Science has changed our lives beyond recognition in the last few decades. A key part of this journey was mobile network technology that continuously evolves and provides us with ubiquitous internet access. The next stage of this evolution is 5G tech that promises unprecedented connectivity and internet speed. More than that, fifth generation connectivity will power self-driving cars, smart cities, and connected factories, turning them into the pillars of modern economic and social systems. Total connectivity is well upon us, and we’re witnessing the beginning of that transformation.
Digital transformation is human transformation and that’s where you need to start. The first step towards a successful digital transformation is not the technology itself, but thinking about how you can empower your people through it. Where do you expect value to shift to? What new skills will your people need to learn in order to succeed? How can technology help them get where they need to be to serve your customers well?
For nearly a decade, healthcare professionals have been introduced to “quick-fix” automation solutions that have failed to work cohesively with existing systems. As a result, these implementations have largely failed – causing distrust amongst employees about the value of new technologies. However, RPA is different in that it is quick to implement, seamlessly integrates with existing systems and delivers near-immediate value. Here, we debunk common myths about RPA in healthcare to explain how technology can free up time and resources spent on administrative tasks.
It is crucial for the companies to give a cultural work environment to its employees if the employers want to increase the total output productivity. The culture-building process cannot simply be effective enough for implementation without the use of technology. Technology and culture building are two aspects that can be integrated into the immense growth of the business. In other words, building a culture at work is impossible without the integration of technology.
Data Science is an exciting job, but it can be very difficult to perform if you speak to a non-technical audience. Data and business are intimately related to each other and you must remember this point when you work with business-oriented people. The only way to survive is to find a middle point between a data-driven bottom-up approach and a business-driven top-down approach. Finally, as Data Science is hard and time-consuming, delivering small results with a constant delivery rate is the only way you can keep your customers engaged.
Understanding a phenomenon is the first step toward engineering it, so if we have an explanation of consciousness we can hope to succeed in building the same functionality into our AIs. In any case, it is very unlikely that artificial intelligence with no ability to explain its reasoning with human concepts will be socially acceptable. Equip it with human characteristics such as consciousness would probably be the only way for us to trust it and solve the black box problem, that is to say artificial consciousness out of necessity.
The term digital transformation is often used in relation to IT transformation or business transformation but even though they are closely related, the terms are distinctly different. But digital transformation does not really refer to IT systems but rather to an organisation’s underlying business and its business processes going digital and becoming more agile. Digital transformation will also require the use of various technologies such as cloud computing, business analytics (BA), artificial intelligence (AI) and machine learning (ML) and, going forward, the Internet of Things. Here are five digital trends to note.
This article familiarizes you with data storage in Docker. There are many ways to save data with Docker. Data in Docker can either be temporary or persistent. Data can be kept temporarily inside a Docker container in two ways. Many times you will want your data to exist even after the container is long gone. Many times you will want your data to exist even after the container is long gone. You need to persist your data.
As for dark data, it’s all the information companies collect in their regular business processes, don’t use, have no plans to use, but will never throw out. Its web logs, visitor tracking data, surveillance footage, email correspondences from past employees, and so much more. While dark data may never be used or be useful for many organizations, it’s something that should not be ignored. Then what are some of the best practices with dark data? What can be done to get the most value from it?
We’ve already seen some of the threats manifest themselves in various ways, including biased algorithms, AI-based forgery and the spread of fake news during important events such as elections. The past few years have seen the development of a growing discussion around building trust in artificial intelligence and creating safeguards that prevent abuse and malicious behavior of AI models. The various efforts are focused in three fields of fairness, explainability and robustness. It’s important to create robust AI models and to evaluate the resilience of artificial intelligence algorithms against abuse and erratic behavior.
As AI gets better and more sophisticated, it also enables cybercriminals to use deep learning and AI to breach security systems just as cybersecurity experts use the same technology tools to detect suspicious online behavior. Deepfakes, using AI to superimpose one person's face or voice over another in a video, for example, and other advanced AI-based methods will probably play a larger role in social media cybercrime and social engineering. It sounds scary, and it's not science fiction.
Today, when customers seek instant responses to their queries, chatbots are a popular solution. They are already making their way to messaging mediums. The real challenge, though, lies in building a chatbot that is engaging, interactive and is able to provide real value to users. With easy-to-use chatbot platforms, making a chatbot is now quick and hassle-free. And with unprecedented technological advancements in artificial intelligence and machine learning, the future of chatbots looks bright.
Recall that a Docker image is made of a Dockerfile + any necessary dependencies. Also recall that a Docker container is a Docker image brought to life. To work with Docker commands, you first need to know whether you’re dealing with an image or a container. Once you know what you’re working with you can find the right command for the job. This article highlights the key commands for running vanilla Docker. Here are a few things to know about Docker commands.
You could say there is too much AI hype. It is not a contentious thing to say, it just is. Maybe it would help if we re-defined it. Instead is saying AI means artificial intelligence, maybe we should return to an earlier definition, algorithmic intelligence, instead. Artificial intelligence is the application of algorithmic computation to large data sets. To see through the hype, just remember that AI could just as easily mean algorithmic intelligence.
HR representatives can dig into data to tackle their needs. Doing so can help their companies and employees thrive in an ever-demanding landscape. HR data can help companies figure out what factors make employees most likely to quit. It can also pinpoint which workers might be feeling upset about their roles, allowing HR representatives to intervene proactively. HR people often use technologies to impact their work, big data among them. Here are six ways big data in HR has had an impact.
If robots are truly taking over, then why are having trouble finding enough humans to do work that needs being done? The truth is that automation doesn’t replace jobs, it replaces tasks and when tasks become automated, they largely become commoditized. So while there are significant causes for concern about automation, such as increasing returns to capital amid decreasing returns to labor, the real danger isn’t with automation itself, but what we choose to do with it.
By enabling the digitization of assets, blockchain technology is driving a fundamental shift from the Internet of information, where we can instantly view, exchange and communicate information to the Internet of value, where we can instantly exchange assets. A new global economy of immediate value transfer is on its way, where big intermediaries no longer play a major role. Blockchain will profoundly disrupt hundreds of industries that rely on intermediaries, including banking, finance, academia, real estate, insurance, legal, healthcare and the public sector — amongst many others.
Today’s consumers are digital natives who are media-savvy, and that’s made them impervious to conventional marketing techniques. Rather than just trying to sell them something, businesses need to find a way to reach them on a deeper, more personal level. And it’s easy to understand why technology has taken on an increasingly prominent role in this sector in recent years, forcing companies to adapt or risk being left behind. In fact, it’s increasingly obvious that tech is the future of marketing.
In the past few years, telepresence robots have been around, but have not gained widespread traction in the enterprise. Yet, increasingly we are seeing classes of robots designed to interact with people in a distributed physical space. Moving into 2019, we will see an expansion of these office and personal robots meant to help us collaborate and coordinate action with others. As the year comes to an end, here are some predictions on digital workplace, specifically, the rise of the cobot - a robot interacting and functioning with people.
Digital transformation is bringing fundamental change to businesses across all sectors. Business leaders and IT professionals know tsunami of data is here, but many don’t yet have a comprehensive strategy to integrate it all, so they rely on stop-gap measures. Four data myths in particular hold companies back from creating an effective, long-term data integration strategy. Here’s a brief overview of each myth — and an explanation of why it’s leading businesses astray.
Despite chatbots sometimes being regarded as an overhyped technology, most realistic scenarios indicate that the bots will inevitably take on the role of being the first line of technical support. By its very essence, this technology opens up access to the new market opportunities by encouraging scalable, one-on-one conversations between brands and consumers. Creating a world where companies are building stronger relationships with their clientele is definitely worth it. There is no doubt that in the foreseeable future, chatbots will ultimately become what they had been designed to - an irreplaceable assistant for humanity.
The blockchain value framework is aimed at helping organizations identify the concrete value of blockchain technology in their use-case proposals and build a corresponding business case. The framework has three distinct dimensions: improved productivity and quality, increased transparency among parties, and reinventing products and processes. Each dimension includes a distinct set of blockchain-enabling capabilities that provide a solution to a concrete pain point or present an area of opportunity. In addition, the framework will help identify where the real value will be created.
Facial recognition holds so much more potential. Massive opportunity lies in facial recognition software, from helping to identify missing or exploited children to speeding up lines in the airport. As marketers trying to leverage facial recognition as a vehicle to improving customer experience, is there still a way for us to use the technology? How can we tap into the benefits without crossing the line of personal privacy? Before the technology is unleashed into the corporate world, companies need to fully understand it.
Marketers use big data to sell more products. Big data marketing, however, analysing data to improve marketing activities isn’t about more ads. It’s about serving the right ads to the right people at the right time. It’s about answering questions regarding what type of message resonates with customers, which landing page is the most efficient, or which social media platform should the company use to reach their target audience. Without big data, companies operate on unproven assumptions, which is never a good idea. That’s why businesses use big data marketing to solve puzzling issues.