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Weekly Research Newsletter - KIIT - Issue #18

Weekly Research Newsletter - KIIT - Issue #18
10th July 2021,
We are excited to share this week’s picks for the research newsletter. We hope you’ll enjoy reading them over the weekend.

Evaluating Large Language Models Trained on Code
By Mark Chen, Jerry Tworek, Heewoo Jun et al.
We introduce Codex, a GPT language model fine-tuned on publicly available code from GitHub, and study its Python code-writing capabilities. A distinct production version of Codex powers GitHub Copilot. On HumanEval, a new evaluation set we release to measure functional correctness for synthesizing programs from docstrings, our model solves 28.8% of the problems, while GPT-3 solves 0% and GPT-J solves 11.4%. Furthermore, we find that repeated sampling from the model is a surprisingly effective strategy for producing working solutions to difficult prompts. Using this method, we solve 70.2% of our problems with 100 samples per problem. Careful investigation of our model reveals its limitations, including difficulty with docstrings describing long chains of operations and with binding operations to variables. Finally, we discuss the potential broader impacts of deploying powerful code generation technologies, covering safety, security, and economics.
Deep Learning for AI
By By Yoshua Bengio, Yann Lecun, Geoffrey Hinton
We reviewed the basic concepts and some of the breakthrough achievements of deep learning several years ago.63 Here we briefly describe the origins of deep learning, describe a few of the more recent advances, and discuss some of the future challenges. These challenges include learning with little or no external supervision, coping with test examples that come from a different distribution than the training examples, and using the deep learning approach for tasks that humans solve by using a deliberate sequence of steps which we attend to consciously—tasks that Kahneman calls system 2 tasks as opposed to system 1 tasks like object recognition or immediate natural language understanding, which generally feel effortless.
Did you enjoy this issue?
Priyansi, Junaid Rahim and Biswaroop Bhattacharjee

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