Keeping up with an industry as fast-moving as AI is a tall order. So until an AI can do it for you, here's a handy roundup of the last week's stories in the world of machine learning, along with notable research and experiments we didn't cover on their own. The biggest news of the last week (we politely withdraw our Anthropic story from consideration) was the announcement of Bedrock, Amazon's service that provides a way to build generative AI apps via pretrained models from startups including AI21 Labs, Anthropic and Stability AI. Currently available in "limited preview," Bedrock also offers access to Titan FMs (foundation models), a family of AI models trained in-house by Amazon. It makes perfect sense that Amazon would want to have a horse in the generative AI race.

It's inspired by the daily TechCrunch column where it gets its name. This week, I am diving deeper into what generative AI means, or doesn't mean, for enterprise buyers. I also have some notes on why your company may want to be like Figma, and how the investing side of the market is adjusting to down rounds being the new normal. When The Exchange looked into Battery Ventures' state of cloud software spending report, we started by focusing on what the title promised: fresh data on cloud software spend. And it turned out to be more encouraging than we expected.

Federated learning allows for the training of machine learning models on multiple decentralized local datasets without requiring explicit data exchange. However, data pre-processing, including strategies for handling missing data, remains a major bottleneck in real-world federated learning deployment, and is typically performed locally. This approach may be biased, since the subpopulations locally observed at each center may not be representative of the overall one. To address this issue, this paper first proposes a more consistent approach to data standardization through a federated model. Additionally, we propose Fed-MIWAE, a federated version of the state-of-the-art imputation method MIWAE, a deep latent variable model for missing data imputation based on variational autoencoders. MIWAE has the great advantage of being easily trainable with classical federated aggregators. Furthermore, it is able to deal with MAR (Missing At Random) data, a more challenging missing-data mechanism than MCAR (Missing Completely At Random), where the missingness of a variable can depend on the observed ones. We evaluate our method on multi-modal medical imaging data and clinical scores from a simulated federated scenario with the ADNI dataset. We compare Fed-MIWAE with respect to classical imputation methods, either performed locally or in a centralized fashion. Fed-MIWAE allows to achieve imputation accuracy comparable with the best centralized method, even when local data distributions are highly heterogeneous. In addition, thanks to the variational nature of Fed-MIWAE, our method is designed to perform multiple imputation, allowing for the quantification of the imputation uncertainty in the federated scenario.

Data storytelling plays an important role in data workers' daily jobs since it boosts team collaboration and public communication. However, to make an appealing data story, data workers spend tremendous efforts on various tasks, including outlining and styling the story. Recently, a growing research trend has been exploring how to assist data storytelling with advanced artificial intelligence (AI). However, existing studies may focus on individual tasks in the workflow of data storytelling and do not reveal a complete picture of humans' preference for collaborating with AI. To better understand real-world needs, we interviewed eighteen data workers from both industry and academia to learn where and how they would like to collaborate with AI. Surprisingly, though the participants showed excitement about collaborating with AI, many of them also expressed reluctance and pointed out nuanced reasons. Based on their responses, we first characterize stages and tasks in the practical data storytelling workflows and the desired roles of AI. Then the preferred collaboration patterns in different tasks are identified. Next, we summarize the interviewees' reasons why and why not they would like to collaborate with AI. Finally, we provide suggestions for human-AI collaborative data storytelling to hopefully shed light on future related research.

Deep generative models are becoming increasingly powerful, now generating diverse high fidelity photo-realistic samples given text prompts. Have they reached the point where models of natural images can be used for generative data augmentation, helping to improve challenging discriminative tasks? We show that large-scale text-to image diffusion models can be fine-tuned to produce class conditional models with SOTA FID (1.76 at 256x256 resolution) and Inception Score (239 at 256x256). The model also yields a new SOTA in Classification Accuracy Scores (64.96 for 256x256 generative samples, improving to 69.24 for 1024x1024 samples). Augmenting the ImageNet training set with samples from the resulting models yields significant improvements in ImageNet classification accuracy over strong ResNet and Vision Transformer baselines.

Generative AI platforms have been the rage since the second half of last year with Microsoft and Google pushing these programs into their existing services. Even the Ministry of Electronics and Information Technology (MeitY), on 3 February, said it is "cognizant" of the emergence and proliferation of generative AI and noted that AI can be a "kinetic enabler" for growth in India. However, researchers at institutes underline a host of challenges for generative AI projects in academia, the biggest of which lie in sourcing ample data of Indic languages, the cost of such projects, and the scale of computing power needed. Indian researchers have been working on such projects for more than three years. "In academia, we're using techniques from language models, namely the transformer architecture, for different tasks such as classification of data, answering questions, machine translation and building chatbots," said Tapas Kumar Mishra, assistant professor of computer science engineering at National Institute of Technology (NIT), Rourkela.

While tech giants across the world work on materializing the idea of having a generative artificial intelligence (AI) to aid humans in their daily lives, the risk of the nascent technology going rogue remains imminent. Considering this possibility, Tesla and Twitter chief Elon Musk reminded the people of AI's potential to destroy civilization. On March 15, Musk's plan of creating a new AI startup surfaced after the entrepreneur was reportedly assembling a team of AI researchers and engineers. However, Musk continues to highlight the destructive potential of AI -- just like any other technology -- if it goes into the wrong hands or is being developed with ill intentions. According to Musk, AI can be dangerous. In a FOX interview, he said that AI can be more dangerous than mismanaged aircraft design or production maintenance, for example.

MobileGPT is a cutting-edge personalised AI assistant on WhatsApp powered by OpenAI ChatGPT. Chat, create AI images and documents, all in one convenient service.

Ian Sansavera, a software architect at a New York startup called Runway AI, typed a short description of what he wanted to see in a video. "A tranquil river in the forest," he wrote. Less than two minutes later, an experimental internet service generated a short video of a tranquil river in a forest. The river's running water glistened in the sun as it cut between trees and ferns, turned a corner and splashed gently over rocks. Runway, which plans to open its service to a small group of testers this week, is one of several companies building artificial intelligence technology that will soon let people generate videos simply by typing several words into a box on a computer screen.

While tech giants across the world work on materializing the idea of having a generative artificial intelligence (AI) to aid humans in their daily lives, the risk of the nascent technology going rogue remains imminent. Considering this possibility, Tesla and Twitter chief Elon Musk reminded the people of AI's potential to destroy civilization. On March 15, Musk's plan of creating a new AI startup surfaced after the entrepreneur was reportedly assembling a team of AI researchers and engineers. However, Musk continues to highlight the destructive potential of AI -- just like any other technology -- if it goes into the wrong hands or is being developed with ill intentions. According to Musk, AI can be dangerous. In a FOX interview, he said that AI can be more dangerous than mismanaged aircraft design or production maintenance, for example.