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The #BigData Evolution and Revolution in 2017

Big data, a buzz word of overloaded information, gathers a set of technologies and practices for storing large amount of data and analyse in a blink of an eye. Nowadays, Big data is shaking our ways of doing business and the ability to manage and analyse this data depends on the competitiveness of companies and organisations. The phenomenon of Big Data is therefore considered one of the great IT challenges of the next decade.

 

4 major technological axes are at the heart of the digital transformation:

 

  • Mobile and Web: The fusion of the real and virtual worlds,
  • Cloud computing: The Web as a ubiquitous platform for services,
  • Big Data: The data revolution,
  • Social empowerment: The redistribution of roles.

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Interconnected and feeding each other, these 4 axes are the foundations of digital transformation. Data, global or hyperlocal, enables the development of innovative products and services, especially through highly personalised social and mobile experiences. As such, data is the fuel of digital transformation.

The intelligent mobile terminals and the permanent connectivity form a platform for social exchanges emergence new methods of work and organisation. Social technologies connect people to each other, to their businesses and to the world, based on new relational models where power relations are profoundly altered. Finally, cloud computing makes it possible to develop and provide, in a transparent way, the information and services needed by users and companies.

According to Eric Schmidt, Chairman of Google, we are currently creating in two days as much information as we had created since the birth of civilisation until 2003. For companies, the challenge is to process and activate the available data in order to improve their competitiveness. In addition to the “classical” data already manipulated by companies and exploited by Business Intelligence techniques, there is now added informal data, essentially stemming from crowdsourcing, via social media, mobile terminals and, increasingly via the sensors integrated in the objects.

 

Why Big and why now?

 

3 factors explain the development of Big Data:

    • The cost of storage: the latter is constantly decreasing and is becoming less and less a relevant criterion for companies. Cloud computing solutions also allow for elastic data management and the actual needs of enterprises.
    • Distributed storage platforms and very high-speed networks: with the development of high speed network and cloud computing, the place of data storage is no longer really important. They are now stored in distinct, and sometimes unidentified, physical locations.
    • New technologies for data management and analysis: among these Big Data-related technological solutions, one of the references is the Hadoop platform (Apache Foundation) allowing the development and management of distributed applications addressing huge and scalable amounts of data.

 

These 3 factors combined tend to transform the management and storage of data into a “simple” service.

 

Sources of Data: 

 

To understand the phenomenon of Big Data, it is interesting to identify the sources of data production.

 

    • Professional applications and services: these are management software such as ERP, CRM, SCM, content and office automation tools or intranets, and so on. Even if these tools are known and widely mastered by companies, Microsoft has acknowledged that half of the content produced via the Office suite is out of control and is therefore not valued. This phenomenon has experienced a new rebound with the eruption of e-mail. 200 million e-mails are sent every minute.
    • The Web: news, e-commerce, governmental or community-based websites, by investing the Web, companies and organizations have created a considerable amount of data and generated ever more interactions, making it necessary to develop directories and search engines, the latter generating countless data from users’ queries.
    • Social media: by providing crowdsourcing, Web 2.0 is at the root of the phenomenal growth in the amount of data produced over the past ten years: Facebook, YouTube and Twitter, of course, but also blogs, sharing platforms like Slideshare, Flickr, Pinterest or Instagram, RSS feeds, corporate social networks like Yammer or BlueKiwi, etc. Every minute, more than 30 hours of video are uploaded to YouTube, 2 million posts are posted on Facebook and 100,000 Twitter tweets.
    • Smartphones: as the IBM specifies, the mobile is not a terminal. The mobile is the data. There are now 4 times more mobile phones in use than PCs and tablets. A “standard” mobile user has 150 daily interactions with his smartphone, including messages and social interactions. Combined with social media and Cloud Computing services, mobile has become the first mass media outlet. By the end of 2016, Apple’s App Store and Google Play had over 95 billion downloaded apps.
    • IOT: mobile has opened the way to the Internet of Things. Everyday objects, equipped with sensors, in our homes or in industry, are now a potential digital terminal, capturing and transmitting data permanently. The industrial giant General Electric is installing intelligent sensors on most of its products, from basic electrical equipment to turbines and medical scanners. The collected data is analysed in order to improve services, develop new ones or minimise downtime.

 

Data visualization:

 

An image is better than a big discourse … Intelligent and usable visualization of analytics is a key factor in the deployment of Big Data in companies. The development of infographics goes hand in hand with the development of data-processing techniques.

 

The data visualization allows to:

 

    • show “really” the data: where data tables are rapidly unmanageable, diagrams, charts or maps provide a quick and easy understanding of the data;
    • reveal details: data visualization exploits the ability of human view to consider a picture as a whole, while capturing various details that would have gone unnoticed in a textual format or in a spreadsheet;
    • provide quick answers: by eliminating the query process, data visualization reduces the time it takes to generate business-relevant information, for example, about the use of a website;
    • make better decisions: by enabling the visualization of models, trends and relationships resulting from data analysis, the company can improve the quality of its decisions;
    • simplify the analyzes: datavisualizations must be interactive. Google’s Webmaster tools are an example. By offering simple and instinctive functionality to modify data sets and analysis criteria, these tools unleash the creativity of users.

 

Big Data Uses: 

 

The uses of Big Data are endless, but some major areas emerge.

 

Understand customer and customize services

This is one of the obvious applications of Big Data. By capturing and analyzing a maximum of data flows on its customers, the company can not only generate generic profiles and design specific services, but also customize these services and the marketing actions that will be associated with them. These flows integrate “conventional” data already organized via CRM systems, as well as unstructured data from social media or intelligent sensors that can analyze customer behavior at the point of purchase. Therefore, the objective is to identify models that can predict the needs of clients in order to provide them with personalized services in real time.

 

Optimize business processes

Big Data have a strong impact on business processes. Complex processes such as Supply Chain Management (SCM) will be optimized in real time based on forecasts from social media data analysis, shopping trends, traffic patterns or weather stations. Another example is the management of human resources, from recruitment to evaluating the corporate culture or measuring the commitment and needs of staff.

 

Improve health and optimize performance

Big Data will greatly affect individuals. This is first of all due to the phenomenon of “Quantified Self”, that is to say, the capture and analysis of data relating to our body, our health or our activities, via mobiles, wearables ( watches, bracelet, clothing, glasses, …) and more generally the Internet of the Objects. Big Data also allow considerable advances in fields such as DNA decoding or the prediction of epidemics or the fight against incurable diseases such as AIDS. With modeling based on infinite quantities of data, clinical trials are no longer limited by sample size.

 

Making intelligent machines

The Big Data is making most diverse machines and terminals more intelligent and more autonomous. They are essential to the development of the industry. With the multiplication of sensors on domestic, professional and industrial equipment, the Big Data applied to the MTM (MachineTo Machine) offers multiple opportunities for companies that will invest in this market. Intelligent cars illustrate this phenomenon. They already generate huge amounts of data that can be harnessed to optimize the driving experience or tax models. Intelligent cars are exchanging real-time information between them and are able to optimize their use according to specific algorithms.

Similarly, smart homes are major contributors to the growth of M2M data. Smart meters monitor energy consumption and are able to propose optimized behaviors based on models derived from analytics.

Big Data is also essential to the development of robotics. Robots are generating and using large volumes of data to understand their environment and integrate intelligently. Using self-learning algorithms based on the analysis of these data, robots are able to improve their behavior and carry out ever more complex tasks, such as piloting an aircraft, for example. In the US, robots are now able to perceive ethnic similarities with data from crowdsourcing.

 

Develop smartcities

The Big (Open) Data is inseparable from the development of intelligent cities and territories. A typical example is the optimization of traffic flows based on real-time “crowdsourced” information from GPS, sensors, mobiles or meteorological stations.

The Big Data enable cities, and especially megacities, to connect and interact sectors previously operating in silos: private and professional buildings, infrastructure and transport systems, energy production and consumption of resources, and so on. Only the Big Data modeling makes it possible to integrate and analyze the innumerable parameters resulting from these different sectors of activity. This is also the goal of IBM’s Smarter Cities initiative.

 

In the area of ​​security, authorities will be able to use the power of Big Data to improve the surveillance and management of events that threaten our security or predict possible criminal activities in the physical world (theft, road accidents , disaster management, …) or virtual (fraudulent financial transactions, electronic espionage, …).

Challenges in #MachineLearning Adaptation 

It’s very possible that at the time you read these lines, you’ve already use the result of machine learning algorithms several times today: your favorite social network might have already suggested you a new friend-list, search motor certain pages relevant to your history etc. You’ve dictated a message on your phone, read an article that was in your news feed based on your preferences and that may have been translated automatically. And even without using a computer, you may have been listening to news or just heard the weather forecast.

 

We are living in a world where most transactions and stock decisions that make and undo an economy, and more and even medical diagnoses are based on the qualities of the algorithm than on those of human experts, incapable of treating the mountain of information necessary for a relevant decision-making.

 

Such algorithms learn from data in order to issue predictions and make data based decisions and are called machine learning. These automated learning algorithms and systems have undergone significant advances in recent years thanks to the availability of large volumes of data and intensive computing, as well as interesting advances in optimization. A major feature of deep learning is its ability to learn descriptors while clustering the data. However, there are, many limitations and challenges that we have classified as: Data sources; Symbolic representations vs continuous representations; Continuous and endless learning; Learning under constraints; Computing architectures; Unsupervised learning; Learning process with human intervention, explanations …

 

Data Sources

There are many challenges in this area such as, learning from heterogeneous data available on multiple channels; manage uncertain information; identify and process with rare events beyond the purely statistical approaches; work by combining sources of knowledge and data sources; integrating models and ontologies into the learning process; and finally get good learning performance with little data, when massive data sources are not available.

 

Symbolic Representations vs Continuous Representations

Continuous representations allow the machine learning (ML) algorithm to approach complex functions, while symbolic representations are used to learn rules and symbolic models. The most significant recent advances concern continuous representations. These, however, leave out the reasoning while it would be desirable to integrate it in the continuous representation to be able to make inferences on the numerical data. Moreover, in order to exploit the power of deep learning, it may be useful to define continuous representations of symbolic data, as has been done for example for text with word2vec and text2vec representations.

 

Continuous and endless learning

Some AI systems are expected to be resilient, in order to be able to operate 24/7 without interruption. Interesting advances have been made in lifelong learning systems that will continually build new knowledge while they are operating. The challenge here is the ability of AI systems to operate online in real time, while being able to revise existing beliefs, learned from previous cases, independently. Self-priming is an option for these systems because it allows the use of elementary knowledge acquired at the beginning of the operation to guide future learning tasks, as in the NELL (NeverEnding Langage Learning) system developed at the Carnegie-Mellon University.

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Learning under constraints

Privacy Protection is undoubtedly the most important constraint to be taken into account. Researchers specializing in Machine learning recently recognized the need to protect privacy while continuing to learn from personal data (from records about individuals). To fulfill this purpose, privacy-oriented learning systems are being developed by researchers. Generally, machine learning must take into account other external constraints such as decentralized data or energy limitations. Researches on the general problem of machine learning with external constraints is therefore necessary.

 

Computing architectures

Modern machine learning systems require intensive computing performances and efficient data storage to scale up data size and problem dimensions. Algorithms will be run on GPUs and other powerful architectures; Data and processes must be distributed across multiple processors. New research needs to focus on improving machine learning algorithms and problem formulation to make the most of these computing architectures.

 

Unsupervised Learning

The most remarkable results obtained in the field of machine learning are based on supervised learning, that is, learning from examples in which the expected result is provided with the input data. This involves prior labeling of data with the corresponding expected results, a process that requires large-scale data. The Amazon’s Mechanical Turk (www.mturk.com) is a perfect example of how large companies mobilize human resources to annotate data. But the vast majority of data exists with no expected result, ie without desired annotation or class name. It is therefore necessary to develop unsupervised learning algorithms to manage this enormous amount of unlabeled data. In some cases, a minimal amount of human supervision can be used to guide the unsupervised algorithm.

 

Learning process with human intervention, explanations

The challenges relate to the establishment of a natural collaboration between the machine learning algorithms and the users in order to improve the learning process. To do this, machine learning systems must be able to show their progress in a form that is understandable to humans. Moreover, it should be possible for the human user to obtain explanations from the system on any result obtained. These explanations would be provided during the learning process and could be linked to input data or intermediate representations. They could also indicate levels of confidence, as appropriate.

 

Transfer Learning

Transfer learning is useful when little data is available for learning a task. It consists in using for a new task knowledge that has been acquired from another task and for which more data is available. This is a rather old idea (1993), but the results remain modest because it is difficult to implement. Indeed, it implies being able to extract the knowledge that the system has acquired in the first place, but there is no general solution to this problem (how, how to reuse them …). Another approach to transfer learning is “shaping”. It involves learning a simple task and then gradually becoming more complex until it reaches the target task. There are some examples of this procedure in the literature, but no general theory.

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?

 

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?
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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.

The Impact and Challenges of Artificial Intelligence for Next-Gen Enterprises

Artificial Intelligence (AI) is not a new phenomenon. It continues to develop and its applications are already very present in our personal daily life (gaming, robotics, connected objects …), arousing as much enthusiasm as fear. This complex concept gained its success in the science fiction world. Although AI is still calling for a more or less fantasized imagination, it is an integral part of reality, and it can be found in many services, systems and applications.

 

What can be the role of artificial intelligence in the enterprise of the future? Will AI make organizations smarter? These are the main questions that have motivated big companies, with the objective of analyzing and anticipating the impacts of this revolution in progress. In this post, I’ll be discussing organizational, legal and ethical issues related to the governance of artificial intelligence in large enterprises.

 

A critical factor in adapting the company to the evolutions and challenges of AI environment is to rethink relationship with the company’s stakeholders, and in particular with the customer. It doesn’t mean that one must highlight that “the customer is important” but to emphasize the interaction with the customer. With that being said, client exists only through the interest and interactions developed with them.

So, the question companies should ask is how can they develop a successful interaction with the help of artificial intelligence? What does this mean concretely in terms of channels, content, customer knowledge and, above all, commitment to the customer?

 

Some companies have “Innovation and prospective” unit to carry out an analysis and a reflection of AI impact within the company. This is to take the step without neglecting the employees who are at the center of the subject. These cells allow the sharing of ideas. As the applications of artificial intelligence within the company are diverse, such as increase of human expertise through virtual assistants; optimization of certain products and services; new perspectives in research and development through the evolution of self-learning programs. The objective of this unit is to exchange in order to make the prospective, in a participatory way, through conferences, roundtables, written reports or scenarios, depending on the choice of the structures.

 

The Impact of Artificial Intelligence for Enterprises

 

Artificial intelligence technologies are already anchored in our daily lives. These technological advances intensely question the managerial and organizational practices around innovation in large companies. Many conducted surveys demonstrate that in general, companies do not have a dedicated budget for artificial intelligence. Nevertheless, there are either investment projects or resources that can be allocated to artificial intelligence teams integrated into the wider data teams. Be that as it may, the subject of artificial intelligence is present in large enterprises; It may remain theoretical but may also be the subject of initial experiments, notably concerning the predictive algorithms. Artificial intelligence does not fundamentally change everything in the company, it will rather “increase” performance, automating or perfecting certain processes and / or operations.

 

Benefits for organizations:

 

Today, artificial intelligence already generates many benefits for organizations, notably by:

  • Responding to Big Data issues; Artificial intelligence relies in large part of the search and mass analysis of data from which it can learn;
  • Increasing human decision-making expertise, online help assistant: a Hong Kong-based company, Deep Knowledge Venture (DKV), possesses, for example, artificial intelligence at its board of directors. Vital (Validating Investment Tool for Advancing Life Sciences) who makes investment recommendations and is also entitled to vote;
  • Optimizing services and products: improving customer knowledge, decision-making and operational processes;
  • Strengthening systems security: in the area of ​​cybersecurity, artificial intelligence becomes a structuring element of IT infrastructures, in order to secure networks. Automatic recognition is well established for the detection of fraud, and experts are under way to create algorithms that will identify threats that human brains and traditional security mechanisms fail to recognize.
  • Helping to make discoveries: some companies in the field of health analyze all the scientific publications related to a particular area of ​​research, which allows them to look for new properties, new molecules.

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Challenges:

 

The challenges for large companies are numerous. Starting with cultural and organizational changes. As noted in the Telecom Foundation’s Watchbook No. 8: “The craze for artificial intelligences has been accelerated by the availability of AI capabilities in the form of APIs (on the one hand, Vision or predictive), and the source code of the platforms of Machine Learning released by major Internet operators, on the other hand “.

These technology facilitators will keep pushing companies to become APIs (APIs) in order to optimize their resources. It is therefore necessary to understand the world of APIs in this transversal, cross-enterprise approach, which is not without posing a number of challenges for large companies. To succeed one must develop the fallowing roadmap strategy:

  • Build stronger relationships with clients;
  • Optimize internal processes;
  • Accelerate the development of new developments.

 

To conclude I’ll say that we live a golden age of artificial intelligence, boosted by the increasing interest of web giants for the stakes of Big Data. The first AI investors are indeed the pure players of the internet and the main players of the software. The movement is launched, and it is our responsibility to anticipate the effects of this revolution on large companies.

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.

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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.

IoT: Biggest Revolution in Retail

If the IoT represents a huge opportunity for almost every facet of the business, this is particularly true for supply chain specialists, operations and analysis. The leaders of e-commerce and traditional commerce see an opportunity of competitive advantage in IoT.

 

Even though I’ve already wrote about IoT in my previous posts, let me give you again a quick definition of it. In 1999, Kevin Ashton (MIT Auto-ID Center) describes the Internet of Things as a network of interconnected objects that generates data without any human intervention. Today, Gartner describes the IoT as “the network of physical objects containing embedded technology to communicate, detect or interact with their internal states or the external environment.”

 

estimates for IoT revenue by region in 2020

For some IoT is only a new name of an old concept, the only thing which has recently changed in this existing concept, is the evolution of Cloud technology. According to a recent survey by Gartner, IoT is one of the fastest-growing technological trend. Estimation says that by 2020, the number of connected objects will be multiplied by 26 to $ 30 billion. Main reason behind IoT success is the development of solutions based in clouds; which allows to actually have access to the data generated by the connected objects.

 

The growth of IoT relies on three levers: reduction in integrated chips costs, technologies supported by a cloud platform and powered by analyzing Big Data and finally the Machine Learning. A case study of IBM named “The smarter supply chain of the future” revels that in near future the entire supply chain will be connected – not just customers, suppliers and IT systems in general, but also parts, products and other smart objects used to monitor the supply chain. Extensive connectivity will enable worldwide networks of supply chains to plan and make decisions together.

 

The main objective of such connective supply chain is to gain better visibility and to reduce the impact of volatility in all stages of the chain and get better returns by being more agile product flow. Several developments are already underway in the IoT and are revolutionizing the retail supply chain at various levels:

 

– At the client side: integration of end consumer in the IoT. The main objective of this step is to collect customer data to create customized product, personalized offers while simplifying the purchasing process. Devices such as health trackers, connected watches etc. continuously collect the data from consumers, prescribers. The collected data represents a great opportunity of positioning product/services. For example, from a person’s browsing history, its culinary tastes and influences on social networks, information on a nutrition bar can be offered to him. Recommendations may also be appropriate if the person enrolled in a sports club or acquired a fitness tracker and so on.

 
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– As for retailers: Beyond the preparation of the assortment by merchants, there are smart shelves and organization of sales outlet. Moreover, we are witnessing a rapidly changing purchasing behavior so with smart shelves a retailer’s system can analyze inventory, capacity and shipment information sent by suppliers. Via the predicted system retailers and suppliers can avoid costly out-of-stocks or missed sales.

To take the example of nutrition bar, time spent in front of a specific category of products (yogurt lightened for example) can be an early indicator to change suggestions or promotions. In addition, the integration of the retail IoT can allow the line to automatically trigger orders. The whole environment can be configured to access a library of planograms, to store inventory data and related warehouses to automatically run restocking. As the elements of this environment are already used independently, we can predict that we are at the dawn of IoT in retail.

 

If the store are at a less advanced stage in the application of IoT, transportation and warehousing are well connected. The integration of RFID shows a first generation data-oriented machine. Integrated tracking systems have long been used in transport and warehouse systems. RFID tagging of pallets has to have better visibility on the status of stocks and the location. The convergence of demand signals and increased visibility on the state of stocks and their location results in scenarios such as the anticipated shipment for which Amazon has filed a patent. Increasing integration of IoT can lead to efficient use of robots for material handling and delivery by drones. These innovations are challenging the effectiveness of existing systems by optimizing the machine learning an effective alternative.

 

Even with all the benefits it promises to offer companies, IoT is still a gamble, with big risks and unsolved problems. For any organization that decided to embark on the IoT, a number of questions remain open whether in technology, integration with file distribution systems to traditional ERP API to communicate with sensors and application languages ​​(Python, ShinyR, et AL.)

 

There are several interfaces that work well in specific areas, but it needs more standardized platforms. Industry experts have launched PaaS (Platform as a Service) to integrate this growing IoT technology. Despite these challenges, the technology seems a surmountable obstacle. Only the legislation on collected data is a real problem so far. Even the customer acceptance remains a challenge. In 2013, Nordstorm had to backtrack on his program which was to track customer movements by the Wi-Fi use on smartphones and via video analysis due to customers demand.

 

Finally, the important thing to remember is that the IoT is a revolutionary technology. A lot of expert retailers, e-commerce players and technology solutions providers will rethink and adapt the model and evolve in processes designed for organizations wishing to adopt the IoT. Retailers that take the lead in this space stand to gain an important advantage in an already competitive environment. Early adopters will be positioned to more quickly deliver IoT-enabled capabilities that can increase revenue, reduce costs and drive a differentiated brand experience. The IoT will be a disruptive force in retail operations.

 

 

Sources:

The Smarter Supply Chain Of The Future

The CEO Perspective: IOT for Retail Top Priorities to build a Successful Strategy

Machine Learning: An Artificial Intelligence approach

I’ve heard a lot of people saying that Machine Learning is nothing else than a synonymous of Artificial Intelligence but that’s not true at all. The reality is that Machine Learning is just one approach to AI (in fact it’s called the statistic approach).

 

Let me first give a definition of Machine Learning. It’s a type of artificial intelligence that gives computers the ability to learn to do stuff via different algorithms. On the other hand Artificial Intelligence is used to develop computer programs that perform tasks that are normally performed by human by giving machines (robots) the ability to seem like they have human intelligence.

 

If you are wondering what it means for a machine to be intelligent, it’s clear that “learning” is the KEY issue. Stuffing a lot of knowledge into a machine is simply not enough to make it intelligent. So before going far in the article, you must know that in the field of Artificial Intelligence, there are 2 main approaches about how to program a machine so it can perform human tasks. We’ve a Statistical Approach (also known as probabilistic) and Deterministic Approach. None of these two approach are superior to the other, they are just used in different cases.

 

The Machine Learning (=Statistic AI) is based on, yes you’ve guessed right, statistics. It’s a process where the AI system gather, organize, analyze and interpret numerical information from data. More and more industries are applying AL to process improvement in the design and manufacture of their products.

 

There’ll be around 5 to 20 billion connected devices within 3 years and so many capture points will be used to make live decisions, to recommend, provide real-time information and detect weak signals or plan of predictive maintenance. Whether it’s at the level of business uses, the sectors of industry and services (health, distribution, automotive, public sector …) or even the use of Business Intelligence, everything is changing! With the Machine Learning and voice recognition technology based on AI, even the Big Data technology might be quickly overtaken by real-time information.

 

In a preview of an upcoming e-book, “AI & Machine Learning”, UMANIS talks about The Data, machinery and men. In the e-book they have elaborated problems and expectations that different companies are facing in the technological era.

 

Based on the responses of 58 participants who responded to the survey “AI & Machine Learning”, here below you’ll find identified trends and indicators.

 

  • 44% of companies believes that AI and Machine Learning have become essential and latest trend in various fields including education, healthcare, the environment and business sector,
  • One company on two is curious about the technological innovations in order to understand the collection of data (via machine)
  • 1/3 of companies are currently on standby on AL & Machine Learning topics,
  • 21% of IT decision makers were informed about Cortana suites (Microsoft) and Watson (IBM)
  • 36% want to go further on this type of technology,
  • 88% are planning to launch an AL project within more than 6 months,
  • 50% of respondents are unaware of the purpose of these technologies in the company.

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TOP 5 issues:

  • Detect abnormalities
  • Using machine learning to optimize the automation
  • Integrating a Learning Machine module into an existing SI
  • Remodeling of the real-time Data architecture to gather big volumes with high computing power
  • Finding a permanent solution of storage and backup of the collected data

 

There’s no doubt that machine learning area is booming. It can be applied to high volumes of data to obtain a more detailed understanding of the implementation process and to improve decision making in manufacturing, retail, healthcare, life sciences, tourism, hospitality, financial services and energy. The machine learning systems can make precise result predictions based on data from training or past experiences. By gathering relevant information for making more accurate decisions, machine learning systems can help manufacturers improve their operations and competitiveness.

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