All Videos Channels

Country Song Writer's Playbook

51 Followers

This channel is dedicated to songwriting that speaks through both lyrics and arrangements. My goal is to create songs that connect with listeners on an emotional level, resonating with both the heart and mind. I am shifting my focus for now. I’m writing songs and using AI-generated voices to perform them. I’m really curious to see where AI is heading musically. The AI will handle the voicing, while I combine my creativity with AI technology to produce the 'artwork.'"

K. A. Patterson - American Songwriter

12 Followers

MUSIC Promotion - Mostly Classic & Neo-Traditional Country Mainly promoting Songwriter K. A. Patterson (yours truly), showcased mostly through performances by the Middle Tennessee studio ensemble K. A. Patterson and the Paradise Band (KAP&TPB). Earlier tunes are reminiscent of Country\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'s "golden age", the mid 1960\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'s to mid 1980\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'s. Charlie Pride, Linda Ronstadt, Merle Haggard, Dottie West, Glen Campbell, Emylou Harris, Conway & Loretta, Kenny & Dolly, Randy Travis, Highway 101, etc. Later tracks are much like the Country Music from 1985-2005. Restless Heart, Patty Loveless, Garth Brooks, Kathy Mattea, Lonestar, Reba McEntire, Diamond Rio, Martina McBride, etc. Throughout K. A. Patterson music, you\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'re likely to hear John Denver, The Eagles, Pure Prairie League, Marshall Tucker, Paulette Carlson, and similar Artist styles. Some KAP music bridges the gap from the afore-mentioned to "Today\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'s Country". Other Country Artists+great music from Artists of other genres are also featured.

Ai Generate

10 Followers

This stunning artwork, titled "Beauty Queen," captures the intricate beauty of a woman through the unique perspective of AI technology. The use of bold, vibrant colors and detailed geometric shapes creates a striking portrayal of femininity and elegance. The interplay of light and shadow creates an intriguing depth and texture to the piece, giving it a sense of movement and life. Overall, this artwork is a testament to the power of artificial intelligence as a tool for creative expression and the unlimited potential of technology in the world of art.

AI-Generated Hits

7 Followers

Welcome to AI-Generated Hits, the ultimate destination for music lovers who crave cutting-edge sounds and revolutionary technology. Our channel is dedicated to exploring the world of artificial intelligence and machine learning as it intersects with the art of music production. Here, you'll find incredible performances, covers, and remixes that push the boundaries of what's possible with AI technology. From viral hits to experimental soundscapes, we showcase the best and brightest of the AI music scene. Join us on this exciting journey and discover the future of music today!

Original Artist Songwriter Contestants Verified

5 Followers

ORIGINAL ARTIST is a competitive music platform designed ONLY for SONGWRITERS performing their OWN music. ORIGINAL ARTIST is designed to honor songwriter musicians and to raise awareness as a charity fundraiser event series. All download purchase votes, donations, and fees fund aftercare for the survivors of child sex trafficking. Details and Submissions are to be sent to the originalartist.me website details on originalartist.me This competitive music series only allows each participant to submit their own copywritten music to be submitted into the categories of: Country, Rock, Urban and Latin with their respective sub-genres.

Users can generate videos up to 1080p resolution, up to 20 sec long, and in widescreen, vertical or square aspect ratios. You can bring your own assets to extend, remix, and blend, or generate entirely new content from text.

4 Followers

We’ve discovered neurons in CLIP that respond to the same concept whether presented literally, symbolically, or conceptually. This may explain CLIP’s accuracy in classifying surprising visual renditions of concepts, and is also an important step toward understanding the associations and biases that CLIP and similar models learn. Fifteen years ago, Quiroga et al.1 discovered that the human brain possesses multimodal neurons. These neurons respond to clusters of abstract concepts centered around a common high-level theme, rather than any specific visual feature. The most famous of these was the “Halle Berry” neuron, a neuron featured in both Scientific American⁠(opens in a new window) and The New York Times⁠(opens in a new window), that responds to photographs, sketches, and the text “Halle Berry” (but not other names). Two months ago, OpenAI announced CLIP⁠, a general-purpose vision system that matches the performance of a ResNet-50,2 but outperforms existing vision systems on some of the most challenging datasets. Each of these challenge datasets, ObjectNet, ImageNet Rendition, and ImageNet Sketch, stress tests the model’s robustness to not recognizing not just simple distortions or changes in lighting or pose, but also to complete abstraction and reconstruction—sketches, cartoons, and even statues of the objects. Now, we’re releasing our discovery of the presence of multimodal neurons in CLIP. One such neuron, for example, is a “Spider-Man” neuron (bearing a remarkable resemblance to the “Halle Berry” neuron) that responds to an image of a spider, an image of the text “spider,” and the comic book character “Spider-Man” either in costume or illustrated. Our discovery of multimodal neurons in CLIP gives us a clue as to what may be a common mechanism of both synthetic and natural vision systems—abstraction. We discover that the highest layers of CLIP organize images as a loose semantic collection of ideas, providing a simple explanation for both the model’s versatility and the representation’s compactness.