Artificial Intelligence might not be the newest entrant in smart technology but, its redefined usage is. It has recently gained mass popularity. Now, it is being accepted across many industries. With the general public embracing AI, government agencies are also considering this fantastic technology. AI can imitate human's decision-making skills. Thus, it has the potential to improve the decision-making ability of various applications. The AI engine is useful for multiple applications such…Continue
Added by Amit Dua on April 15, 2019 at 9:38pm — No Comments
We have been in existence for over 10 years now, with content in many different places, lists, categories, and databases. This is an attempt to put everything together in one place, and help our readers (re-)discover some great articles and resources that were lost on the Internet over the years, but still sit on our web servers. We are making them come back to life. We are in the process of organizing it in a way that is user-friendly. Some of the resources below are very recent, and some…Continue
Added by Vincent Granville on April 3, 2019 at 5:30pm — No Comments
We investigate a large class of auto-correlated, stationary time series, proposing a new statistical test to measure departure from the base model, known as Brownian motion. We also discuss a methodology to deconstruct these time series, in order to identify the root mechanism that generates the observations. The time series studied here can be discrete or continuous in time, they can have various degrees of smoothness (typically measured using the Hurst exponent) as well as long-range or…Continue
Machine Learning (ML) has been among the top strategies for almost every organization - whoever adopts the new methodology early and quickly establishes the corporate capability will gain competitive advantages in the market.
However, because ML is still a relatively new field and data science is a skillset that is scarce for many companies, how to make the journey move at optimal speed without disaster or time wasted…Continue
Added by Stephanie Shen on March 24, 2019 at 2:30pm — No Comments
BigQuery is Google’s serverless, highly scalable, enterprise data warehouse designed to make all your data analysts productive at an unmatched price-performance. Because there is no infrastructure to manage, you can focus on analyzing data to find meaningful insights using familiar SQL without the need for a database administrator.
Analyze all your data by…Continue
Added by satyajit maitra on March 22, 2019 at 3:49am — No Comments
Summary: Based on a McKinsey study we reported that 47% of companies had at least one AI/ML implementation in place. Looking back at the data and the dominance of RPA as the most widely reported instance makes us think that the number is probably significantly lower.
We’ve all experienced the great data rush as companies push to use analytics to drive business decisions. After all, the proliferation of data and its intelligent analysis can change entire company trajectories. But to make the quintillions of data created each day truly useful, as well as all that has come before, it must be understandable to an artificial intelligence (AI) system.
Dealing with numbers is one thing, but human language is…Continue
Added by Rosaria Silipo on March 18, 2019 at 7:41am — No Comments
Summary: In the literal blink of an eye, image-based AI has gone from high cost, high risk projects to quick and reasonably reliable. C-level execs looking for AI techniques to exploit need to revisit their assumptions and move these up the list. Here’s what’s changed.
For data scientists these are miraculous times. We tend to think of miracles as something that occurs instantaneously but in our world that’s not quite so. Still the rate…Continue
Added by William Vorhies on March 4, 2019 at 9:41am — No Comments
Data Science is a combination of data inference, algorithms, and technology that solves complex problems. The core of this technology is data that is initially raw, then is streamlined, and stored in a data warehouse. These vast amounts of data can help generate significant business values.…Continue
Added by Amit Dua on March 3, 2019 at 8:30pm — No Comments
Summary: Adoption of AI/ML by larger companies has more than doubled since last year according to these survey results from McKinsey and Stanford’s Human-Centered AI Institute. This new data gives us a much better idea of which global regions and which industries are adopting which AI/ML techniques.
Added by William Vorhies on February 25, 2019 at 9:22am — No Comments
Visualization has become a key application of data science in the telecommunications industry.
Specifically, telecommunication analysis is highly dependent on the use of geospatial data. This is because telecommunication networks in themselves are geographically dispersed, and analysis of such dispersions can yield valuable insights regarding network structures, consumer demand and availability.
To illustrate this point, a k-means clustering algorithm is used…
Added by Michael Grogan on February 19, 2019 at 3:44am — No Comments
Image recognition and classification is a rapidly growing field in the area of machine learning. In particular, object recognition is a key feature of image classification, and the commercial implications of this are vast.
For instance, image classifiers will increasingly be used to:
Added by Michael Grogan on February 17, 2019 at 11:00am — No Comments
The key to perform any text mining operation, such as topic detection or sentiment analysis, is to transform words into numbers, sequences of words into sequences of numbers. Once we have numbers, we are back in the well-known game of data analytics, where machine learning algorithms can help us with classifying and clustering.
We will focus here exactly on that part of the analysis that transforms words…Continue
Added by Rosaria Silipo on February 11, 2019 at 3:09pm — No Comments
Summary: Not enough labeled training data is a huge barrier to getting at the equally large benefits that could be had from deep learning applications. Here are five strategies for getting around the data problem including the latest in One Shot Learning.
Understanding customer transactional behaviour pays well for any business. With the tsunami of start ups in recent times and the immense money flow in businesses, customers find lucrative offers from companies for acquisition, retention & referrals strategies. Understanding transactional behaviour of a customer has become even more complex with the invent of new business houses everyday. Although, with the rise of powerful machines, one can…Continue
Added by PS Dhillon on January 28, 2019 at 4:00am — No Comments
Summary: The world of healthcare may look like the most fertile field for AI/ML apps but in practice it’s fraught with barriers. These range from cultural differences, to the failure of developers to really understand the environment they are trying to enhance, to regulatory and logical Catch 22s that work against adoption. Part 3 of 3.
Summary: Despite hundreds of projects and thousands of data scientists devoted to bringing AI/ML to healthcare, adoption remains low and slow. A good portion of this problem is our own fault for failing to see the processes being disrupted through the eyes of the physician users. Here we lay out the healthcare opportunity landscape but for data scientists following classical disruption strategies, it may be more of a minefield. Part 2 of…Continue
Added by William Vorhies on January 14, 2019 at 8:00am — No Comments
Deep Learning is picking momentum in Quantitative Finance, outside the obvious application to the prediction of asset prices (where to my knowledge it is not particularly effective) and spreading into the more serious application area of option pricing and risk management.
These two recent papers clearly demonstrate the benefits of DL as a pricing technology alternative to the classical FDM and Monte-Carlo in certain contexts:…Continue
Added by Antoine Savine on January 11, 2019 at 5:30am — No Comments
Data science is changing the rules of the game for decision making. Artificial intelligence is living its golden years where abundance of data, cheap computing capacity, and devoted talent depicts an unstoppable intelligence assisted life for humans. While it is common to hear about AI advice on health or financial investments, the same in business strategy is not so common. Maybe it is just a matter of time that AI learns how to handle data to support…Continue
Summary: If you want to understand the promise of AI/ML in healthcare you need to see it through the eyes of physicians, the ultimate users. Turns out these folks aren’t the rapid adopters you’d think they’d be and the problem is largely with the way data scientists have tried to implement. Part 1 of 3.