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Thanks to AI, the coder is no longer king: All hail the QA engineer

How will that situation change development teams? A common ratio of developers to testers is three to one. At a big bank with 40,000 software engineers, 10,000 might do security, reliability, and quality control. But the AI effect is like squeezing a balloon so it expands on the other side. The coding productivity jump is offset by a dramatic increase in cycles spent on testing.

How Development Teams Can Get Ahead

For software teams, the pressure is on to adapt. Companies that want to stay ahead of the game should first get a handle on a long-time adversary: toil.

Korean researchers power-shame Nvidia with new neural AI chip — claim 625 times less power draw, 41 times smaller

A team of scientists from the Korea Advanced Institute of Science and Technology (KAIST) detailed their ‘Complementary-Transformer’ AI chip during the recent 2024 International Solid-State Circuits Conference (ISSCC). The new C-Transformer chip is claimed to be the world’s first ultra-low power AI accelerator chip capable of large language model (LLM) processing.

In a press release, the researchers power-shame Nvidia, claiming that the C-Transformer uses 625 times less power and is 41x smaller than the green team’s A100 Tensor Core GPU. It also reveals that the Samsung fabbed chip’s achievements largely stem from refined neuromorphic computing technology.

Though we are told that the KAIST C-Transformer chip can do the same LLM processing tasks as one of Nvidia’s beefy A100 GPUs, none of the press nor conference materials we have provided any direct comparative performance metrics. That’s a significant statistic, conspicuous by its absence, and the cynical would probably surmise that a performance comparison doesn’t do the C-Transformer any favors.

The AI Takeover In Cinema: How Movie Studios Use Artificial Intelligence

The film industry, always at the forefront of technological innovation, is increasingly embracing artificial intelligence (AI) to revolutionize movie production, distribution, and marketing. From script analysis to post-production, Already AI is reshaping how movies are made and consumed. Let’s explore the current applications of AI in movie studios and speculates on future uses, highlighting real examples and the transformative impact of these technologies.

AI’s infiltration into the movie industry begins at the scriptwriting stage. Tools like ScriptBook use natural language processing to analyze scripts, predict box office success, and offer insights into plot and character development. For instance, 20th Century Fox employed AI to analyze the script of Logan, which helped in making informed decisions about the movie’s plot and themes. Consider, in pre-production, AI has also aided in casting and location scouting. Warner Bros. partnered with Cinelytic to use AI for casting decisions, evaluating an actor’s market value to predict a film’s financial success. For example, let’s look at location scouting. AI algorithms can sift through thousands of hours of footage to identify suitable filming locations, streamlining what was once a time-consuming process.

During filmmaking, AI plays a crucial role in visual effects (VFX). Disney’s FaceDirector software can generate composite expressions from multiple takes, enabling directors to adjust an actor’s performance in post-production. This technology was notably used in Avengers: Infinity War to perfect emotional expressions in complex CGI scenes. Conversely, AI-driven software like deepfake technology, though controversial, has been used to create realistic face swaps in movies. For instance, it was used in The Irishman to de-age actors, offering a cost-effective alternative to traditional CGI. Additionally, AI is used in color grading and editing. IBM Watson was used to create the movie trailer for Morgan, analyzing visuals, sounds, and compositions from other movie trailers to determine what would be most appealing to audiences.

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