With local lockdowns being a new threat, councils face fresh calls to gather and understand social distancing requirements. This isn’t just in large towns and cities; local authorities need to be able to assess and understand risk across broader geographical areas to keep people safe. More small towns and villages are already installing cameras and sensors (or upgrading their current infrastructure) to capture data in their streets to identify places where people struggle to social distance. The city of Oxford, too, has implemented a large scale deployment of cycling specific sensors. Councils and other local authorities are taking their responsibilities seriously. Aiding all this is AI. Artificial intelligence can underpin a council’s strategy for coping with the Active Travel boom. In practical terms, this means positioning cameras at busy junctions, on popular footpaths, and around town and city centres, then analysing what those cameras see. It’s not just a numbers game, although knowing with confidence how many people are travelling in a certain area on a given day will certainly be useful. AI can quickly identify where social distancing is struggling to be adhered to due to road or path layout, and spot dangerous behaviour such as undertaking or cyclists riding on pavements.
Our current way of protecting online data is to encrypt it using mathematical problems that are easy to solve if you have a digital “key” to unlock the encryption but hard to solve without it. However, hard does not mean impossible and, with enough time and computer power, today’s methods of encryption can be broken. Quantum communication, on the other hand, creates keys using individual particles of light (photons) , which – according to the principles of quantum physics – are impossible to make an exact copy of. Any attempt to copy these keys will unavoidably cause errors that can be detected. This means a hacker, no matter how clever or powerful they are or what kind of supercomputer they possess, cannot replicate a quantum key or read the message it encrypts. This concept has already been demonstrated in satellites and over fibre-optic cables, and used to send secure messages between different countries. So why are we not already using in everyday life? The problem is that it requires expensive, specialised technology that means it’s not currently scalable. Previous quantum communication techniques were like pairs of children’s walkie talkies.
One of the key value propositions of data management is to deliver data to internal and external stakeholders in required quality for different purposes. Data management sets up data value chains that turn raw data into meaningful information. Different data management capabilities should enable data value chains. The core data management capabilities taken into the “Orange” model are data modeling, information systems architecture, data quality, and data governance. In Figure 1, they are marked orange. These capabilities are performed by data management professionals. Other capabilities that belong to other domains like IT, security, and other business support functions. To implement a data management capability, a company should establish a formal data management function. The data management function will become operational by implementing four key components that enable data management capability such as processes, roles, tools, and data ... To make the evidence objective, it should be measurable. This is the second criterion. For example, you can prove your progress by demonstrating the number of data quality issues resolved within a specified period. You should also compare the planned and achieved resolved issues.
The fourth generation of AI is ‘artificial intuition,’ which enables computers to identify threats and opportunities without being told what to look for, just as human intuition allows us to make decisions without specifically being instructed on how to do so. It’s similar to a seasoned detective who can enter a crime scene and know right away that something doesn’t seem right, or an experienced investor who can spot a coming trend before anybody else. The concept of artificial intuition is one that, just five years ago, was considered impossible. But now companies like Google, Amazon and IBM are working to develop solutions, and a few companies have already managed to operationalize it. So, how does artificial intuition accurately analyze unknown data without any historical context to point it in the right direction? The answer lies within the data itself. Once presented with a current dataset, the complex algorithms of artificial intuition are able to identify any correlations or anomalies between data points. Of course, this doesn’t happen automatically. First, instead of building a quantitative model to process the data, artificial intuition applies a qualitative model.
While size is a factor, both small and large companies can benefit from leveraging the expertise of a partner. Small companies can get enterprise-level services for a fraction of the cost of supporting full time employees; large companies can relieve their IT departments of time-consuming tasks and still save money. This allows for both to focus on their core competencies – the outsource provider brings platform and process expertise to the table to help guide program maturity while handling the grind of scanning, analysis and reporting. This frees up the customer organization to focus on operating their business and handling strategic technology initiatives. A qualified third-party company that specializes in VM already has the certified security professionals on board who are not only up to speed with the latest threats, but always use the most effective detection tools and are in the loop of important new information. If you answered in the affirmative to outsourcing VM, you’ll want to know how to select a company that is truly going to help you shore up the weaknesses in your defenses. First, you want one that has years of experience protecting businesses and offers dedicated support 24/7.
Operationally, challenges stem from misalignment in understanding who the end customer really is. Companies often design products and services for themselves and not for the end customer. Once an organization focuses on the end user and how they are going to use that product and service, the shift in thinking occurs. Now it’s about looking at what activities need to be done to provide value to that end customer. Thinking this way, there will be features, functions, and processes never done before. In the words of Stephen Covey, “Keep the main thing the main thing”. What is the main thing? The customer. What features and functionality do you need for each of them from a value perspective? And you need to add governance to that. Effective governance ensures delivery of a quality product or service that meets your objectives without monetary or punitive pain. The end customer benefits from that product or service having effective and efficient governance. That said, heavy governance is also waste. There has to be a tension and a flow or a balance between Hierarchical Governance and Self Governance where the role of every person in the organization is clearly aligned in their understanding of value contributed to the end customer.
Different microservices teams can have their own lifecycle definitions and different user roles to manage the lifecycle state transfer. That allows teams to work autonomously. At the same time, WSO2 digital asset governance solution allows these teams to create custom lifecycles and attach them to the services that they implement. As part of that, there can be roles that verify the overall governance across multiple teams by making sure that everyone follows the industry best practices that are accepted by the business. As an example, If the industry best practice is to use Open API Specification for API definitions, every microservices team needs to adhere to that standard since it is technology-neutral. At the same time, teams should have the autonomy to select the programming language and the libraries used in their development. Another key aspect of design-time governance is the reusable aspect. Given that microservices teams are stemmed from ideas, there can be situations where certain services that are required to retrieve data for this new microservices implementation is already available via a service developed by another team.
Cloud-native infrastructures and security observability are purposefully designed to remove the security speed bumps that slow innovation down, and instead, leverage a security guardrails approach that supports even faster software integration and delivery. Developers may then focus on serving the customer when they have tailored observability available—driven by automated security feedback cycles—so teams can quickly learn from mistakes and rapidly deliver value and innovation to customers. Optimizing customer experiences on the fly, for example, is just one cloud-native advantage made possible by event-driven architectures (EDAs). DevOps teams are now smartly requiring embedded security context across the development life cycle in order to understand what is going on and to help automate security of their cloud-delivered applications. Any migration into application programming interface (API) and event-driven architectures like cloud-native environments can enjoy the benefits paid forward from preexisting, automated, observable security deployed across your application development life cycle.
While practical uses get the most attention, machine learning also offers advantages for basic scientific research. In high-energy particle accelerators, such as the Large Hadron Collider near Geneva, protons smashing together produce complex streams of debris containing other subatomic particles (such as the famous Higgs boson, discovered at the LHC in 2012). With bunches containing billions of protons colliding millions of times per second, physicists must wisely choose which events are worth studying. It’s kind of like deciding which molecules to swallow while drinking from a firehose. Machine learning can help distinguish important events from background noise. Other machine algorithms can help identify particles produced in the collision debris. “Deep learning has already influenced data analysis at the LHC and sparked a new wave of collaboration between the machine learning and particle physics communities,” physicist Dan Guest and colleagues wrote in the 2018 Annual Review of Nuclear and Particle Science. Machine learning methods have been applied to data processing not only in particle physics but also in cosmology, quantum computing and other realms of fundamental physics, quantum physicist Giuseppe Carleo and colleagues point out in another recent review.
Quote for the day:
"You do not lead by hitting people over the head. That's assault, not leadership." - Dwight D. Eisenhower