Work by Wyss Core Faculty member Peng Yin in collaboration with Collins and others has demonstrated that different toehold switches can be combined to compute the presence of multiple "triggers," similar to a computer's logic board. DNA and RNA have been compared to "instruction manuals" containing the information needed for living "machines" to operate. But while electronic machines like computers and robots are designed from the ground up to serve a specific purpose, biological organisms are governed by a much messier, more complex set of functions that lack the predictability of binary code. Inventing new solutions to biological problems requires teasing apart seemingly intractable variables -- a task that is daunting to even the most intrepid human brains. Two teams of scientists from the Wyss Institute at Harvard University and the Massachusetts Institute of Technology have devised pathways around this roadblock by going beyond human brains; they developed a set of machine learning algorithms that can analyze reams of RNA-based "toehold" sequences and predict which ones will be most effective at sensing and responding to a desired target sequence.

Songwriter's block can be a problem for even the world's most successful musicians. They can sometimes overcome it by taking breaks, seeking new forms of inspiration, or simply pushing through. And if none of that works, they could try out a new AI lyrics generator called keyword2lyrics. Sometimes I have a few ideas that I want to turn into a song, but I'm too lazy for that, so I thought it would be cool to make a program that generates lyrics from isolated keywords or phrases. Gatthi developed the tool by training OpenAI's GPT-2 language model on songs that Google lists when you search for "top artists 20th century" and "top artists 21st century," and extracted keywords from them using a tool called yake.

A GPT-3-powered bot has been caught posing as a human on Reddit after more than a week of rampant posting on one of the site's most popular subreddits. Under the username of thegentlemetre, the bot had been churning out a post per minute on /r/AskReddit, a sub with more than 30 million users. That behavior raised the suspicions of writer Philip Winston. "I read through some of the posts and they reminded me of text I'd seen from OpenAI's language model GPT-3," Winston wrote on his blog. Winston shared his theory on the subreddit /r/GPT3. Another Redditor named Wiskkey noticed that the structure of its writing was similar to that used by the Philosopher AI, a controversial text generator powered by GPT-3.

PyTorch has sort of became one of the de facto standards for creating Neural Networks now, and I love its interface. Yet, it is somehow a little difficult for beginners to get a hold of. I remember picking PyTorch up only after some extensive experimentation a couple of years back. To tell you the truth, it took me a lot of time to pick it up but am I glad that I moved from Keras to PyTorch . With its high customizability and pythonic syntax,PyTorch is just a joy to work with, and I would recommend it to anyone who wants to do some heavy lifting with Deep Learning. So, in this PyTorch guide, I will try to ease some of the pain with PyTorch for starters and go through some of the most important classes and modules that you will require while creating any Neural Network with Pytorch.

You're looking for a complete Convolutional Neural Network (CNN) course that teaches you everything you need to create a Image Recognition model in Python, right? You've found the right Convolutional Neural Networks course! Identify the Image Recognition problems which can be solved using CNN Models. Create CNN models in Python using Keras and Tensorflow libraries and analyze their results. Have a clear understanding of Advanced Image Recognition models such as LeNet, GoogleNet, VGG16 etc.

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You are free to share this article under the Attribution 4.0 International license. A new deep learning-based tool called Metabolic Translator may soon give researchers a better handle on how drugs in development will perform in the human body. When you take a medication, you want to know precisely what it does. Pharmaceutical companies go through extensive testing to ensure that you do. Metabolic Translator, a computational tool that predicts metabolites, the products of interactions between small molecules like drugs and enzymes could help improve the process. The new tool takes advantage of deep-learning methods and the availability of massive reaction datasets to give developers a broad picture of what a drug will do.

We present a deep imitation learning framework for robotic bimanual manipulation in a continuous state-action space. Imitation learning has been effectively utilized in mimicking bimanual manipulation movements, but generalizing the movement to objects in different locations has not been explored. We hypothesize that to precisely generalize the learned behavior relative to an object's location requires modeling relational information in the environment. To achieve this, we designed a method that (i) uses a multi-model framework to decomposes complex dynamics into elemental movement primitives, and (ii) parameterizes each primitive using a recurrent graph neural network to capture interactions. Our model is a deep, hierarchical, modular architecture with a high-level planner that learns to compose primitives sequentially and a low-level controller which integrates primitive dynamics modules and inverse kinematics control. We demonstrate the effectiveness using several simulated bimanual robotic manipulation tasks. Compared to models based on previous imitation learning studies, our model generalizes better and achieves higher success rates in the simulated tasks.

In this paper, we tackle the problem of learning control policies for tasks when provided with constraints in natural language. In contrast to instruction following, language here is used not to specify goals, but rather to describe situations that an agent must avoid during its exploration of the environment. Specifying constraints in natural language also differs from the predominant paradigm in safe reinforcement learning, where safety criteria are enforced by hand-defined cost functions. While natural language allows for easy and flexible specification of safety constraints and budget limitations, its ambiguous nature presents a challenge when mapping these specifications into representations that can be used by techniques for safe reinforcement learning. To address this, we develop a model that contains two components: (1) a constraint interpreter to encode natural language constraints into vector representations capturing spatial and temporal information on forbidden states, and (2) a policy network that uses these representations to output a policy with minimal constraint violations. Our model is end-to-end differentiable and we train it using a recently proposed algorithm for constrained policy optimization. To empirically demonstrate the effectiveness of our approach, we create a new benchmark task for autonomous navigation with crowd-sourced free-form text specifying three different types of constraints. Our method outperforms several baselines by achieving 6-7 times higher returns and 76% fewer constraint violations on average. Dataset and code to reproduce our experiments are available at https://sites.google.com/view/polco-hazard-world/.

Pretrained contextualized embeddings are powerful word representations for structured prediction tasks. Recent work found that better word representations can be obtained by concatenating different types of embeddings. However, the selection of embeddings to form the best concatenated representation usually varies depending on the task and the collection of candidate embeddings, and the ever-increasing number of embedding types makes it a more difficult problem. In this paper, we propose Automated Concatenation of Embeddings (ACE) to automate the process of finding better concatenations of embeddings for structured prediction tasks, based on a formulation inspired by recent progress on neural architecture search. Specifically, a controller alternately samples a concatenation of embeddings, according to its current belief of the effectiveness of individual embedding types in consideration for a task, and updates the belief based on a reward. We follow strategies in reinforcement learning to optimize the parameters of the controller and compute the reward based on the accuracy of a task model, which is fed with the sampled concatenation as input and trained on a task dataset. Empirical results on 6 tasks and 23 datasets show that our approach outperforms strong baselines and achieves state-of-the-art performance with fine-tuned embeddings in the vast majority of evaluations.