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…

Summary:  Here are our 5 predictions for data science, machine learning, and AI for 2019.  We also take a look back at last year’s predictions to see how we did.

It’s that time of year again when we do a look back in order to offer a look forward.  What trends will speed up, what things will actually happen,…

Summary:  There are two definitions currently in use for AI, the popular definition and the data science definition and they conflict in fundamental ways.  If you’re going to explain or recommend AI to a non-data scientist, it’s important to understand the difference.

For a profession as concerned with accuracy as we are, we do a really poor job at naming things, or at least being consistent in the naming.  “Big Data” – totally misleading…

There are transformative technologies in the world today with consistent effect and reliability in their promise to alter or change the ecosystem. Industries have transformed, and early adopters with it, while others race to understand how best to adapt or integrate said emerging technologies into their organizations in an effective and seamless…

Summary:  Even if you’re not big enough to have a full blown data science group that shouldn’t hold you back from benefiting from AI.  The market has evolved so that there are now industry and process specific vertical applications available from 3^rd party AI vendors that you can implement.  There are just a few things to look out for.

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Summary:  How about we develop a ML platform that any domain expert can use to build a deep learning model without help from specialist data scientists, in a fraction of the time and cost.  The good news is the folks at the Stanford DAWN project are hard at work on just such a platform and the initial results are extraordinary.

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In last part we have seen the basics of Artificial intelligence and Artificial Neural Networks. As mentioned in the last part this part will be focused on applications of Artificial neural networks. ANN is very vast concept and we can find its…