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DATA CENTER OF TOMORROW: TOP TECHNOLOGICAL TRENDS

DATA CENTER OF TOMORROW: TOP TECHNOLOGICAL TRENDS

How will be the data center of the future?

IDC, the popular analytics firm, has identified the technological trends that will distinguish data centers of the future. Since ten years ago, data centers’ world is going through an extraordinary transformation, influenced by advanced technologies such as Cloud and Edge Computing, Artificial Intelligence and Machine Learning. Data centers of tomorrow will be increasingly agile, distributed and automated infrastructures, capable to guarantee the services requested with superior performance and efficiency compared to traditional data centers. The paradigm shift comes in part from new business applications that request a different infrastructure management, in part from the need of keep supporting the legacy world.

data center futuro

There will be a sort of middle layer between core and edge: her’s where IDC places data centers of tomorrow. Traditional facilities must evolve according to the new logic of technology: proximity to users and to the Internet of Things is an essential aspect to approach and manage the ever-growing amount of data created every day. Now we will see the top technological trends of data center of the future according to the experts researches.

Turnaround in Data Centers facilities

There will be a radical change for what regards in-house data center facilities. Until now companies have been focusing on the development of big data centers, while with the digital transformation they will invest more and more on smaller centers, located at strategic points to better respond in terms of latency. IDC predicts that during the current year 25% of firms will abandon the consolidation of large data centers in favour of smaller and better placed buildings.

Edge Computing & smart gateway

Edge computing will be an essential element in data centers of the future, because it puts data close to users ad allows to use advanced technologies such as Internet of Things and Artificial Intelligence, that request superior computing power. In this connection, it must be emphasized the importance of IoT gateways, able to collect edge data and to connect them to other networks and data centers. IoT devices are a precious tool for the entire monitoring process and also as concerned the management of possible computing and storage problems. Data centers will become once again the point of reference for edge computing infrastructures, whic will send to dc the information collected in the first instance and will get back from dc the processed data, and must, moreover, provide data protection.

Increase of Data Vaulting strategies

IDC predict that in 2020 40% of firms will use a data vaulting strategy to manage the proliferation of information, by using intermediate data storage centers managed by colocators, without having to build new facilities. Companies can access to those sites through pay-per-use packages including space, connectivity, security and storage, where they can start processes to cleanup and manage the stored data. This way firms can exchange data among cloud data centers, data vault and edge sites.

Automation: Machine Learning & Artificial Intelligence

What is described above means a greater fragmentation, which will bring to an increased use of advanced technologies to optimize the infrastructural management of data centers, data vault and edge networks. In particular, Artificial Intelligence and Machine Learning will affect the operations bringing a superior level of automation which will ensure higher resilience and performance. IDC predicts that within 2022 50% of IT assets of business data centers will operate autonomously thanks to AI technology. A trend that will become more relevant in edge sites, where the human presence will be limited or even absent.

Consolidation of Multi-Cloud environments

Multi-cloud strategies will be further increased. That because from each Cloud environment shall be possible provide custom services according to the client’s needs. Only this way data centers of tomorrow can respond properly to the market demand by ensuring the flexibility and agility requested.

 
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8 BENEFITS OF INTELLIGENT MONITORING FOR BUSINESSES

8 BENEFITS OF INTELLIGENT MONITORING FOR BUSINESSES

The Artificial Intelligence in the service of companies

Monitoring systems are an essential aspect of the IT infrastructure management, because they enable clients to control the functioning, the productivity and optimize processes, but especially they are a crucial tool to prevent blocking issues and react in real time with corrective and efficient actions where a negative event take place. With the evolution of technology, monitoring systems became increasingly complex and advanced, until they come to the monitoring based on Artificial Intelligence. How it differs from the traditional monitoring activities and what are the benefits of that solution? Let’see them now! In the following line we will describe some advantages of the monitoring solution offered by our technological partner, Dynatracethe world leading company in the performance management field.

1. “All-in-one” software
Dynatrace has a great benefit compared to traditional solutions: it includes all the monitoring features in a single software. Real User Monitoring (RUM), Synthetic Monitoring, infrastructure and application monitoring are all integrated in the same tool, simplifying a lot the firms’ life. As well as simple monitoring, Dyatrace is used for the effective management of performances and digital experiences, aiming to provide customers with the highest level of user experiences.
2. Automation
The heart of the platform is made by automation; every single step is automated, including upgrades. Thanks to that, Dynatrace is able to identify any emerging issue and anticipate its impact on the system. All of this will finally result in easier and faster processes and in a great saving of time and costs for the IT department.
3. Artificial Intelligence
What distinguish more Dynatrace from the other monitoring services is that the solution is powered by Artifical Intelligence. With AI the monitoring gets proactive and provides precise real-time insights which help companies in the management of complex systems, otherwise unmanageable. Because of Artificial Intelligence, the monitoring service does not offer only data but actionable answers to best manage mission-critical Cloud infrastructures.
4. Full stack
Dynatrace analyses each component of the system, by offering a 360 degree view of the digital experience. The platform is not just monitoring, it is also able to identify and understand the relationships and interdependences, top to the bottom, which exist in the digital ecosystem.
5. High scalability
Dynatrace is a native-Cloud solution and for that reason it provides a monitoring service with high scalability, availability and security. The platform can easily scale to over 100,000 hosts.
6. Flexible deployment
Dynatrace platform has the maximum flexibility and allows to simplify the deployment process significantly.
7. Enterprise level
A unique role-based access and advanced security measures make Dynatrace an enterprise-quality tool, designed with the aim of being widely used by large firms.
8. Compatible with hybrid Clouds
Dynatrace is a very flexible software, compatible with most of the technologies available on the market.

Do you think that your business needs an advanced monitoring service? Discover more information about Dynatrace solution and reserve now a free consulting with our experts!

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MACHINE LEARNING VS DEEP LEARNING

MACHINE LEARNING VS DEEP LEARNING

We already talked about Artificial Intelligence and today we will look into two subjects strictly connected to that discipline, Machine Learning and Deep learning, trying to figure things out. Those terms are often used improperly as synonymous of AI, but, even if they are “relatives” of that computer science, they are not the same. Let’s see in details the differences between Machine Learning and Deep Learning.

What is Machine Learning?

The term Machine Learning refers to a group of methods to train the Artificial Intelligence so that it can perform activities autonomously without them being previously programmed. Thanks to Machine learning, machines are able not only to solve preset problems but also to learn from experiences just like us, by correcting mistakes and taking decisions autonomously. In general, we can say that are mechanisms through which intelligent machines can improve their abilities and performances over time.

We can identify three different kinds of Machine Learning according to their characteristics:

  • With supervision: it provides the machine with a series of information that allow it to understand how to behave, a sort of database of experiences from which it can gain as it should carry out tasks. Experiences provided are already encoded, and the machine should just analyze them and choose the right response to the stimulus given.
  • Without supervision: this learning method is based on results analysis. Not coded information are provided to machines which should organize them in intelligent way so to learn what are the better results in each situation. Compared to the method with supervision it offers more freedom to machines and it has a higher level of complexity.
  • Reinforcement learning: is a learning system which can be defined as a “meritocratic” method, meaning that the machine learns how to act through rewards: for instance, it will be rewarded when it will achieve its goals. It is the most complex learning model of the three.

What is Deep Learning?

Deep Learning, instead, is a part of Machine Learning, a learning approach which takes the human brain function as model of inspiration. It’s a very complex learning method with different levels of education which needs tailored neural networks and a computing power able to support more layers of calculation and analysis. Although it may seem a futuristic technology, Deep Learning is widely used and present in our daily life. An application example are all the images and vocal recognition systems we use everyday through our smartphones.

 
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7 PREDICTIONS ON THE FUTURE OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING

7 PREDICTIONS ON THE FUTURE OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING

What will happen over the next ten years about Artificial Intelligence and Machine Learning?

Today we will show you the predictions of 7 tech experts about the future of Artificial Intelligence and Machine Learning in the following ten years: how society will change with the application of AI technology and what fields will be most affected by that? According to these specialists, in ten years the Artificial Intelligence will be able to…

1. Increase security
In the opinion of Nicholas Horbaczewski, CEO & Founder at the Drone Racing Leaguedrones will change the way we live. Somehow they represent now what mobile phones were in the ’90s. Drones are devices able to move objects very rapidly and, especially, they can fly. Package delivery, emergency responses or urgent delivery of medical products, anything will become immediate with drones. Horbaczewski consider them also a central point in the security field: they will make the world safer thanks to the possibility of inspect places otherwise difficult to control. Drones will become part of our daily lives and will change it radically, as well as smartphones and internet did.

2. Generate new services (and possible social issues)
Martin Ford, author of NY Times, says that Artificial Intelligence will improve our ability of solving problems and generate new ideas. It is likely that in the following ten years AI and robotics will be completely integrated in the business operations and will have a great impact on organizations’ efficiency: new products and service based on AI will be created, as well as new markets and customers. At the same time, Artificial Intelligence may eliminate certain jobs, which will become automated, and may create critical situations in terms of privacy, security and military applications. According to Ford, in ten years the debate about possible issues deriving from the application of AI will be central both at political and social levels.

3. Empower businesses
Matthew Kamen, VSP of Engineering at Foursquare, thinks that the applications of AI are “stuck” at this moment, and they are limited to reproduce what human beings already can do, or what humans trust to let them do. In ten years these trust barriers against the AI technology will decrease progressively and our addiction to algorithms and intelligent machines will grow. Kamen believes that AI technologies will bring a great change in enterprises and in the development of consumer-based applications, by giving to analysts, developers, marketers and many more professionals the chance to interact and understand users much better.

4. Improve healthcare
In the healthcare field, according to Serkan Kutan – CTO of Zocdoc, intelligent machines would be very useful. Many doctors work too much, they can’t see all their patients and can’t keep up-to-date with the new studies and advancements for lack of time. For that, AI could give a great help, especially for all what is related to patients data analysis and diagnostics. Indeed, machines would have a faster and immediate access to a larger set of clinical data and the doctor, by delegating that part of work, will have more time to interact with his patients and improve outcomes.

5. Facilitate sustainability
Nikita Johnson, Founder of RE.WORK, declares that Artificial Intelligence will have a serious impact on every single industrial field and everything we do. But at a higher level, fields like sustainability, environmental problems and climate changes, AI and Machine Learning would be at the forefront. There are many areas in which the machines could help a lot and create improvements, especially if we talk about the great challenges of our century like urbanization, population increases and energy. So, Artificial Intelligence will be used not only to increase business productivity but also for higher and more significant purposes.

6. Make humans smarter
John Stecher, Group Managing Director at Barclays Investment Bank, says that computing power will increase progressively, by giving us more power in training our Artificial Intelligence models. In addition, the amount of data analyzed will grow exponentially and that allows us to monitor more elements in our platforms and in the world in general. Combining that with Artificial Intelligence, we will have the ability of making more intelligent predictions about future behaviours and events and train smarter knowledge systems and models. Stecher thinks that the worries of many specialists about risks involved in the application of AI technology were groundless, because training a machine is similar to educate a child; if you teach him well, for example what is wrong and what is right, he will grow and become a productive member of the society who cares about people and future as a human being.

7. Inspire artists
Stephanie Dinkins, Transdisciplinary AI Artist, thinks that in ten years the intelligent algorithms will be part of most decisions made, small or big, and that artists may be involved in Artificial Intelligence. Even if that concept may sound daunting, it is just a matter of starting to use and explore AI potentials. Dickins encourages the artists to exploit AI with the purpose of create beautiful and expressive artistic works like they would make with any other medium. On the other side, she refuses completely the idea of the technology that becomes the artist. AI must be considered as a tool to increase human thought and develop creativity, not something to turn creativity or ethics over to the machines. According to the artist, what developers of AI should ask themselves is how can AI systems be used to increase productivity while respecting human diversity, dignity, and our cultural specificities?

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ARTIFICIAL INTELLIGENCE: FROM BIRTH TO THE PRESENT DAY

ARTIFICIAL INTELLIGENCE: FROM BIRTH TO THE PRESENT DAY

Everybody talks about it, but what exactly is meant by Artificial Intelligence?

“Artificial Intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals. In computer science AI research is defined as the study of “intelligent agents”: any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. Colloquially, the term “artificial intelligence” is applied when a machine mimics “cognitive” functions that humans associate with other human minds, such as learning and problem solving”. In other words, the term Artificial Intelligence refers to the ability of a machine to solve problems or in general act in the same manner as human beings. We don’t talk only about simple calculation skills but an intellect which includes different forms of intelligence, from spatial to introspective.

The first steps towards Artificial Intelligence

The first developments of Artificial Intelligence are not so recent, because the first project about AI is to attribute to the studies of two researchers called Warren McCulloch e Walter Pitt, who showed to the scientific world in the 1943 the first artificial neuron. Then, about ’50s, were developed the first prototypes of operating neural networks, that means mathematical algorithms with the purpose of reproduce the functioning of biologic neurons. The term Artificial intelligence is officially connected to another date and person: it was used by the American mathematician John McCarthy during a conference to which participate the major experts of the field that showed some programs able to perform logic thinking, in particular related to mathematics.

’70s: crucial experiments

From that moment, studies and researches in the field of Artificial Intelligence became many and in 1973 was born Lisp, the first program language which remains at the basis of AI for the following thirty years.
In that period the ferment about AI was very high and many sophisticated programs were created, but somehow that was the perception of the difficulties to reproduce the typically human intuitive and thinking abilities. After a phase of slowdown in the investments about this area, a new impulse came from biology: in 1969 some researchers created DENDRAL, a program able to re-build a simple molecule from the information on its molecular mass.

Therefore, the AI found a new development direction based on expert systems, meaning machines that used intensively the knowledge to achieve different solutions for particular contexts with some basic information. After that, the progress was very fast. At the beginning of ’80s the Artificial Intelligence was applied to the business field for the first time and the studies push the geographical boundaries of United States towards Japan and Europe. During these years the turning point was represented by the re-build of the algorithm for the learning through neural nets, already invented in ’60s. The most popular example of the Artificial Intelligence’s use is Deep Blue, a IBM machine which, through learning, defeated the world champion of chess because of a high level of knowledge and creativity that the human player couldn’t achieve.

The Artificial Intelligence nowadays

Nowadays, AI has many applications and is already present in our daily life, just think about voice recognition systems of electronic devices or security systems. Very popular is also the testing of AI on vehicles: equipped with intelligent systems, they are able to drive without a human conductor, thanks to specific cameras and sensors that, just like human eyes and ears, perceive everything while driving and are able to make decisions.

What is the future scenario about Artificial intelligence and related applications? We talk about it in this post: 7 PREDICTIONS ON THE FUTURE OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING

 
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