Blockchain is changing digital marketing in a disruptive way, potentially wiping out a whole new generation of companies built on its very existence. The real impact of blockchain in digital marketing is not just in the new use cases being developed. It’s in how those use cases will impact entire systems that have popped up as a way to manage the digital marketplace. In a time when digital marketing seems to be changing and growing by the moment, blockchain is changing digital marketing in disruptive, perhaps even irreversible ways.
AR and VR are not new, and in retail/e-commerce projects tend to land in one of two buckets: virtual reality and items in physical space. Image recognition paired with AR content can assist in-store shoppers to bypass clunky and complicated mobile navigation. If you want your AR or VR project to be successful it must add value to the customer; be it education, entertainment, or any type of utility, and encourage either a purchase, a repeat visit or positive brand impression.
It has been a long time in the making, but the technology for AI that can write code has finally arrived. In the same way that automation has revolutionised software delivery, AI for code promises to drastically improve the way developers interact with legacy systems. It offers an efficient way—arguably, one of the only viable ways—to upgrade core business applications, speed up the software delivery lifecycle and remove the costs of legacy code.
The cyber security industry is one with many open doors for those who either have the experience or the drive to gain it. Between rising cybercrime activity and more exacting laws and regulatory standards, the demand for skilled and knowledgeable cybersecurity professionals in 2019 continues to rise. However, it’s not just about just traditional training and education, being successful in the field also often involves. Tips How to get the cyber security job you want or to move up in your existing cyber security career
DataOps is a collaborative data manager practice, really focused on improving communication, integration, and automation of data flow between managers and consumers of data within an organization. DataOps is first and foremost a people-driven practice, rather than a technology-oriented one. DataOps is like the DevOps version of anything to do with data engineering. While still in its very early days, data engineers are beginning to embrace DataOps practices. Having a DevOps structure in place can ensure DataOps success
Today, we take automated fingerprint comparison, rapid DNA testing, and other tools for granted. But the technological march in forensics is far from over. With consumers more aware than ever of the value of their data and privacy issues, law enforcement needs new tools to fight these new crimes. That’s where AI can help—by using modern tools to solve modern crimes. Here’s what we can expect from artificial intelligence (AI) and its role in preventing and solving crimes of the future.
It is true that our perception of Artificial Intelligence is formed under the influence of mass culture with all its dreams and fears. Of course, AI plays an increasingly important role in our life and we’ll see tremendous improvements in technology in the following years, but in its essence, AI is a tool. It helps us to enhance our abilities, just like normal computers, or calculators, or a pen and a paper that improve our memory. So, we are, and will be, in charge of what to do with this tool.
How does a company transform its business model midflight, while at the same time competitively operating legacy businesses in order to provide stability and cash flow? AI can help you better understand what is truly driving value in your company. This is hard work, because it requires you to be data driven. Once you get your team and tools together, it’s time to begin the journey of real transformation using AI-driven insights to power platform business models.
Banks have access to enormous amounts of data about their customers, but due to multiple constraints this data is not yet sufficiently converted into useful insights. With competition in the financial services sector getting fiercer, banks need to adopt a data-driven approach if they want to stay competitive. As opportunities for incumbent banks and insurers from these insights are almost unlimited, Big Data will be a strong differentiator in the future competitiveness of financial institutions.
Technology is expected to change travel mostly by making it more convenient. In the future, travelers on business trips will spend less time going through security and more time collaborating with coworkers. Travel plans will be custom-tailored for both individual travelers and the company they work for. And all of these changes could accelerate as mobile devices and WiFi become even more ubiquitous and technology like 5G radically increases mobile connection speeds. These will cause the biggest changes in business travel and may even redefine what a business trip means.
A batched or scheduled approach needs to be considered as complementary and this too can incorporate AI and ML in how decisions are made and actions are taken. The technology has the power to execute for most use cases today, perhaps not at massive scale and perhaps in very specific and possibly narrow or as ‘weak AI’ propositions but these may just be the starting point for what Amazon's Jeff Bezos considers the AI Golden Age.
Container technologies have matured and will gain large-scale enterprise adoption in the next few years. Containers will offer you choice and control, which is very important as you decide when to scale or shift workloads in order to maximise performance and drive cost efficiencies. While change is always uncertain and can be perceived as increasing complexity, done correctly containers offer significant value as you progress your digital strategies. Resisting containers will not just affect your competitiveness it will hinder your ability to shape your IT strategy according to the needs of your business.
Today and well into the coming years, the survival of companies and the ability for humans to meet their material needs will increasingly rely on clean, well-operated, low-waste supply chains. Smart technology is now inescapable for warehouses. The benefits are just the start of what’s possible when companies take even modest steps into the future. Let’s look at some of the constituent components of a smart warehouse and find out how each one contributes to the larger goal of a more stable and efficient supply chain.
Artificial intelligence or AI, the phrase/acronym gets banded about an awful lot. In fact, many experts in AI don’t even like the term, they much prefer to use the words machine and learning, or ML. Delve deeper, and you come across many more terms, neural networks, deep learning, natural language processing and random forests. The trouble is, to a lot of people, the phrase artificial intelligence conjures up images of machines, ruling the world. The reality is very far from this vision of science fiction.
Put the finishing touches on your Data Science skill set by understanding the business perspective. This post explores the Fundamental Business Equation that each Data Scientist should understand. The content aims at educating technical Data Scientists who wish to have a tangible impact through their work. Find out which role Machine Learning can play in your company and how to find opportunities. The goal of a business is to make money, and Data Scientists are hired to help the company achieve this goal. How do you define “making money”?
As we continue to learn about the unique security threats of deep learning algorithms entail, one of the areas of focus are adversarial attacks, perturbation in input data that cause artificial intelligence algorithms to behave in unexpected (and perhaps dangerous) ways. Researchers are working on ways to build robust AI models that are more resilient against adversarial examples. Protecting deep learning algorithms against adversarial perturbation will be key to deploying AI in more sensitive settings.
Predictive analytics is one of the most important tools we have for putting humanity’s zettabytes of data to work for us. The widespread use of data in predictive analytics brings some new types of risks that should be on our radars, as well. The governments of the world are, rightfully, becoming more involved in the politics of privacy, for example. Here are four industries finding consequential ways to put this tech to good use.
Strategic investments in artificial intelligence, machine learning, and other associated technologies are becoming almost mandatory for agencies that want to stay ahead of the technological curve. It makes far more sense for the government to buy commercial technologies rather than build them. In addition, employees, both existing and potential new recruits, must factor into tech decision-making processes for the future. Here are tips from CIOs on where to use AI and emerging technologies.
Visualization tools represent an important bridge between graph data and analysts. It helps surface information and insights leading to the understanding of a situation, or the solving of a problem. Graph visualization tools turn connected data into graphical network representations that take advantage of the human brain proficiency to recognize visual patterns and more pattern variations. Graph visualization brings many advantages to the analysis of graph data. When you apply visualization methods to data analysis, you are more likely to cut the time spent looking for information.
Customers’ returning to your business is much more profitable than gaining new buyers. Always remember that if your ultimate goal is to retain customer loyalty, you need to make sure your customers enjoy interacting with your brand so much that they won’t be tempted to leave even if the competitor’s price is lower. Here are some ideas on how you can enhance customer loyalty based on the latest trends in consumer behavior.
DNA testing is evolving at an incredible pace. Scientists are using big data to develop clearer insights into the human genome and subsequently improve the quality of DNA testing. Big data is playing an important role in reducing false positives in DNA testing. It is also making the reduction of false positives more necessary. Preventing false positives with consumer DNA testing is also crucial. Millions of people are depending on DNA testing for identifying health risks and potentially identifying heirs to property. The problems associated with false positives in these instances could be just as problematic.
The truth is that every disruptive era is not only fraught with danger, but also opportunity. Every generation faces unique challenges and must find the will to solve them. Today, at the beginning of a new century, we are seeing similar shifts that are far more powerful and are moving far more quickly. Disruption is no longer seen as merely an event, but a way of life and the fissures are there for all to see. Our future will depend on our determination to solve problems faster than our proclivity to continually create them.
It’s clear the umbrella of artificial intelligence has advanced in areas like machine learning, which is already impacting areas such as image recognition, one of the biggest current viable use cases of AI-based technology. Any CTO looking at the technology will have to define a relatively narrow use case — ‘what problem will AI help me solve’, they should ask?, and focus on making sure there is enough data, and the right kind of data, to support that use case.
Automation - and specifically robotic process automation (RPA) - is indeed seeing back office processes and procedures handed over to machines (or bots). The front office of many companies, however, is often still very manual and very human. The trick to creating jobs, improving operational efficiencies and unlocking staff value lies in freeing up people in the front office so they can focus on the customer. How do you do that?