the Anderson transition with atomic matter waves. The transition is characterized by a well defined critical point, a divergence of the localization length below the critical point (in the localized regime) and a vanishing of the diffusion constant above the critical point (in the diffusive regime). We have determined the scaling laws and the critical exponent ν of the Anderson transition, which is significantly larger than unity and very close to the value obtained in recent numerical experiments [12,23] on the Anderson model, enforcing the assumption [15] that the two systems are substantially equivalent. Whether this exponent is universal (i.e. independent of the microscopic details) or not remains to be studied. A very interesting point is that our Andersonequivalent quasiperiodic kicked rotor can be easily generalized to higher dimensions simply by adding new incommensurable frequency, which opens perspectives for fascinating studies of the dependence of the critical exponent on the dimension of the underlying Anderson model.

The authors acknowledge D. Shepelyansky for bringing ref. [15] to their attention, and for fruitful discussions. They also acknowledge G. Beck for his help with the experiment.

Figure 1 :

 1 Figure 1: Phase diagram of the quasiperiodic kicked rotor, from numerical simulations. The localized (insulator) region is shown in blue, the diffusive (metallic) region is shown in red. The experimental parameters are swept along the diagonal dash-dotted line. The insets show the experimentally observed momentum distributions, localized in the insulator region and Gaussian in the diffusive (metallic) region.

  Figure 2:Temporal dynamics of the quasi-periodically kicked rotor. We measure the population Π0(t) of the zeromomentum class as a function of time and plotted the quantity Π -2 0 (t) (proportional to p 2 (t)). Clearly, it tends to a constant in the localized regime (blue curve) and increases linearly with time in the diffusive regime (red curve) . Close to the critical point (purple curve), it displays anomalous diffusion Π -2 0 (t) ∼ t 2/3 , as expected from theoretical arguments[24].

Figure 3 :

 3 Figure 3: Finite size scaling applied to the results of numerical simulations [(a) and (b)] and to the experimental results [(c) and (d)]. The rescaled quantity Λ = Π -2 0 (t)t -2/3 is plotted as a function of time (from 30 to 10 4 kicks (simulation) and from 30 to 150 kicks (experiment), for various values of K. Finite-size scaling of the raw data makes it possible to determine both the scaling function f , shown in (a) and (c), and the scaling parameter ξ(K), represented in (b) and (d). Close to Kc, the behavior of ξ in (b) and (d) is well fitted by Eq. (2) (dashed lines), giving Kc = 6.6 (simulation) and Kc = 6.4 (experiment). The critical exponent is ν = 1.60 ± 0.05 (simulation) and ν = 1.4 ± 0.3 (experiment).