Author: Anthony Alford

Article originally posted on InfoQ. Visit InfoQ

Geoffrey Hinton, professor at the University of Toronto and engineering fellow at Google Brain, recently published a paper on the Forward-Forward algorithm (FF), a technique for training neural networks that uses two forward passes of data through the network, instead of backpropagation, to update the model weights.

By Anthony Alford

Article originally posted on InfoQ. Visit InfoQ

Microsoft announced the release of ML.NET 2.0, the open-source machine learning framework for .NET. The release contains several updated natural language processing (NLP) APIs, including Tokenizers, Text Classification, and Sentence Similarity, as well as improved automated ML (AutoML) features.

Program manager Luis Quintanilla announced the release at the recent .NET Conf 2022. The updated NLP APIs are powered by TorchSharp, a .NET wrapper for the popular PyTorch deep learning framework. The release includes the EnglishRoberta tokenization model and a TorchSharp implementation of NAS-BERT, which is used by the Text Classification and Sentence Similarity APIs. Updates to AutoML include an API for automated data pre-processing and a set of APIs for running experiments to find the best models and hyperparameters. Quintanilla also announced a new release of the Model Builder tool for Visual Studio, which includes the new text classification scenario and advanced training options.

The Text Classification API, which was previewed earlier this year, is based on the NAS-BERT model published by Microsoft Research in 2021. This model was developed using neural architecture search (NAS), resulting in smaller models than the standard BERT model, while maintaining accuracy. Users can fine-tune the pre-trained NAS-BERT model with their own data, to fit their custom use cases. The Sentence Similarity API uses the same pre-trained model, but instead of being fine-tuned to classify an input string, the model takes two strings as input and outputs a score indicating the similarity of the meaning of the two inputs.

The AutoML APIs are based on Microsoft’s Fast Library for Automated Machine Learning & Tuning (FLAML). While the Featurizer API is designed for pre-processing, the rest of the APIs work together to search for the best set of hyperparameters. The Experiment API coordinates the optimization of a Sweepable pipeline over a Search Space using a Tuner. Devs can use the Sweepable API to define the training pipeline for hyperparameter optimization of their models; the Search Space API for configuring the range of the hyperparameter search space for that pipeline; and the Tuner API to choose a search algorithm for that space. The release includes several tuner algorithms, including basic grid and random searches as well as Bayesian and Frugal optimizers.

Quintanilla also gave viewers a preview of the ML.NET roadmap. Future plans for deep learning features include new scenarios and APIs for question answering, named-entity recognition, and object detection. There are also plans for TorchSharp integrations for custom scenarios and improvements to the ONNX integration. Other plans include upgrades to the LightGBM implementation and to the implementation of the IDataView interface, as well as improvements to the AutoML API.

At the end of his presentation, Quintanilla answered questions from the audience. One viewer asked about support for different vendors’ GPUs and accelerator libraries, and Quintanilla noted that currently only NVIDIA’s CUDA accelerator is supported. When another viewer asked whether ML.NET’s object detection algorithms would run fast enough to support a live video stream, Quintanilla replied:

We want to focus on performance. We’re introducing new deep learning scenarios and we realized that performance is key there, so performance is a focus for us going forward.

The ML.NET source code is available on GitHub.

Article originally posted on InfoQ. Visit InfoQ

Meta AI Research recently open-sourced CICERO, an AI that can beat most humans at the strategy game Diplomacy, a game that requires coordinating plans with other players. CICERO combines chatbot-like dialogue capabilities with a strategic reasoning, and recently placed first in an online Diplomacy tournament against human players.

CICERO was described in a paper published in the journal Science. CICERO uses a 2.7B parameter language model to handle dialogue between itself and other players. To determine its moves, CICERO’s planning algorithm uses the dialogue to help predict what other players are likely to do, as well as what other players think CICERO will do. In turn, the output of the planner provides intents for the dialogue model. To evaluate CICERO, the team entered it anonymously in 40 online Diplomacy games; the AI achieved a score more than double that of the human average. According to the Meta team,

While we’ve made significant headway in this work, both the ability to robustly align language models with specific intentions and the technical (and normative) challenge of deciding on those intentions remain open and important problems. By open sourcing the CICERO code, we hope that AI researchers can continue to build off our work in a responsible manner. We have made early steps towards detecting and removing toxic messages in this new domain by using our dialogue model for zero-shot classification. We hope Diplomacy can serve as a safe sandbox to advance research in human-AI interaction.

Diplomacy is a strategy board game where players must capture a majority of territories called supply centers to win. There is no random component in the game; instead, battles are determined by numerical superiority. This often requires players to cooperate, so the bulk of game play consists of players sending messages to each other to coordinate their actions. Occasionally players will engage in deceit; for example, promising to help another player, while actually planning to attack that player.

To be successful, therefore, an AI must not only generate messages of human-level quality; the messages must make sense given the state of the game board, and the messages must cause other players to trust the AI. To generate the dialogue, Meta used a pre-trained R2C2 language model that was fine-tuned on a dataset of almost 13M messages from online Diplomacy games. The generated dialogue is conditioned on the intents generated by a planning module; the intents are the most likely actions that message sender and receiver will take after reading that message.

CICERO’s planning module generates intents by predicting other players’ likely actions, given the state of the board and messages from those players, then choosing an optimal action for itself. To model the likely actions of the other players, CICERO uses an iterative planning algorithm called piKL which incorporates information from the dialogues with other players. To train the planning module, the Meta researchers used a self-play algorithm similar to that used by AlphaZero.

The Meta team entered CICERO into anonymous league play for online Diplomacy games. The AI played 40 games, including an 8-game tournament with 21 players; CICERO placed first in the tournament. For its entire 40 games, CICERO was ranked in the top 10 percent of players with an average score was 25.8%, while the average score of its 82 human opponents was 12.4%.

In a Twitter thread about the work, CICERO co-author Mike Lewis replied to a question about whether CICERO would “backstab” (that is, lie to) other players:

It’s designed to never intentionally backstab – all its messages correspond to actions it currently plans to take. However, sometimes it changes its mind…

The CICERO source code is available on GitHub.

Article originally posted on InfoQ. Visit InfoQ

Microsoft Research recently open-sourced FarmVibes.AI, a suite of ML models and tools for sustainable agriculture. FarmVibes.AI includes data processing workflows for fusing multiple sets of spatiotemporal and geospatial data, such as weather data and satellite and drone imagery.

The release was announced on the Microsoft Research blog. FarmVibes.AI is part of Microsoft’s Project FarmVibes, an effort to develop technologies for sustainable agriculture. The key idea in FarmVibes.AI is fusion of multiple data sources to improve the performance of AI models. The toolkit contains utilities for downloading and preprocessing public datasets of satellite imagery, weather, and terrain elevation. It also includes models for removing cloud cover from satellite images and for generating micro-climate forecasts. According to Microsoft:

In addition to research, we are making these tools available to the broader community. Scientists, researchers, and partners can build new workflows leveraging these AI models, to estimate farming practices, the amount of emissions, and the carbon sequestered in soil.

World population growth and climate change are two of the major concerns motivating Project FarmVibes. As the population grows, farmers will need to produce more food; yet farming is not only impacted by climate change, it is also considered to be one of its causes. Project FarmVibes aims to help farmers increase their yields while reducing their use of water and chemicals. The project builds on FarmBeats, a previous research effort that was released as an Azure Marketplace product in 2019.

The core of FarmVibes.AI is a Kubernetes-based computing cluster for executing workflows. The cluster has four components: a REST API for invoking workflows and monitoring results; an orchestration module for managing workflow execution; workers for processing chunks of data through the workflow; and a cache for storing reusable intermediate results. There is also a pre-built Python client for interacting with the REST API.

The system comes with several built-in workflows for data ingestion, data processing, machine learning, and farm-related AI. The data ingestion workflows can download and process nearly 30 publicly available geospatial datasets. The data processing workflows implement several statistical and transformative operations, such as thresholding and Normalized Difference Vegetation Index (NDVI). The ML and AI workflows implement several models for identifying features on a farm, such as crops or pavement, as well as “what-if” scenarios such as water conservation and carbon sequestration.

FarmVibes.AI also includes several example Jupyter notebooks demonstrating data fusion, model training, and inference. These notebooks showcase some of Microsoft’s agriculture-related AI research, including SpaceEye and DeepMC. SpaceEye is a deep-learning computer vision model that can “recover pixels occluded by clouds in satellite images.” This can improve the performance of downstream models that use satellite imagery as input; for example, models that identify crops in satellite images. DeepMC is a model that can make short-term predictions of microclimate parameters, including temperature, humidity, wind speed, and soil moisture, which can help farmers identify optimal times for planting and harvesting.

Besides FarmVibes.AI, Project FarmVibes also includes: FarmVibes.Connect, technology for networking remote farms; FarmVibes.Edge, an IoT solution for processing data locally instead of in the cloud; and FarmVibes.Bot, a chatbot interface for communicating with farmers. Although only the FarmVibes.AI source code is currently available on GitHub, Microsoft says that the other components “will be released to GitHub soon.”