giovedì 8 giugno 2023

AlphaDev Solves Algorithms Faster Than Humans

Deepmind, Google’s AI branch, has just presented AlphaDev, a Reinforcement Learning agent that can discover faster sorting algorithms on its own.

AlphaDev’s advanced computer science algorithms outperform decades of human benchmarks from engineers and scientists.

These new algorithms are already part of two standard C++ coding libraries and are being used trillions of times per day by programmers worldwide. In addition, AlphaDev has also demonstrated potential in enhancing other essential computer science algorithms, such as hashing.

This represents a significant step towards using AI to optimize the world's code, one algorithm at a time.


mercoledì 31 maggio 2023

Repair slave in MySQL GTID replication, after fatal error 1236 (slave has more GTIDs than the master)

For repairing a broken slave after fatal error 1236.

Last_IO_Error: Got fatal error 1236 from master when reading data from binary log: 'Slave has more GTIDs than the master has, using the master's SERVER_UUID. This may indicate that the end of the binary log was truncated or that the last binary log file was lost, e.g., after a power or disk failure when sync_binlog != 1

slave# stop slave for channel 'mymaster';

slave# reset slave for channel 'mymaster';

slave# set gtid_next='automatic';

slave# change master to master_auto_position=1 for channel 'mymaster';

slave# start slave for channel 'mymaster';

slave# show slave status for channel 'mymaster'\G

lunedì 19 dicembre 2022

OpenAI ChatGPT

ChatGPT, which stands for Chat Generative Pre-trained Transformer, is a chatbot developed by OpenAI. ChatGPT is built on top of OpenAI's GPT-3.5 family of large language models, and is fine-tuned with both supervised and reinforcement learning techniques.

ChatGPT was launched as a prototype in November 2022, and quickly garnered attention for its detailed responses and articulate answers across many domains of knowledge.

It was fine-tuned on top of GPT-3.5 using supervised learning as well as reinforcement learning. Both approaches used human trainers to improve the model's performance. In the case of supervised learning, the model was provided with conversations in which the trainers played both sides: the user and the AI assistant. In the reinforcement step, human trainers first ranked responses that the model had created in a previous conversation. These rankings were used to create 'reward models' that the model was further fine-tuned on using several iterations of Proximal Policy Optimization (PPO). Proximal Policy Optimization algorithms present a cost-effective benefit to trust region policy optimization algorithms; they negate many of the computationally expensive operations with faster performance. The models were trained in collaboration with Microsoft on their Azure supercomputing infrastructure.

ChatGPT is part of a series of releases around GPT 3.5 that are highlighting some of the capabilities of the upcoming GPT-4 model. One of the key differences of ChatGPT with previous models is its ability to follow instructions. This is powered another model called InstructGPT which OpenAI quietly unveiled at the beginning of the year.

Large language models like GPT-3 are often used to follow instructions to execute user’s tasks. However, quite often, these models generate toxic or untruthful outputs that are not related to the input instructions. This is mostly due to the fact that models like GPT-3 are trained to predict the next word in a sentence rather than to execute a specific task. This is precisesly the problem OpenAI tried to address with InstructGPT, a language model that builds upon GPT-3 language capabilities but improves it its ability to follow instructions.

More about InstructGPT
Training language models to follow instructions with human feedback.

Making language models bigger does not inherently make them better at following a user's intent. For example, large language models can generate outputs that are untruthful, toxic, or simply not helpful to the user. In other words, these models are not aligned with their users. In this paper, we show an avenue for aligning language models with user intent on a wide range of tasks by fine-tuning with human feedback. Starting with a set of labeler-written prompts and prompts submitted through the OpenAI API, we collect a dataset of labeler demonstrations of the desired model behavior, which we use to fine-tune GPT-3 using supervised learning. We then collect a dataset of rankings of model outputs, which we use to further fine-tune this supervised model using reinforcement learning from human feedback. We call the resulting models InstructGPT. In human evaluations on our prompt distribution, outputs from the 1.3B parameter InstructGPT model are preferred to outputs from the 175B GPT-3, despite having 100x fewer parameters. Moreover, InstructGPT models show improvements in truthfulness and reductions in toxic output generation while having minimal performance regressions on public NLP datasets. Even though InstructGPT still makes simple mistakes, our results show that fine-tuning with human feedback is a promising direction for aligning language models with human intent.

martedì 5 aprile 2022

Pathways Language Model (PaLM): Scaling to 540 Billion Parameters for Breakthrough Performance

Google AI introduced the 540 billion parameter Pathways Language Model. Trained on two Cloud #TPU v4 pods, it achieves state-of-the-art performance on benchmarks and shows exciting capabilities like mathematical reasoning, code writing, and even explaining jokes. https://goo.gle/3j6eMnK

PaLM demonstrates the scaling capability of the Pathways system to thousands of accelerator chips across two TPU v4 Pods by training a 540-billion parameter model efficiently with a well-studied, well-established recipe of a dense decoder-only Transformer model. Pushing the limits of model scale enables breakthrough few-shot performance of PaLM across a variety of natural language processing, reasoning, and code tasks.

PaLM paves the way for even more capable models by combining the scaling capabilities with novel architectural choices and training schemes, and brings us closer to the Pathways vision:

<<Enable a single AI system to generalize across thousands or millions of tasks, to understand different types of data, and to do so with remarkable efficiency.>>

giovedì 17 giugno 2021

How to Kill MySQL Queries

The command SHOW PROCESSLIST lists all the open connections to the server


In order to kill a thread completely, use the KILL command followed by the thread ID returned by SHOW PROCESSLIST:

mysql> KILL 1234;

martedì 20 aprile 2021

Repair slave in MySQL GTID replication, after fatal error 1236 (master has purged binary logs)

For repairing a broken slave after fatal error 1236.

Last_IO_Error: Got fatal error 1236 from master when reading data from binary log: 'The slave is connecting using CHANGE MASTER TO MASTER_AUTO_POSITION = 1, but the master has purged binary logs containing GTIDs that the slave requires.'

slave# reset master;

slave# source /pathto/dump.sql;

slave# start slave for channel 'mymaster';

slave# show slave status for channel 'mymaster'\G

Here is another useful tutorial, if the problem persists


venerdì 18 settembre 2020

Demo of OpenAI GPT-3

Incredible demos of OpenAI GPT-3, watch this video!

Mind-blowing conversations with OpenAI GPT-3


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