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 9:00am — No Comments
Some months ago, booking.com joined the ginger group of brands to combine artificial intelligence (AI) with mobile to get a headsup in anticipating a customer’s purchase intent.
Booking.com app users now not only receive instant booking access to a destination with a single QR code but also get…Continue
Added by Hemant Warudkar on January 22, 2019 at 9:05pm — 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
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.
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.
Added by William Vorhies on December 17, 2018 at 8:50am — No Comments
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.
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 — No Comments
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 — No Comments
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.
Added by William Vorhies on November 5, 2018 at 4:18pm — No Comments
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 — No Comments
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 — No Comments
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.
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 — No Comments
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 — No Comments
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 — No Comments
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 — No Comments
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.
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.
Added by William Vorhies on July 17, 2018 at 7:00am — No Comments
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 — No Comments