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Category: AI

Replace your whole dev team with AI

Replace your whole dev team with AI

Front end and back end developers, security experts, social media engagement managers, and marketing. Replace everyone with an AI powered bot and even create/train your own code GPT for your internal code.

How easy mass AI surveillance is – today

How easy mass AI surveillance is – today

If armies of AI spies are not a thing now, they soon will be.

Imagine having thousands and thousands of agents watching every single one of your government and company officials morning, noon, and night. Now, imagine they never get tired and only cost as much as a high-end PC.

Using data that most companies collect on users and a few planted agent employees to give you key access and your leaders data is ripe for the picking and exploiting. Low-tier hackers may hack you to ransom your data – the big guys will quietly watch you to see if you’re vulnerable in ways never before.

Here’s just one simple example of what it might look like:

Y combiner group wants to hire AI bots

Y combiner group wants to hire AI bots

Y Combinator-backed startup Firecrawl has placed three new ads on YC’s job board for “AI agents only” and has set aside a $1 million budget total to make it happen.

They don’t want to hire people, but AI agents – and they are paying $1 million to ‘hire’ them.

What they do want is fully AI powered content creation agents “that never sleeps and always ships”. It will autonomously produce “high-quality” SEO-pleasing blog posts and tutorials on how to use its product, then wants the AI to watch engagement metrics and use that to autonomously improve the audience for its content, too.

Within a week, they had about 50 applicants.

Articles:

Tool for measuring AI enhanced GPU image quality

Tool for measuring AI enhanced GPU image quality

Engineers at Intel released an open-source tool that tries to quantify the issues from increasing amounts of upscalers, frame generators, and AI rendering techniques. Ironically, the tool itself is an AI trained on large datasets. Their paper about the methodology is located here.

CGVQM is a video quality metric that predicts perceptual differences between pairs of videos.
Like PSNR and SSIM, it compares a ground-truth reference to a distorted version (e.g. blurry, noisy, aliased).

What sets CGVQM apart is that it is the first metric calibrated for distortions from advanced rendering techniques, accounting for both spatial and temporal artifacts.

CGVQM is available for free on github and uses PyTorch optimized for CUDA GPUs though it does work on CPUs.

Other links:

AI GPU/Video Quality Metrics

AI GPU/Video Quality Metrics

How do you know when AI generated frames on your graphics card or in video playback look bad? Some folks at Intel published a paper and shared a dataset for a technique called CGVQM+D: Computer Graphics Video Quality Metric and Dataset – which tries to quantify the image quality issues created by using AI generation techniques like neural supersampling, novel-view synthesis, path tracing, neural denoising, frame interpolation, and variable rate shading.

AI to AI communication

AI to AI communication

It’s no secret AI agents are starting to take over customer service and other tasks. So, instead of communicating via expensive text-to-speech conversion, why not use their own language.

Gibberlink allows AIs to skip unnecessary speech synthesis and recognition and communicate using beeps and bleeps like R2D2.