Human Machine Partnership – Is 2018 the year of #MachineLearning?

Human Machine Partnerships2018 is all about the further rapprochement of man and machine. Dell Technologies predicts the key IT trends for 2018. Driven by technologies such as Artificial Intelligence, Virtual and Augmented Reality and the Internet of Things, the deepening of cooperation between man and machine will drive positively the digitization of companies. The following trends will and are shaping 2018:


Companies let AI to do data-driven thinking


In the next few years, companies will increasingly use the opportunity to let artificial intelligence (AI) think for themselves. In the AI systems, they set the parameters for classifying desired business outcomes, define the rules for their business activities, and set the framework for what constitutes an appropriate reward for their actions. Once these sets of rules are in place, the AI systems powered by data can show new business opportunities in near real time.


The “IQ” of objects is increasing exorbitantly


Computing and networking items over the Internet of Things are becoming increasingly cost effective. The embedding of intelligence into objects will therefore make gigantic progress in 2018. Networked device data, combined with the high levels of computing power and artificial intelligence, will enable organizations to orchestrate physical and human resources automatically. Employees are becoming “conductors” of their digital environments and smart objects act as their extension.


IQ of Things


AR headsets ultimate comeback in 2018


Its economic benefits have already been proven by augmented reality (AR). Many teams of designers, engineers or architects are already using AR headsets. Whether to visualize new buildings, to coordinate their activities on the basis of a uniform view of their developments or to instruct new employees “on the job” even if the responsible instructor cannot be physically present at the moment. In the future, AR will be the standard way to maximize employee efficiency and leverage the “swarm intelligence” of the workforce.


AR headsets


Strong bond of customer relationship


Next year, companies will be able to better understand their customers through predictive analytics, machine learning (ML), and artificial intelligence (AI) and use these technologies to improve their customer first strategies. Customer service will perfectly maintain the connection between man and machine. It will not be first-generation chatbots and pre-made messages that address customer concerns in the service, but teams of people and intelligent virtual agents.


Deeper Relationship with Customers


The “Bias Check” will be the new spell checker


Over the next decade, technologies such as AI and Virtual Reality (VR) will enable those responsible to evaluate information without prejudgment and make decisions in an entirely balanced way. In the short term, AI will be used in application and promotion procedures to bring out conscious or unconscious prejudices. VR is increasingly being used as an interviewing tool to cover the identity of applicants with the help of avatars. “Bias checks” – “prejudice checks” – could become the standard procedure in decision-making processes in the future, just as spell-checking is today when it comes to writing texts.


Bias check


The mega-cloud is coming up

In 2018, an overwhelming majority of companies will adopt a multi-cloud approach and combine the different cloud models. To overcome the associated cloud silos, the next step will be the mega-cloud. It will interweave the different public and private clouds of companies in such a way that they behave as a single holistic system. With the help of AI and ML, this IT environment will be fully automated and consistently evaluated.




IT security is becoming more important than ever


In today’s increasingly connected world, IT security companies need more than ever to rely on third parties. They are no longer individual instances, but parts of a bigger whole. Even the smallest errors in any of the connected subsystems can potentiate to fatal failures in the entire ecosystem. In particular, for multinational corporations, it’s a must in 2018 to prioritize the implementation of security technologies. This development is further fueled by new regulations, such as the GDPR regulation of the EU.



E-sports gaming industry ready for mainstream


Not least driven by virtual reality, the phenomenon of e-sports for companies in the media and entertainment industry 2018 finally become a fixture. Millions of other players and viewers are jumping on the bandwagon and making continuity e-sports mainstream for 2018. This phenomenon is representative of a bigger trend: even original physical activities such as sports are digitized. In the future, every business will be a technological business, and people’s free time will be shaped by networked experiences.


“People have been living and working with machines for centuries,” says Dinko Eror, Senior Vice President and Managing Director, Dell EMC Germany. “In 2018, however, this relationship is reaching a whole new level: man and machine will be more intertwined than ever, and that will change everything – from the way we do business to the design of leisure and entertainment.”

Artificial Intelligence and the Corporate World Transformation

Worldwide Analytics Cognitive AI  and Big Data Predictions

Worldwide, companies collect and own huge amounts of data in the form of documents. Due to a lack of digitization, these can often not be served for business processes – or only with a huge manual effort behind. These documents usually contain important and business-critical information, so the loss or even the time delay in gathering information can have a major impact on the success of a business.


However, with the rapid advances in automated text capture cognitive technology, organizations are now able to easily digitize, classify, and automatically read their unstructured business documents for transfer to relevant business processes. With such fully automated solutions, companies can not only save time and money, but also greatly improve the data quality in their systems and massively accelerate response times and important decisions.


Especially computer vision has evolved enormously in recent years. The ability to quickly recognize and process text on each device has greatly improved since the time when documents had to be scanned and analysed with OCR technology. This rapid development is also reflected in the numbers in the industry: IDC predicts that the world market for content analytics, discovery and cognitive systems software will reach $ 9.2 billion by 2019 – more than twice as much as in 2014. To make the most of these market changes, IT solution providers need to better serve the rapidly growing needs of machine learning and artificial intelligence (AI). Only then can they meet the customer requirements of tomorrow and remain relevant.


Employees in the center of each business


There is a groundless fear that artificial intelligence automation solutions could replace skilled employees in companies. Despite or because of solutions based on artificial intelligence, well-trained employees are needed who understand the core values of the company as well as the technological processes. People have qualities that AI solutions depend on, such as empathy, creativity, judgment, and critical thinking. That’s why qualified employees are essential for the success of a company in the future as well.


Companies as drivers of digital transformation


Businesses first and foremost require systems that support and relieve their professionals of their day-to-day routine work, enabling them to work more productively and creatively. Above all else, modern systems must be capable – on the basis of past experience – of learning behaviour independently and of making suggestions for the future course of action. To do this, companies need professionals who are able to lead these systems to enable automated workflows in the first place.


Robotic Process Automation (RPA) and machine learning drive the automation of routine repetitive tasks. RPA software is a powerful solution for more efficient manual, time-consuming and rule-based office activities. They reduce throughput times and at a lower cost than other automation solutions. In addition, artificial intelligence will make more types of these tasks automatable. The combination of RPA and machine learning will undoubtedly create a large market segment with high demand; namely for the identification of processes and their intelligent implementation.


The next five years


It is expected that once companies have automated various tasks through the use of artificial intelligence, they will increasingly want to monitor and understand the impact of these processes on their organization. As a result, they will undergo a fundamental change over the next three to five years. This is mainly due to the convergence of RPA and AI in the following areas:

The use and advancement of RPA will entail a wave of machine learning advancements, such as: for task automation or document processing. Even processes that affect basic decision-making benefit greatly from RPA. Use cases traditionally associated with capturing data from documents, on the other hand, will converge with ever new document-based RPA use cases. AI technology is now being used more widely and offers advantages for the identification and automation of processes as well as their analysis.


AI will also lead to the automation of basic tasks performed today by qualified staff. It will have a major impact on the composition and size of the workforce of companies, especially in the fintech, health, transport and logistics sectors. Above all, companies from all industries benefit from optimized processes for customer relations. However, authorities can also offer citizens quicker reaction times and improved service through intelligent automation.

And finally, robotics is much more than just R2-D2 or C-3PO. Software robotics will think much faster than most people, penetrate the work environment in companies – in data and document capture, RPA, analytics and for monitoring and reporting – intelligent and situational.


Ready for change


Businesses need to prepare for the age of AI today to stay successful. This requires a significant shift in the required skills in the company. Above all, it is up to the employees to be open to the new technologies and to see them as an opportunity to gain competitive advantages.

In general, intelligent systems will do more work in the future. For example, in the case of lending, the role of the person in charge will continue to decline because the system will be able to independently make intelligent decisions based on the borrower’s previous financing behaviour. Ultimately, the clerk only has to worry about rule-based exceptions. This will relieve the loan officers of many routine tasks, allowing them to spend more time on customer care. Overall, this significantly increases bank productivity.

A further shift in competence results from the fact that the process requires less human control and expertise. As software becomes increasingly knowledgeable, it becomes less dependent on employees. This means that their duties are smaller, but at the same time more responsible.

2017 Digital Evolution Report – CyberCrime, Digitization, Blockchain and Artificial Intelligence

Cyber-crime, Smart-Cities, Digitization, Blockchain and Artificial Intelligence are those words which really got the hype on the platform of IT in 2017. Cybercriminals have smacked many companies many times. Digitization is progressing despite lame internet connections. Blockchain became Gold Chain and Artificial Intelligence is experiencing an incredible revival.

Key Technologies 2017

Ransomware: The ransom and the cyber blackmailer


Ransomware remains a leader in digital security threats. According to ITRC Data Breach report, in 2015 more than 177,866,236 personal records exposed via 780 data security breaches, and the previous mentioned number lift up to 30% in 2016 with security breaches arising on multiple fronts, companies, healthcare systems, governmental and educational entities, and individuals started to realize how real the threat of cybersecurity attacks was. 2017 so far, was a very highlighted year for cyber-crimes. 519 Cyber-attacks were placed from Jan 2017 until September 2017 affecting financial sectors, health-care sectors, gaming companies, containing information about credit cards, health data of billions of people around the world. With all these attacks phishing, spying on webcams or networked household appliances (IoT) remain risky.


Very popular in this year’s cyber attack list are the #wannacry and Equifax data breach attacks. These attacks unbaled 300000 computer systems for 4 days and affected financial data on more than 800 million customers and 88 million businesses worldwide and more than 45% of all detected ransomware.

Cyber policies are currently very much in vogue, but in which cases of damage do these insurances actually comes in? ABA, American Bankers Association, explains how companies should best go about finding a suitable policy and what makes good cyber insurance.


The General Data Protection Regulation (GDPR): What needs to be changed?


Companies only have a few months left to prepare for the new European #DataProtection Regulation. On 25 May 2018, all companies managing personal data of citizens of the European Union will be required to comply with the new regulations and requirements of the General Data Protection Regulation (GDPR).

This regulation will impose significant new obligations on companies that manage personal data, as well as severe penalties for those who’ll violate these rules, including fines of up to 4% of global turnover or € 20 million highest amount being withheld. But what is to change concretely? Here is a “Guide to compliance with the EU GDPR” and a framework to become step by step GDPR-fit.


Digital Transformation: Slow Internet connections as a brake pad


Digitization is progressing, but most users still complain about slow Internet connections. Despite the 7th place in the worldwide internet ranking, Belgium is still far behind the world’s fastest internet country. Notwithstanding all the shortcomings of the national IT infrastructure, companies are dealing with the technical and organizational challenges that result from the digital IT transformation.


The crazy rise of Bitcoin


In the period of a year the value of bitcoin has been multiplied by ten. A bitcoin was worth “only” 1000 dollars on January 1, 2017 … and 8000 dollars ten days ago. In April 2017 Japan officially recognised bitcoin and virtual currencies as legal methods of payment. You should know that Bitcoin represents less than 50% of the money supply of all cryptocurrencies in circulation. this is partly explained by the network situation and the rise of the Ethereum currency. Even if bitcoin is a legal in the vast majority of countries around the world, only a few governments have recognized the legal status of bitcoin in a particular regulatory manner.


IoT Projects: The 5 Biggest Mistakes and the Five Steps to Success


Closely linked to Digital Change is Internet of Things (IoT) and Industry 4.0 projects. Pioneers already pointed out the four biggest mistakes in IoT projects. If a company wants to exploit the potential of the IOT, it means a lot of work and often frustration – the technical, commercial and cultural challenges are manifold. Until an IoT solution is successfully established on the market, many decisions have to be carefully considered.

But how does an IoT project succeed? Four steps are needed to make an IoT project a success.


Blockchain: The new gold chain

The blockchain is a much-debated technology with disruptive potential and three key characteristics: decentralization, immutability, and transparency. It could help to automate business processes, increase the security of transactions and replace intermediaries such as notaries or banks. Blockchain turns out to be the silent revolution that will change our lives. On top of that, it can turn into a gold chain for early adopters.


Cloud: Companies use public cloud despite security concerns

For years, companies have avoided the public cloud, as it is difficult to get a grip on in terms of security. However, this year, companies in the EMEA region increased their investment in the public cloud despite ongoing security concerns and lack of understanding of who is responsible for data security. However, caution is still needed to provide attacks such as wannacry.


Artificial intelligence

In 2016, Gartner put artificial intelligence and advanced machine learning in first place in its forecast for 2017, stating that this trend was really pronounced during 2017. Briefly 80 % of companies have already invest in Artificial Intelligence (AI). Nevertheless, one out of every 3 deciders believes that their organization needs to spend more on AI technology over the upcoming years if they want to keep pace with their competitors. Artificial intelligence penetrates into all areas of life. But how does it work?

One example is the automated and personalized customer approach to AI. With personalized campaigns and individual customer approach, the marketing of the future wants to win the battle for the buyer. As a rule, the necessary data are already available in companies, but the resources and software tools for their profitable use are not.
In 2018 Businesses will have an availability of AI-supported applications and should therefore focus on the commercial results achieved through these applications that exploit narrow AI technologies and leave the AI in the general sense to researchers and writers of science fiction;


The future of the human worker

AI systems can be used without a doubt. The world is becoming increasingly complex, which requires a thoughtful and wise use of our human resources. This can support high-quality computer systems. This also applies to applications that require intelligence. The flip side of AI is that many people are scared about the possibility of smart machines, arguing that intelligence is something unique, which is what characterizes Homo Sapiens. Not only that but many people still think that Artificial intelligence is the new threat to employment. It will replace the man and steal all the jobs. And they thinks that the future is dark.

Yet technological progress has never caused unemployment. On the contrary, since the industrial revolution, employment has multiplied. But, always, with each progress, fears resurge. Today, it is artificial intelligence that scares, or is used to scare. Economic history, and economic science therefore invites us to remain calm in the face of technological progress in general, and artificial intelligence in particular. By allowing the invention of new things to be exchanged, by stimulating entrepreneurship, it is not a danger but only an opportunity.


DATA based business models

Data Driven Business Model puts data at the center of value creation. This central place of data in the Business Model can be translated in different ways: analysis, observation of customer behaviour, understanding of customer experience, improvement of existing products and services, strategic decision-making, and marketing of data.

These data can be gathered from different sources, generated directly by the company, processed and enriched by various analyses and highlighted by data access and visualization platforms. Once data is collected, It’s essential to manage the multiple sources of data and identify which areas will bring the most benefit. Tracking the right data points within an organization can be profitable during the decision-making process. This allows an organization’s management to make data-driven decisions while amplifying synergy within the day-to-day operations.
As for revenue models, these can be based on a direct sale of data, a license, a lease, a subscription or a free provision financed by advertising.


Chatbots – Trends and Opportunities in E-Commerce

Evolutions of #Ecommerce is nothing without #Chatbots, #ArtificialIntelligence and #MachineLearning. These notions represent the new technologies trends that increase the competitiveness of an e-commerce. By 2016, 9 out of 10 customers globally were using messaging to interact with companies. To remain competitive, e-commerce must adapt to the rapid evolution of digital technologies and the behavior of Internet users.


Chatbot and ecommerce

Statistics shows that average time saving per chatbot inquiry when compared with traditional call centers is 4+ minutes in chatbots for the banking & healthcare sectors. By 2022 $8 billion in cost savings is expected. Therefore, application leaders need to include bots in their mobile app strategies to get ahead of this trend.


The phenomenon of Chatbots should transform the relationship between companies and their customers and evolve it towards a personalized one-to-one relationship. Indeed, Chatbots technology comes at a time when with the rise of messaging apps, the way many of us use social media to share and interact is fundamentally changing. According to Business Insider report, 80% of businesses want chatbots by 2020.


Fact: statistics shows the number of global messaging apps users in the first Q1 of 2017 is increased of 17% compared to Q4 of 2016. Messenger, WhatsApp and WeChat are leading with 1.2 Billion monthly active users fallowing by Viber which has 889 million monthly active users. On next spot, we have Skype with 260 million users. Click here to know more stats.


The evolution of e-commerce applications (on-line ordering, on-demand service) is based mainly on the responsiveness and dynamism of Chatbots that adapt to the user-friendly environment. The integration of Chatbots to mobile applications will bring more user-friendliness and ergonomics. The companies will be able to respond to the user’s needs directly via the conversation without having them to change the application. According to Gartner chatbots will power 85% of all customer service interactions by the year 2020.


The Chatbots are positioning more and more in the lives of individuals. For e-commerce companies, chat bot presents these advantages:

  • Enhance the user experience: Virtual assistants are committed to improve the user experience on smartphones by providing them with practical information and by offering them the possibility to interact with their apps.
  • Set up a new chat channel: chatbots, mostly on messaging platforms, allows customers to place orders and follow them via a conversational interface.
  • Inform and facilitate access to information: The most intuitive feature is to use Chatbots as an enhanced search engine by helping the user to search and access the right information.
  • Guide: Chatbots accompany customers in their product choices by giving them personalized advice and responding to their questions.
  • Sell differently: Chatbots are able to search, plan, reserve and place orders from a single conversation.
  • Assisting and retaining Chatbots by using messaging platforms as an additional channel for customer relations, is an effective tool for keeping customers loyal to their orders.


Chatbots, a phenomenon to follow closely

Chatbot services have enormous potential. But, as with any new technology, companies need to carefully consider what implementation challenges might come across. For example, they must not forget that with the use of Chatbots, they won’t have entire control over their client’s experience, so developing great services will be hard. As the number of chatbots is set to explode, how do they plan to ensure their stands out? What makes their service essential compared with their competitors?

There is also the challenge of to the point communication with a client. Customers will quickly turn away from chatbots that can’t comprehend straightforward questions. So companies must think how quickly can you shift customers to a human interaction?

As with all customer-facing technologies, privacy and security are critical. Security issues should be considered strictly while integrating a chatbot strategy. Customers won’t use services they don’t trust with their data.


While AI is gaining momentum and investment, chatbots are getting better with natural language and learning. This increasing facility has enabled better customer experiences, cost efficiencies and potential revenue increases within the e-commerce sphere. Chatbots are therefore a phenomenon to continue to follow closely. And Organizations wanting to deploy messenger chatbots, marketers and chatbot developers should consider compatibility, the consumers’ lifestyle and shopping preferences, for a successful implementation. Similarly, the consumers’ privacy concerns and resistance to intrusive mobile advertisement are important topics to be considered.

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.