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"The tool has determined that there is a 99.61% probability this text was generated using OpenAI GPT. Please note that this tool like everything in AI, has a high probability of detecting GPT output, but not 100% as attributed by George E. P. Box "All models are wrong, but some are useful"."

"DeepCoder uses a technique called program synthesis: creating new programs by piecing together lines of code taken from existing software - just like a programmer might. Given a list of inputs and outputs for each code fragment, DeepCoder learned which pieces of code were needed to achieve the desired result overall. "It could allow non-coders to simply describe an idea for a program and let the system build it""

"This study therefore concludes that file sharing has not reduced the creation of new original music. It may have led to fewer works as a result of fewer new artists entering the field, but it was also associated with an increase in output by those artists who chose, despite the lower returns, to devote their talents to making music. Given that file sharing undeniably promotes the broad dissemination of existing works, this conclusion suggests that file sharing is both fully consonant with copyright's constitutionally-delimited purposes and welfare enhancing."

" Critics have pointed out that the old computing adage "garbage in, garbage out" also applies to ML. If the data from which a machine "learns" is biased, then the outputs will reflect those biases. And this could become generalised: we may have created a technology that - however good it is at recommending films you might like - may actually morph into a powerful amplifier of social, economic and cultural inequalities."

"There's a similar problem in artificial intelligence: The people who develop AI are increasingly having problems explaining how it works and determining why it has the outputs it has. Deep neural networks (DNN)-made up of layers and layers of processing systems trained on human-created data to mimic the neural networks of our brains-often seem to mirror not just human intelligence but also human inexplicability."

"Getty points to images produced by Stable Diffusion which contain its copyright watermark, suggesting that Stable Diffusion has ingested and is reproducing copyrighted material without permission (Stability AI has not yet commented publicly on the lawsuit). The same level of evidence is harder to come by when examining ChatGPT's text output, but there is no doubt that it has been trained on copyrighted material. OpenAI will be hoping that its text generation is covered by "fair use", a provision in copyright law that allows limited use of copyrighted material for "transformative" purposes. That idea will probably one day be tested in court."

"Most AI programs function like a "black box." "We know exactly what a model does but not why it has now specifically recognized that a picture shows a cat," said computer scientist Kristian Kersting of the Technical University of Darmstadt in Germany to the German-language newspaper Handelsblatt. That dilemma prompted Kersting-along with computer scientists Patrick Schramowski of the Technical University of Darmstadt and Björn Deiseroth, Mayukh Deb and Samuel Weinbach, all at the Heidelberg, Germany-based AI company Aleph Alpha-to introduce an algorithm called AtMan earlier this year. AtMan allows large AI systems such as ChatGPT, Dall-E and Midjourney to finally explain their outputs."

"In the future, it may be possible to guard against this kind of photo misuse through technical means. For example, future AI image generators might be required by law to embed invisible watermarks into their outputs so that they can be read later, and people will know they're fakes. But people will need to be able to read the watermarks easily (and be educated on how they work) for that to have any effect. Even so, will it matter if an embarrassing fake photo of a kid shared with an entire school has an invisible watermark? The damage will have already been done."

"The listeners accurately heard the sentences 43 percent of the time when given a set of 25 possible words to choose from, and 21 percent of the time when given 50 words, the study found."