In 2015, Klaus Schwab, founder of the World Economic Forum, asserted that we were on the brink of a “Fourth Industrial Revolution,” one powered by a fusion of technologies, such as advanced robotics, artificial intelligence, and the Internet of Things.
“[This revolution] will fundamentally alter the way we live, work, and relate to one another,” wrote Schwab in an essay published in Foreign Affairs. “In its scale, scope, and complexity, the transformation will be unlike anything humankind has experienced before.”
The recent surge of developments in AI and robotics — and their deployment into the workforce — seems right in line with his predictions, although almost ten years on. — Read More
Recent Updates
Meet Amazon Q, the AI assistant that generates apps for you
Amazon Web Services (AWS) has long offered generative AI solutions to optimize everyday business operations. Today, AWS added to those offerings with the general availability of its AI assistant Amazon Q.
AWS first announced Amazon Q in November 2023; on Tuesday, the company made the AI-powered assistant generally available for developers and businesses, as well as released free courses on using the AI assistant and a new Amazon Q capability in preview. — Read More
BBC presenter’s likeness used in advert after firm tricked by AI-generated voice
There was something strange about her voice, they thought. It was familiar but, after a while, it started to go all over the place.
Science presenter Liz Bonnin’s accent, as regular BBC viewers know, is Irish. But this voice message, ostensibly granting permission to use her likeness in an ad campaign, seemed to place her on the other side of the world.
The message, it turns out, was a fake – AI-generated to mimic Bonnin’s voice. Her management team got hold of it after they saw the presenter’s face on online ads for an insect repellant spray this week, something for which she did not sign up. — Read More
Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone
We introduce phi-3-mini, a 3.8 billion parameter language model trained on 3.3 trillion tokens, whose overall performance, as measured by both academic benchmarks and internal testing, rivals that of models such as Mixtral 8x7B and GPT-3.5 (e.g., phi-3-mini achieves 69% on MMLU and 8.38 on MT-bench), despite being small enough to be deployed on a phone. The innovation lies entirely in our dataset for training, a scaled-up version of the one used for phi-2, composed of heavily filtered web data and synthetic data. The model is also further aligned for robustness, safety, and chat format. We also provide some initial parameter-scaling results with a 7B and 14B models trained for 4.8T tokens, called phi-3-small and phi-3-medium, both significantly more capable than phi-3-mini (e.g., respectively 75% and 78% on MMLU, and 8.7 and 8.9 on MT-bench). — Read More
GitHub previews Copilot Workspace, an AI developer environment to turn ideas into software
GitHub has revealed Copilot Workspace, its AI-native developer environment. Using natural language, developers can brainstorm, plan, build, test and run code faster and easier than before. First teased in 2023 at its user conference, GitHub Copilot Workspace is now available in technical preview and interested developers can sign up for the waitlist. — Read More
China’s S1 robot impresses with its ‘human-like’ speed and precision
The era of humanoid robots seems to flourish, with new models being developed and trained at exceptional speeds.
Another Chinese firm making advanced strides in this realm is Astribot. The Senzhen-based subsidiary of Stardust Intelligence is a robotics firm focused on developing AI robot assistants.
In a video released by the firm, its humanoid S1 is seen doing household tasks at an unprecedented pace, which marks a significant advancement for a robot. — Read More
Video
What can LLMs never do?
Every time over the past few years that we came up with problems LLMs can’t do, they passed them with flying colours. But even as they passed them with flying colours, they still can’t answer questions that seem simple, and it’s unclear why.
And so, over the past few weeks I have been obsessed by trying to figure out the failure modes of LLMs. This started off as an exploration of what I found. It is admittedly a little wonky but I think it is interesting. The failures of AI can teach us a lot more about what it can do than the successes. — Read More
The Rise of Large-Language-Model Optimization
The web has become so interwoven with everyday life that it is easy to forget what an extraordinary accomplishment and treasure it is. In just a few decades, much of human knowledge has been collectively written up and made available to anyone with an internet connection.
But all of this is coming to an end. The advent of AI threatens to destroy the complex online ecosystem that allows writers, artists, and other creators to reach human audiences. — Read More
Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time
The conventional recipe for maximizing model accuracy is to (1) train multiple models with various hyperparameters and (2) pick the individual model which performs best on a held-out validation set, discarding the remainder. In this paper, we revisit the second step of this procedure in the context of fine-tuning large pre-trained models, where fine-tuned models often appear to lie in a single low error basin. We show that averaging the weights of multiple models fine-tuned with different hyperparameter configurations often improves accuracy and robustness. Unlike a conventional ensemble, we may average many models without incurring any additional inference or memory costs — we call the results “model soups.” When fine-tuning large pre-trained models such as CLIP, ALIGN, and a ViT-G pre-trained on JFT, our soup recipe provides significant improvements over the best model in a hyperparameter sweep on ImageNet. The resulting ViT-G model, which attains 90.94% top-1 accuracy on ImageNet, achieved a new state of the art. Furthermore, we show that the model soup approach extends to multiple image classification and natural language processing tasks, improves out-of-distribution performance, and improves zero-shot performance on new downstream tasks. Finally, we analytically relate the performance similarity of weight-averaging and logit-ensembling to flatness of the loss and confidence of the predictions, and validate this relation empirically. Code is available at this https URL. — Read More
Evolutionary Optimization of Model Merging Recipes
We present a novel application of evolutionary algorithms to automate the creation of powerful foundation models. While model merging has emerged as a promising approach for LLM development due to its cost-effectiveness, it currently relies on human intuition and domain knowledge, limiting its potential. Here, we propose an evolutionary approach that overcomes this limitation by automatically discovering effective combinations of diverse open-source models, harnessing their collective intelligence without requiring extensive additional training data or compute. Our approach operates in both parameter space and data flow space, allowing for optimization beyond just the weights of the individual models. This approach even facilitates cross-domain merging, generating models like a Japanese LLM with Math reasoning capabilities. Surprisingly, our Japanese Math LLM achieved state-of-the-art performance on a variety of established Japanese LLM benchmarks, even surpassing models with significantly more parameters, despite not being explicitly trained for such tasks. Furthermore, a culturally-aware Japanese VLM generated through our approach demonstrates its effectiveness in describing Japanese culture-specific content, outperforming previous Japanese VLMs. This work not only contributes new state-of-the-art models back to the open-source community, but also introduces a new paradigm for automated model composition, paving the way for exploring alternative, efficient approaches to foundation model development. — Read More
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