Challenges of #ArtificialIntelligence


Until few years ago, #ArtificialIntelligence (#AI) was similar to nuclear fusion in unfulfilled promise. It had been around a long time but had not reached the spectacular heights foreseen in its initial stages. However now, Artificial intelligence (AI) is no longer the future. It is here and now. It’s realizing its potential in achieving man-like capabilities, so it’s the right time to ask: How can business leaders adapt AI to take advantage of the specific strengths of man and machine?


AI is swiftly becoming the foundational technology in areas as diverse as self-driving cars, financial trading, connected houses etc. Self-learning algorithms are now routinely embedded in mobile and online services. Researchers have leveraged massive gains in processing power and the data streaming from digital devices and connected sensors to improve AI performance. Therefore, the progress in robotics, self driving cars, speech processing, natural language understanding is quite impressive.


But with all the advantages AI can offer, there are still some challenges for the companies who wants to adapt #AI. As AI is a vast domain, lisitng all challenges is quite impossible, yet we’ve listed few generic challenges of Artificial Intelligence here below, such as: AI situated approach in the real-world; Learning process with human intervention; Access to other disciplines; Multitasking; Validation and certification of AI systems.


Artificial Intelligence’s Situated Approach:

Artificial Intelligence systems must operate and interact with the real world and their environment, receiving sensor data, determining the environment in which they operate, act on the real world, are such examples. Artificial Intelligence systems must behave autonomously and maintain their integrity under various conditions. To meet these requirements, AI systems must manage unstructured data as well as semantic data.


AL system and Human Intervention:

AI systems are programmed to interact with human users: they must therefore be able to explain their behavior, justify in a certain way the decisions they make so that human users can understand their actions and motivations. If this understanding is not forthcoming, human users will have little or no confidence in the AI’s systems, which isn’t acceptable. In addition to that, AI systems need some flexibility and adaptability in order to manage different users and different expectations. It is important to develop interaction mechanisms that promote good communication and interoperation between humans and AI systems.


AI, Opening to other disciplines:

An AI will often be integrated into a larger system of many other elements. Openness therefore means that AI scientists and developers will have to collaborate with specialists in other computer science disciplines (ex, modelling and predicting, verification and validation, networks, visualization, human-machine interaction, etc.) to compose a competitive and wider system of AI. The second aspect to consider is the impact of AI systems on many facets of our lives, our economy and our society, and therefore the collaboration with non-computer specialists such as psychologists, biologists, mathematicians, economists, environmentalists and lawyers is a must.



Many AI systems are excellent-competent in a specific area, but turn out incompetent outside of their specific areas. However, systems operating in a real environment, such as robots, must be able to perform several parallel actions, such as memorizing facts, assimilating new concepts, acting on the real world and interacting with humans.


Validation and certification: 

The essential element of AI’s critical systems, the certification of AI systems or their validation by appropriate means, are real challenges, especially if they meet the expectations mentioned above (adaptation, multitasking, learning processes with human intervention). The verification, validation and certification of conventional systems (which are therefore not part of the AI) is already a difficult task – even if there are already exploitable technologies. The application of these tools to complex AI systems is a daunting task that needs to be addressed in order to be able to use these systems in environments such as airplanes, nuclear power plants, hospitals, and so on.


Other Generic Challenges: 

In addition to the previous challenges, the following requirements for AI systems should lead to new research activities: some are extremely complex and cannot be satisfied in the short term, but worth attention.
Implanting norms and values ​​into AI systems goes far beyond existing science and technology: for example, should a robot that will buy milk for its owner stop on the way to help a person whose life is in danger? Could a powerful AI technology be used by artificial terrorists? At present, AI research is far from being able to meet these requirements.
The privacy requirement is particularly important for AI systems confronted with personal data, such as intelligent assistants / companions or data mining systems. This requirement was already valid for conventional systems, but AI systems have the particularity that they will generate new knowledge from private data and will probably make them public if there are no technical means capable of imposing restrictions.

Final challenge concerns the scaling-up. AI systems must be able to manage large amounts of data and situations. We’ve seen learning algorithms that absorb millions of data points (signals, images, videos, etc.) and large-scale reasoning systems, such as the IBM Watson system, using encyclopedic knowledge. However, the question of scaling for the many V’s (variety, volume, speed, vocabularies, etc.) remains unanswered.


AI Applications: 

This is not strictly a challenge for AI, but it is important to highlight that AI systems contribute to resolve societal problems: AI applications cover the entire range of human activities, such as environment and energy, health and assisting living and home maintenance, transportation and smart cities, etc. They can be beneficial to mankind and the economy, but they can also pose threats if they are not controlled as planned.

How Artificial Intelligence is impacting the Tourism Sector?

Artificial intelligence has existed for several years, yet we witness that it is now reaching another dimension, thanks to more powerful computers and the multiplication of available data. By its capacity to raise all sectors of activity, it is undeniable that it represents a great interest for Tourism. With the wealth of data available to professionals, today there are a multitude of technologies and recommendations applications, real time chatbot and personalized concierge services. The aim is to simplify the work of tourism industry professionals so that they can return to their core business with powerful tools and technologies and make an important difference in terms of profit and customer satisfaction. But the question one must ask is how to use Artificial Intelligence wisely?

Artificial Intelligence and Tourism

The first point: if we think about tourism future, in terms of types of travelers, its certain that we will be dealing with several categories of profiles, which may overlap. Our first category, for example, will be constituted, as is the case today, of travelers wishing to disconnect radically from their “everyday” environment in order to immerse themselves in another culture. And this, by all possible means.

Others, more vigilant, are the second category that will want to practice simple trips, without risks, even without surprises, neither good nor bad. This does not exclude, on the contrary, the survival of an adventure tourism.

For, the last profile, the purpose of a journey will be less the destination than the experience that one can have there. They will travel to learn how to cook a rare product or to learn a new activity based on information provided by our peers. The purpose of their travel will be based on learning.

Whatever the size of the group and the number of establishments it counts, it seems to me that we are moving towards a world where the tourist supply will continue to increase, thanks to two levers: new destinations and new traveler’s profiles. It will be required to be extremely flexible towards the customer’s expectations, to which one must respond with the development of innovative services to accompany them at each stage of their journey before, during and after their stay .


How can AI added value be applied to Tourism?
By Customization. And that is what profoundly changes the ins and outs. Rather than bringing the same experience to the same type of travel, artificial intelligence offers the possibility of matching the desires, habits, preferences of the tourist with the proposed product. “Artificial intelligence makes a data pool meaningful. By learning what the customer is looking for, buying, and loving, it makes it possible to generate customized and targeted offers that are more likely to be converted into a purchase.

Today, cognitive systems are capable of interacting in natural language, they can process a multitude of structured and unstructured data, developed with geo-localized content, and learn from each interaction. These systems will rapidly become essential in the development of strategic topics for the industry, such as “smarter destination”, on the personalization of the customer experience and its loyalty, as well as on the provision of management, analysis and Marketing, all this by using BigData. These services will be an asset to make the whole of the tourism sector more efficient by helping the actors and structures in place.


How far can artificial intelligence push the tourism industry?
Not up to replace the human. Robots are used for certain tasks, but not as a replacement for humans, although, in the long term, this could happen, but the problem of the energy that robots consume must be solved. Referring to artificial intelligence is often trying to compare with human intelligence, it’s important to notice that the aim of cognitive systems is NOT to replace human beings; Robots cannot reason or learn as a human being can do. They serve the needs and imagination of tourism professionals who, with the help of partners, take benefit from them thanks to their knowledge.


Like I’ve mentioned above that AI isn’t a new technology, we have been interested init since the 50/60 years, but if today the subject seems quite new, it is because the data is only available now. Tourism, like all industries, is digitized and gives a potentiality of data where one can apply machine learning. So AI is a revolution in progress, to the extent that it leads to new ways of thinking about the supplier’s offer.

Understanding the #Blockchain Economic Revolution

The Blockchain is a revolution that is undoubtedly leading to a complete overhaul of economic activity. It’s not a simple geek trend but still most people have absolutely no idea what the blockchain stands for. It’s essential to distinguish clearly the differences between bitcoin, crypto-currency and the breakthrough of technology underlying below the nameà the #Blockchain. You must know that there are several types of blockchains on the market, and bitcoin is another version of it which got huge success in recent years.


To be short, #Blockchain is an information storage and transmission technology that is transparent, secure and operates without a central control unit. Transactions between network users are grouped in blocks. Each block is validated by the nodes of the network called “minors”, according to the techniques that depend on the type of block. This process puts everything on trust between the market players without going through a central authority. It’s an open source system where each link in the chain offers autonomous legitimacy. The decentralized nature of the chain, coupled with its security and transparency, suggests a revolution of an unimaginable enigma. The fields of opportunity open far beyond those who have access to the monetary sector.

Understanding the Blockchain Economic Revolution


In fact, it is a revolution, as has been in human history, the advent of commerce. When individuals bought and sold their products face to face, with the handshake, the trust was established. Second, globalization has created new needs. Entities have been set up to protect sellers and buyers. Laws and legal services have developed around financial exchanges. Each market had to have intermediaries at the grass-roots level, without it being possible to assess or quantify a degree of trust between people. What changes with the blockchain is not only its decentralized aspect, but also absence of intermediates. Blockchains could replace most “trusted third parties” centralized by distributed computing systems. More than that, many observers highlight the blockchain as an alternative to any back-office systems in the banking sector. It would also help eradicate corruption in global supply chains.


The boom of the Internet offers some good indications on how the blockchain could develop. The Internet has reduced communication and distribution costs. For ex, the cost of a WhatsApp message is much cheaper than an SMS. Just as the cost of a software or an online platform is cheaper than having to sell its products through a physical store. The marginal operating costs, thanks to the Web, have been reduced to almost zero. This has caused profound changes in the telecommunication, media and software markets. The Blockchains result allowed to limit all marginal transaction costs close to 0.


Blockchains are a low-cost market disruptor for any business that acts as an intermediate in market. They allow things that have never been possible by using existing infrastructure and financial resources. We can exchange things that were not previously considered assets. It can be data, our reputation or unused power. The possibilities are as vast as they are unimaginable, but that does not mean that each type of element will be profitable for a company.


It is preferable not to dwell first on the technological aspect. It is much better to focus on the root of your customer’s problem. Successful businesses know how to identify, what is missing or a concern to their prospects, and know how to solve it. Blockchain technology is valuable in a setting where data has to be shared and edited by many unapproved parties. That is the infrastructure. The added value comes from the services that are built around it, with applications or modules.

Currently we are in the infrastructure market phase, there are still standards or platforms to democratize blockchain technology. In the near future, thanks to the crazy pace of development of this system, it will be easier for developers and entrepreneurs to use the blockchain on a daily basis. As easily as the MySQL or MongoDB databases we use today. Once the infrastructure stage is over, the evolution of blockchains will really become exciting. The infrastructure will be a huge database on which companies will be able to operate all kinds of connected objects or devices. The connected devices will collect data, blockchains will ensure, shear and process data; Artificial intelligence applications will automate activities.


Just imagine these farms where the product is grown and picked up by robots, delivered at home via drones, with a connected refrigerator that alerts us when we need something from there. An artificial intelligence system manages presets objectives to perfectly match the supply and demand. Blockchains are much more than just a bitcoin. They are the real building blocks of our future world.

Artificial Intelligence vs Human Intelligence

Whatever the encouraging results and the progress of #ArtificialIntelligence (#AI) the world can see, we are far from the development of an intelligence such as human intelligence. More and more studies show the major importance of our sensory relation to our environment.

In his book, Descartes’ Error, neuroscientist Antonio Damasio writes that “Nature appears to have built the apparatus of rationality not just on top of the apparatus of biological regulation, but also from it and with it “. In other words, the human thinks with all his body, not just with his brain.

This need of physical survival in an uncertain world can be at the root of the suppleness and power of human intelligence. But few AI researchers have really embraced the implications of these ideas.

Artificial Intelligence vs Human Intelligence

The motivation of most artificial intelligence algorithms is to conclude patterns from vast data sets – so it could require millions or even billions of individual cat’s photos to gain a high degree precision in the recognition of cats in general, for example.

But when a human confronts a new problem, most of the hardest work has already been done. In a way that we are just beginning to understand, our body and brain have already built a model of the world that we can apply almost instantly to a wide range of challenges. But for an AI algorithm, the process starts at zero each time. There is an active and important line of research, known as “inductive transfer”, based on the use of knowledge previously learned by machines to illuminate new solutions. However, as is, it is doubtful that this approach is capable of imitate something like the richness of our own body models.


If Stephen Hawking’s caveat that smart machines could put an end to humanity is relevant, technology is still far from proposing something that is more or less approaching human intelligence. And it will be impossible to achieve this goal if experts won’t think carefully about how to give the algorithms a kind of long-term relationship and embodied with their environment.


Currently #AI has beaten humans in poker, but not to forget that a computer can’t win at poker if great set of algorithms aren’t behind it. The victory of AI proves the ability of a computer program to learn from human to surpass him but doesn’t mean that human intelligence has been left behind by the artificial intelligence.


At the same time that the American Gafa (Google, Amazon, Facebook, Apple), the Chinese BATX (Baidu, Alibaba, Tencent and Xiaomi), car manufacturers and all industries and services make the bet of artificial intelligence, Would it be necessary to conclude that future sounds the death of human intelligence?


Will we be, in near future, led by robots that have become, thanks to humans, more intelligent and more effective in managing our lives? Or, conversely, is not the victory of a computer program ultimately only the success of computing and calculating power … and nothing else than that. This question will be asked repetitively and it’s up to each individual to decide the answer we want to give.


We must not forget that #ArtificialIntelligence only helps us in our daily lives, by assisting us in various activities that we are moreover willing to entrust to more efficient and technical than we do to realize them. For example, to order a lunch/dinner, to chat with an after-sales service or to drive a car is in no way the demonstration of talent, intelligence or the human mind, nor of the domination of artificial intelligence. Is it not, moreover, the characteristic of human intelligence to know how to invent machines to replace it and make it better in such activities?


The recent success of “chatbots”, these small programs able to make the machine interact with the man thanks to the artificial intelligence, testifies in the same way. Thanks to them, it is now easier to manage your gas bill or know the terms of voting at an election. And tomorrow, they will allow us to dialogue with our car, our house and all connected objects of our world …


For it is ultimately the primary meaning that must be restored to artificial intelligence: to be at the service of human. Whether it deals with the recognition of voice, images, movements or their interpretation, artificial intelligence is only a tool, designed by humans, to render a service more efficient and to free man from the constraint of Activities entrusted to him.


Emotion, the shielded hunt of the human

True, technological breakthroughs frighten us as much as they fascinate us, but we have the certainty that human intelligence will remain dominant as long as it knows the only thing a machine can never transmit: emotion. This means that artificial intelligence can only assist us with functions and tasks that do not involve emotion and feelings. The man will then take over the artificial intelligence.


It is also in this case that technology may well make us much more human by reminding us of our irrevocable comparative advantage: our ability to experience feelings, to understand others, to anticipate their expectations, to guess their Fears … No machine is capable of feeling emotions with the finesse, precision, delicacy but also the weakness and sometimes the charm of the emotional intelligence of man.


Faced with the increasing irruption of artificial intelligence into everyday life, the man-machine relationship “remains to be defined”, but leave impression that the “upheaval will be profound”.

How #DeepLearning is revolutionizing #ArtificialIntelligence

This learning technology, based on artificial neural networks, have completely turned upside down the field of artificial intelligence in less than five years. “It’s such a rapid revolution that we have gone from a somewhat obscure system to a system used by millions of people in just two years” confirms Yann Lecun, one of deep learning and artificial intelligence’s creator.

All major tech companies, such as Google, IBM, Microsoft, Facebook, Amazon, Adobe, Yandex and even Baidu, are using. This system of learning and classification, based on digital “artificial neural networks”, is used concurrently by Siri, Cortana and Google Now to understand the voice, to be able to learn to recognize faces.


What is “Deep Learning”?


In concrete terms, deep learning is a learning process of applying deep neural network technologies enabling a program to solve problems, for example, to recognize the content of an image or to understand spoken language – complex challenges on which the artificial intelligence community has profoundly worked on.


To understand deep learning, we must return to supervised learning, a common technique in AI, allowing the machines to learn. Basically, for a program to learn to recognize a car, for example, it is “fed” with tens of thousands of car images, labeled etc. A “training”, which may require hours or even days of work. Once trained, the program can recognize cars on new images. In addition to its implementation in the field of voice recognition with Siri, Cortana and Google Now, deep learning is primarily used to recognize the content of images. Google Maps uses it to decrypt text present in landscapes, such as street numbers. Facebook uses it to detect images that violate its terms of use, and to recognize and tag users in published photos – a feature not available in Europe. Researchers use it to classify galaxies.


Deep learning also uses supervised learning, but the internal architecture of the machine is different: it is a “network of neurons”, a virtual machine composed of thousands of units (Neurons) that perform simple small calculations. The particularity is that the results of the first layer of neurons will serve as input to the calculation of others. This functioning by “layers” is what makes this type of learning “profound”.


One of the deepest and most spectacular achievements of deep learning took place in 2012, when Google Brain, the deep learning project of the American firm, was able to “discover” the cat concept by itself. This time, learning was not supervised: in fact, the machine analyzed, for three days, ten million screen shots from YouTube, chosen randomly and, above all, unlabeled. And at the end of this training, the program had learned to detect heads of cats and human bodies – frequent forms in the analyzed images. “What is remarkable is that the system has discovered the concept of cat itself. Nobody ever told him it was a cat. This marked a turning point in machine learning, “said Andrew Ng, founder of the Google Brain project, in the Forbes magazine columns.


Why are we talking so much today?


The basic ideas of deep learning go back to the late 80s, with the birth of the first networks of neurons. Yet this method only comes to know its hour of glory since past few years. Why? For if the theory were already in place, the practice appeared only very recently. The power of today’s computers, combined with the mass of data now accessible, has multiplied the effectiveness of deep learning.


“By taking software that had written in the 1980s and running them on a modern computer, results are more interesting” says Andrew Ng. Forbes.


This field of technology is so advanced that experts now are capable of building more complex neural networks, and the development of unsupervised learning which gives a new dimension to deep learning. Experts confirms that the more they increase the number of layers, the more the networks of neurons learn complicated and abstract things that correspond more to the way of a human reasoning. For Yann Ollivier, deep learning will, in a timeframe of 5 to 10 years, become widespread in all decision-making electronics, as in cars or aircraft. He also thinks of the aid to diagnosis in medicine will be more powerful via some special networks of neurons. The robots will also soon, according to him, endowed with this artificial intelligence. “A robot could learn to do housework on its own, and that would be much better than robot vacuums, which are not so extraordinaire for him!


At Facebook, Yann LeCun wants to use deep learning “more systematically for the representation of information”, in short, to develop an AI capable of understanding the content of texts, photos and videos published by the surfers. He also dreams of being able to create a personal digital assistant with whom it would be possible to dialogue by voice.


The future of deep learning seems very bright, but Yann LeCun remains suspicious: “We are in a very enthusiastic phase, it is very exciting. But there are also many nonsense told, there are exaggerations. We hear that we will create intelligent machines in five years, that Terminator will eliminate the human race in ten years … There are also great hopes that some put in these methods, which may not be concretized”.


In recent months, several personalities, including Microsoft founder Bill Gates, British astrophysicist Stephen Hawking and Tesla CEO Elon Musk, expressed their concerns about the progress of artificial intelligence, potentially harmful. Yann LeCun is pragmatic, and recalls that the field of AI has often suffered from disproportionate expectations of it. He hopes that, this time, discipline will not be the victim of this “inflation of promises”.



#MachineLearning: How #PredictiveAnalytics reinvents Customer’s Satisfaction

Billions and trillions of data is collected on customer behavior from the huge platform called internet. To these are added valuable information gathered by every organization from different sectors. In this mine of information, Machine learning, pursues an ultimate goal: to better understand customers in order to offer them the best experience possible by offering them the product or service most likely to their need. It’s analytical power and the advance in artificial intelligence allows companies to take advantage of the wealth of data they collect.

At this point we all know that #bigdata is worth nothing, nada, without proper decryption. This is where machine learning or “automatic learning” comes into action. With its power of analysis, this field of artificial intelligence extracts the valuable information from the mass data. In other words: it enables to turn the lead into gold by simplifying the life of the customer and improving its satisfaction thanks to the precise analysis of its way of purchase.


Artificial Intelligence: algorithms and insights

Since its first use in the general public in the late 1990s, the machine learning have never stopped to make talk about it. Its recent victory was in March 2016 via AlphaGo, the software of Google, against the legendary Lee Sedol. We’ve witnessed AlphaGo’s most notable examples of deep learning, which was, the ability of a machine to independently analyze sums of data with an extremely high level of performance.

If such technological power remains exceptional, all of us daily experience the small machine learning without knowing it. How? Well, just surf on Amazon, LinkedIn, Spotify or even Netflix to see these platforms automatically offer suggestions according to their precise taste. These associations of ideas remain pertinent on subjects as fine as the interest for a film, a song, a service or a cross purchase. It is a much less superficial intelligence than it seems but with concrete results.


From big data to automatic learning

Well-resourced with quality data, the algorithm analyze deeply in the vast meadows of digital world. They cross distant data from each other to reveal information never brought to light. These algorithms bring us the astonishing results which a human mind would have swept away. For example, in a customer journey, deep learning allows to discover that the intention of purchase can be correlated with an action at precise moment of purchasing action. With automatic learning, one can therefore target with precision every important thing that human understanding can escape.


Machine learning: better tracking of customer routes

According to Salesforce’s state-of-the-art survey published in 2016, customer engagement is a top priority for organizations. Customer satisfaction is the main reason for success, even surpassing revenue growth and the acquisition of new customers. In this context, Machine learning is thus a major ally.

From an operational point of view, most of the machine learning applications used today are subject to a pre-learning phase. A large amount of data is thus processed, during algorithm design, to better guide the search and automate more easily the answers that will be offered to online surfers. It comes to deal with a combination between human intelligence and artificial intelligence. The goal still to be reached, for each organization, is a user experience that is as simple and fluid as possible. The machine learning has already made it possible to take a major step forward thanks to the ultra-segmentation of the profiles for a refined follow-up of the customer routes.


Sharing Data: the essence of war

In order to function at full capacity, machine learning must benefit from first-class information. How it’s possible? By adapting an omnivorous diet. Depending on the project, companies use the information they collect through cookies, geolocation, social networks, loyalty programs (which typically collect data on age, location, purchase history …).

Contrary to popular belief, consumers are rather inclined to share their data, but not at any price. This is evidenced by the Columbia Business School’s “What is the future of data sharing” study conducted by the Columbia Business School Center for Global Brand Leadership in 2015 with 8,000 Internet users in the United Kingdom, the United States, Canada, France and India. “Consumers are much more knowledgeable about the issue of data sharing than we originally suspected. According to our study, one of the determining factors in the decision to share them is trust in the brand, “says Matthew Quint, director of the Center for Global Brand Leadership. Researchers at Columbia Business School have come to the conclusion that more than 75% of Internet users more readily share their data with a brand they trust.


Customer data: Give and Take

Beyond trust, the sharing of information is based on a give-and-take approach. According to the same Columbia Business School study, 80% of consumers agree to share confidential information in exchange for a reward. It must be a “valuable offer, but this value must be clearly defined and easy to understand to hope for the best possible return on investment,” says Matthew Quint. Young consumers would be more favorable than their elders to concede their personal information. What promises beautiful days to machine learning.


All the above points ends on the same conclusion that organizations can get a better understanding and add a new layer of intelligence on their customers behavior by using predictive analysis.

Big Data: 2017 Major Trends

big data trends 2017

Over the past year, we’ve seen more and more organizations store, process and exploit their data. By 2017, systems that support a large amount of structured and unstructured data will continue to grow. The devices should enable data managers to ensure the governance and security of Big Data while giving end-users the possibility to self-analyze these data.

Here below the hot predictions for 2017.


The year of the Data Analyst – According to forecasts, the Data Analyst role is expected to grow by 20% this year. Job offers for this occupation have never been more numerous before. Similarly, the number of people qualified for these jobs is also higher than ever. In addition, more and more universities and other training organizations offer specialized courses and deliver diplomas and certifications.


Big Data becomes transparent and fast – It is obviously possible to implement machine learning and perform sentiment analysis on Hadoop, but what will be the performance of interactive SQL? After all SQL is one of powerful approach to access, analyze, and manipulate data in Hadoop. In 2017, the possibilities to accelerate Hadoop will multiply. This change has already begun, as evidenced by the adoption of high performance databases such as Exasol or MemSQL, storage technology such as Kudu, or other products enabling faster query execution.


The Big Data is no longer confined to Hadoop – In recent years, we have seen several technologies developing with the arrival of Big Data to cover the need to do analysis on Hadoop. But for companies with complex and heterogeneous environments, the answers to their questions are distributed across multiple sources ranging from simple file to data warehouses in the cloud, structured data stored in Hadoop or other systems. In 2017, customers will ask to analyze all their data. Platforms for data analytics will develop, while those specifically designed for Hadoop will not be deployable for all use cases and will be soon forgotten.


An asset for companies: The exploitation of data lakes – A data lake is similar to a huge tank, it means one needs to build a cluster to fill up the tank with data in order to use it for different purpose such as predictive analysis, machine learning, cyber security, etc. Until now only the filling of the lake mattered for organizations but in 2017 companies will be finding ways to use data gathered in their reservoirs to be more productive.


Internet of Objects + Cloud = the ideal application of Big Data – The magic of the Internet of Objects relies on Big Data cloud services. The expansion of these cloud services will allow to collect all the data from sensors but also to feed the analyzes and the algorithms that will exploit them. The highly secure IOT’s cloud services will also help manufacturers create new products that can safely act on the gathered data without human intervention.


The concentration of IoT, Cloud and Big Data generates new opportunities for self-service analysis – It seems that by 2017 all objects will be equipped with sensors that will send information back to the “mother server”. Data gathered from IoT is often heterogeneous and stored in multiple relational or non-relational systems, from Hadoop cluster to NoSQL databases. While innovations in storage and integrated services have accelerated the process of capturing information, accessing and understanding the data itself remains the final challenge. We’ll see a huge demand for analytical tools that connect natively and combine large varieties of data sources hosted in the cloud.


Data Variety is more important than Velocity or Volume – For Gartner Big Data is made of 3 V: Large Volume, Large Velocity, Large Variety of Data. Although these three Vs evolve, the Variety is the main driver of investment in Big Data. In 2017, analysis platforms will be evaluated based on their ability to provide a direct connection to the most valuable data from the data lake.


Spark and Machine Learning makes Big Data undeniable – In a survey for Data Architect, IT managers and analysts, almost 70% of respondents favored Apache Spark compared to MapReduce, which is batch-oriented and does not lend itself to interactive applications or real time processing. These large processing capabilities on Big Data environments have evolved these platforms to intensive computational uses for Machine Learning, AI, and graph algorithms. Self-service software vendor’s capabilities will be judged on the way they will enable the data accessible to users, since opening the ML to the largest number will lead to the creation of more models and applications that will generate petabytes of data.


Self-service data preparation is becoming increasingly widespread as the end user begins to work in a Big Data framework – The rise of self-service analytical platforms has improved the accessibility of Hadoop to business users. But they still want to reduce the time and complexity of data preparation for analysis. Agile self-service data preparation tools not only enable Hadoop data to be prepared at source, but also make it accessible for faster and easier exploration. Companies specialized in data preparation tool for Big Data end-user, such as, Alteryx, Trifacta and Paxata are innovating and consistently reducing entry barriers for those who have not yet adopted Hadoop and will continue to gain ground in 2017.


Data management policies in hybrid cloud’s favor – Knowing where the data come from (not just which sensor or system, but from which country) will enable governments to implement more easily national data management policies. Multinationals using the cloud will face divergent interests. Increasingly, international companies will deploy hybrid clouds with servers located in regional datacenters as the local component of a wider cloud service to meet both cost reduction objectives and regulatory constraints.


New safety classification systems ensures a balance between protection and ease of access- Consumers are increasingly sensitive to the way data is collected, shared, stored – and sometimes stolen. An evolution that will push to more regulatory protection of personal information. Organizations will increasingly use classification systems that organize documents and data in different groups, each with predefined rules for access, drafting and masking. The constant threat posed by increasingly offensive hackers will encourage companies to increase security but also to monitor access and use of data.


With Big Data, artificial intelligence finds a new field of application – 2017 will be the year in which Artificial Intelligence (AI) technologies such as automatic learning, natural language recognition and property graphs will be used routinely to process data. If they were already accessible for Big Data via API libraries, we will gradually see the multiplication of these technologies in the IT tools that support applications, real-time analyzes and the scientific exploitation of data.


Big Data and big privacy – The Big Data will have to face immense challenges in the private sphere, in particular with the new regulations introduced by the European Union. Companies will be required to strengthen their confidentiality control procedures. Gartner predicts for 2018 that 50% of violations of a company’s ethical rules will be data-related.



Top 10 Big Data Trends 2017 – Tableau

Big Data Industry Predictions for 2017 – Inside Bigdata

From #BigData to #IoT, The Key Technologies of 2020

“Innovation, by definition, is unpredictable”. A Gartner study predict the impact of new technologies in the professional world. There are 47 main technologies that’ll help companies to industrialize the process of innovation. Here below we’ve resumed the most important and trendy ones.


Big Data: 


The collection of massive data has become a major issue, especially in an era where governments are increasingly on the lookout for personal information. As we’ve all seen in the strategy of Facebook addition of smileys to the simple “like”, the personal data represents an economic value and holding such data may well be a powerful output for the government. Experts have predicted that in 2020 there’ll be 10 400 billion gigabytes of data that’ll be shared every month on the web. This is why the analysis of massive data is a key technology for companies in competition and these business analysis can be helpful for their business strategy to improve management / client relationship.


The sensors: 


The global sensor market is estimated at 154.4 billion dollars by 2020. This figure is explained because of its multiple use, either for the water management, energy management, the analysis of chemical and microbiological pollutants, inventory control in industries or activities tracers in the health field.

A new market is developing around the sensors: biosensors. The biosensor is an analytical tool consisting of an organic compound that allows the connection between biological material and the transducer, which transform the biochemical signal into quantifiable physical signal. It serves in particular to health, environment, safety and food field. It is estimated that sensor market will reach 2.78 billion dollars in 2020.


Autonomous Robot: 

Autonomous Robot

Robotics is considered one of the 9 industrial solutions. From a kitchen blander to a human size robot, robotics certainly has a bright future in the new technologies market. As predicted by many experts, robots will soon replace humans to do repetitive or dangerous tasks. For example, when it will be to visit the disaster site or rescue missions, we will send robots instead. However, there’s still a lot to work on robotics before they can perform to any unexpected situations. This is why investments are growing to make it completely autonomous.

On environmental issues, various robots are set up as Diya One of Partnering Robotics for purifying indoor air or Xamen who unveiled a surveillance drone in 2015 to inspect industrial facilities. Robots like Nao will attend everyday life of the older persons. And other will help in the driving area as Valeo, which takes care of parking.

One of the main issues will be the “Sense and Avoid” or “See and Avoid” so that robots can evolve while adhering to the laws of robotics, on which the European Parliament is currently working. The park of “service robots” will reach $ 20 billion and is estimated at 18 million units by 2020. These include agricultural and logistics robots that will get a significant share in the market.


Artificial Intelligence: 


Biggest dream of AI is to copy and perform exactly like human intelligence, may be in a much better way. A dream slowly becoming reality … or not. Meanwhile, the current IA operates on 3 stages: perception of the environment, a decision that involves the reasoning and learning and environment-oriented actions.

More and more AI is used within the services but it’s also as key elements in decision making support in medical diagnostics (IBM Watson) or in the financial markets sector (algorithms of high frequency trading (THF)) . Thus, 40% of transactions on the stock markets are generated without human intervention. Some AI are also capable of providing decision support such as VITAL algorithm, the Board of Directors of Deep Knowledge Ventures, which participates in investment decisions by analyzing balance sheets of potentially interesting companies. A BBC Research has estimated the global market for intelligent machines (expert systems, autonomous robots, digital support systems) to $ 15.3 billion in 2019.


The 5G Infrastructure: 


By 2020, the infrastructure of the 5th generation will replace 4G. Faced with the development of the IoT, M2M and environmentally friendly services, it is essential to go beyond 4G. 5G will ensure the continuity and quality of the user experience anywhere anytime. The main quality of 5G compared to 4G will be speed. “As the IoT revolution gets underway, 5G networks will be able to handle the hundreds of millions of devices and sensors that will join the network” says Roger Entner, expert on 5G wireless networks. With this new infrastructure, it will be possible to quickly respond to the challenge of energy efficiency and ensure connectivity with massive data objects Internet

Currently, the European Commission has set up the 5G Infrastructure Public Private Partnership consortium which aims to support the development of 5G standards and strengthen European industry to successfully transition to 5G. The EU is associated with many countries drivers of mobile, broadband including Japan and South Korea. The latter announced that it would invest $ 1.5 billion to deploy the 5G services. A partnership is also planned with China: China’s Huawei has announced an investment of $ 600 million in the development of 5G. What more we can ask for?


Internet of Things: 


According to the Institute of Audiovisual and Telecommunications in Europe (IDATE), in 2020 the Internet of Things will be made of 85% of connected objects, 11% of communicating terminals and 4% will be dedicated to M2M (Machine-to -Machine). The global market for the Internet Of Things will reach 1.525 trillion euros by 2020.


All these technologies are game changer and will make our future even brighter. Most of us aren’t well-equipped emotionally and culturally to have this much technology entering into our lives but we’ve to embrace them now before it gets too late!!!

Artificial Intelligence Techniques to detect Cyber Crimes

Artificial Intelligence Techniques to detect Cyber Crimes

When we talk about artificial intelligence, many imagine a world of science fiction where robots dominate. In reality, artificial intelligence is already improving current technologies such as online shopping, surveillance systems and many others.


In the area of ​​cyber security, artificial intelligence is being used via machine learning techniques. Indeed, the machine learning algorithms allow computers to learn and make predictions based on available known data. This technique is especially effective for daily process of millions of malware. According to AV-Test statistics, security analysts must examine more than 400,000 new malicious programs every day.


Security experts affirms that the traditional detection methods (the signature-based systems) are no longer really proactive in most cases. The task is even more difficult as, in a world dominated by copy-paste exploit cloning, security vendors must also manage third-party services, and focus on detecting the obfuscated exploit variant, to be able to provide protection to their customers. Attackers are numerous, but the automatic learning balance the chances of struggle.


Applying Artificial Intelligence to cyber Security: More and more technology companies and security vendors are beginning to look for ways to integrate artificial intelligence to their cyber security arsenal. Many clustering and classification algorithms can be used to quickly and correctly answer the crucial question: “This file is it healthy or malicious?” For example, if a million files must be analyzed, the samples can be divided into small groups (called clusters) in which each file is similar to the others. The security analyst only has to analyze later, a file in each group and apply the results to others.

More importantly, machine learning gets a high detection rate for new malicious software in circulation as the famous ransomware malware and zero-day, and against whom, a security solution must be as efficient as possible. In order to be practical, each machine learning classifiers used for malware detection must be set to obtain a very small amount, preferably zero, of false positives. It is also a way to form with very large databases (using the graphics processor or parallelism).

The fundamental principle of machine learning is to recognize the trends of past experiences, and make predictions based on them. This means that security solutions can react more effectively and more quickly to new invisible cyber threats compared to traditional techniques and automated cyber-attack detection systems that were used before. Artificial Intelligence is also suitable to fight against sophisticated attacks such as APT (Advanced Persistent Threats), where attackers take special care to remain undetected for indefinite periods of time.


Man against the machine:  breaking the boundaries between man and machine, artificial intelligence is a very important cyber weapon, but cannot alone take on any fight against cyber threats. As I’ve mentioned in previous paragraphs, the machine learning systems can get false positives, the decision of a human is needed to sort algorithms with appropriate data.

Les algorithmes d’apprentissage automatique sont, dans l’ensemble, plus précis dans l’évaluation des menaces potentielles de malwares au sein de grandes quantités de données de renseignement, que leurs homologues humains. Ils savent aussi repérer plus rapidement les intrusions.

The machine learning algorithms are, overall, more accurate in assessing potential malware threats in large quantities of intelligence data, than humans. They also know how to quickly detect breach. The current hybrid approach that is generally used today is to oversee automatic learning by human analysts. This allowed better results so far.


Regarding the future of AI, it is almost impossible to predict the future. Who knows that may be next year, machine learning will most likely focus on the creation of specific profiles for each user. Where an action or a user’s behavior does not correspond to the predefined templates, the user will be informed. For example, a peak of downloads in a short time will be marked as suspect, and analyzed closely by a human expert.

Google reveals five security issues concerning Artificial intelligence

In a recent article published by Google, they’ve reveled five major security problems related to Artificial Intelligence. From now on, companies will have to fallow a guide on their future Al system in order to control robots before they can actually interact with humans.

security issues concerning Artificial intelligence

The artificial intelligence is designed to mimic the human brain, or at least its logic when it comes to making decisions. Before worrying about whether an artificial intelligence (AI) could become so powerful that can dominate humans, it would be better to make sure that robots (also called our future colleagues and household companions) are trustworthy. That’s what Google have tried to explain us. Google’s artificial intelligence specialists have worked with researchers from the Universities of Stanford and Berkeley (California, USA) and with the Association OpenAI on concrete security issues that we must work to resolve.


In white paper titled “Concrete Problems in AI Safety” this team describes five “practical problems” of accidents in artificial intelligence based machine could cause if they aren’t designed properly. Al specialists define accidents as “unexpected and harmful behavior that may emerge from poor design of real world machine learning systems”. In short, these are not potential errors of robots we should be feared but those of their designers.


To concretely illustrate their point of view, the authors of this study voluntarily took a random example of a “cleaning robot”. However, it’s quite clear that the issues apply to all forms of AI controlling a robot.



Pour prévenir ce cas de figure, la solution pourrait consister à créer des « contraintes de bon sens » sous la forme de pénalités infligées à l’IA lorsqu’elle cause une perturbation majeure à l’environnement dans lequel le robot évolue.

  • A robot may disrupt the environment :

The first two risks identified by the researchers from Google and their acolytes are related to a poor coordination and allocation of the main objective. There is first what they call “Avoiding Negative Side Effects”. Specifically, how to avoid environment related problems caused by a robot while it’s accomplishing its mission. For example, the cleaner could well topple or crush what is on his way because he calculated the fastest route to complete its task. To prevent this scenario, the solution may be to create “constraints of common sense” in the form of penalties imposed on the IA when he causes a major disruption to the environment in which the robot moves.

  • The machine can cheat :  

Second risk of Al based machines is to avoiding reward hacking. For IA, the reward is the success of the goal. Avoid the quest reward from turning into a game and the machine trying to get by all means, even skip steps or cheat. In the case of cleaning robot, it would for example to hide the dirt under the rug in order to say “that’s it, I’m done.”
A difficult problem to solve as an Al can be interpreted in many different ways a task and the environment it meets. One of the ideas in the article is to truncate the information so that the program does not have a perfect knowledge of how to get a reward and thus does not seek to go shorter or easier.

  • How to setup the robot go to the basics?

The third risk is called scalable oversight. More the goal is complex, AI will have to validate his progress with his human referent, which would quickly become tiresome and unproductive. How to proceed so the robot can accomplish itself certain stages of its mission to be effective while knowing seek approval in situations that he will know how to interpret? Example: tidy and clean the kitchen, but ask what to do in the saucepan on the stove. It would simplify to the maximum step of the cooking task so that the robot goes to the point without coming to disturb you during your nap every time.

  • How much independence can you give to an AI?

The next identified problem is the safe exploration of Al. How much independence can you give an AI? The whole point of artificial intelligence is that it can make progress by experimenting different approaches to evaluate the results and decide to keep the most relevant scenarios to achieve its objective. Thus, Google says, if our brave robot would be well advised to learn to perfect its handling of the sponge, we wouldn’t want it to clean an electrical outlet! The suggested solution is to train these Al with simulated environment in which their empirical experiments will not create any risk of accident.

  • Does AI will adapt the change?

Fifth and final problem: robustness to distributional shift or how to adapt to change. “How to be ensured that AI recognizes and behaves properly when it is in a very different environment from the one in which it was being driven? It is clear that we wouldn’t want the robot who was trained to wash the floor of a factory with detergent products does not apply the same technique if asked to clean home.

The article ends by saying that these problems are relatively easy to overcome with the technical means currently available but it’s better to be prudent and develop security policies that can remain effective as the autonomous systems will gain in power. Google is also working on an “emergency stop” button for all menacing AI, if eventually one or several of these risks were not fully mastered,