Another area of massive opportunities includes ethics, philosophy, policy making, and civic planning.
Real-world problems, life-long learning , keeping yourself up-to-date with new research, expanding your expertise to other domains — if this excites you then you are already on the path to preparing yourself for the economy of the future. If you can plan and persistently invest a good chunk of time then take various courses from Coursera, Edx, Udacity; they can provide you enough understanding of Artificial Intelligence. Make sure you do all projects and assessments. After completing learning you need to build a few small projects, and slowly you will figure it out what you like most, what you can directly relate to your area of expertise.
Artificial Intelligence is a vast field, it includes multiple disciplines and a variety of tools and platforms. Any of my attempt to structure how once should learn AI would just attract more debate. My alert readers and AI professionals would recognize that AI is more than programming languages, tools, and algorithms. The rapid growth of online education means that employees can re-skill themselves faster than ever before, and mostly for free.
Learning Artificial Intelligence using various self-learning platforms allows you the flexibility of economy, learning on your own timelines and opportunities to deal with real-world issues, and early industry experience. If you are already in a specific industry and want to build more relevant experience in the same industry then online learning is a great help. If you are at the beginning of your career and crave to immerse yourself in deep and interesting problems — formal education from the top institution will provide right launching platform.
You can spend time with highly technical and creative people. Your formal AI study Ph. Like every Ph. A lot will depend on the quality of your publications and thesis, letters of recommendations, and how effectively you can sell yourself. Keep in mind that Ph. Most of the industry jobs are not pure research-based and spending 4 to 6 years to get a Ph.
Artificial Intelligence should be used to identify domains more likely to benefit from the technology innovations.
eScienceCommons: Predictive Health: A call to reinvent medicine
In the years to come, there will be more technological building blocks for artificial intelligence. With more data and more people interested in solving more problems, you should be able to imagine how artificial intelligence can supercharge your learning and career. We can use various languages like any of the Python, R, but the bigger story is how we apply Artificial Intelligence to solve a given problem determines your success. You need to be a thinker and craftsman with a lot of imagination. The only way to understand Artificial Intelligence is to solve some problem quickly, learn it better and improve.
If you are into managed service business you should be able to think of various ways to automate, extend and improve existing processes and methods. You could come up with ideas that cross-pollinate multiple domains.
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From end-users, service agents to applications, service delivery process at every level can be automated using Artificial Intelligence and Cognitive Computing. Robotic process automation RPA can be used to execute repetitive tasks that were earlier performed by humans. These digital labor algorithms can become very complex and sometimes capable of learning and adjusting on its own. I was overwhelmed and highly discouraged. They were helpful as their curriculum was mostly code-less.
The first machine learning algorithm I wrote was kind of funny, it was able to predict whether our housemaid is coming to work or not at least it was predicting better than me and my wife. It involved a lot of learning and hacking.
- Search Harvard Health Publishing.
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- Cloud Computing: a Catalyst for Reinvention | AmbitiousThinking.
- Minding Evil: Explorations of Human Iniquity (At the Interface Probing the Boundaries, 23).
- The story of an analytics-driven precision medicine company.
- Cinderellas Sisters: A Revisionist History of Footbinding (Philip A. Lilienthal Asian Studies Imprint).
I also had to deal with data ethics issue that was quite unexpected. From sorting cucumbers to curing cancers , machine learning algorithms are highly prominent in our daily lives. Current efficiencies of AI is so great that the greatest minds on this planet are predicting that in near future AI will triumph over humanity and will present a direct threat to human survival.
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- Grammar Practice for Pre-Intermediate Students (GRPR).
- Predictive Health: How We Can Reinvent Medicine to Extend Our Best Years;
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Their revenue and cash flows 1 1. Cash flow is measured here in terms of earnings before interest, taxes, depreciation, and amortization EBITDA minus capital expenditures. Consumption of mobile data boomed, as masses of new wireless customers used their handsets to spend ever-increasing amounts of time online. Companies responded by investing heavily in their wireless networks, even as subscriber growth slowed. As a result, the average ratio of capital spending to revenues has remained stubbornly high, at around 15 percent, for the major players Exhibit 1. What can companies do to alleviate the squeeze on margins and create more value?
With the newest software and hardware, along with digital-age management practices, mobile operators can achieve breakthrough cost savings and capital intensity while maintaining or even increasing their scale. To capitalize on these opportunities, executives must take bold action to transform their businesses.
Managing networks with next-generation technologies can cut the capital-spending and operating expenses of wireless operators. And digital technology can help them to streamline their business functions and please their customers, reducing costs and raising sales. Wireless operators have little reason to wait before making these moves—the necessary technologies and management methods are available now. Moreover, operators can launch a digital transformation and begin reaping the benefits even as they move into fast-growing adjacent markets or await favorable regulatory changes.
In this article, we take a closer look at the prospects of mobile operators for digital reinvention and how they can exploit those opportunities. Doing more with less is seldom easy. But leading-edge technologies help mobile operators do just that to meet the burgeoning demands on their networks. The shift to small-cell networks has been one fundamental step toward next-generation technologies. Until recently, mobile networks consisted of expansive cells with modest capacity.
Now, technological advances have made it possible for wireless operators to set up and operate dense networks of small, high-capacity cells.
These networks typically cost less to upgrade than networks of large cells do. Network equipment has gotten better as well: it is less costly to buy and operate, more flexible, and more powerful. Mobile operators can also use sophisticated analytical tools to gain insights into capturing the maximum value from capital investments. Network technology is improving all the time, and the advances will probably accelerate in several years, with the establishment of 5G wireless standards, which make it possible to serve more mobile users in a given area.
The following applications can help these companies create more value right away. Operators collect ample data about where, when, and how much subscribers use their mobile handsets. These data are precise: they can establish usage patterns within five-by-five-meter squares, roughly the size of a studio apartment, over the course of days and weeks.
By running the data through algorithms, a wireless operator can pinpoint where and when network overloads happen and which customers they affect most. With that information, it can project how much a possible upgrade might improve the satisfaction—and ultimately the retention—of its more profitable customers. An operator can also determine the highest levels of network performance that do not yield diminishing returns in customer satisfaction.
Such findings let the company avoid investments that would make their networks better than necessary. With these techniques, mobile operators planning capital expenditures can prioritize value creation rather than network performance. Networks made up of small cells are not only less expensive to maintain than networks of large cells but also more flexible. Adjusting capacity is harder with large cells. Even if some areas in such a cell are experiencing low demand, its capacity has to be kept uniformly high to maintain the quality of service in areas where demand is strong.
Much as operators can use analytics to determine where to make upgrades, they can also use machine learning to adjust wireless networks automatically as demand changes or even to base adjustments on predictions. If a machine-learning model has records of network usage and other conditions such as traffic or weather and then receives new data in real time, it can predict when usage might rise or fall across a network and adjust capacity preventively. Improvements in software allow mobile companies to get significantly increased performance from hardware they already own or to use less expensive hardware.
New methods for compressing and managing video, for example, let wireless companies offer video-streaming services to ten times more households, thus creating new revenue streams. These methods can also cut data-storage costs by 60 percent. These allow wireless operators to centralize the functions for controlling networks and to administer changes and upgrades remotely rather than in the field.
Another benefit is that they let operators use generic network hardware, which tends to be more cost-effective. All of these network technologies promise to lower costs and make it faster and easier to change networks in response to problems or new customer needs.
We estimate that the newest technologies would let operators lower their capital expenditures by up to 40 percent—thus pushing these costs down to under 10 percent of revenues—and their network-operating expenses by a similar amount Exhibit 2. Mobile operators lag behind companies in some other industries in doing business digitally. For example, they make a smaller share of their sales online than insurance and retail-banking companies do.
Using digital technology to automate operations makes them leaner and more productive, which leads to lower costs. McKinsey research has also found that providing customer service through digital channels improves customer satisfaction —and that in turn leads to increases in revenue. The opportunities to digitize support for mobile customers are especially promising.