The Digital HR enterprise is a harbinger of change in the global business environment and may be defined as an encapsulation of Cloud Enterprise, Cognitive Enterprise (Conversational UX, Intelligent Apps, Advance Analytics), and Connected Enterprise (Digital Integration, IoT, Blockchain). The digital workforce comprises a multi-generational, digitally and socially connected, tech-savvy workforce creating a compelling focus on employee experience. The digital workplace is the inevitable shift to next-gen technologies encompassing Cloud, Mobility, Robotic Process Automation, Conversational AI, Machine Learning, Analytics and Insights, Gamification, and IoT, all of which are the building blocks for enhancing employee experience. Every organization working in a highly competitive market is required to redraw its focus on employee experience to maximize productivity. The increased focus on HR transformation engagements has forced the organisations to fast track the employee experience to extraordinary levels.
Genotyping makes it possible to know an individual’s alleles, i.e. the genetic variations received from his or her parents. However, without knowing the parental genome, we do not know which alleles are simultaneously transmitted to children, and in which combinations. “This information – haplotypes – is crucial if we really want to understand the genetic basis of human variation,” explains Emmanouil Dermitzakis, a professor at the Department of Genetic Medicine and Development at UNIGE Faculty of Medicine and SIB, who co-supervised this work. “This is true for both population genetics or in the perspective of precision medicine.” To determine the genetic risk of disease, for example, scientists assess whether a genetic variation is more or less present in individuals who have developed the disease in order to determine the role of this variation in the disease being studied. “By knowing the haplotypes, we conduct the same type of analysis, says Dermitzakis. However, we are moving from a single variant to a combination of many variants, which allows us to determine which allelic combinations on the same chromosome have the greatest impact on disease risk. It is much more accurate!”
Building on the proven success of these IPA use cases, IDC predicts that by 2022, 75% of enterprises will embed intelligent automation into technology and process development, using AI-based software to discover operational and experiential insights to guide innovation. And by 2024, AI will be integral to every part of the business, resulting in 25% of the overall spend on AI solutions as “Outcomes-as-a-service” that drive innovation at scale and superior business value. AI will become the new UI by redefining user experiences where over 50% of user touches will be augmented by computer vision, speech, natural language and AR/VR. Over the next several years, we will see AI and the emerging user interfaces of computer vision, natural language processing, and gesture, embedded in every type of product and device. Emerging technologies are high-risk technologies. In 2020, warns Forrester, 3 high-profile PR disasters will “rattle reputations,” as the potential areas for AI malfunction and harm will multiply: The spread of deepfakes, incorrect use of facial recognition, and over-personalization.
In OAM, an Application is made from three core concepts. The first is the Components that make up an application, which might comprise a collection of microservices, a database and a cloud load balancer. The second concept is a collection of Traits which describe the operational characteristics of the application such as capabilities like auto-scaling and ingress which are important to the operation of applications but may be implemented in different ways in different environments. Finally, to transform these descriptions into a concrete application, operators use a configuration file to assemble components with corresponding traits to form a specific instance of an application that should be deployed. OAM’s core ideas came from our years of hard work on the application platform, and our extensive experience with the development and operation of Kubernetes in both internal cluster and public cloud offering. As engineers, we thrive on innovation-based learning from past failures and mistakes.
Personalization is about taking a product that we think someone wants and putting it in front of that person and having a win. And so how do we do that? How do we go grab the product and show it to the person? Recently, I've seen companies who say they provide personalization, and what they're really doing is they're segmenting the audience into a small group. The first group to play with is gender, then maybe you segment by age, and so now you've got four different groups. That's not personalization, it's segmentation. Sure, it starts to get towards personalization, but it's not very informed. It's not what we might call intelligent use of data. When we start getting more predictive instead of descriptive, we start to look at past behaviors and how they predict future behaviors. That's where the really interesting work happens in the recommender space, or even in the classification space, where we might have a user onboarding with us and the user fills out a bunch of information, and then immediately we can treat the customer differently based on how we predict the customer's going to act in the future.
The development of the payment industry is already showing where the journey of the retail banking industry is going. It is surprising that while payment is one of the main tasks of banking, besides lending and investment, banks outsourced significant payment processes to third parties already decades ago. New business areas, such as the credit card business, banks have left to the credit card companies. Amazing, because with payments the private as well as business customers experience their banking daily and thus payments contribute to a substantial part of the customer loyalty. Last but not least, the payment transactions of the customers also give very good indications of the respective life and business situation and potential financial services needs. But banks have not only left the credit card business largely to third parties, but also the fast-growing market for online payments. This is the only way to explain that PayPal has now attracted more than 20 million users in Germany, PAYONE handles more than 2.6 billion transactions per year, and Wirecard has a larger market capitalization than Deutsche Bank- all of these would have been unthinkable ten years ago!
Let's face it, for most users, if a computer boots up and they can start using it, then the computer is working fine—well, at least good enough. However, this apathetic view of computer health and maintenance can lead to serious problems down the road, especially if your computer is using an outdated device driver. I recently repurposed an old gaming laptop as my new business PC. This process included updating the OS from Windows 7 to Windows 10. After the update, I noticed that the CPU fan was constantly running, and the PC performance was sluggish at best. So, I did some troubleshooting with the Windows Task Manager. I discovered that, for unexplained reasons and even after a lengthy update process, the computer was still using an outdated and deprecated device driver. This was causing major performance issues and was obviously unacceptable. This tutorial shows you how to use the Task Manager to identify bad acting devices, how to troubleshoot the problem, and then how to fix it.
When structuring the data locally, I didn’t go down to the most granular level identified by the UTZappos50k dataset, such as the ankle, knee-high, mid-calf, over the knee, etc. classification labels for boots. My local data are kept at the highest level of classification, which includes only boots, sandals, shoes, and slippers. ... In DJL terms, a dataset simply holds the training data. There are dataset implementations that can be used to download data (based on the URL you provide), extract data, and automatically separate data into training and validation sets. The automatic separation is a useful feature as it is important to never use the same data the model was trained with to validate the model’s performance. The training dataset is used by the model to find trends and patterns in footwear data. The validation dataset is used to qualify the model’s performance by providing an unbiased estimate of the model’s accuracy at classifying footwear. If the model were validated using the same data it was trained with, our confidence in the model’s ability to classify shoes would be much lower because the model is being tested with data it has already seen.
It’s every chief information security officer’s (CISO) worst nightmare—a cyberattack in which hackers obtain access to private customer information like names, birth dates and credit card and social security numbers. If this data is compromised, erosions in consumer trust, brand reputation and investor confidence are possible. Tossing salt on the wounds are punitive fines for a data breach from regulations in Europe and California. Effective January 1, 2020, violations of the California Consumer Privacy Act (CCPA) can result in civil penalties of up to $7,500 per infringement. ... Cybercriminals are always au courant. They are well aware that investors are increasingly concerned over companies’ reputational risks, such as poor environmental sustainability practices and reported employee ethics violations. They also know that these reputational events could have a material impact on the organization’s share value. Hence, hackers continually scour IT networks and systems looking for evidence of such corporate fallibilities. If discovered, a ransom may be demanded to keep a lid on the bombshell information.
There’s obviously a big problem with this. Many people don’t trust Facebook with their private data, see it as a threat to democracy, or both. Now the company wants to launch its own currency? Is it just trolling us? At this point the company might emphasize that it won’t be in charge of the Libra Association, the nonprofit it created to manage the currency. The group, made up of 20 other firms in addition to Facebook, will manage a “reserve” of government-issued money that is supposed to back each digital unit and keep the currency stable. Half of that reserve will be US dollars, and the other half will be made up of British pounds, Japanese yen, euros, and Singapore dollars. Fine, but the design itself has worried US policymakers, who say they can’t even tell what Libra is, much less how to deal with it. Whatever it is, it will probably be hard to ignore in 2020. ... Many cryptocurrency enthusiasts argue that true cryptocurrency is the product of a decentralized, “permissionless” network like Bitcoin. Bitcoin is designed to provide freedom from corporate and government censorship, and its network is controlled by a global public network made up of thousands of computers.
Quote for the day:
"Leadership is a dynamic process that expresses our skill, our aspirations, and our essence as human beings." -- Catherine Robinson-Walker