Google reveals five security issues concerning Artificial intelligence

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

security issues concerning Artificial intelligence

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


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


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



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

  • A robot may disrupt the environment :

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

  • The machine can cheat :  

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

  • How to setup the robot go to the basics?

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

  • How much independence can you give to an AI?

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

  • Does AI will adapt the change?

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

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

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.


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.




The Smarter Supply Chain Of The Future

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

Big Data revolution in Sales and Marketing industry

Big Data revolution in Sales and Marketing industry

Digital technologies, particularly Social, Cloud, Mobile and Big Data, are transforming the industry and the way companies used to operate. These technologies are creating new business opportunities by launching new services and/or establish new businesses by optimizing operations, reducing costs, improving services and/or launching new services along companies.


Big data, the most hyped terms of past couple of years, is still a catchall term. The ever-increasing amount of data generates enormous challenges, but also generates significant business opportunities for sales and marketing professionals of all sizes of enterprises. You must have noticed that in today’s ever changing world, unstructured data such as digital photos, videos and other connected social media activities are growing much faster than structured data so we can say that data processing is no longer the sole domain of relational databases.


Therefore, an entire new industry has formed around technologies in order to store, sort, organize, and analyze the large volume of data and give business insights. Companies such as Xorlogics helps business to provide them high quality software, manage and transform their data, thanks to their deep expertise and leading edge technologies that they have established in the areas of technology and development.


Ok so enough with the history and technology lessons, let’s talk about what does #BigData has to offer and can change the field of sales and marketing. Well, in my humble opinion, in terms of needs addressed and core functionality, Big Data can be seen as an evolution of business analytics and can be used in customer relationship better than ever before. A survey by Skytree ran in January confirms that sales and marketing gained most from Machine Learning and advanced analytics projects. Basically now instead of giving ads in magazines and newspapers, or billboards and reaching out only a limited local audience, it is time to start thinking outside of the box. Big data and predictive analytics technologies represent the opportunity to turn the tables. In other words, sales and marketing can finally become more about facts, analytics, and math rather than only a magical feeling of rather it’ll work or not.


Here’s how I think Sales and Marketing are gaining profits and new Business opportunities by the correct input of Big Data and Analytics.


Customer segmentation:    

By using correctly the analytic tools, one can segment the market into granular micro-segments and then offer personalized services and increase effectiveness, efficiency, and satisfaction. Data analytical tools let you personalize marketing and campaigns, promotions and discounts and customized goods and services. One can even get the prediction and advanced analytics of customers buying behavior at a nearly “personal” level


Social & mobile media real-time data analysis:     

Gathering and tracking a real-time data from the Web allows adapting and evaluating business strategies and marketing champagnes that respond best to web-based consumer’s behavior and choices. Real-time sales data visualization technologies enable sales managers to adjust battlefields tactics based on live data feeds.


Product cross selling:            

Best opportunity of cross-selling by using all the data that can be known about a customer, including the customer’s demographics, purchase history, preferences, real-time locations, and other facts to increase the average basket size.


Dynamic pricing:        

Increasing the level of granularity of data on pricing and sales – Pricing optimization leveraging demand-elasticity models based on analysis of historical sales to derive insights – Assessing and informing pricing decisions in near real time. – Integrating promotions and pricing seamlessly, whether consumers are online, instore, or browsing a catalog – Leveraging performance based pricing plans and risk sharing schemes.


Location-based marketing and sales:          

The growing adoption of smart phones and other personal location data enabled mobile devices to target consumers who are close to stores or already in them and letting customers “check-in” in their favorite places. By geo-targeting mobile advertising, companies can create a multichannel experience to drive sales, customer satisfaction, and loyalty and create value from the use of personal location data.


Customer service:      

By developing product sensor data analysis for after-sales service, big data can be used to predict purchases, analyze customer behavior and better understand the people buying your product.


In conclusion I’ll say that it’s true that large enterprises were the first one to adopt BigData because their need to explore and gain insight from their enormous data is profound in comparison of a small business, however, if you delay a lot, you will be left alone far behind in this high-tech connected world. So more quickly you adapt changes of the tsunami of data and powerful business insights, better impact your business can get!

Machine Learning: An Artificial Intelligence approach

machine learning


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.


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.

Hybrid Cloud Myths Busted

Often presented as a third path between the private cloud and public cloud, companies, consulting firms, suppliers and hosts have started to take their interest hybrid cloud. What is the hybrid cloud? There is no strict and standardized definition of what the hybrid cloud is, each player on the market (consulting firms, hosting companies…) have their own definition.




For Forrester, the hybrid cloud presents itself as “an IT infrastructure model in which at least one external cloud service is integrated with an application, data source, or internalized infrastructure element”. For Gartner, “Hybrid cloud refers to policy-based and coordinated service provisioning, use and management across a mixture of internal and external cloud services”. Basically, it’s a mix use of public and private cloud which are used together to create value.

benefits of Hybrid cloud

Combining the benefits of public clouds such as agility and low cost, with the strengths of private clouds (control, performance and safety) the hybrid cloud brings the best of both technologies. These assets play an essential role in the success of companies, even if few of them still wonders what really a „hybrid cloud“ is. Two options are offered to businesses: get on the board or remain docked. To see more clearly, I propose to demystify the five major myths that usually surround the hybrid cloud.


Private + Public = Hybrid:


It is not enough to place together these two infrastructures to create a hybrid cloud. In fact, you may not gain any of the respective advantages of both types of clouds but end up multiplying the risk by both of them! First, by moving secure data to the public cloud, security breach can result in brand damage and loss of customers’ trust, and requires significant time and effort to remediate; secondly, migration of apps from a public cloud to a private cloud can lead to unexpected costs. By having a hybrid cloud, you control your workload, your network and storage resources while minimizing risk and increasing productivity.


A Hybrid Cloud is Complex and difficult to implement:


The use of complete IT solutions allows you to reduce complexity and to choose standard technology – (Microsoft, OpenStack, Vmware) on which your hybrid cloud is standardized, but also the type of public cloud with which the private cloud deployed on site by customer can interact. A complete solution accelerates three essential elements of development: – Integration of end-to-end testing to verify that all components work together; – Use of a converged infrastructure that simplifies the implementation and deployment; – Predefined plans for services, with workflows that must automate provisioning through a self-service portal.


The public cloud is more cost-efficient:

Cost savings via Hybrid cloud

A New study by IDG Research Services shows that if we take into account the governance issues, risk and compliance, the hybrid cloud displays in fact a lower total cost of ownership. When data or workloads migrate to the public cloud, it is easy to override local or international regulations on data protection. The local laws and requirements vary from market to market, and some are so complex that companies simply prefer to avoid public clouds. May be that’s why you may want to choose private cloud for sensitive workloads. In Germany for example, the rules on how data is stored and processed are especially strict. The solution lies in how to mix public cloud and private cloud offering each workload the advantages of one or the other, depending on their requirements.


On the cloud, the data control escapes you:


While extraction or data migration can be difficult via some particularly cloud service providers, a well-orchestrated hybrid cloud environment enable you to keep hand on your work. A well-managed hybrid cloud can provide quickly required public and private resources, provide IT departments a high level of visibility and control, as well as self-service and on demand access for developers and applications users.


It is difficult to know which applications are suitable for cloud:                


Companies are often hampered by the critical interdependencies of IT infrastructure, ignorance of their IT assets and their relationships to business applications. With a simple spreadsheet and without rigorous methodology, it’s impossible to know precisely if an application is suitable or not for cloud – and even less to know the position in a cloud architecture. Experts who use automated platforms for collecting and analyzing data can provide a complete comprehensive view of the application portfolio and tell whether you need to migrate, consolidate, modernize or simply stop the use of an application.          


hybrid Cloud as digital transformation


Even if the definition of hybrid cloud is still unclear, its undeniable benefits in terms of agility and cost reduction are now the essential model of tomorrow. Companies that are preparing or starting their digital transformation have every reason to anticipate a future adoption and now choose technologies that will integrate public and private worlds, i.e. converged infrastructure and proven software solutions.