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Summary AIAS Logistica is a delivery company based in Sicily, Italy that was contracted to deliver a 5000L water cistern purchased from Tecnomat in Ragusa. Despite clear arrangements for delivery on specific dates (April 16-17), multiple confirmations, and full advance payment, the company repeatedly failed to honor scheduled deliveries without providing notice. When delivery was finally attempted, the driver refused to complete proper placement of the item despite having appropriate equipment, leaving the heavy cistern in the middle of the road. This article details a customer’s frustrating experience with AIAS Logistica’s unreliable service, poor communication, and unprofessional conduct that resulted in significant inconvenience and physical hardship.   The Order I ordered a 5000L water cistern from Tecnomat (Ragusa) after confirming I would only be available to take delivery on April 16th or 17th. My contact at Tecnomat assured me this wouldn’t be an issue. On March 15th, I ...

Introduction Several months ago, I posed a question in the r/audacity subreddit regarding the differences between various OpenVINO Whisper Transcription models and their impact on transcription quality. Having received no response, I conducted this comparative study independently. Evaluating the performance of these models across diverse audio content is essential for a comprehensive assessment. This report compares four audio processing models: base , small , medium , and large-v3 . The analysis encompasses scores achieved by each model on ten different audio tracks (labelled Track 1 through Track 10), along with their respective processing durations. The objective of this analysis is to provide a data-driven foundation for assessing each model’s effectiveness, examining their processing efficiency, and identifying the strengths and weaknesses of each model.   Analysis Data All tracks, outputs from the different Audacity models, source code used to generate the scores, and in...

In this How-To, we’re going to explore how to install the OpenVINO AI plug-in for Audacity. The OpenVINO AI plug-in adds additional functionality to Audacity. One feature is music separation, allowing you to split a mono or stereo audio track into individual stems, such as vocals, drums, and other instruments. This AI plug-in also includes noise suppression capabilities, enabling you to remove background noise from audio recordings. Another feature is music generation and continuation. It generates music based on a prompt and settings that you can adjust. The final feature is audio transcription. You can highlight the section of audio you want to transcribe, select OpenVINO's Whisper Transcription option, and Audacity will add a timestamped text track with the transcribed content. The OpenVINO LLM is installed locally on your computer, ensuring that no data is transmitted to third-party companies during processing.   The installation process. You need to have the latest version of ...

We evaluate the ability of different AI systems to comprehend complex narratives. We use a modified version of the nursery rhyme "Jack and Jill," where they ascend a hill and return on their own two feet, unlike the original version where they tumble "head over heels." The story begins with Jack and Jill having 10 apples. However, it introduces additional details about the distribution of apples between them. Notably, Jill also has oranges, and at some point, Jack is said to pick up two shrivelled apples, which ultimately leads to the correct answer. Some AI systems incorrectly interpret the scenario. Most provide insight into their reasoning processes, which can help readers arrive at the correct conclusion by analysing these explanations. An intriguing observation from this exercise is that semantically confusing information can lead AI systems to produce incorrect outputs. For content creators, particularly in corporate environments where AI may be used to summar...

We examine scenarios where artificial intelligence (AI) lacks sufficient training data (as in this case) or is intentionally provided with incorrect or misleading information. The latter scenario is known as AI poisoning. We posed questions about three structures: the Eiffel Tower, Big Ben, and the bastions of Valletta. For the first two, we inquired about their heights, while for the third, we requested the mean height. To test the AI's ability to handle errors, we intentionally misspelled "Eiffel Tower." The AI was able to recognise and correct the typo, due to advancements in spell-checking algorithms and its exposure to a wide array of misspelled variations of the name during training. We also requested that the AI provide responses in both metric and imperial units, specifying the country where each structure is located. Additionally, we asked that the information be presented in descending order of height. For the Eiffel Tower and Big Ben, the AI consistently deli...

We asked different AI engines to identify the largest of three numbers. The integer part (before the decimal point) is the same for all three numbers; only the fractional part (after the decimal point) differs. The three fractional parts are .11, .9, and .099999. The correct answer is the number ending in .9, yet some AI engines incorrectly identify the number ending in .11 as the largest. When comparing decimal fractions, the magnitude of each digit position must be considered sequentially from left to right, not by evaluating the fractional part as a whole number. This is a fundamental principle of decimal representation. A possible explanation for this error is that some AI engines have not been adequately trained on decimal number comparisons and their proper mathematical handling. As a follow-up test, we asked the different AI engines to pronounce the number they identified as largest. The engines that correctly identified the number ending in .9 also correctly pronounced it, as t...