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Music makes a big difference to the baby brain. One study from the Institute of Learning and Brain Sciences detected that after babies listen to music, their auditory and prefrontal cortexes look different. These are the regions of the brains in charge of processing both music and speech.

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Bridge

Spirit lead me where my trust is without borders

Let me walk upon the waters

Wherever You would call me

Take me deeper than my feet could ever wander

And my faith will be made stronger

In the presence of my Saviour

Whether your child is just approaching adolescence or fully submerged in it, you may have noticed a difference. Your cuddly baby is now a teenager, and it can be challenging to navigate your relationship.

How strange, a movie where a bad man becomes better, instead of the other way around. "Tsotsi," a film of deep emotional power, considers a young killer whose cold eyes show no emotion, who kills unthinkingly, and who is transformed by the helplessness of a baby. He didn't mean to kidnap the baby, but now that he has it, it looks at him with trust and need, and he is powerless before eyes more demanding than his own.

He goes from here to there. He has a strange meeting with a man in a wheelchair, and asks him why he bothers to go on living. The man tells him. Tsotsi finds himself in an upscale suburb. Such areas in Joburg are usually gated communities, each house surrounded by a security wall, every gate promising "armed response." An African professional woman gets out of her Mercedes to ring the buzzer on the gate, so her husband can let her in. Tsotsi shoots her and steals her car. Some time passes before he realizes he has a passenger: a baby boy.

Tsotsi is a killer, but he cannot kill a baby. He takes it home with him, to a room built on top of somebody else's shack. It might be wise for him to leave the baby at a church or an orphanage, but that doesn't occur to him. He has the baby, so the baby is his. We can guess that he will not abandon the boy because he has been abandoned himself, and projects upon the infant all of his own self-pity.

We realize the violence in the film has slowed. Tsotsi himself is slow to realize he has a new agenda. He uses newspapers as diapers, feeds the baby condensed milk, carries it around with him in a shopping bag. Finally, in desperation, at gunpoint, he forces a nursing mother (Terry Pheto) to feed the child. She lives in a nearby shack, a clean and cheerful one. As he watches her do what he demands, something shifts inside of him, and all of his hurt and grief are awakened.

What a simple and yet profound story this is. It does not sentimentalize poverty or make Tsotsi more colorful or sympathetic than he should be; if he deserves praise, it is not for becoming a good man but for allowing himself to be distracted from the job of being a bad man. The nursing mother, named Miriam, is played by Terry Pheto as a quiet counterpoint to his rage. She lives in Soweto and has seen his kind before. She senses something in him, some pool of feeling he must ignore if he is to remain Tsotsi. She makes reasonable decisions. She acts not as a heroine but as a realist who wants to nudge Tsotsi in a direction that will protect her own family and this helpless baby, and then perhaps even Tsotsi himself. These two performances, by Chweneyagae and Pheto, are surrounded by temptations to overact or cave in to sentimentality; they step safely past them and play the characters as they might actually live their lives.

Monalisa was a hit long before global pop star Chris Brown jumped on its remix in May. But Brown, who has shown on recent afrobeats features alongside Rema and Davido that his versatility knows no bounds, elevated the song and gave it a global appeal. It feels organic and right. The perfect addition to what was already a flawless song.

The Google Research team addresses the problem of the continuously growing size of the pretrained language models, which results in memory limitations, longer training time, and sometimes unexpectedly degraded performance. Specifically, they introduce A Lite BERT (ALBERT) architecture that incorporates two parameter-reduction techniques: factorized embedding parameterization and cross-layer parameter sharing. In addition, the suggested approach includes a self-supervised loss for sentence-order prediction to improve inter-sentence coherence. The experiments demonstrate that the best version of ALBERT sets new state-of-the-art results on GLUE, RACE, and SQuAD benchmarks while having fewer parameters than BERT-large.

Masked language modeling (MLM) pre-training methods such as BERT corrupt the input by replacing some tokens with [MASK] and then train a model to reconstruct the original tokens. While they produce good results when transferred to downstream NLP tasks, they generally require large amounts of compute to be effective. As an alternative, we propose a more sample-efficient pre-training task called replaced token detection. Instead of masking the input, our approach corrupts it by replacing some tokens with plausible alternatives sampled from a small generator network. Then, instead of training a model that predicts the original identities of the corrupted tokens, we train a discriminative model that predicts whether each token in the corrupted input was replaced by a generator sample or not. Thorough experiments demonstrate this new pre-training task is more efficient than MLM because the task is defined over all input tokens rather than just the small subset that was masked out. As a result, the contextual representations learned by our approach substantially outperform the ones learned by BERT given the same model size, data, and compute. The gains are particularly strong for small models; for example, we train a model on one GPU for 4 days that outperforms GPT (trained using 30 more compute) on the GLUE natural language understanding benchmark. Our approach also works well at scale, where it performs comparably to RoBERTa and XLNet while using less than 1/4 of their compute and outperforms them when using the same amount of compute.

The pre-training task for popular language models like BERT and XLNet involves masking a small subset of unlabeled input and then training the network to recover this original input. Even though it works quite well, this approach is not particularly data-efficient as it learns from only a small fraction of tokens (typically ~15%). As an alternative, the researchers from Stanford University and Google Brain propose a new pre-training task called replaced token detection. Instead of masking, they suggest replacing some tokens with plausible alternatives generated by a small language model. Then, the pre-trained discriminator is used to predict whether each token is an original or a replacement. As a result, the model learns from all input tokens instead of the small masked fraction, making it much more computationally efficient. The experiments confirm that the introduced approach leads to significantly faster training and higher accuracy on downstream NLP tasks.

The authors from Microsoft Research propose DeBERTa, with two main improvements over BERT, namely disentangled attention and an enhanced mask decoder. DeBERTa has two vectors representing a token/word by encoding content and relative position respectively. The self-attention mechanism in DeBERTa processes self-attention of content-to-content, content-to-position, and also position-to-content, while the self-attention in BERT is equivalent to only having the first two components. The authors hypothesize that position-to-content self-attention is also needed to comprehensively model relative positions in a sequence of tokens. Furthermore, DeBERTa is equipped with an enhanced mask decoder, where the absolute position of the token/word is also given to the decoder along with the relative information. A single scaled-up variant of DeBERTa surpasses the human baseline on the SuperGLUE benchmark for the first time. The ensemble DeBERTa is the top-performing method on SuperGLUE at the time of this publication.

Large language models have been shown to achieve remarkable performance across a variety of natural language tasks using few-shot learning, which drastically reduces the number of task-specific training examples needed to adapt the model to a particular application. To further our understanding of the impact of scale on few-shot learning, we trained a 540-billion parameter, densely activated, Transformer language model, which we call Pathways Language Model PaLM. We trained PaLM on 6144 TPU v4 chips using Pathways, a new ML system which enables highly efficient training across multiple TPU Pods. We demonstrate continued benefits of scaling by achieving state-of-the-art few-shot learning results on hundreds of language understanding and generation benchmarks. On a number of these tasks, PaLM 540B achieves breakthrough performance, outperforming the finetuned state-of-the-art on a suite of multi-step reasoning tasks, and outperforming average human performance on the recently released BIG-bench benchmark. A significant number of BIG-bench tasks showed discontinuous improvements from model scale, meaning that performance steeply increased as we scaled to our largest model. PaLM also has strong capabilities in multilingual tasks and source code generation, which we demonstrate on a wide array of benchmarks. We additionally provide a comprehensive analysis on bias and toxicity, and study the extent of training data memorization with respect to model scale. Finally, we discuss the ethical considerations related to large language models and discuss potential mitigation strategies. e24fc04721