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.
Added by William Vorhies on January 7, 2019 at 8:00am —
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,… Continue
Added by William Vorhies on December 17, 2018 at 8:50am —
Summary: We’re rapidly approaching the point where AI will be so pervasive that it’s inevitable that someone will be injured or killed. If you thought this was covered by simple product defect warranties it’s not at all that clear. Here’s what we need to start thinking about.
So far the press… Continue
Added by William Vorhies on December 10, 2018 at 10:07am —
Summary: Sales is supposed to be an area that is more immune to replacement by AI than many others because of the high level of impromptu and improvisational human contact required. That remains true. But AI is showing that it can be a valuable augment to B2B sales and some early adopters are scoring big gains.
Added by William Vorhies on December 4, 2018 at 9:59am —
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… Continue
Added by William Vorhies on November 27, 2018 at 8:23am —
Summary: Looking for your next job in an early stage company but want to make sure your startup has staying power. Follow the expert rankings by CB Insights that also show us the changing trends in how AI startups should be focusing their offerings.
Let’s suppose you’re early in your data science career and your credentials… Continue
Added by William Vorhies on November 5, 2018 at 4:18pm —
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 3rd party AI vendors that you can implement. There are just a few things to look out for.
Added by William Vorhies on October 23, 2018 at 7:30am —
Given the touting of recent analytic and machine learning results in healthcare, why haven't doctors been replaced by computers yet? The truth is that there are many obstacles that stand in the way of implementing analytics in healthcare. Ethical issues introduced by this technology are also fiercely debated and must be considered. Finally, while obstacles imply a possibility of being overcome, there are also limitations to this technology,… Continue
Added by Vik Kumar on October 13, 2018 at 11:55am —
Summary: We are entering a new phase in the practice of data science, the ‘Code-Free’ era. Like all major changes this one has not sprung fully grown but the movement is now large enough that its momentum is clear. Here’s what you need to know.
We are entering a new phase in the practice of data science, the ‘Code-Free’… Continue
Added by William Vorhies on October 9, 2018 at 9:54am —
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.
Added by William Vorhies on September 4, 2018 at 8:02am —
The second edition (fully revised, extended, and updated) of Machine Learning Algorithms has been published (Packt).
From the back cover:
Machine learning has gained tremendous popularity for its… Continue
Added by Giuseppe Bonaccorso on September 2, 2018 at 7:18am —
Congestive heart failure (CHF) has been called an "epidemic" and a "staggering clinical and public health problem" (Roger, 2013). It can be defined as the impaired ability of the ventricle to fill or eject with blood. Consequences include difficulty breathing, coughing fits, leg swelling, decreased quality of life, and ultimately death. As life expectancy increases globally, we can only expect to see this syndrome more frequently. Fortunately, the advents of… Continue
Added by Vik Kumar on August 28, 2018 at 3:17am —
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… Continue
Added by Jayesh Bapu Ahire on August 25, 2018 at 9:00pm —
Summary: Our recent series of articles on AI strategies shows the options available for the strategic direction of your AI-first company. Here are some thoughts on moving from strategy to implementation, including some useful tools to help in planning.
Hope you’ve been following our latest series of articles describing and… Continue
Added by William Vorhies on August 7, 2018 at 7:25am —
Summary: Getting an AI startup to scale for an IPO is currently elusive. Several different strategies are being discussed around the industry and here we talk about the horizontal strategy and the increasingly favored vertical strategy.
Looks like there’s a problem brewing in AI startup land. While AI is most certainly… Continue
Added by William Vorhies on July 17, 2018 at 7:00am —
Amazon is every online retailer’s forbidding nightmare. Last year, it dominated 44 percent of the US eCommerce market and about 4 percent of all domestic retail sales. One Click Retail, an eCommerce analysis provider, explains its dominance with the fact that millennials, Amazon’s core demographic, are getting older and starting to spend more. Moreover, advanced marketing capabilities for sellers, developments in Alexa, and pioneering in applications of the hottest technologies make it… Continue
Added by Maryna Ivakhnenko on July 11, 2018 at 11:00pm —
Never before have customers been more in control of the retail trade than today. But are they really? Or has the retailer wrested control of the exchange? Let’s revisit this in the light of new technologies and sensors deployed in this “game”.
In the sixties through the eighties, the Sears, Walmart and K-mart kind of super stores aggregated purchase information to decide what to buy and stock their shelves. Improving…
Added by Hemant Warudkar on May 14, 2018 at 7:00pm —
This is a continuation of my previous blog, “Natural Language Understanding – Application Notes with Context Discriminant”.
Natural Language Understanding (NLU) is a subtopic of Natural Language Processing (NLP). Successful implementations of NLU are difficult because of limitations in prevailing technology. SiteFocus solved these limitations with a new approach to NLU. This approach has been successfully… Continue
Added by Sing Koo on April 10, 2018 at 1:30pm —
How good is a certain soccer player? Let’s find out applying Machine Learning to Fifa 18!
I’m sure you’ve probably heard about the 2018 FIFA Football World Cup in Russia everywhere during the last few months. And, if you are a techy too, I guess you also have realized that Machine Learning and Artificial Intelligence are buzzwords too. So, what better way to get ready for the World Cup than by practicing in a project that combines these two hot… Continue
Added by Regiane Folter on March 28, 2018 at 4:30am —
This is an AI related post on the nature and philosophy of intelligence. In the various fields that study the mind, human or otherwise, there are many definitions (and lack of) for the term 'intelligence'. What is it, how can we measure it, how can we reproduce it? What implications does this have in the fields of AI, machine learning, and data science? A paper  by Shane Legg and Marcus Hutter, attempted to survey the definition from these various fields. The following are some sample… Continue
Added by Kostas Hatalis on March 15, 2018 at 12:30pm —