Addressing ever changing challenges
A number of evolving challenges do not arise from the technologies themselves, but to the way they are used or perhaps abused.
Interview with Frédéric Oblé
@fredericoble - Member of the Atos Scientific Community
A growing number of ethical challenges are raised by the application of digital technologies in the IT for Life sciences area, such as advancement in medical technology and food production technology.
In the area of analytics, we are faced with the challenge of Fast Data and the ability to analyse data streams in real time. At the other extreme we have the challenge of Deep Learning as we look to exploit an ever increasing pool of data, using machine learning and algorithmic searching to unlock otherwise hidden insights.
Identity and Privacy and Security debates will continue as the needs, wishes and expectations of stakeholders like citizens, governments and businesses evolve.
With a move to increasingly collaborative working between potentially trustless parties and with the promised advent of high disruptive technologies like Quantum Computing, these topics are more relevant and impactful than ever.Read More & Download PDF
Emerging & evolving technologies
Privacy, labor and cyber-physical security will remain open challenges by 2020, as businesses and societies continue to learn that no technology can fully solve the world’s problems.
Demographic trends and climate change are putting increasing pressure on some of the basic human needs like food, drinking water and healthcare.
Add to this the challenges of sustainability in areas of energy production and raw material consumption, and it is clear that current thinking and practices need to change.
In a number of respects Information Technology and Digital Technologies can be significant enablers of radically new approaches that could help transform the way we face these challenges. Particularly in the area of Health, our understanding of the human genome together with ever increasing quantities of data relating to patient medical histories is facilitating a shift towards a healthcare model that is predictive, preventative, personalised and participatory.
Natalia Jimenez Lozano
Fast Data puts the emphasis on generating actionable intelligence at high speed, enabling immediate response based on insights derived from deep analytics of incoming data streams.
Fast Data is time-critical: its value and derived insights exist within a small window of opportunity as it initiates actions or decisions based on the events identified and on the analytics applied thanks to the historical analysis.
Sources such as Telecom networks, smart sensors, & connected devices provide fast event streams that exceed several thousand events per second and now are approaching millions per second.
This reality has prompted the development of distributed streaming computing platforms: the seed for Industrial Data Platforms.
Algorithms, and their optimized computation architecture, will be the critical differentiator in the successful implementation of Fast Data. HPC technologies will enable multiple use cases such as image analysis in autonomous cars and usage of Blockchain in energy gateways.
The adoption of Fast Data also requires organization agility to address and make real-time decisions, thus driving a need for coordination between networks of people. Since the value generated from fast streaming data depreciates rapidly with time, businesses need to reduce the gap between events and decisions in order to exploit transient business moments
Deep Learning is one branch of technology in the field of Machine Learning and Artificial Intelligence. It involves very large and multi-layered networks of artificial neurons that mimic the behavior of the human brain.
Using large, multi-layered, Neural Networks with advanced algorithms it is possible to model, teach and ultimately analyse sets of data to develop understanding and make decisions.
These techniques will be applied to autonomous vehicle guidance, weather prediction and health monitoring. When used in image processing scenarios, Deep Learning will aid in facial recognition, video surveillance, handwriting analysis and object identification.
With audio, it will provide automatic speech recognition and translation services.
Billions of connected devices will be part of new ecosystems, interacting dynamically and without strict supervision, giving rise to new challenges and threats in the context of Cybersecurity.
In addition, new and exciting opportunities are provided to offer deeper integrated and open solutions aimed at improving overall situational awareness and increasing organizational and system resilience.
A number of upcoming technologies will by 2020 combine to become important drivers of simplicity and automation in cybersecurity engineering and operations. Those technologies will support concepts for the security of interconnected value chains – like trusted brokers, dynamic access control, application shielding or cyber ecosystems.
New security architectures will break traditional infrastructure silos. Find out what we will foresee as essential abilities for cyber resilient system.
Real-time security analytics will help to reduce the detection time of attacks and their neutralization.
Next generation cryptography with blockchain or homomorphic encryption will secure the communication and the data itself.
Have a deeper look on our prediction to the market adoption of Cybersecurity until 2020 and how it impacts your business.
Marc Llanes Badia
Ubiquitous computing in combination with a high volume of information has all the characteristics to make privacy a concept of the past.
Consumers are aware that sharing data is key to simplify many of their daily activities, but they expect increased protection of their identity as well as value and rewards in exchange for the exploitation of their usage patterns and data.
Global surveillance, recurring identity thefts and data breaches have created mistrust between individuals, governments and corporations. Data protection legislations will be adopted across the globe to strengthen the obligations of data processing entities.
Data operators will re-invent and re-think the allocation of their respective obligations and their relationships with public authorities. They will progressively adopt and publish their common data protection policies.
Consumers are widely adopting “privacy enhancing” technologies, techniques such as ad blockers or the intentional “dirtying” of databases, and will be ready to swap provider if they do not perceive appropriate data protection.