It is a common problem for aggregation sites.

How can we leverage self-supervised learning in evaluating "aestheticsness" of an image? Does anyone have experience with that?

Basically it comes down to what you want to do and if you want to work on the go. Personally I have a MacBook Pro and I ssh into my workstation at home. My workstation is pretty old, so I might update it soon. Personally I prefer Mac Os and I use it at home too. My WS doesnt even have a monitor.

Here's what you need to know in business news. The city's Board of Supervisors voted on Tuesday to prohibit the use of facial recognition technology within city limits. It's a somewhat symbolic move: The police there don't currently use the stuff, and the places where it is in use -- seaports and airports -- are under federal jurisdiction and therefore unaffected by the new regulation. The major television networks tried to sell their fall advertising slots in an annual pageant known as the upfronts. In a week of star-studded presentations, skits and boozy mingling, representatives of major advertisers flocked to New York to see what the networks have in store.

A new startup has created an artificial intelligence system capable of mimicking voices that are unprecedentedly close to the real thing. In a video from Dessa, an AI company staffed by former employees of Google, IBM, and Microsoft, multiple audio clips demonstrate a machine-learning software that parrots the voice of popular podcaster, Joe Rogan to a degree that's almost indiscernible from the real thing. In the clips, the computer-generated Rogan muses on topics like chimpanzee's who can play hockey; it pulls off some adept tongue-twisters; and it even pontificates theories about how we're all living in a simulation, which as noted by The Verge, are some of Rogan's favorite topics. Joe Rogan is one of the most popular podcasters in the world, giving AI plenty of data to choose from when trying to mimic the host's voice In a response, even Rogan himself called the demonstration'terrifyingly accurate' reports CNET. What makes the demonstration more intriguing, or perhaps scary, according to Dessa is that software like the one demonstrated channeling Rogan could soon be commonplace.

Spotify has launched a new voice-controlled smart device, marking a debut in the hardware industry. Dubbed'Car Thing,' it plugs into a vehicle's cigarette lighter and allows users to turn on their favorite playlist hands-free while they're driving. The device is being rolled out among a small group of test users in the coming weeks, according to the Verge. Spotify has launched a new voice-controlled smart device, marking a debut in the hardware industry. It allows users to turn on their favorite playlist hands-free while they're driving Users plug it into their car's 12-volt outlet, or cigarette lighter.

Spotify announced Friday that the music streaming service is test driving some hardware. The company is trying to learn more about what you do and listen to in your car by publicly testing out a voice-controlled music and podcast device dubbed "Car Thing." The device reportedly plugs into your vehicle's 12-volt outlet, which is also known as a cigarette lighter, for power and the automotive gadget connects to your car and phone via Bluetooth. Don't make plans to go out and buy the device anytime soon, though. Spotify says it's only testing the devices, making them available to a few premium users.

Abstract: Compressed sensing (CS) provides an elegant framework for recovering sparse signals from compressed measurements. For example, CS can exploit the structure of natural images and recover an image from only a few random measurements. CS is flexible and data efficient, but its application has been restricted by the strong assumption of sparsity and costly reconstruction process. A recent approach that combines CS with neural network generators has removed the constraint of sparsity, but reconstruction remains slow. Here we propose a novel framework that significantly improves both the performance and speed of signal recovery by jointly training a generator and the optimisation process for reconstruction via meta-learning.

Seeing the out-of-sight has turned a new corner. Now, digital cameras can take an image of an object hidden around a wall, which could help autonomous cars detect hazards in blind spots. In principle, any vertical edge can act as an accidental camera, by projecting subtle patterns of light onto the ground. These patterns reveal a semblance of what is happening on the other side of the edge and, though too faint to be noticed by the human eye, can be enhanced and interpreted by imaging algorithms.

This paper introduces Seeker, a system that allows users to interactively refine search rankings in real time, through feedback in the form of likes and dislikes. When searching online, users may not know how to accurately describe their product of choice in words. An alternative approach is to search an embedding space, allowing the user to query using a representation of the item (like a tune for a song, or a picture for an object). However, this approach requires the user to possess an example representation of their desired item. Additionally, most current search systems do not allow the user to dynamically adapt the results with further feedback. On the other hand, users often have a mental picture of the desired item and are able to answer ordinal questions of the form: "Is this item similar to what you have in mind?" With this assumption, our algorithm allows for users to provide sequential feedback on search results to adapt the search feed. We show that our proposed approach works well both qualitatively and quantitatively. Unlike most previous representation-based search systems, we can quantify the quality of our algorithm by evaluating humans-in-the-loop experiments.