If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Since it launched in 2017, Facebook's machine-learning framework PyTorch has been put to good use, with applications ranging from powering Elon Musk's autonomous cars to driving robot-farming projects. Now pharmaceutical firm AstraZeneca has revealed how its in-house team of engineers are tapping PyTorch too, and for equally as important endeavors: to simplify and speed up drug discovery. Combining PyTorch with Microsoft Azure Machine Learning, AstraZeneca's technology can comb through massive amounts of data to gain new insights about the complex links between drugs, diseases, genes, proteins or molecules. Those insights are used to feed an algorithm that can, in turn, recommend a number of drug targets for a given disease for scientists to test in the lab. The method could allow for huge strides in a sector like drug discovery, which so far has been based on costly and time-consuming trial-and-error methods.
Until now, few companies outside of Google and Facebook have had the AI foresight and resources to leverage graph embeddings. This powerful and innovative technique calculates the shape of the surrounding network for each piece of data inside of a graph, enabling far better machine learning predictions. Neo4j for Graph Data Science version 1.4 democratizes these innovations to upend the way enterprises make predictions in diverse scenarios from fraud detection to tracking customer or patient journey, to drug discovery and knowledge graph completion. Caption: Graph embeddings are a powerful tool to abstract the complex structures of graphs and reduce their dimensionality. This technique opens up a wide range of uses for graph-based machine learning.
I recently started a new newsletter focus on AI education. TheSequence is a no-BS( meaning no hype, no news etc) AI-focused newsletter that takes 5 minutes to read. The goal is to keep you up to date with machine learning projects, research papers and concepts. One of the arguments that is regularly used in favor of machine learning systems is the fact that they can arrive to decisions without being vulnerable to human subjectivity. However, that argument is only partially true.
As of October 2020, the COVID-19 pandemic has claimed over 1 million lives across the world and over 41 million people have been infected. Understanding the factors and policies that influence the spread of the virus can help governments make informed decisions in order to control infections and deaths until a vaccine becomes widely available. The data used for this project can be divided into four different parts, each represented as separate data frames/tables in the code: policy data, mobility data, demographic data, and COVID-19 time-series statistics. The policy data, extracted from the OxCGRT dataset, contains information about the policies implemented by the government in each country to control the spread of COVID-19. The policy data is available for each day after the start of the pandemic.
Technology evolution is no longer keeping pace with the growth of data. We are facing problems storing and processing the huge amounts of data produced every day. People rely on data-intensive applications and new paradigms (for example, edge computing) to try to keep computation closer to where data is produced and needed. Thus, the need to store and query data in devices where capacity is surpassed by data volume is routine today, ranging from astronomy data to be processed by supercomputers, to personal data to be processed by wearable sensors. The scale is different, yet the underlying problem is the same.
A pair of statisticians at the University of Waterloo has proposed a math process idea that might allow for teaching AI systems without the need for a large dataset. Ilia Sucholutsky and Matthias Schonlau have written a paper describing their idea and published it on the arXiv preprint server. Artificial intelligence (AI) applications have been the subject of much research lately, with the development of deep learning networks, researchers in a wide range of fields began finding uses for it, including creating deepfake videos, board game applications and medical diagnostics. Deep learning networks require large datasets in order to detect patterns revealing how to perform a given task, such as picking a certain face out of a crowd. In this new effort, the researchers wondered if there might be a way to reduce the size of the dataset.
The graph represents a network of 1,805 Twitter users whose tweets in the requested range contained "#selfdrivingcars", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Wednesday, 21 October 2020 at 12:45 UTC. The requested start date was Wednesday, 21 October 2020 at 00:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 8-day, 6-hour, 12-minute period from Monday, 12 October 2020 at 17:48 UTC to Wednesday, 21 October 2020 at 00:00 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.
The gradient descent algorithm is an approach to find the minimum point or optimal solution for a given dataset. It follows the steepest descent approach. That is it moves in the negative gradient direction to find the local or global minima, starting out from a random point. We use gradient descent to reach the lowest point of the cost function. In Machine Learning, it is used to update the coefficients of our model.
Neo4j, the leader in graph technology, announced the latest version of Neo4j for Graph Data Science, a breakthrough that democratizes advanced graph-based machine learning (ML) techniques by leveraging deep learning and graph convolutional neural networks. Until now, few companies outside of Google and Facebook have had the AI foresight and resources to leverage graph embeddings. This powerful and innovative technique calculates the shape of the surrounding network for each piece of data inside of a graph, enabling far better machine learning predictions. Neo4j for Graph Data Science version 1.4 democratizes these innovations to upend the way enterprises make predictions in diverse scenarios from fraud detection to tracking customer or patient journey, to drug discovery and knowledge graph completion. Neo4j for Graph Data Science version 1.4 is the first and only graph-native machine learning functionality commercially available for enterprises.
In computer vision object detection is used for identification and localization of objects in different kind of applications such as face recognition, anomaly detection, counting of different types of objects. One possible way to run inference both on CPU and GPU is to use an Onnx Runtime, which is since 2018 an open source. A corresponding CPU or GPU (Microsoft.ML.OnnxRuntime for CPU and Microsoft.ML.OnnxRuntime.Gpu for GPU) libraries may be included in the project via Nuget Package Manger. Including both libraries in the project results in "Unable to load DLL'onnxruntime.dll': The specified module could not be found.