They also test the sentences written in CrowS and compare to StereoSet, indicating that the data quality in CrowS could be higher. Then said “It’s a shame, really. Now, researchers with Google Brain have turned their attention to learning how to learn ML optimizers – this is a (potentially) big deal, because an optimizer, like ADAM, is fundamental to the efficiency of training machine learning models. Now, we’re entering a new era where researchers are developing chips even more specialized for matrix multiplication than today’s GPUs. We should also ask how much damage the ‘virus’ could do before an ‘antidote’ is available. Unsupervised translation seems to work well. So perhaps we can be necromancers with our robots and they will teach us something about ourselves? Import AI 213: DeepFakes can lipsync now; plus hiding military gear with adversarial examples. Optimism was highest in East Asia (11% mostly harmful vs. 59% mostly helpful); Southeast Asia (25% vs. 37%), and Africa (31% vs. 41%). I work as the Strategy and Communications Director @ OpenAI and formerly worked as the world’s only neural network reporter at Bloomberg, and before that as the world’s only distributed systems reporter at The Register. But people are trying to change this – the main effort here is one called Masakhane, a pan-African initiative to essentially cold start a thriving NLP community that pays attention to local data needs. If you can get this number to zero, then you’re going to be doing. This app lets a patient use their phone to either take a picture or record a live video and runs detection against this. Gpt-f: Deep learning for automated theorem proving:What happens if you combine transformers pre-training, and the task of learning to prove mathematical statements? If successful, the work will factor into broader alignment initiatives for OpenAI technology, or that of other organizations.Read more here; apply for engineer and researcher roles. This underlines the importance of research aimed at understanding things like — the compute requirements for human intelligence (Import 214); measuring and comparing compute and other drivers of AI progress (Import 199): trends in the cost of compute (Import 127). You do have four AI systems, all significantly below the grade where we’d need to pay attention to them. In fact, the above happened in 2012, when a team from the University of Toronto demonstrated good results on the ‘ImageNet’. He reveals the deep connections between problems (RL and behaviourist psychology; bias and alignment; alignment and moral philosophy). It’s philosophically more concise in that it doesn’t rely on the existence of a universal class of all sets,” and another said ““I had a look at the proofs—very impressive results! OpenAI’s ‘Reflection’ team is hiring engineers and researchers to help align GPT-3. Other features, such as the new data types should be seen more as an ease-of-use-feature as they provide the same performance boost as Turing does but without any extra programming required,” writes Dettmers. Applications close 19th October. Whenever something becomes much cheaper, we usually see major changes in adoption and usage. So they switched to voice. Read more: Datasets for Language in Sub-Saharan Africa (Lacuna Fund website).Check out the full RFP here (PDF).###################################################KILT: 11 data sets, 5 types of test, one big benchmark:…Think your AI system can use its own knowledge? Play begins amid a specific global situation and spans five years. One of the notable things about GPT-3 was how it did very well on the difficult multi-task SuperGLUE benchmark without SuperGLUE-specific pre-training – instead, OpenAI loaded SuperGLUE problems into the context window of an already trained GPT-3 model and tried to get it to output the correct answer. Google uses ML to learn better ML optimization – a surprisingly big deal:…Yo dawg, we heard you like learning to learn, so we learned how to learn a learning optimizer…In recent years, AI researchers have used machine learning to do meta-optimization of AI research; we’ve used ML to learn how to search for new network architectures, to learn how to distribute nets across chips during training, and learning how to do better memory allocation. If you can make this, you must have spent a long time studying. He put his earbuds in and fiddled with an application on his phone till he’d tuned the frequencies so he was hyper-sensitive to the sounds of movement in the forest – breaking twigs, leaves crushed under foot, atypical rhythms distinct from that caused by wind. ARM’s RISC-architecture chips don’t lend themselves as well to the sort of massively parallelized computing operations required to train AI systems efficiently. A single model can therefore be trained using these sampled subgraphs, and can be deployed at scale.”. 2) Models can get much better — GPT is an old approach with some major flaws, and is far from an ‘ideal’ transformer, so there is significant room for improvement. Proofs that humans like: GPT-f contributed 23 shortened proofs of theorems to the Metamath library. Unsupervised data techniques give us a way to increase the size and breadth of our datasets without a substantial increase in economic costs. …Like GPT3, but for protein sequences instead of text… Matthew’s view: This is non-fiction at its best — a beautifully written, and engaging book. Today, you need to get a bunch of permissions to deal with this data, but if you could find a way to encrypt it, you’d be able to work with it in a privacy-preserving way. One use of GeDi is to intervene on model outputs that could display harmful or significant biases about a certain set of people.Why this matters: GeDi is an example of how researchers are beginning to build plug-in tools, techniques, and augmentations, that can be attached to existing pre-trained models (e.g, GPT3) to provide more precise control over them. In the company’s own words, it uses “a large unlabeled dataset to help with improving the performance of a supervised task defined by a labeled dataset”.The ingredients: Google’s system uses a large-scale ‘Conformer’, a convnet-transformer hybrid model, along with pre-training, Noisy Student training, and some other tricks to gets its performance.How good is it?
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