In this paper, we propose a new framework for fine-grained emotion prediction in the text through emotion definition modeling. Our approach involves a multi-task learning framework that models definitions of emotions as an auxiliary task while being trained on the primary task of emotion prediction. We model definitions using masked language modeling and class definition prediction tasks. Our models outperform existing state-of-the-art for fine-grained emotion dataset GoEmotions. We further show that this trained model can be used for transfer learning on other benchmark datasets in emotion prediction with varying emotion label sets, domains, and sizes. The proposed models outperform the baselines on transfer learning experiments demonstrating the generalization capability of the models.

All the sessions from Transform 2021 are available on-demand now. As the IBM Watson experience shows, the path to AI success is fraught with challenges. Yet overall, it has been a very good year for AI and the companies developing it. So much so that Alphabet CEO Sundar Pichai, in a recent podcast recorded by BBC, says: "I view [AI] as a very profound enabling technology. If you think about fire or electricity or the internet, it is like that, but I think even more profound."

One month ago, GitHub announced its latest, shiny product: an artificial intelligence tool developed by GitHub and OpenAI to assist users of Visual Studio Code by autocompleting code, but on the next level. It's a machine learning-powered software that can write code by itself, generating quite impressive programming functions. Here is the catch, there are a lot of dislikes going on for this cute little fellow. And I don't get why so much hate for this, how can you not like something like this in the market, and that too for free. Let's address them one by one So many articles are flooding to criticize, just for the sake of writing something.

In the programming world most of the time whenever we get stuck at something, all it takes is an instant google search to stackoverflow and you know the drill Ctlr C Ctlr V. What if the same can be achieved with the help of AI by just typing out the name of the function in your ide. The tech world is evolving at a rapid pace and at times it can be intimidating, especially for programmers. And that's what the new GitHub COPILOT powered by GPT-3 is all about. And now that I have access to the technical preview, here is my point of view. As the name suggests copilot is here to assist you with your programming journey.

British artificial intelligence giant DeepMind has released a database of nearly all human protein structures that it amassed as part of its AlphaFold program. Last year, the organisers of the biennial Critical Assessment of protein Structure Prediction (CASP) recognised AlphaFold as a solution to the grand challenge of figuring out what shapes proteins fold into. "We have been stuck on this one problem – how do proteins fold up – for nearly 50 years. To see DeepMind produce a solution for this, having worked personally on this problem for so long and after so many stops and starts, wondering if we'd ever get there, is a very special moment." AlphaFold is a major scientific advance that will play a crucial role in helping scientists to solve important problems such as the protein misfolding associated with diseases such as Alzheimer's, Parkinson's, cystic fibrosis and Huntington's disease.

The human mediator complex has long been one of the most challenging multi-protein systems for structural biologists to understand.Credit: Yuan He The human genome holds the instructions for more than 20,000 proteins. But only about one-third of those have had their 3D structures determined experimentally. And in many cases, those structures are only partially known. Now, a transformative artificial intelligence (AI) tool called AlphaFold, which has been developed by Google's sister company DeepMind in London, has predicted the structure of nearly the entire human proteome (the full complement of proteins expressed by an organism). In addition, the tool has predicted almost complete proteomes for various other organisms, ranging from mice and maize (corn) to the malaria parasite (see'Folding options').

Marcelo Sousa, a biochemist at the University of Colorado Boulder, had spent ten years trying to crack a particularly tricky puzzle. Sousa and his team had collected reams of experimental data on a single bacterial protein linked to antibiotic resistance. Working out its structure, they hoped, would help to find inhibitors that could stop that resistance from building. But, year after year, the puzzle remained unsolved. Within 15 minutes, DeepMind's machine learning system had solved the structure.

Protein structures to represent the data obtained via AlphaFold. DeepMind and EMBL release the most complete database of predicted 3D structures of human proteins. Partners use AlphaFold, the AI system recognized last year as a solution to the protein structure prediction problem, to release more than 350,000 protein structure predictions including the entire human proteome to the scientific community. DeepMind today announced its partnership with the European Molecular Biology Laboratory (EMBL), Europe's flagship laboratory for the life sciences, to make the most complete and accurate database yet of predicted protein structure models for the human proteome. This will cover all 20,000 proteins expressed by the human genome, and the data will be freely and openly available to the scientific community.

T-cell immunomodulatory protein homolog, from the AlphaFold Protein Structure Database, reproduced under a CC-BY-4.0 license. DeepMind and the European Molecular Biology Laboratory (EMBL) have partnered to produce a database of predicted protein structure models. The first release covers all 20,000 proteins expressed in the human proteome, and the proteomes of 20 other biologically significant organisms, totalling over 350k structures. In the coming months they plan to expand the database to cover a large proportion of all catalogued proteins (the over 100 million in UniRef90). The data is freely and openly available to the scientific community. You can access the AlphaFold Protein Structure Database here.

Over the last several years, end-to-end neural conversational agents have vastly improved in their ability to carry a chit-chat conversation with humans. However, these models are often trained on large datasets from the internet, and as a result, may learn undesirable behaviors from this data, such as toxic or otherwise harmful language. Researchers must thus wrestle with the issue of how and when to release these models. In this paper, we survey the problem landscape for safety for end-to-end conversational AI and discuss recent and related work. We highlight tensions between values, potential positive impact and potential harms, and provide a framework for making decisions about whether and how to release these models, following the tenets of value-sensitive design. We additionally provide a suite of tools to enable researchers to make better-informed decisions about training and releasing end-to-end conversational AI models.