Over the last few years, Excel has been redesigned from the ground up. Currently, Microsoft is making the new Excel core-features available to every user, regardless of your Office 365 license. Thanks to the Microsoft naming conventions, it is easy to confuse the new features with existing ones. That being said, Power Query and Power Pivot are not the same things as Pivot Tables, which you have likely been using for years.…

Summary:  Too many solutions.  We are at an inflection point where too many vendors are offering too many solutions for moving our AI/ML models to production.  The very real risk is duplication of effort, fragmentation of our data science resources, and incurring unintended new technical debt as we bind ourselves to platforms that have hidden assumptions or limitations in how that approach problems.

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When we take into account the insurance and banking industry, data plays a central role in it. Insurance is a people-centric business. A business that heavily relies on an individual’s data to the maximum extent possible. And the data Insurance providers and carriers have access to more of such data than ever before. The amount of data, we as humans have created in the past two years is simply unprecedented. …

Ever since the payment systems existed, there were always people who would find sophisticated ways to get to someone’s finances illegally. It has become a major problem in the modern era when all transactions can easily be completed online with only entering your credit card information. Even in the 2010s quite a lot of American retail website…

Summary:  Contextually intelligent, NLP-based interactive assistants are one of the next big things for AI/ML.  The tech is already here from recommendation engines.  The need to be more efficient and to become AI-augmented in our decision making is now.  Getting the contextual awareness is the hard part.

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Summary:  AI/ML itself is the next big thing for many fields if you’re on the outside looking in.  But if you’re a data scientist it’s possible to see those advancements that will propel AI/ML to its next phase of utility.

“The Next Big Thing in AI/ML is…” as the lead to an article is probably the most…

Summary:  Data Scientists from Booking.com share many lessons learned in the process of constantly improving their sophisticated ML models.  Not the least of which is that improving your models doesn’t always lead to improving business outcomes.

The adoption of AI/ML in business is at an inflection point. …

There are a lot of AI Companies that offer Machine Learning development. Even choosing the right ones for your project could take a decent amount of time, not even mentioning to pick the one that fits.

We decided to help CEOs, established companies, and startups that are looking for AI & ML Developer to save time…

Summary:  More data means better models but we may be crossing over a line into what the public can tolerate, both in the types of data collected and our use of it.  The public seems divided.  Targeted advertising is good but the increased invasion of privacy is bad.

Headlines are full of alarm.  The public is up in arms.  The internet is stealing their privacy.  Indeed, the Future of Humanity Institute at Oxford rates this as the most…

As we all know that artificial Intelligence is slowly slowly becoming the most important and most integral part for the small to big businesses. It helps the business from the purchase of a product or manufactures the products to deliver the product to the client or customers. Here we have come with a small analysis of big business Company named coca…

Summary:  Despite our concerns about China taking the lead in AI, our own government efforts mostly through DARPA continue powerful leadership and funding to maintain our lead.  Here’s their plan to maintain that lead over the next decade. 

Think all those great ideas that have powered AI/ML for the…

Summary:  If you are guiding your company’s digital journey, to what extent should you be advising them to adopt deep learning AI methods versus traditional and mature machine learning techniques.

By now everyone is at least familiar with using AI/ML as a required cornerstone of company strategy.  Frequently…

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.

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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.

As AI/ML becomes increasingly…

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 been trying to get a handle on…

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.

We know…

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.

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Leveraging the abbreviation "vs" in and of itself begins to stir the insides of the ever-faithful, "until death do we part" neural network enthusiasts because, lets face it, they are riding a wave that is driving the stock prices in both the private commercial and public commercials sectors. Most of the applications talked about today which leverage the all-too-mysterious but oh-so-exciting "AI" or "Artificial Intelligence" are implementing supervised learning approaches to solve their…

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…

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.

It’s…