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#BigData: Jobs and Key Skills Businesses Need

Data is an organization’s most valuable asset, and the best way to nurture and protect it is through a governing body that is responsible for setting consistent data standards for the organization. The production of data is expanding at an astonishing pace. Experts at EMC point to a 4300% increase in annual data generation by 2020. Due to this digital Data increase, it is, now more than ever, essential for companies who intend to take full advantage of the real value of big data, to recruit new talent to improve their productivity.

Rare profiles, with degrees from different backgrounds have the task of extracting this unstructured data, to transform them into beneficial actions and operations for the company. Companies working with big data are already facing challenges when it comes to finding and hiring the best possible talent. Recognizing this growing need, these companies will have to recruit distinct profiles with knowledge of training and data-oriented diplomas.

 

But what are these jobs?

 

Chief Data Officer (CDO): 

He is the Director of the data, the #DataGuard. He leads a team that specializes in the acquisition, analysis and data mining. Its main function is governance of his team for the supply of the most interesting and valuable data for the interest of the company. Based on statistics, computing and digital knowledge, he gives insights to each department such as, marketing, human resources, engineering, quality department, accounting and management. Graduation from engineering school is required, as well as skills and experience in the fields of management, IT and marketing are needed.

 

Business Intelligence Manager: 

His job is to facilitate the decisions of the CDO. He use new technologies to develop dashboards, reporting tools, in order to integrate the computer system and make them available to company users. This profession requires a solid knowledge of English, computer and data management. Just like CDO, graduation from engineering school is required.

 

Data Scientist: 

He is responsible for the collection, processing, evaluation and analysis of big data to optimize the company’s strategy. His role is to create for the Company, algorithms that produce useful information, particularly in order to offer customers the products they want. These profiles combines management, IT and statistics skills. They master the techniques of data mining, as well as technologies and IT tools databases such as Hadoop, Java, MapReduce, Bigtable, NoSQL … A degree from engineering schools is essential.

 

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Data Analyst: 

He works with statistical tools and specialized computer technology to organize, synthesize and translate the information companies need to make better decisions. Data analysts guard and protect the organization’s data, making sure that the data repositories produce consistent, reusable data.Graduated from engineering school is required.

 

Le Data Miner: 

He is the “data excavator,” the Sherlock Holmes of the company’s data. His role is to find the information from multiple data sources to make them usable and useful for the company. He must have excellent computer, business and statistical skills. It is possible to become Data Miner from a computer or marketing Degree. He can potentially evolve into a Data Analyst and Data Scientist.

 

Master Data Manager: 

Data Manager acquires and organizes information from the company for their optimal use. He is an expert in database including, the reference data (related to supplier catalogs, customers, products, etc …) and structural metadata (related to regulatory standards and methods). He must ensure that these data are consistent and well organized according to defined business rules and properly integrated into the information system operated by the business teams.

 

Data Protection Officer: 

This person is the guarantor of keeping a record of all the processing operations on personal data carried out by the company where he works. This position could become mandatory in all companies with more than 250 employees. His challenge is to be informed of all data processing projects within company so he can input his upstream recommendations. It must not only bring together computer and law skills, but also strong communication skills. This is a new job of digital business that appears in a highly competitive environment where data protection issues is the heart of business and represents a major challenge for the economy.

 

The big data experts are both very rare and in high demand. They are found mainly in large groups such as banking, insurance and finance, or in the operators that store and process data such as data centers, internet service providers and web hosts. But the regulation of rapidly changing data, business data and opportunities are multiplying, so all companies will be surrounded, near and far, with similar profiles to those presented above.

So if you are looking for a job, recruit talent, or simply want to learn more about these new jobs, feel free to contact us on LinkedIn: https://www.linkedin.com/company/xorlogics

Bridging the skills gap in #CyberSecurity

cybersuverillance

Attackers are not robots or software. They are human beings. As soon as you deploy new defenses, they react quickly to change tactics to cross or break the security bridge. In short, it is an eternal chase. However, effective protection requires both skills and knowledge, an essential aspect often neglected.

 

Technology is only a starting point: 

Security teams are gradually becoming aware of required effort to not get left behind by the advanced attackers. Thus, to better detect the presence of hackers on their networks, advanced technologies are being successfully deployed within companies. Despite this huge investment in high tech security, the attacks continue, and the worst part is that these attacks cause extreme damage. The teams now understand that if the detection is the first important step, they must also be able to prevent, analyze and neutralize attacks. Hence a need for sophisticated security expertise. But then, it is extremely difficult to recruit and retain qualified employees, able to exploit the latest technology and block determined attackers.

 

Security, much more than only a matter of technology: 

Security not only suffers from company’s skimpy budgets, but also a skill gap that threatens so many organizations today in security era. So attracting the right talent and keep them often takes a challenge. Thus, the constraints of resources and personnel can stand in the way of the most effective strategies. According to a recent report from FireEye, that despite threats detection devices, over two-thirds of victims companies were unable to realize themselves. For this they have had to rely on a third party.

 

Also according to this study, even when the company had found itself the incident, the attackers remained on the network for 250 days in average. Knowing that network monitoring tools generate thousands of alerts each day, how can a diligent RSSI distinguish a dangerous threat in those mass alerts?

 

Real time cyber-surveillance: 

At the time when we are inundated with alerts, it’s crucial to understand their meaning and relative importance. Which alerts are actually useful? Which require our immediate attention and which we can be ignored? Identify the attacker and his goals allow you to better assess the risk it represents. Better yet, if you know the procedure, you can anticipate his next actions.

 

To identify and neutralize the attacks, security teams must not only detect but also establish their priority and eliminate false positives. Determined to circumvent detection devices, attackers constantly adapting their tactics.

 
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So you need a system to stay on top of their latest methods and limit the impact of security breaches. Organizations must be able to adapt as attackers change their tactics. This is precisely the role of cyber surveillance.

But again, the cyber surveillance alone is not enough. Security teams must be able to analyze their terminals, networks and newspapers in the light of this information.

 

“Identify the attacker and their goals allows you to better assess the risk it represents”

 

Taking the normal activity as reference point in a given environment, they can identify potential gaps and identify any anomalies that might reveal the presence of attackers.

 

Choosing the right solution: 

How a CISO can ensure that the organization’s staff, policies, processes, practices, and technologies can be proactively protect, shield, and defend the enterprise from cyber threats, and prevent the occurrence and recurrence of cybersecurity incidents commensurate with the organization’s risk tolerance. Here’s what might look like the solution:

Skills – An effective solution must provide the company the expertise and personnel to track the possible signs of advanced network threats. In case of proven violation, the company must have the services of an advanced team to neutralize attacks.

Cyber surveillance – This solution should offer your staff a clear understanding of the context within which the threats target your environment. This information must be complete and validated by experts in malware and cyber surveillance.

Technology – As we’ve mentioned, the technology is the fundamental bedrock of your security architecture. They must be able to identify known and unknown threats. Moreover, whatever the technology deployed, they must be able to protect your business on all the major attack vectors: web, email, mobile devices and terminals.

 

Organizations must ensure that their security architecture must be agile. It must be deeply integrated for an end-to-end view of attacks. It must present a full picture of threats by incorporating internal and external intelligence. And it must take an active, “lean-forward” posture that doesn’t just wait for attacks but anticipates them.

 

Xorlogics is a provider of proven High Quality low cost Software Development and Outsourcing Services. We provide a full suite of information security services and software consultancy that help define cyber security strategy, identify and remediate threats and risks, select and deploy the right technology, and achieve operational readiness to protect from malicious attack. Feel free to contact-us, because we are more than willing to help you!

5 Pitfalls in Data Protection Strategy

The new digital era requires that the data protection strategy must not be limited to simple backup and recovery system. The evolution of data center requires disposing of a solution for protecting data to which companies can entrust their business and career.

A well thought out data protection strategy is a key differentiator for your business because it helps you with fallowing points:

 

  • eliminate complexity and access interruptions to IT services;
  • reduce inefficiencies within the team and operating expenses;
  • make faster decisions on reliable information and reflect strategically;
  • accelerate the return on investment.

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Exploring a variety of data protection solutions, from physical to virtual and private clouds to public clouds, while ensuring scalability of the company, is a long and tough journey filled with pitfalls.

Here are five pitfalls to avoid at all costs in developing an effective strategy for data protection.

 

    • Adapt new technologies and save yourself by falling to the wayside:
      By stop being a cost and finally promote the company’s activity center requires an IT more agile and responsive to business user’s needs and risks. The rapid adoption of new technologies, whether virtual machines or deployment strategies in the cloud is a way for IT to respond quickly to new requirements.But this can lead to new inefficiencies if we keep using basic solutions for our data protection. According to Gartner, “Enterprises need a global protection strategy to manage the diversity of data and workloads via a solution or a single platform, and should avoid multiple point of solutions, which can cause more costs and time waist.Choose a unified platform for data protection with the flexibility and scalability to meet current and future needs of your business. Contact a supplier, such as Xorlogics, recognized for its expertise of both classic environments (on site) that virtualized and cloud, always at the forefront of technological developments.

 

    • Save your staff productivity:
      Companies today are struggling with huge data volume and colossal infrastructure deployments and are growing, weighing heavily on the budgets and IT staff capabilities. According to ESG, “64% of IT budgets are used to ensure the proper function of existing operations, often by doing as we have always done.”Now we all understand that it’s difficult to meet new demands when one spends so much time in routine processes. Migrate your home media servers to integrated backup appliances is a way to recover a lot of work hours which you can use elsewhere. Indeed, home media servers mobilize time, either to acquire, install and integrate, manage, apply their patch or update. In addition, to which provider do you contact in case of problems?Integrated appliances enable you to consolidate multiple disparate point products and thus increase the effectiveness of your team and the use of resources. So we suggest you to select a provider that allows you to choose an integrated appliance. This will save valuable time for your IT staff, who can then devote to more strategic projects. Studies show that it is normally possible to reduce 30% operating expenses (OpEx) and return on the initial investment in 15 months.

 

    • Impossible to protect what we can’t see:
      For many companies, Oracle is the leading database solution. But in many cases, the team responsible for the implementation and the responsible for data protection have no visibility on their mutual process. If the database application and data protection software are not closely related, both teams can see what the other is working on. This can lead to gray areas where the team responsible for data protection isn’t able to know if backups have been made and where they are stored, while none of the two groups has full control on Data protection.Knock down those barriers through an integrated solution providing backup administrators and those databases the same visibility of data protection, with self-service functionality to maximize their effectiveness. Look for a solution that allows Oracle administrators to continue to use their preferred tools and provides backup administrators the expected visibility. Or contact Xorlogics to create a situation that can be benefit to both groups and protect applications in reliable and flexible way.

 

    • Stop wasting on unnecessary licenses:
      Every transaction has a cost, whether withdrawals in ATMs, credit cards or of holding checking accounts. But some companies even pay software license fees to create additional copies of their own data. Does it sound logic to you? What’s reasonable for us is ONLY to pay for the data you want to protect. One must be able to make as many copies they want of their data without paying any extra fee of it.Unfortunately, many providers charge back-end treatments. Every time a backup is performed or even a copy to another location, one is forced to pay additional license fees. It is quite unusual since the purpose of a backup is precisely to have several doubles in multiple locations!Claim software licenses calculated based on the front end capacity you want to protect. In addition, you must have separate licenses for software and hardware in order to best protect your investment. Independent software licenses give you the flexibility to redeem your licenses from a home media server and appliance, or move them to a new or larger appliance. This dual approach allows you to say goodbye to unnecessary spending on software licenses, and prevents you to buy these licenses whenever you upgrade the hardware platform.

 

    • Modulate the degree of protection: 
      Today, companies have very little visibility into their data infrastructure: they are unable to distinguish between important and simple data. This produces considerable inefficiencies, since all data must be treated equally, whether it’s customer’s data or just employee’s personal videos. It is extremely difficult to have a Strategic reflection and improve the effectiveness of data protection if all the data must be protected in the same way.
      Companies need to have visibility and understanding of their data to manage and protect them in full knowledge of the facts. Knowing where the data resides, know their type, identify their owner and seniority are all factors of prime importance. This knowledge can improve the efficiency of your team and reduce costs, especially on primary storage and the one dedicated to data protection. Look for a solution that already includes an understanding of the type of data you have to avoid excessive or unjustified charges. And choose one that fits your backup solution in order to follow easily the continued growth of your applications and your data.

 

Xorlogics can meet all these challenges by providing customized solutions designed for your specific needs, with which no other product provider can match. We collect the approved solutions, innovative technologies and professional services to meet the needs of our customers in terms of data protection. So don’t hesitate to fill this form and let our experts help you out!

Challenges of #BigData

Behind the name of #BigData is hidden an astronomical amount of data produced anywhere, everywhere at any moment by men and machines to each action they perform together and separately.

This production is exploding because 90% of the available data was created only in the last two years. Big Data today is being analyzed to discover the insights that lead to better decisions and strategic business moves.

 

Big data apps are being used to improve offers, service levels and customer support and many more. The following numbers will certainly show you the economic potential of well-established data: Only 17% of companies haven’t plan at all to launch a Big Data project but over 70% of companies have already made use of Big Data, either by integrating their business or as part of a pilot project process. The Data technologies are maturing to a point in which more and more organizations are prepared to pilot and adopt big data as a core component of the information management and analytics infrastructure. It’s an area of research that is booming but still faces many challenges in leveraging the value that data have to offer.

 

Here are so called “big challenges” of Big Data.

 

Find a language for Big Data:

All sciences, chemistry and mathematics have experienced a tremendous boost by adopting a specific language. Don’t you think we must follow the same path in the area of Big Data and invent an algebraic notation and an adapted programming language to better share and facilitate its analysis?

 

Work on reliable data: 

With the explosion in the volume of available data, the challenge is how to separate the “signal” of “data” and “valuable information”. Unfortunately at this point, a lot of companies have difficulty to identify the right data and determine how to best use it. The fight against “spam data” and data quality is a crucial problem. Companies must think outside of the box and look for revenue models that are very different from the traditional business.

 

Data access: 

Data access and connectivity can be an obstacle. McKinsey survey shows that still a lot of data points aren‘t yet connected today, and companies often do not have the right platforms to manage the data across the enterprise.

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Embedding increasingly complex data: 

If the Big Data was first concerned the “simple” data (tables of numbers, graphs …), the processed data is now more and more complex and varied: images, videos, representations of the physical world and the living world. It is therefore necessary to rethink and reinvent the big data tools and architectures to capture, store and analyze this data diversity.

 

Better integrate time variable: 

The time dimension is also an important challenge for the development of Big Data, both to analyze causalities in the long term than to treat accurate information in real time in a large data flow. Finally, the problem also arises in terms of storage. The volume of created data will exceed the storage capacities and will require careful selection.

 

IT architecture: 

 The technology landscape in the data world is changing extremely fast. Delivering valuable data means collaboration with a strong and innovative technology partner that can help create the right IT architecture that can adapt to changes in the landscape in an efficient manner.

 

Security: 

Last but not the least, we’ve security issue. Keeping such vast lake of date secure is a big challenge itself. But if companies limit data access based on a user’s need, make user authentication for every team and team member accessing the data and make a proper use of encryption on data, we can avoid a lot of problems.

 

The change of scale offered by the technologies of Big Data have generated profound paradigm shifts in scientific, economic and political fields. But it also impacts the human field.

 

Xorlogics cognitive abilities are indeed developed to treat and represent all number of data. Big Data thus puts us to the test to challenge our analytical capabilities and our perception of the world. As we change and grow, the beliefs that are most vital to us is to put the people first, follow excellence, embrace the change and act with integrity to serve the world.We at Xorlogics have exceptional expertise in the domain of Big Data like Hadoop EcoSystem (HDFS), MapReduce, Pig, Spark, Storm HBase, Cassandra, MongoDB, Hive, Sqoop, Thrift, Zookeeper, HUE, Nutch Tika, Kafka.

 

So if you are looking for more information or to gain a better understanding of big data terms, tools and methodologies don’t hesitate to contact our experts in the data field!

Machine Learning and a powerful Customer-Service

Machine learning: powerful customer service

Every business-customer couple is looking for a certain harmony. But like any other private couple, it cannot exist without first having a strong knowledge of one another.

Monday, is your birthday. You open your emails and, wow surprise, your favorite shoe brand address their vows with a discount code. But before you go further to use this delicate attention, you note that the recipient is not good, means the mail and promo code might not be for you. And what’s more annoying than receiving a mailing from one’s favorite brand with such error? Unfortunately, this kind of mistake is not so rare in the context of written exchanges between a customer and a business. And although regrettable, this is sometimes the tip of the iceberg in terms of cutting edge of customer relationship.

 

Actually such errors in the commercial couple not only irritate, but they can also be the cause of branch of the company-client marriage contract. All loyal customers may fly away if their trusted brand is not even able to store essential information about them. Because Customers expect to be heard and acknowledged, to be treated with the utmost care and personalization, and to receive responses promptly.

As any other couple, the business couples thus have its ups and downs. However, with a little effort we can make this relation stable. And for a business, knowing a customer on the fingertips is a must.

 

Machine learning is based on algorithms that can learn from data without relying on rules-based programming. To be able to work on its commercial couple and ensure its present and future, every company therefore needs to know how to collect and operate effectively its customer data.

Development of data gathering tools, databases, behavioral segmentation techniques, connected data feedback from the field, are just as many opportunities on which it is necessary to invest in order to create a certain connection with the customer. Only by knowing the “who”, “what”, “when”, “how” and “why” about the act of buying, companies will be able to provide personalized service.

 

But so far, the collection of data alone is not the happy ending of the story. Like any old couple who knows each other by heart, if one doesn’t anticipate the desires, expectations and limits of others and does not act according, misunderstandings and conflicts takes birth. For a long term harmonious relationship, companies must therefore capitalize on these new gathered data and to do this machine learning is the best option. By describing the ability of a computer to not only calculate but to learn without being explicitly programmed, machine learning analyzes the raw data, synthesizes and then leaves it to companies to operate according to the relevance of their “data driven strategy”.

 
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Duo of anticipation and empathy, a win-win for commercial couple:

Today, the customer data volumes is exploding, more data has been created in the past two years than in the entire previous history of the human race thanks to the advent of digital channels and connected communication tool for customer interactions and business have thus become very complex. Machine learning makes it possible not only to sort and keep only the essentials, but also helps to learn about what are needs, expectations and requirements of customers, so companies can anticipate their actions and harmonize the client relationship.

 

A well-synthesized insight, driven by approaches such as machine learning, can provide opportunity for any company to predict. In particular, I would say that customer-profiling based on data from touch-points can allow companies to not only determine the stage of a customer within the sales funnel, but to predict their actions and reactions in the future. While only the biggest players have access to the technological know-how to do this well right now, it’s only a matter of time before SMEs can replicate it and take advantage of the computing power already at their disposal.

 

Customer service teams are expected not only to react to requests and questions coming their way, but to also proactively anticipate customer needs. Machine learning is also anticipation. It’s a powerful tool to analyze the actions of customers and sales assistant, but also to identify some keywords used throughout their conversations to recognize problems and find within the company’s knowledge base information a solution to the problem. Companies can identify urgent request of customers and respond quickly. A bit like when it comes to detect in the long conversations THE topic which should not be overlooked and which fully deserves our attention.

 

Beyond anticipation, machine learning also increases the empathy of any business capacity. By learning from their exchanges, companies eventually learn a lot about their customers and can offer personalized services. The challenge is then to provide new goods and services by knowing from customer purchase history, to give free shipping or reductions when it comes to a loyal customer or his birthday. A professional error can happen, in that case a company must admit their mistake and do everything to compensate the client at the right time, but also must learn from their errors and take extra precautions to reduce the risk of problem in the future.

 

So Machine learning is a predictive (and increasingly prescriptive) analytics approach that teaches computers to think and solve problems like a human, continuously adapting to new information. With machine learning, you can monitor the entire customer experience to not only gain new perspective but actual guidance on the best next steps to take, because it’s virtually impossible to grow your business over time without putting the customer first. While there are plenty of tools and services that allow you to streamline aspects of marketing and customer service, be wary of letting these resources overtake your entire business model. The only way to build a profitable business is by humanizing your brand and developing lasting connections with your customer base.

 

Internet of Things, a booming connectivity for Africa

African economic pulse has quickened, infusing the continent with a new commercial technology known as The Internet of Things. This new technology offers many prospects for the continent, and can help solve many problems.
In terms of IT infrastructure, Africa is currently very behind compared to other more developed nations. However, more than half sub-Saharan African population has a mobile phone. Therefore, the Internet of Things is the logical continuation in terms of connectivity for the African continent.

IoT in Africa

A driver for economic growth for Africa:
The Internet of Things is much more than a simple technology. This is a product and services ecosystem, from the simple device to the technology of Cloud. As you might know an efficient connectivity adds real value to businesses. This value provides an exciting prospect for Africa, and could result in significant economic growth. The African IT could also quickly catch up and align with the rest of the world.

 

A booming connectivity: 
The adoption of the Internet of Things in Africa is nothing fancy. According to a McKinsey study, the penetration of internet in Africa will triple by 2025 to exceed 50%. This represents 600 million regular Internet users. The study also predict a strong potential for the Internet of Things in developing countries. By 2020, these countries could represent 40% of the global market value of the IoT.
Currently 15% of the world population lives in Africa. More than half of global population growth between now and 2050 is expected to occur in Africa. Therefore, the deployment of a connected system is essential to this.

 

IoT promises: 
The Internet of Things has the potential to solve many problems on the African continent. Many African countries have already embarked on the IoT adventure. Caregivers in Ethiopia monitor patient health status to adapt and adjust their treatment. Nairobi’s connected Traffic lights helps to regulate traffic. In South Africa, utilities suppliers use connected measuring tools to prevent possible overloads. Wildlife is monitored and maintained through connected DNA analysis applications and satellite imagery. DNA analysis has proved a game changer in wildlife.
The potential of the Internet of Things in Africa is unlimited. As technology advances and integrates daily life of most of citizens, we always expect more from IoT solutions to solve the problems.

 

A solution to the agricultural problems: 
In sub- Saharan Africa, 95% of usable land depend on rain. Therefore, food crops are often minimal, and the risk of famine continues to loom as a threat. With the IoT, wireless sensors can monitor the growth, soil moisture levels and water tanks. Smart vehicles can reduce the required physical labor. Thus, cultures can be more prolific, for a lower cost. According to the United Nations Food and Agriculture Organization, agricultural production needs to increase by 60% to feed the entire population expected to reach nine billion by 2050.
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The sensors on their equipment help farmers to manage their vehicles and tractors, reduces time usage while saving fuel. The information is combined with historical and meteorological data, or data relating to ground conditions.

 

Limiting the effects of natural disasters and epidemics: 
Connected Robots can help limit the effects of natural disasters. Still in development pharse, future robots, connected IoT technologies and control mechanisms as SORMAS of SAP could reduce the impact of epidemics such as Ebola. We all know when the powerful earthquake in March 2011 triggered a tsunami that devastated Japan’s Fukushima-Daiichi nuclear plant and raised radiation to alarming levels, authorities contemplated sending in robots first to inspect the facility, assess the damage and fix problems where possible. Ever since, Defense Advanced Research Projects Agency (DARPA), an agency under the U.S. Department of Defense, has been working to improve the quality of robots. It is now conducting a global competition to design robots that can perform dangerous rescue work after nuclear accidents, earthquakes and tsunamis.

 

Several obstacles:
The future looks bright, but there are still many obstacles to overcome. The implementation cost of the IoT infrastructure is very high, and the investments will likely come from outside. Moreover, the hacking risk is a major threat. In addition, it is imperative to deploy training programs to educate and enable it to exploit the opportunities offered by this new technology.

 

The overall connectivity is essential. For now, many African nations are lagging behind in this area. The lack of infrastructure, however, can be beneficial for Africa. Instead of incremental updates techniques, the continent can directly jump into the wagon of new technologies in a way that is not possible for developed countries.
The Internet of Things happen in Africa, and African companies cannot ignore this novelty. Also, be prepared to face challenges in terms of security, and be able to articulate the return on investment are two key points to enjoy this new boom.

 

Sources:
World Population Prospects
What’s driving Africa’s growth
Lions go digital: The Internet’s transformative potential in Africa

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.

Google reveals five security issues concerning Artificial intelligence

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

 

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 has tried to explain to 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 :

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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.

 
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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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