Not a robot so much as a hi-tech Frankenstein's monster, stitched together with bits of Robocop and Terminator, but cheekily enjoyable just the same. This is a sci-fi chiller co-written by horror experts Akela Cooper and James Wan and directed by Gerard Johnstone. M3gan, or Model 3 Generative Android, is an eerily self-possessed blond tweenage girlbot, voiced by Jenna Davis, a state-of-the-art toy from the near future developed as a personal passion project by engineer Gemma (Allison Williams, from Get Out and HBO's Girls) to the exasperation of her highly stressed boss David, amusingly played by Ronny Chieng. To be properly developed, M3gan needs to "pair" with a little girl owner; she needs to sync up with an actual human, to learn her owner's speech patterns, behavioural traits and emotional needs, so she can be properly close with her. And Gemma doesn't have anyone to fill that post – until her nine-year-old niece Cady (Violet McGraw) is orphaned after a car crash and comes to live with Gemma, who must furthermore honour her late parents' wish that she is homeschooled.

Tech companies of all sizes are showing off their latest products at CES, formerly known as the Consumer Electronics show. The show is getting back to normal after going completely virtual in 2021 and seeing a significant drop in 2022 attendance because of the pandemic. You might see the next big thing or something that will never make it past the prototype stage. On Tuesday night, the show kicked off with media previews from just some of the 3,000 companies signed up to attend. Bird Buddy showed off a smart bird feeder that takes snapshots of feathered friends as they fly in to eat some treats.

The proliferation of rumors on social media has become a major concern due to its ability to create a devastating impact. Manually assessing the veracity of social media messages is a very time-consuming task that can be much helped by machine learning. Most message veracity verification methods only exploit textual contents and metadata. Very few take both textual and visual contents, and more particularly images, into account. Moreover, prior works have used many classical machine learning models to detect rumors. However, although recent studies have proven the effectiveness of ensemble machine learning approaches, such models have seldom been applied. Thus, in this paper, we propose a set of advanced image features that are inspired from the field of image quality assessment, and introduce the Multimodal fusiON framework to assess message veracIty in social neTwORks (MONITOR), which exploits all message features by exploring various machine learning models. Moreover, we demonstrate the effectiveness of ensemble learning algorithms for rumor detection by using five metalearning models. Eventually, we conduct extensive experiments on two real-world datasets. Results show that MONITOR outperforms state-of-the-art machine learning baselines and that ensemble models significantly increase MONITOR's performance.

Deep learning is a type of machine learning that involves using artificial neural networks to process large amounts of data. These neural networks are designed to mimic the way that the human brain processes information, using a series of interconnected nodes to analyze and interpret complex data sets. One of the key advantages of deep learning is its ability to automatically learn and improve from experience without the need for explicit programming. This allows deep learning algorithms to automatically identify patterns and trends in data and make predictions or decisions based on those patterns. Deep learning is a subfield of artificial intelligence (AI) that is inspired by the structure and function of the human brain, specifically the neural networks that make up the brain.

Could artificial intelligence provide a snapshot of what's to come in the new year?

Benefiting from large-scale datasets and pre-trained models, the field of generative models has recently gained significant momentum. However, most datasets for symbolic music are very small, which potentially limits the performance of data-driven multimodal models. An intuitive solution to this problem is to leverage pre-trained models from other modalities (e.g., natural language) to improve the performance of symbolic music-related multimodal tasks. In this paper, we carry out the first study of generating complete and semantically consistent symbolic music scores from text descriptions, and explore the efficacy of using publicly available checkpoints (i.e., BERT, GPT-2, and BART) for natural language processing in the task of text-to-music generation. Our experimental results show that the improvement from using pre-trained checkpoints is statistically significant in terms of BLEU score and edit distance similarity. We analyse the capabilities and limitations of our model to better understand the potential of language-music models.

Automatic music generation with artificial intelligence typically requires a large amount of data which is hard to obtain for many less common genres and musical instruments. To tackle this issue, we present ongoing work and preliminary findings on the possibility for deep models to transfer knowledge from language to music, by finetuning large language models pre-trained on a massive text corpus on only hundreds of MIDI files of drum performances. We show that by doing so, one of the largest, state-of-the-art models (GPT3) is capable of generating reasonable drum grooves, while models that are not pre-trained (Transformer) shows no such ability beyond naive repetition. Evaluating generated music is a challenging task, more so is evaluating drum grooves with little precedence in literature. Hence, we propose a tailored structural evaluation method and analyze drum grooves produced by GPT3 compared to those played by human professionals, exposing the strengths and weaknesses of such generation by language-to-music transfer. Our findings suggest that language-to-music transfer learning with large language models is viable and promising.

But, Google has its own contender, LaMDA. It is only available to a closed set of testers, but it could be a serious contender in the AI Language Model race. Click on the article above to learn more! Come back here for all the latest Artificial Intelligence News.

Recently, an independent artist Kelly McKernan posted on her Instagram how she felt when she found out that she was one of the first 400 artists whose artwork was used to train AI. I'm credited (…) on the Wikipedia page for AI art as a "style prompt." I'm getting tagged in image prompts and met with indignation when I request my name removed. She continues that to her, much of this is unethical and feels violative. Current art students are discouraged from continuing to study; professors don't know what to tell them; emerging artists are feeling hopeless and giving up.

Let me begin with a quick story of two friends, Peter and Henry. Two young boys who lived in a small village – shared a common dream of becoming successful musicians. Despite facing many challenges and setbacks, they never gave up on their dream. Eventually, their hard work and determination paid off, as they landed a record deal and became household names, inspiring people worldwide with their music. Now my question is: have you heard of these two musicians: Peter & Henry?