Artificial Intelligence has been developing for many years. This is a great historical perspective documentary video that gives information on the history of A.I. As we progress forward with our journeys in life, the A.I. development will continue to grow in unimaginable ways. If you want to learn some of the historical origins of A.I. this is a good place to begin.

A recent string of problems suggests facial recognition's reliability issues are hurting people in a moment of need. Motherboard reports that there are ongoing complaints about the ID.me facial recognition system at least 21 states use to verify people seeking unemployment benefits. People have gone weeks or months without benefits when the Face Match system doesn't verify their identities, and have sometimes had no luck getting help through a video chat system meant to solve these problems. ID.me chief Blake Hall blamed the problems on users rather than the technology. Hall instead suggested that people weren't sharing selfies properly or otherwise weren't following instructions. Motherboard noted that at least some people have three attempts to pass the facial recognition check, though.

In this article, we will be discussing Support Vector Machines. Before we proceed, I hope you already have some prior knowledge about Linear Regression and Logistic Regression. If you want to learn Logistic Regression, you can click here. You can also check its implementation here. By the end of this article., you will get to know the basics involved in the Support Vector Machine.

I randomly encountered chefboost in my Twitter feed and given that I never heard about it before, I decided to have a quick look into it and test it out. In this article, I will briefly present the library, mention the key differences from the go-to library which is scikit-learn, and show a quick example of chefboost in practice. I think the best description is provided in the library's GitHub repo: "chefboost is a lightweight decision tree framework for Python with categorical feature support". Following the last point, chefboost provides three algorithms for classification trees (ID3, C4.5, and CART) and one algorithm for regression trees. To be honest, I was not entirely sure which one is currently implemented in scikit-learn, so I checked the documentation (which also provides a nice and concise summary of the algorithms).

All around us, developers now leverage machine learning capabilities in their applications to amplify human effort. Tensorflow enables developers to make mind blowing capabilities like tracking your pose with a web cam, object detection using images, sentiment analysis in text, and computer generated art/music. In this session, we'll explore the opportunities for web developers to create "plug and play" machine learning experiences using TensorFlow.JS and related JavaScript libraries. We'll explore ways that you can make an impact using pre-trained models from tfhub.dev. To learn more, check out https://www.tensorflow.org/js/ .

Sustainable development goals (SDGs) are becoming more and more important for companies of all shapes and sizes. Put simply, SDGs are a collection of 17 interlinked goals designed to help companies achieve a more sustainable future. Set in 2015 by the UN General Assembly, these goals aim to support such efforts as making processes more efficient, reducing waste, creating diversity, and improving education. Artificial intelligence is one way that these sustainable development goals can be achieved, but leveraging the technology is no simple task. To learn more about the use of machine learning and AI technology for SDGs, we talked to Tiago Ramalho, the founder of Recursive.

Git is a powerful tool, but it can be overwhelming especially for newcomers. Even for experienced developers, getting stuck in a merge or a rebase conflict is pretty common. Even with extensive blogs available, it can be sometimes tricky to identify the cause, ultimately ending up wasting our productive time. There are a plethora of tutorials out there already, but most of them simply talk about high-level user commands, syntax, and how to use them abstracting out most of the internal details. This article tries to uncover how Git works under the hood.

Machine Learning features are derived from an organization's raw data and provide a signal to an ML model. A very common type of feature transformation is a rolling time window aggregation. For example, you may use the rolling 30-minute transaction count of a credit card to predict the likelihood that a given transaction is fraudulent. It's easy enough to calculate rolling time window aggregations offline using window functions in a SQL query against your favorite data warehouse. However, serving this type of feature for real-time predictions in production poses a difficult problem: How can you efficiently serve such a feature that aggregates a lot of raw events ( 1000s), at a very high scale ( 1000s QPS), at low serving latency ( 100ms), at high freshness ( 1s) and with high feature accuracy?

In the current "space weather" study, an international team headed by the Central Institute of Meteorology and Geodynamics (ZAMG) and the Institute for Space Research (IWF) of the Austrian Academy of Sciences was able to create static solar wind models using new machine learning – combining algorithms and thus improving space weather forecasting. June 17, 2021 – Space weather not only ensures remarkable light processes, also known as polar lights, but can also have a huge impact on our modern technologies. So-called geomagnetic storms, for example, can have a significant impact on power supplies, GPS and other communications systems that our modern society depends on. The expansion of our space programs and the increasing human presence in space, such as the International Space Station or soon again on the Moon, require an accurate prediction of the solar wind. The solar wind is a stream of charged particles that spreads from our central star into space and also hits the Earth's magnetic field.

Most of the modern-day NLP systems have been following a pretty standard approach for training new models for various use-cases and that is First Pre-train then Fine-tune. Here, the goal of pre-training is to leverage large amounts of unlabeled text and build a general model of language understanding before being fine-tuned on various specific NLP tasks such as machine translation, text summarization, etc. In this blog, we will discuss two popular pre-training schemes, namely, Masked Language Modeling (MLM) and Causal Language Modeling (CLM). Under Masked Language Modelling, we typically mask a certain % of words in a given sentence and the model is expected to predict those masked words based on other words in that sentence. Such a training scheme makes this model bidirectional in nature because the representation of the masked word is learnt based on the words that occur it's left as well as right.