The internet’s steady fall into the AI-garbled dumpster continues. As Vice reports, a recent stud y conducted by researchers at the Amazon Web Services (AWS) AI Lab found that a “shocking amount of the web” is already made up of poor-quality AI-generated and translated content.

The paper is yet to be peer-reviewed, but “shocking” feels like the right word. According to the study, over half — specifically, 57.1 percent — of all of the sentences on the internet have been translated into two or more other languages. The poor quality and staggering scale of these translations suggest that large language model (LLM)-powered AI models were used to both create and translate the material. The phenomenon is especially prominent in “lower-resource languages,” or languages with less readily available data with which to more effectively train AI models.

In other words, in what the researchers believe to be a ploy to garner clickbait-driven ad revenue, AI is being used to first generate poor-quality English-language content at a remarkable scale, and then AI-powered machine translation (MT) tools transcribe said content into several other languages. The translated material gets worse each time — and as a result, entire regions of the web are filling to the brim with degrading AI-scrambled copies of copies.

Give people a barrier, and at some point they are bound to smash through. Chuck Yeager broke the sound barrier in 1947. Yuri Gagarin burst into orbit for the first manned spaceflight in 1961. The Human Genome Project finished cracking the genetic code in 2003. And we can add one more barrier to humanity’s trophy case: the exascale barrier.

The exascale barrier represents the challenge of achieving exascale-level computing, which has long been considered the benchmark for high performance. To reach that level, however, a computer needs to perform a quintillion calculations per second. You can think of a quintillion as a million trillion, a billion billion, or a million million millions. Whichever you choose, it’s an incomprehensibly large number of calculations.

On May 27, 2022, Frontier, a supercomputer built by the Department of Energy’s Oak Ridge National Laboratory, managed the feat. It performed 1.1 quintillion calculations per second to become the fastest computer in the world.

A study pitting human children against large language models in basic reasoning problems revealed AIs to have one big blind spot: innovation.

On Thursday, OpenAI and ASU announced the first-of-its-kind partnership, which has reportedly been in the works for six months.

Students, professors, and researchers are set to get access to the tech in February. The university plans to build personalized AI tutors and avatars for students and expand its prompt engineering course.

In a press release, Arizona State University said the partnership would set a new precedent for how universities “enhance learning, creativity and student outcomes.”

AI tools like ChatGPT can draft letters, tell jokes and even give legal advice – but only in the form of computerized text.

Now, scientists have created an AI that can imitate human handwriting, which could herald fresh issues regarding fraud and fake documents.

Amazingly, the results are almost indistinguishable from the real thing drafted by human hands.

(Bloomberg) — Google DeepMind, Alphabet Inc.’s research division, said it has taken a “crucial step” towards making artificial intelligence as capable as humans. It involves solving high-school math problems. Most Read from BloombergWall Street Dials Back Fed Wagers After Solid Data: Markets WrapMusk Pressures Tesla’s Board for Another Massive Stock AwardChina’s Economic Growth Disappoints, Fueling Stimulus CallsChina Population Extends Record Drop on Covid Deaths, Low BirthsApple to Allow Outsi.

How hard would it be to train an AI model to be secretly evil? As it turns out, according to AI researchers, not very — and attempting to reroute a bad apple AI’s more sinister proclivities might backfire in the long run.

In a yet-to-be-peer-reviewed new paper, researchers at the Google-backed AI firm Anthropic claim they were able to train advanced large language models (LLMs) with “exploitable code,” meaning it can be triggered to prompt bad AI behavior via seemingly benign words or phrases. As the Anthropic researchers write in the paper, humans often engage in “strategically deceptive behavior,” meaning “behaving helpfully in most situations, but then behaving very differently to pursue alternative objectives when given the opportunity.” If an AI system were trained to do the same, the scientists wondered, could they “detect it and remove it using current state-of-the-art safety training techniques?”

Unfortunately, as it stands, the answer to that latter question appears to be a resounding “no.” The Anthropic scientists found that once a model is trained with exploitable code, it’s exceedingly difficult — if not impossible — to train a machine out of its duplicitous tendencies. And what’s worse, according to the paper, attempts to reign in and reconfigure a deceptive model may well reinforce its bad behavior, as a model might just learn how to better hide its transgressions.

What is happening at the frontier of research and application, and how are novel techniques and approaches changing the risks and opportunities linked to frontier, generative AI models?

One brain to rule them

Two researchers have revealed how they are creating a single super-brain that can pilot any robot, no matter how different they are.

Sergey Levine and Karol Hausman wrote in IEEE Spectrum that generative AI, which can create text and images, is not enough for robotics because the Internet does not have enough data on how robots interact with the world.

The brain, despite its comparatively shallow structure with limited layers, operates efficiently, whereas modern AI systems are characterized by deep architectures with numerous layers. This raises the question: Can brain-inspired shallow architectures rival the performance of deep architectures, and if so, what are the fundamental mechanisms that enable this?

Neural network learning methods are inspired by the brain’s functioning, yet there are fundamental differences between how the brain learns and how deep learning operates. A key distinction lies in the number of layers each employs.

Deep learning systems often have many layers, sometimes extending into the hundreds, which allows them to effectively learn complex classification tasks. In contrast, the human brain has a much simpler structure with far fewer layers. Despite its relatively shallow architecture and the slower, noisier nature of its processes, the brain is remarkably adept at handling complex classification tasks efficiently.