Emerging applications like machine learning (ML), big data analytics, and artificial intelligence (AI) has created the need for many companies to hire highly skilled and experienced work force. Demand for data scientists, ML engineers and data engineers is booming and will only increase in the next years. The January report from Indeed, one of the top job sites, showed a 29% increase in demand for data scientists year over year and a 344% increase since 2013.
Added by Chris Kachris on May 17, 2019 at 4:50am — No Comments
Machine learning applications require powerful and scalable computing systems that can sustain the high computation complexity of these applications. Companies that are working on the domain of machine learning have to allocate a significant amount of their budget for the OpEx of machine learning applications whether this is done on cloud or on-prem.
Typical machine learning application…Continue
Added by Chris Kachris on May 14, 2019 at 11:30pm — No Comments
Summary: Especially in consumer goods and retail the value of AI/ML is only part of the story. AI/ML will increasingly need to integrate with helper technologies to deliver maximum value. Up your game in IoT, 5G, and robotics to ensure you’re giving your operating team all the best options for their investment.Continue
Added by William Vorhies on May 13, 2019 at 8:06am — No Comments
Artificial intelligence (AI) seemingly has been discussed everywhere over the last few years, and now it’s made its way into the commercial insurance industry. Organizations are using AI and machine learning for everything from streamlining operations to offering more personalized care and better customer service. There is an increasing sense of urgency about getting started on the AI journey. The question is how. Do they develop a custom solution in-house or purchase a third-party solution…Continue
Added by Ji Li on May 2, 2019 at 3:00pm — No Comments
Added by satyajit maitra on April 30, 2019 at 6:00am — No Comments
Summary: Communicating with your Board of Directors about AI/ML is different from conversations with top operating executive. It’s increasingly likely your Board will want to know more and planning that communication in advance will make your presentation more successful.
Added by William Vorhies on April 29, 2019 at 9:35am — No Comments
In his book, The Master Algorithm, Pedro Domingos imagines the following experiments:
Take a building, extremely well–built for two purposes: Nothing can enter and most importantly nothing can…Continue
Added by Paul Pinard on April 16, 2019 at 6:00am — No Comments
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
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
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
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.