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?
AI and Big Data are actually transforming the roles of traditional developers. It’s no longer just about jamming lots of code and creating full-blown applications. The bottom line is that the development process must not be an assembly line; rather, it should be a true collaboration. People who understand data and analytics are the need of the hour. Part of this is about understanding statistics, like Bayesian inference, but also grasping the nuances of data. Those people that have these valuable skills will be the next generation developers.
What are some of the trends that look most interesting within healthcare AI? One of the key trends is the use of health AI to spur the transition of medicine from reactive to proactive care. Machine learning-based applications will preempt and prevent disease on a more personal level, rather than merely reacting to symptoms. Ultimately, patients will benefit from timely and personalized treatment to improve outcomes and potentially increase survival rates.
AI systems and algorithms are created by people with their own experiences, backgrounds and blind spots which can unfortunately lead to the development of fundamentally biased systems. If we allow this incredible technology to continue to advance but fail to address questions around biases, our society will undoubtedly face a variety of serious moral, legal, practical and social consequences. It’s important we act now to mitigate the spread of biased or inaccurate technologies. Then what can be done?
The reaction to the phrase artificial intelligence was mixed. Did it really explain the technology? Was there a better way to word it? Well, no one could come up with something better–and so AI stuck. Since then, we’ve seen the coining of plenty of words in the category, which often define complex technologies and systems. The result is that it can be tough to understand what is being talked about. So to help clarify things, let’s take a look at the AI words you need to know.
RPA has been shown to generate quick and high levels of ROI (Return On Investment) for customers. And with all the money sloshing around in the RPA space, we should expect quite a bit of action next year. RPA has been concentrated in certain industries, like finance. But it seems like a pretty good bet that the technology will see expansion across more sectors. In terms of the core RPA technology, expect to see dynamism in the new year as well. Here are some areas to consider.
Next week, the California Consumer Privacy Act (CCPA ) will go into effect. It really hasn’t gotten much attention–but it should. The law is likely to have a far-reaching impact on the tech world, especially in categories like AI (Artificial Intelligence). So what is the CCPA? Actually, it is the most thorough privacy regulation in the US. It even goes beyond the requirements of the General Data Protection Regulation (GDPR) act, which is focused on Europe.
APIs (Application programming interfaces) have been a game-changer. Though APIs have been around for decades, but during the past decade, this technology has become a major force. APIs create powerful ways for businesses to streamline how they engage with partners and together deliver new generations of applications that empower consumers with more services and options. APIs enable companies to more easily build products and services that would otherwise take too long to build. The prospects for the API economy are exponential.
Artificial intelligence for IT operations (AIOps) refers to the spectrum of AI capabilities used to address IT operations challenges–for example, detecting outliers and anomalies in the operations data, identifying recurring issues, and applying self-identified solutions to proactively resolve the problem, such as by restarting the application pool, increasing storage or compute, or resetting the password for a locked-out user. With AIOps, there is the potential for achieving scale and efficiencies. Such benefits can certainly move the needle for a company, especially as IT has become much more strategic.