Although automatic speech recognition (ASR) can perform well in common non-overlapping environments, sustaining performance in multi-talker overlapping speech recognition remains challenging. Recent research revealed that ASR model's encoder captures different levels of information with different layers -- the lower layers tend to have more acoustic information, and the upper layers more linguistic. This inspires us to develop a Sidecar separator to empower a well-trained ASR model for multi-talker scenarios by separating the mixed speech embedding between two suitable layers. We experimented with a wav2vec 2.0-based ASR model with a Sidecar mounted. By freezing the parameters of the original model and training only the Sidecar (8.7 M, 8.4% of all parameters), the proposed approach outperforms the previous state-of-the-art by a large margin for the 2-speaker mixed LibriMix dataset, reaching a word error rate (WER) of 10.36%; and obtains comparable results (7.56%) for LibriSpeechMix dataset when limited training.

Prompt tuning, in which a base pretrained model is adapted to each task via conditioning on learned prompt vectors, has emerged as a promising approach for efficiently adapting large language models to multiple downstream tasks. However, existing methods typically learn soft prompt vectors from scratch, and it has not been clear how to exploit the rich cross-task knowledge with prompt vectors in a multitask learning setting. We propose multitask prompt tuning (MPT), which first learns a single transferable prompt by distilling knowledge from multiple task-specific source prompts. We then learn multiplicative low rank updates to this shared prompt to efficiently adapt it to each downstream target task. Extensive experiments on 23 NLP datasets demonstrate that our proposed approach outperforms the state-of-the-art methods, including the full finetuning baseline in some cases, despite only tuning 0.035% as many task-specific parameters. Finetuning pretrained language models (PLMs) has led to significant improvements across various downstream NLP tasks (Devlin et al., 2019; Howard & Ruder, 2018; Raffel et al., 2020). However, the conventional paradigm of full task-specific finetuning (FT) is difficult to scale to multiple tasks, given that modern PLMs can have hundreds of millions (or even billions) of parameters. There thus has been a growing interest in developing parameter-efficient methods for model tuning (Houlsby et al., 2019; Lester et al., 2021; Ding et al., 2022), where the goal is to learn only a small number of additional parameters per task while achieving performance comparable to full finetuning. Work done during an internship at MIT-IBM Watson AI Lab. Figure 2: Parameter efficiency on GLUE (left) and SuperGLUE (right). Our multitask prompt tuning (MPT) approach, which transfers a single shared prompt learned from multiple source tasks using prompt decomposition and distillation, maintains high accuracy (y-axis) while finetuning only a small number of parameters per task (x-axis).

Deploying robots in real-world domains, such as households and flexible manufacturing lines, requires the robots to be taskable on demand. Linear temporal logic (LTL) is a widely-used specification language with a compositional grammar that naturally induces commonalities across tasks. However, the majority of prior research on reinforcement learning with LTL specifications treats every new formula independently. We propose LTL-Transfer, a novel algorithm that enables subpolicy reuse across tasks by segmenting policies for training tasks into portable transition-centric skills capable of satisfying a wide array of unseen LTL specifications while respecting safety-critical constraints. Experiments in a Minecraft-inspired domain show that LTL-Transfer can satisfy over 90% of 500 unseen tasks after training on only 50 task specifications and never violating a safety constraint. We also deployed LTL-Transfer on a quadruped mobile manipulator in an analog household environment to demonstrate its ability to transfer to many fetch and delivery tasks in a zero-shot fashion.

We present multiplexed gradient descent (MGD), a gradient descent framework designed to easily train analog or digital neural networks in hardware. MGD utilizes zero-order optimization techniques for online training of hardware neural networks. We demonstrate its ability to train neural networks on modern machine learning datasets, including CIFAR-10 and Fashion-MNIST, and compare its performance to backpropagation. Assuming realistic timescales and hardware parameters, our results indicate that these optimization techniques can train a network on emerging hardware platforms orders of magnitude faster than the wall-clock time of training via backpropagation on a standard GPU, even in the presence of imperfect weight updates or device-to-device variations in the hardware. We additionally describe how it can be applied to existing hardware as part of chip-in-the-loop training, or integrated directly at the hardware level. Crucially, the MGD framework is highly flexible, and its gradient descent process can be optimized to compensate for specific hardware limitations such as slow parameter-update speeds or limited input bandwidth.

For years, creating good voice overs meant investing hundreds if not thousands of dollars in hiring voice artists, renting a recording studio to get the script recorded, investing in expensive recording equipment (if you are recording from home), and recruiting or outsourcing the entire project to an audio editor to mix the audio and produce a high-quality voiceover. Not to mention, the valuable hours dedicated to the entire process. Even after all this, the quality of the produced audio file may be subpar. What if there was an alternative to creating studio-quality voiceovers, and that too from the comfort of your own homes? Introducing Murf AI voice generator, which eliminates the entire process of generating voiceovers manually and enables you to quickly produce human-like voiceovers without any specialized hardware or professional. Leveraging advanced AI algorithms and deep learning, the realistic online voice generator tool allows you to convert text into natural-sounding speech, in a matter of just a few minutes.

Machine Learning Essentials (2023) Kickstart Machine Learning, understand maths behind essential algorithms, implement them in python & build 8 projects! This hands-on course is Machine Learning Essentials (2023) designed for absolute beginners as well as for proficient programmers who want kickstart Machine Learning for solving real life problems. You will learn how to work with data, and train models capable of making "intelligent decisions" Data Science has one of the most rewarding jobs of the 21st century and fortune-500 tech companies are spending heavily on data scientists! Data Science as a career is very rewarding and offers one of the highest salaries in the world. Unlike other courses, which cover only library-implementations this course is Machine Learning Essentials (2023) designed to give you a solid foundation in Machine Learning by covering maths and implementation from scratch in Python for most statistical techniques.

As marketing director of Attensi, Anthony Wong helps people learn faster and better by making learning more fun and fruitful. ChatGPT has as many people scared as it does excited. Popular culture has many of us believe that the development of AI is akin to bringing dinosaurs back to life and that humanity's desire for progress will eventually be its downfall. The Terminator, 2001: A Space Odyssey and more recently Ex Machina, all depict an AI that goes rogue and turns on its human creators in a fantastically violent fashion. As is often the case, the reality is far less cinematic. The World Economic Forum, WEF, has predicted that more work tasks will be handled by machines in a "robot revolution," leaving many people out of work.

By India Today Education Desk: AI chatbot ChatGPT first made the news in the context of exams when it cleared the University of Pennsylvania's Wharton School's final exam for MBA students. Since its introduction in November 2022, it has become immensely popular across the world, causing numerous educational institutions to ban the use of the tool in exam halls. It has also performed highly in the US medical exam. However, ChatGPT has now failed to clear the UPSC exam, an experiment administered by Analytics India Magazine (AIM). AIM asked the chatbot 100 questions from UPSC Prelims 2022 Question Paper 1 (Set A) to which it could only answer 54 questions properly. The general cut-off score is 87.54.

Hate speech in social media is a growing phenomenon, and detecting such toxic content has recently gained significant traction in the research community. Existing studies have explored fine-tuning language models (LMs) to perform hate speech detection, and these solutions have yielded significant performance. However, most of these studies are limited to detecting hate speech only in English, neglecting the bulk of hateful content that is generated in other languages, particularly in low-resource languages. Developing a classifier that captures hate speech and nuances in a low-resource language with limited data is extremely challenging. To fill the research gap, we propose HateMAML, a model-agnostic meta-learning-based framework that effectively performs hate speech detection in low-resource languages. HateMAML utilizes a self-supervision strategy to overcome the limitation of data scarcity and produces better LM initialization for fast adaptation to an unseen target language (i.e., cross-lingual transfer) or other hate speech datasets (i.e., domain generalization). Extensive experiments are conducted on five datasets across eight different low-resource languages. The results show that HateMAML outperforms the state-of-the-art baselines by more than 3% in the cross-domain multilingual transfer setting. We also conduct ablation studies to analyze the characteristics of HateMAML.

Continual learning (CL) refers to a machine learning paradigm that learns continuously without forgetting previously acquired knowledge. Thereby, major difficulty in CL is catastrophic forgetting of preceding tasks, caused by shifts in data distributions. Existing CL models often save a large number of old examples and stochastically revisit previously seen data to retain old knowledge. However, the occupied memory size keeps enlarging along with accumulating seen data. Hereby, we propose a memory-efficient CL method by storing a few samples to achieve good performance. We devise a dynamic prototype-guided memory replay module and incorporate it into an online meta-learning model. We conduct extensive experiments on text classification and investigate the effect of training set orders on CL model performance. The experimental results testify the superiority of our method in terms of forgetting mitigation and efficiency.