We are dealing with plethora of data and information in the world today and expectation is to predict and forecast how we can gain competitive advantage based on the information that we have, to act in advance. We look forward to define and furnish various methods based on our gut feel, past historical data, simple mathematical averages, and many more to get an incredibly precise prediction. With advanced analytics and data science, we develop "always-on" forecasting models which enable our clients to take their decisions effectively. From intuition to traditional algorithms to machine learning, phases have been evolving over a period. Processes 2 & 3 are iterative and 6 & 7 are iterative. We try to look for answers to various questions in the process – is the goal / business objective descriptive or predictive in nature?

Natural language processing is an ever-growing interest area in the analytics application spectrum and is relevant to HR. In fact, it can revolutionize the quality of insights. In this article, we will explain you how. Did you know that text analysis has been the most prevalent productivity tool over the past 3 decades or so for HR? It is very familiar to HR. HR has been using Boolean keyword searches for identifying good resumes/ job applications for a long time already.

Sooner or later, most of our jobs to be stolen by Artificial Intelligence. We are introducing 5 Human Jobs to be Replaced by A.I. Artificial Intelligence -powered Substitutes for Couriers may appear in 2 forms either as a drone in the air or as a robot on the ground.

Machine learning is becoming extremely powerful, but it requires extreme amounts of data. You can, for instance, train a deep-learning algorithm to recognize a cat with a cat-fancier's level of expertise, but you'll need to feed it tens or even hundreds of thousands of images of felines, capturing a huge amount of variation in size, shape, texture, lighting, and orientation. It would be lot more efficient if, a bit like a person, an algorithm could develop an idea about what makes a cat a cat from fewer examples. A Boston-based startup called Gamalon has developed technology that lets computers do this in some situations, and it is releasing two products Tuesday based on the approach. If the underlying technique can be applied to many other tasks, then it could have a big impact.

Findo wanted to use the results achieved in image analysis with deep statistical models and to apply them to text analysis. Text data is extremely sparse: the more discrete the data, the more data is required to successfully train statistical models. The solution to this obstacle was vector solutions. Artificial Intelligence generally does involve machines producing language responses to a natural (meaning human) language query. But recent advances in fields like generative variational text modeling, distributed vector space modeling of sentences and documents, and topic modeling have made the problem of sparseness more tractable.

Move over mobile: artificial intelligence is the next big disruptive trend in the tech world. While the proliferation of smartphones has dominated the tech cycle over the past couple of years, the growth of artificial intelligence will drive the next technological revolution. Artificial intelligence gives machines the capacity...

A much discussed area in technology these days is artificial intelligence, a type of machine learning. Artificial intelligence is a workload that requires an immense amount of processing power, which is why companies like microprocessor giant Intel (NASDAQ:INTC) -- a company that brings in tens of billions of dollars from sales of processors -- see this market as an interesting long-term growth opportunity. Interestingly, although Intel is a major supplier of processors for artificial intelligence workloads, the company doesn't get nearly as much attention for its efforts in this market as does graphics specialist NVIDIA (NASDAQ:NVDA) -- a company that has seen significant revenue and profit growth from artificial intelligence applications as its long-term investments in this space are paying off. Intel went over its artificial intelligence strategy at its Feb. 9 investor meeting. Let's look at what the company had to say about the market and how it plans to win in it.

From my perspective, it is the smart thing to do for entrepreneurs to involve humans when necessary, as long as it is a means to an end, with the ultimate goal remaining full automation. Worth noting: should they remain at the stage where they use a lot of humans and little automation, entrepreneurs will be stuck with a low margin business that will be increasingly hard to finance and will have low acquisition potential and/or value – probably not a great long-term strategy.

Feature engineering and feature extraction are key, and time consuming, parts of the machine learning workflow. They are about transforming training data, augmenting it with additional features, in order to make machine learning algorithms more effective. Deep learning is changing that according to its promoters. With deep learning, one can start with raw data as features will be automatically created by the neural network when it learns. The feature engineering approach was the dominant approach till recently when deep learning techniques started demonstrating recognition performance better than the carefully crafted feature detectors.

AI is clearly a hot topic for insurers. And, as Rick's article aptly points out, InsurTech startups are creating compelling new use cases and applications for data, algorithms and AI within the insurance space. It is, indeed, an exciting time for insurance. Yet many traditional insurers may read Rick's article with concern and potential fear. In a recent survey by KPMG International, 91 percent of insurance CEOs admitted being worried about the challenge of integrating automation, AI and cognitive robotics into their existing business and operating models.