As part of intelligent automation investigations, robotic process automation (RPA) is being explored in many sectors across the globe, to increase the efficiency of operations and create new products and services. Are you exploring automation and AI for your organization?
A relation with a customer is not over after the delivery of a product or a service, in fact a relation with a customer goes way beyond making a sale. This is why businesses need customer relationship management, to help out a customer facing a problem with your service/product.
Are AI-based systems the next step in a continuous tech evolution that’s allowing us to achieve more, or something alarming that society should be concerned about? Before leaning on either side of the coin, it is important to first understand the misconceptions of AI as portrayed in popular media.
With more and more money pouring into internet companies, companies have been collectively spending billions of dollars in growing the user base on their platform and fight off the cutthroat competition to survive in their respective domain or vertical. Not many companies can survive at scale if the Churn problem is not mitigated as soon as possible.
Are you a tech geek? A business owner? Do you know how technology affects marketing? Are you constantly looking for solutions to make your processes easier? Here are some of the most interesting current and upcoming future trends in technology.
Many companies are thinking about their survival after the apocalypse that will be produced by the mix of IoT, AI and blockchain. CEOs must make decisions that prevent their companies from disappearing or worse, becoming walking dead.
There are really two meanings of “AI” and they are routinely conflated. To un-conflate what people mean by “AI,” I’m going to refer to this as “Artificial Sentience.” Then there is what people call “AI” today—basically, a variety of software that tries, tests, and auto-corrects its strategies for a given task.
If you find data science a tempting opportunity, you’ll benefit from this overview of big data basics for beginners. We’ll discuss what the requirements for jobs are and which skills you should master in order to start a successful data science career.
Machine Learning builds algorithms, which when exposed to high volumes of data, can self-teach and evolve. When this unique technology powers Artificial Intelligence (AI) applications, the combination can be powerful. We can soon expect to see smart robots around us doing all our jobs – much quicker, much more accurately, and even improving themselves at every step.
As companies in a variety of industries plan and execute their digital transformation strategies, powered by the Internet of Things (IoT), they should be designing everything—products, applications, sensors, networks, services, etc.—with one all encompassing goal: to maximize the value of the data they create or ingest.
Improving the IoT device experience doesn’t have a single solution. It needs design. It probably needs new technologies and approaches that make set up and maintenance more automated. And even after all that, a lot of devices will still be useless.
We are approaching the world of artificial intelligence (AI). The world is changing; the law will change; lawyers must change. Unimaginable and exciting challenges are ahead of us.
Machine learning is not a new concept — it’s actually been around since the late 1950s. So, why all the hype? Why do businesses feel like now is the time to adopt? Before businesses start to develop a strategy around machine learning and AI, it’s important to review how machines really learn, and how this can impact your AI and machine learning strategies.
While big data has dropped off the hype cycle, it’s not going away; in fact it will only get “bigger”. Likewise, the Hadoop ecosystem has matured significantly and will continue to do so with all of the big distributions offering data science capabilities. The desire amongst enterprises to migrate big data to the cloud will continue to increase with managed services gaining momentum. There are many challenges that come with all of these things – but with the right strategy and foresight, enterprises can truly maximize big data’s value.
With an almost endless list of sources – including map and satellite data, catchment areas, service points, building and customer locations, land use data, urban data, and communication pathways – spatial data is a valuable global commodity which comes in many forms.
AI has the potential to create more jobs than it kills. In 10 years, most cognitive workers will be working in creative collaboration with intelligent machines. New industries yet unborn will almost certainly rely on the complementary strengths of artificial and human intelligence.
Machine Learning (ML) has given a whole new meaning to data. It has, in all true sense, completely transformed the way we look at data and information. It can indeed be a game changer and be very useful in proving very useful insights that can prove to be very critical for many businesses.
Machine learning is a reality that we are just waking up to. The eventual evolution of machine learning is predictive automation. Predictive automation might be a reality in the next couple of years itself. If IoT is the destination then we are on the right track.
There’s been so much hype in recent months around opportunities in artificial intelligence that it’s starting to feel a bit like a second gold rush. Thinking of joining or investing in an AI startup? Here are 5 questions that cut through the noise
Real-time agricultural data contains knowledge about what the farming industry needs, how to make production more efficient and how to establish a better IoT market for the agriculture industry.
The concept of IoT as an ecosystem just isn’t enough: It’s too large and it’s too diverse. We might, for example, reasonably talk about the IoT wearable device ecosystem, or the Industrial IoT ecosystem — but how do we accurately capture the scope and the scale that is IoT in its entirety?
Digital transformation in insurance is not limited to one country or contained within geographic boundaries. Increasingly, compelling ideas spread from one geography to others as digital initiatives are launched in one part of the world, perfected, and then adopted by other insurers in different countries.
Collecting data is most effective when it is done seamlessly across the Internet of Things. Want to join me for a cup of cappuccino? Be careful, you don’t know the information that might be collected about you. Here’s why
As the technology progresses at a rapid pace, it is a critical time for governments and policymakers to think about how we can safeguard the effects of Artificial Intelligence on a social, economic and political scale. Artificial Intelligence is not inherently good or bad, but the way we use it could well be one or the other.