Straight from the AI's mouth ChatGPT is a natural language processing model developed by OpenAI that is capable of generating human-like text. It has the potential to revolutionize the field of education in several ways. One potential benefit of ChatGPT is its ability to generate personalized learning materials. By asking questions and providing input, students can use ChatGPT to generate tailored study guides and practice exercises. This can be particularly useful for students who struggle to keep up with the pace of a traditional classroom or who need extra help to understand a particular subject.

Baidu today revealed more details about its much-anticipated intelligent Ernie Bot, widely seen as the search giant's counterpart to ChatGPT. Ernie Bot is built atop Baidu's large language model Erniethat released in 2019. In May last year, the third generation of Ernie launched. Here's how Baidu plans to integrate Ernie Bot into its ecosystem of services, said the firm's founder and CEO Robin Li in a letter to staff today. "The integration of ERNIE Bot with Baidu Search will lead to a generational change in the search experience."

This post explores the various components of crafting high-quality prompts for different Artificial Intelligence (AI) tools like DALLE-2 and ChatGPT. I share the CREATE framework to communicate best practices and critical guidelines. The framework aims to help people write better prompts and improve their prompt craft skills.

"Write an article on'What is payment gateway?'" I recently typed into a ChatGPT window. ChatGPT, an artificial intelligence-powered writing generator, quickly obliged. Sure, the tone was inhuman and the structure as sophisticated as a college essay, but the key points, the grammar and the syntax were all spot on. After a bit of a punch-up, it was perfectly passable as a sponsored content article designed to drum up business leads for a software provider – an article like the one that I, a professional copywriter, had just spent hours writing. My amusement quickly turned to horror: it had taken ChatGPT roughly 30 seconds to create, for free, an article that I charged £500 for.

Let's go back to 1997. Steve Jobs returns to Apple and everything is in the mud. The company needs to turn the tables and come up with an epic campaign that would solidify the brand for decades to come. Would the final campaign be as good as the "Think Different" campaign that we ended up getting? Can AI come up with campaigns like "Just Do It," "Finger Lickin' Good," and "The Happiest Place on Earth"? AI is bringing a significant revolution in the marketing landscape, with ChatGPT leading the charge.

Sulagna Saha is a writer at Gestalt IT where she covers all the latest in enterprise IT. She has written widely on miscellaneous topics. A writer by day and reader by night, Sulagna can be found busy with a book or browsing through a bookstore in her free time. She also likes cooking fancy things on leisurely weekends. Traveling and movies are other things high on her list of passions.

While ML generates much economic value, many of us have problematic relationships with social media and other ML-powered applications. One reason is that ML often optimizes for what we want in the moment, which is easy to quantify but at odds with what is known scientifically about human flourishing. Thus, through its impoverished models of us, ML currently falls far short of its exciting potential, which is for it to help us to reach ours. While there is no consensus on defining human flourishing, from diverse perspectives across psychology, philosophy, and spiritual traditions, love is understood to be one of its primary catalysts. Motivated by this view, this paper explores whether there is a useful conception of love fitting for machines to embody, as historically it has been generative to explore whether a nebulous concept, such as life or intelligence, can be thoughtfully abstracted and reimagined, as in the fields of machine intelligence or artificial life. This paper forwards a candidate conception of machine love, inspired in particular by work in positive psychology and psychotherapy: to provide unconditional support enabling humans to autonomously pursue their own growth and development. Through proof of concept experiments, this paper aims to highlight the need for richer models of human flourishing in ML, provide an example framework through which positive psychology can be combined with ML to realize a rough conception of machine love, and demonstrate that current language models begin to enable embodying qualitative humanistic principles. The conclusion is that though at present ML may often serve to addict, distract, or divide us, an alternative path may be opening up: We may align ML to support our growth, through it helping us to align ourselves towards our highest aspirations.

Fine-tuning large language models is becoming ever more impractical due to their rapidly-growing scale. This motivates the use of parameter-efficient adaptation methods such as prompt tuning (PT), which adds a small number of tunable embeddings to an otherwise frozen model, and in-context learning (ICL), in which demonstrations of the task are provided to the model in natural language without any additional training. Recently, Singhal et al. (2022) propose ``instruction prompt tuning'' (IPT), which combines PT with ICL by concatenating a natural language demonstration with learned prompt embeddings. While all of these methods have proven effective on different tasks, how they interact with each other remains unexplored. In this paper, we empirically study when and how in-context examples improve prompt tuning by measuring the effectiveness of ICL, PT, and IPT on five text generation tasks with multiple base language models. We observe that (1) IPT does \emph{not} always outperform PT, and in fact requires the in-context demonstration to be semantically similar to the test input to yield improvements; (2) PT is unstable and exhibits high variance, but combining PT and ICL (into IPT) consistently reduces variance across all five tasks; and (3) prompts learned for a specific source task via PT exhibit positive transfer when paired with in-context examples of a different target task. Our results offer actionable insights on choosing a suitable parameter-efficient adaptation method for a given task.

Recent advances in robot learning have shown promise in enabling robots to perform a variety of manipulation tasks and generalize to novel scenarios. One of the key contributing factors to this progress is the scale of robot data used to train the models. To obtain large-scale datasets, prior approaches have relied on either demonstrations requiring high human involvement or engineering-heavy autonomous data collection schemes, both of which are challenging to scale. To mitigate this issue, we propose an alternative route and leverage text-to-image foundation models widely used in computer vision and natural language processing to obtain meaningful data for robot learning without requiring additional robot data. We term our method Robot Learning with Semantically Imagened Experience (ROSIE). Specifically, we make use of the state of the art text-to-image diffusion models and perform aggressive data augmentation on top of our existing robotic manipulation datasets via inpainting various unseen objects for manipulation, backgrounds, and distractors with text guidance. Through extensive real-world experiments, we show that manipulation policies trained on data augmented this way are able to solve completely unseen tasks with new objects and can behave more robustly w.r.t. novel distractors. In addition, we find that we can improve the robustness and generalization of high-level robot learning tasks such as success detection through training with the diffusion-based data augmentation. The project's website and videos can be found at diffusion-rosie.github.io

In Federated Learning (FL), accessing private client data incurs communication and privacy costs. As a result, FL deployments commonly prefinetune pretrained foundation models on a (large, possibly public) dataset that is held by the central server; they then FL-finetune the model on a private, federated dataset held by clients. Evaluating prefinetuning dataset quality reliably and privately is therefore of high importance. To this end, we propose FreD (Federated Private Fr\'echet Distance) -- a privately computed distance between a prefinetuning dataset and federated datasets. Intuitively, it privately computes and compares a Fr\'echet distance between embeddings generated by a large language model on both the central (public) dataset and the federated private client data. To make this computation privacy-preserving, we use distributed, differentially-private mean and covariance estimators. We show empirically that FreD accurately predicts the best prefinetuning dataset at minimal privacy cost. Altogether, using FreD we demonstrate a proof-of-concept for a new approach in private FL training: (1) customize a prefinetuning dataset to better match user data (2) prefinetune (3) perform FL-finetuning.