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Patterns purposely buried in AI-generated texts could help identify them as such, allowing us to tell whether the words are written by an AI or not. These “watermarks” are invisible to the human eye but let computers detect that the text probably comes from an AI system. If embedded in large language models, they could help prevent some of the problems that these models have already caused. For example, since OpenAI’s chatbot ChatGPT was launched in November, students have already started cheating by using it to write essays for them.

Building the watermarking approach into systems before they are released could help address such problems. AI language models work by predicting and generating one word at a time. After each word, the watermarking algorithm randomly divides the language model’s vocabulary into words on a “greenlist” and a “redlist” and then prompts the model to choose words on the greenlist.

The more greenlisted words in a passage, the more likely it is that the text was generated by a machine. Text written by a person tends to contain a more random mix of words. For example, for the word “beautiful,” the watermarking algorithm could classify the word “flower” as green and “orchid” as red. The AI model with the watermarking algorithm would be more likely to use the word “flower” than “orchid”.

The breathtaking speed of AI development means that new, more powerful models quickly make our existing tool kit for detecting synthetic text less effective. It is a constant race between AI developers to build new safety tools that can match the latest generation of AI models.

Getty Images has filed a landmark lawsuit against Stability AI, creators of open-source AI art generator Stable Diffusion, escalating its legal battle against the firm.

Getty accuses Stability AI of a “brazen infringement of Getty Images’ intellectual property on a staggering scale.” It claims that Stability AI copied more than 12 million images from its database without permission or compensation as part of its efforts to build a competing business and that the start-up has infringed on both the company’s copyright and trademark protections.

The lawsuit is the latest volley in the ongoing legal struggle between the creators of AI art generators and rights-holders. AI art tools require illustrations, artwork, and photographs to use as training data, and often scrape it from the web without the creator’s consent. Once the AI has been trained, it can create original and unique images.

Getty announced in December 2022 that it commenced legal proceedings in the High Court of Justice in London against Stability AI. However, that claim has not yet been served, and the company did not say at the time whether it also intended to pursue legal action in the US. Stability AI is also being sued in the US along with another AI art start-up, Midjourney, by a trio of artists who are seeking a class action lawsuit. Legal experts say Getty Images’ case is on stronger footing than the artist-led lawsuit, but caution that in such unknown legal territory it is impossible to predict any outcome. Stability AI wilfully scraped its images without permission.

After Atlas robots were shown parkouring through a complex obstacle course, jumping over gaps, balancing on narrow beams, and performing backflips in a 2021 video, new footage has now been released by Boston Dynamics showing the humanoid robot manipulating the world around it.

The last time Atlas was seen in a video, the robots did not have hands that could grip objects. However, this time, Atlas is now seen with crab claw-style hands and wrist mobility. In the demonstration, Atlas is placed in a construction worksite environment and is asked to move objects around to get to its end destination. Atlas is seen moving planks to create a bridge, throwing a bag full of two 10-pound (5kg) weights, pushing over a large wooden box, and performing an impressive double flip.

The new capabilities continue to push the limits of locomotion, sensing, and athleticism as Atlas is put to work. The hope is for the new abilities to have real-world applications in fields such as manufacturing, factory work, construction, and disaster response.

Boston Dynamic has been releasing these videos of Atlas doing increasingly more complicated things for years now. However, key questions about the use of these robots in work scenarios are still unanswered.

While these videos are indeed impressive, we suspect that Atlas has been specifically programmed to perform every action and is not doing them intuitively. We also do not know how much ‘run-time’ Atlas would have before needing to be charged again and these issues along with the cost and maintenance of the robot are large obstacles to widespread adoption in our view.

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