Lifeboat Foundation

If your work involves analyzing and reporting on data, then it’s understandable that you might feel a bit concerned by the rapid advances being made by artificial intelligence (AI). In particular, the viral ChatGPT.

AI, particularly ChatGPT, has raised job security concerns among data analysts. Here we look at the potential impact and discuss that despite limitations like frequent mistakes and limited data upload capabilities, ChatGPT has the potential to automate data gathering and analysis tasks in the future.

Google announced today that it will enable a new SafeSearch blurring setting by default for all users in the coming months. The filter is designed to help people protect themselves and their families from inadvertently encountering explicit imagery on Search. The search giant says it’s announcing the feature today to mark Safer Internet Day.

The setting will soon be the new default for people who don’t already have the SafeSearch filter turned on. As a result, Google will blur explicit imagery if it appears in Search results. Explicit results include sexually explicit content like pornography, violence and gore. Google notes that users have the option to adjust the setting at any time. Prior to this expansion, the filter was already on by default for signed-in users under 18.

Once the setting becomes the default, Google will notify you that it has turned on SafeSearch blurring. If you come across an explicit image, you can choose to see it by clicking on the “view image” button. Or, you can select the “manage setting” button to adjust the filter or turn it off altogether. For instance, you can choose the “filter” option, which helps filter explicit images, text and links. Or, you can select the “off” option, which means that you will see all of the relevant results for your query, even if they’re explicit.

After six years of peace, the two tech giants are on course to butt heads again over the future of artificial intelligence.

Microsoft is about to go head-to-head with Google in a battle for the future of search. At a press event later today, Microsoft is widely expected to detail plans to bring OpenAI’s ChatGPT chatbot to its Bing search engine. Google has already tried to preempt the news, making a rushed announcement yesterday to introduce Bard, its rival to ChatGPT, and promising more details on its AI future in a press event on Wednesday.

The two tech giants are on course to butt heads again.

The minor planet, considered small, is about the size of the Colosseum in Rome.

Why ‘panspermia theory’ – the wild idea of microbes ‘hitchhiking’ on meteoroids through the cosmos – is now taken seriously.

Setting boundaries for these tools, then, could be crucial, some researchers say. Edwards suggests that existing laws on discrimination and bias (as well as planned regulation of dangerous uses of AI) will help to keep the use of LLMs honest, transparent and fair. “There’s loads of law out there,” she says, “and it’s just a matter of applying it or tweaking it very slightly.”

At the same time, there is a push for LLM use to be transparently disclosed. Scholarly publishers (including the publisher of Nature) have said that scientists should disclose the use of LLMs in research papers (see also Nature 613, 612; 2023); and teachers have said they expect similar behaviour from their students. The journal Science has gone further, saying that no text generated by ChatGPT or any other AI tool can be used in a paper⁵.

One key technical question is whether AI-generated content can be spotted easily. Many researchers are working on this, with the central idea to use LLMs themselves to spot the output of AI-created text.

Individual atoms trapped by optical ‘tweezers’ are emerging as a promising computational platform.

Diffusion models have recently produced outstanding results on various generating tasks, including the creation of images, 3D point clouds, and molecular conformers. Ito stochastic differential equations (SDE) are a unified framework that can incorporate these models. The models acquire knowledge of time-dependent score fields through score-matching, which later directs the reverse SDE during generative sampling. Variance-exploding (VE) and variance-preserving (VP) SDE are common diffusion models. EDM offers the finest performance to date by expanding on these compositions. The existing training method for diffusion models can still be enhanced, despite achieving outstanding empirical results.

The Stable Target Field (STF) objective is a generalized variation of the denoising score-matching objective. Particularly, the high volatility of the denoising score matching (DSM) objective’s training targets can result in subpar performance. They divide the score field into three regimes to comprehend the cause of this volatility better. According to their investigation, the phenomenon mostly occurs in the intermediate regime, defined by various modes or data points having a similar impact on the scores. In other words, under this regime, it is still being determined where the noisy samples produced throughout the forward process originated. Figure 1(a) illustrates the differences between the DSM and their proposed STF objectives.

Figure 1: Examples of the DSM objective’s and our suggested STF objective’s contrasts.

Rare clouds that give off bright, multi-colored light like an aurora were recently spotted at multiple locations in the Arctic. But what causes them?