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…Continue
Added by William Vorhies on June 10, 2019 at 8:28am — No Comments
Added by Max Ved on May 27, 2019 at 11:02pm — No Comments
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.
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
When we look at broadly different kinds of Machine Learning that are used in practice in Artificial Intelligence
Historically, there have been several approaches in Machine learning for AI like supervised learning, unsupervised learning, reinforcement learning, case-based reasoning, inductive logic programming, experience based generalisation etc. there have been several examples of waves of machine learning for different AI problems. But, of them the 3 most important categories of…Continue
Added by Mahesh Kumar CV on May 5, 2019 at 1:00am — No Comments
AI is a new buzzword but there has been a lot of talk around analytics and prospects over a decade. Here we present a perspective that AI is a continuum of Analytics
Let us see how...
Different approaches in Data Science and analytics have been originated in the field of statistics.
AI has emerged on the other hand out of computer science as a practice and science of studying "intelligent agents".
The way is to treat AI as extending conventional analytics with a…Continue
Added by Mahesh Kumar CV on May 4, 2019 at 9:28am — 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
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
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
Summary: In the literal blink of an eye, image-based AI has gone from high cost, high risk projects to quick and reasonably reliable. C-level execs looking for AI techniques to exploit need to revisit their assumptions and move these up the list. Here’s what’s changed.
For data scientists these are miraculous times. We tend to think of miracles as something that occurs instantaneously but in our world that’s not quite so. Still the rate…Continue
Added by William Vorhies on March 4, 2019 at 9:41am — 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
Artificial intelligence has been fascinating to the human imagination since the term was first used by the first science fiction writers.
The roots of the concept of "artificial intelligence" must be sought deep in the ancient world, where folklore, legends and myths in almost every culture spoke of artificially created creatures endowed with supernatural intelligence, consciousness or other human qualities. The only factor uniting the myths of the whole world is that artificial…Continue
Added by Melissa Crooks on February 27, 2019 at 12:42am — 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
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.
In the late 1990s, AI rose to prominence. In 1997, IBM's Deep Blue…Continue
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