Will artificial intelligence (AI) create an environment where design thinking skills are more valuable than data science skills? Will AI alter how we define human intelligence? Creativity is the application of imagination plus exploration with a strong tolerance to learn through failure. Innovation and creativity are the human ability and willingness to ask provocative questions (like Tom Hanks in the movie Big); embrace diverse ideas and perspectives; blend these different ideas into a new perspective (frame); and explore, test, fail, and learn to apply the new blended perspective to real-world challenges. No, that definition doesn't exactly fit into our ACT, SAT, GMAT tests of intelligence, and that is exactly the point!

Last week, my colleague Tristan wrote about an AI developer who used machine learning to upscale the famous 1895 train scene to 4K at 60 frames per second. While this was a great short watch, it made me wonder about using AI to restore and enhance old videos. Thankfully, I stumbled upon a paper featured by the Two Minute Papers YouTube channel over the weekend that aims to improve and colorize these videos. The model uses Temporal Neural Network to identify and correct defects such as flickers in vintage videos. The remastering of vintage films comprises of a diversity of sub-tasks including super-resolution, noise removal, and contrast enhancement which jointly aim to restore the deteriorated film medium to its original state.

Motorola has hit back at claims that its resurrected Razr can only withstand 27,000 folds before showing signs of damage. CNET conducted a durability test with the new foldable phone last week with a FoldBot and after thousands of rapid folds and just three hours, the Razr's hinge was failing and not fully closing the foldable device. However, the US smartphone-maker says the robot'put undue stress on the hinge' and did not allow the foldable phone to'open and close as intended', the firm told Engadget in a statement. Motorola conducted its own tests with a robot it claims folds the phone properly and revealed users should get'years of use.' In a statement to Engadget, Motorola said: 'razr is a unique smartphone, featuring a dynamic clamshell folding system unlike any device on the market.

We introduce a data-driven approach to automatic pitch correction of solo singing performances. The proposed approach predicts note-wise pitch shifts from the relationship between the respective spectrograms of the singing and accompaniment. This approach differs from commercial systems, where vocal track notes are usually shifted to be centered around pitches in a user-defined score, or mapped to the closest pitch among the twelve equal-tempered scale degrees. The proposed system treats pitch as a continuous value rather than relying on a set of discretized notes found in musical scores, thus allowing for improvisation and harmonization in the singing performance. We train our neural network model using a dataset of 4,702 amateur karaoke performances selected for good intonation. Our model is trained on both incorrect intonation, for which it learns a correction, and intentional pitch variation, which it learns to preserve. The proposed deep neural network with gated recurrent units on top of convolutional layers shows promising performance on the real-world score-free singing pitch correction task of autotuning.

The use of machine learning to model motor learning mechanisms is still limited, while it could help to design novel interactive systems for movement learning or rehabilitation. This approach requires to account for the motor variability induced by motor learning mechanisms. This represents specific challenges concerning fast adaptability of the computational models, from small variations to more drastic changes, including new movement classes. We propose a short review on machine learning based movement models and their existing adaptation mechanisms. We discuss the current challenges for applying these models in motor learning support systems, delineating promising research directions at the intersection of machine learning and motor learning.

Aside from the immature design of many AI applications, another ethical pitfall presents itself in the way we are using these applications. We are designing AI for tasks that could (and should) be done by humans and neglecting to use AI where it is urgently needed -- namely, in repairing our online ecosystem. It is human nature to have opinions. Many of pride ourselves on the fact-based nature of our opinions -- whether they be based on news stories, personal experience, a book we read recently, etc. However, even the strongest opinions are subject to bias, and it is becoming easier than ever to bias our opinions through social media.

When the 50-second silent short film L'Arrivée d'un train en gare de La Ciotat premiered in 1896, some theatergoers reportedly ran for safety at the sight of a projected approaching train, thinking that a real one would burst through the screen at any moment, Looney Tunes-style. A wild thought, given the blurry, low-resolution quality of the original film. Thankfully those panicky cinephile pioneers never saw the AI-enhanced upscaled version released by Denis Shiryaev, or they would have absolutely flipped their lids. Shiryaev leveraged a pair of publically available enhancement programs, DAIN and Topaz Labs' Gigapixel AI, to transform the original footage into a 4K 60FPS clip. Gigapixel AI uses a proprietary interpolation algorithm which "analyzes the image and recognizes details and structures and'completes' the image" according to Topaz Labs' website.

Extraction of symbolic information from signals is an active field of research enabling numerous applications especially in the Musical Information Retrieval domain. This complex task, that is also related to other topics such as pitch extraction or instrument recognition, is a demanding subject that gave birth to numerous approaches, mostly based on advanced signal processing-based algorithms. However, these techniques are often non-generic, allowing the extraction of definite physical properties of the signal (pitch, octave), but not allowing arbitrary vocabularies or more general annotations. On top of that, these techniques are one-sided, meaning that they can extract symbolic data from an audio signal, but cannot perform the reverse process and make symbol-to-signal generation. In this paper, we propose an bijective approach for signal/symbol translation by turning this problem into a density estimation task over signal and symbolic domains, considered both as related random variables. We estimate this joint distribution with two different variational auto-encoders, one for each domain, whose inner representations are forced to match with an additive constraint, allowing both models to learn and generate separately while allowing signal-to-symbol and symbol-to-signal inference. In this article, we test our models on pitch, octave and dynamics symbols, which comprise a fundamental step towards music transcription and label-constrained audio generation. In addition to its versatility, this system is rather light during training and generation while allowing several interesting creative uses that we outline at the end of the article.

With the overwhelming online products available in recent years, there is an increasing need to filter and deliver relevant personalized advice for users. Recommender systems solve this problem by modeling and predicting individual preferences for a great variety of items such as movies, books or research articles. In this chapter, we explore rigorous network-based models that outperform leading approaches for recommendation. The network models we consider are based on the explicit assumption that there are groups of individuals and of items, and that the preferences of an individual for an item are determined only by their group memberships. The accurate prediction of individual user preferences over items can be accomplished by different methodologies, such as Monte Carlo sampling or Expectation-Maximization methods, the latter resulting in a scalable algorithm which is suitable for large datasets.

For those unfamiliar, it's a sort of cousin of AR that taps into computer vision and machine learning to identify items you point your phone at.