Correcting biases in image generator models

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Image generator models—systems that produce new images based on textual descriptions—have become a common and well-known phenomenon in the past year. Their continuous improvement, largely relying on developments in the field of artificial intelligence, makes them an important resource in various fields.


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Bitcoin poised to reclaim $90,000, according to derivatives metrics

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Bitcoin (BTC) failed to sustain levels above $85,000 on March 14, despite a 1.9% gain in the S&P 500 index. More importantly, it has been over a week since Bitcoin last traded at $90,000, prompting traders to question whether the bull market is truly over and how long selling pressure will persist. Bitcoin basis rate rebounds from bearish levels From a derivatives perspective, Bitcoin metrics have shown resilience despite a 30% drop from its all-time high of $109,354 on Jan. 20. The Bitcoin basis rate, which measures the premium of monthly contracts over spot markets, has recovered to healthy levels after briefly signaling bearish sentiment on March 13. Bitcoin 2-month futures contracts annualized premium. Source: Laevitas.ch Traders typically demand a 5% to 10% annualized premium to compensate for longer settlement periods. A basis rate below this threshold signals weak demand from leveraged buyers. While the current 5% rate is lower than the 8% recorded two weeks ago, i...
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Google DeepMind’s latest medical breakthrough borrows a trick from AI image generators

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Much of the recent AI hype train has centered around mesmerizing digital content generated from simple prompts, alongside concerns about its ability to decimate the workforce and make malicious propaganda much more convincing. (Fun!) However, some of AI’s most promising — and potentially much less ominous — work lies in medicine. A new update to Google’s AlphaFold software could lead to new disease research and treatment breakthroughs. AlphaFold software, from Google DeepMind and (the also Alphabet-owned) Isomorphic Labs, has already demonstrated that it can predict how proteins fold with shocking accuracy. It’s cataloged a staggering 200 million known proteins, and Google says millions of researchers have used previous versions to make discoveries in areas like malaria vaccines, cancer treatment and enzyme designs.  Knowing a protein’s shape and structure determines how it interacts with the human body, allowing scientists to create new drugs or improve existing ones. But the ne...
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