Tag Archives: Artificial Intelligence
Understanding Artificial Intelligence
When I published the article “Understanding Blockchain” many of you wrote me to ask me if I could make one dedicated to Artificial Intelligence. The truth is that I hadn’t had time to get on with it and before sharing anything, I wanted to finish some courses in order to add value to the recommendations.
The problem with Artificial Intelligence is that it’s much more fragmented, both technologically and in use cases, than Blockchain, making it a real challenge to condense all the information and share it meaningfully. Likewise, I have tried to make an effort in the summary of key concepts and in the compilation of interesting sources and resources, I hope it helps you as well as it did to me! Read More
Artificial Intelligence in Industry with Dan Faggella
Podcast for those interested in learning applying machine learning and AI technologies within their company or organization. The host, Dan Faggella, interviews top AI and machine learning researchers and executives on how they are using AI and machine learning. Read More
Teaching AI the Concept of ‘Similar, but Different’
As a human you instinctively know that a leopard is closer to a cat than a motorbike, but the way we train most AI makes them oblivious to these kinds of relations. Building the concept of similarity into our algorithms could make them far more capable, writes the author of a new paper in Science Robotics.
Convolutional neural networks have revolutionized the field of computer visionto the point that machines are now outperforming humans on some of the most challenging visual tasks. But the way we train them to analyze images is very different from the way humans learn, says Atsuto Maki, an associate professor at KTH Royal Institute of Technology.
“Imagine that you are two years old and being quizzed on what you see in a photo of a leopard,” he writes. “You might answer ‘a cat’ and your parents might say, ‘yeah, not quite but similar’.” Read More
Building Better Deep Learning Requires New Approaches Not Just Bigger Data
In its rush to solve all the world’s problems through deep learning, Silicon Valley is increasingly embracing the idea of AI as a universal solver that can be rapidly adapted to any problem in any domain simply by taking a stock algorithm and feeding it relevant training data. The problem with this assumption is that today’s deep learning systems are little more than correlative pattern extractors that search large datasets for basic patterns and encode them into software. While impressive compared to the standards of previous eras, these systems are still extraordinarily limited, capable only of identifying simplistic correlations rather than actually semantically understanding their problem domain. In turn, the hand-coded era’s focus on domain expertise, ethnographic codification and deeply understanding a problem domain has given way to parachute programming in which deep learning specialists take an off-the-shelf algorithm, shove in a pile of training data, dump out the resulting model and move on to the next problem. Truly advancing the state of deep learning and way in which companies make use of it will require a return to the previous era’s focus on understanding problems rather than merely churning canned models off assembly lines. Read More
AI+EI – A recipe for success or disaster?
If one thing is for sure, it is that businesses are reaping the benefits of AI’s ability to free us from the more repetitive tasks in the workplace. AI is changing the nature of work. It’s helping to remove the mundane, enabling us to make more informed decisions with its analytical capabilities and its ability to wade through large amounts of data through machine learning.
Yet, according to a report from Gartner, EI accounts for more than 90% of a person’s performance and success in a technical and leadership role. With this in mind, it would be unlikely for AI to completely replace human beings in the workplace at this stage, given its lack of emotional intelligence (among other things). Emotional intelligence, deep domain expertise and a set of “soft skills” cannot yet be automated by current AI technologies. Read More
Blockchain and AI Bond, Explained
Artificial intelligence (AI) and blockchain are two of the most-talked-about technologies of the past 10 years, and their evolution has led to significant and promising innovations. The idea of combining them is particularly intriguing.
The profit potential for these two technologies is forecast to be in the billions for the foreseeable future. Gartner, a global technology research firm, estimates that the business value created by AI will near $3.9 trillion in 2022, while some anticipate the blockchain market will be worth roughly $23 billion by 2023.
The drivers behind this tremendous predicted growth are increased adoption, as well as the potential use cases that have been emerging across both sectors. AI, which is technically not a new technology, has taken on a prominent role in the tech world over the past two years. While we are still far from fully thinking machines, AI has been deployed in everything from marketing and sales to manufacturing and even health care. The technology has become a crucial part of most businesses’ plans moving forward. Read More
Data in the Life: Authorship Attribution in Lennon-McCartney Songs
The songwriting duo of John Lennon and Paul McCartney, the two founding members of the Beatles, composed some of the most popular and memorable songs of the last century. Despite having authored songs under the joint credit agreement of Lennon-McCartney, it is well-documented that most of their songs or portions of songs were primarily written by exactly one of the two. Furthermore, the authorship of some Lennon-McCartney songs is in dispute, with the recollections of authorship based on previous interviews with Lennon and McCartney in conflict. For Lennon-McCartney songs of known and unknown authorship written and recorded over the period 1962-66, we extracted musical features from each song or song portion. These features consist of the occurrence of melodic notes, chords, melodic note pairs, chord change pairs, and four-note melody contours. We developed a prediction model based on variable screening followed by logistic regression with elastic net regularization. Out-of-sample classification accuracy for songs with known authorship was 76%, with a c-statistic from an ROC analysis of 83.7%. We applied our model to the prediction of songs and song portions with unknown or disputed authorship. Read More
Eight technologies you should learn to love
A new AI is Using News Outlets to Produce Fake Article and it is quite Concerning!
Over the last few years, the rise of fake news and information has been off the charts. To tackle this issue, various measures have been implemented. Most recently, the Allen Institute for Artificial Intelligence developed Grover.
Grover is basically a neural network that can produce fake articles by imitating the writing style of actual journalists. The motivation behind its creation is to identify fake news, and generate fake stories as well. Read More
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