Google is paying tribute Tuesday to the computer scientist who created the mathematical framework "fuzzy logic." On this day in 1964, Zadeh submitted the paper "Fuzzy Sets," which laid out the concept of "fuzzy logic." The logo featured on Google.com "The theory he presented offered an alternative to the rigid'black and white' parameters of traditional logic and instead allowed for more ambiguous or'fuzzy' boundaries that more closely mimic the way humans see the world," reads a biography of Zadeh by Google. The theory has been used in various tech applications, including anti-skid algorithms for cars.

All industries have one thing in common, data and lots of it. The amount of data is related to the volume of'things' now connected to the internet, from personal devices, the office printer, all the way to the sensor on a pump that is helping generate the electricity necessary to keep the power on. They say data is the new oil; however, far too many industrial companies are finding little to no use or benefit from all the data they are generating. In fact, the Mining and Resources sector is reported to use less than 1 percent of the data collected from their equipment. So how do companies ensure they are getting the most value from the data generated, and how do we ensure that the project is a success and doesn't become another statistic in the 70 percent of all digital transformations that fail?

When these events occur, decision-makers within financial organisations need to analyse every scenario and act quickly – using data-extracted insights to guide the way. In this piece, InterSystems' Director of Product Management Jeff Fried explains how machine learning can play an integral role in building resilience within the financial sector, using real-time examples such as automated trading, fraud detection, and customer experience (CX) initiatives that utilise modern data management and analytics capabilities to make it through the most strenuous business challenges with almost no effect on the operations of the organisation itself. Before the pandemic, increasing business agility was of utmost importance for organisations across industries. But what was once a battle of speed and first-to-market has now expanded its focus to include business resilience. Recent events have underscored the need to weather the storm during volatile operational or security-related situations.

The topic of AI fairness metrics is as important to society as it is confusing. Confusing it is due to a number of reasons: terminological proliferation, abundance of formulae, and last not least the impression that everyone else seems to know what they're talking about. This text hopes to counteract some of that confusion by starting from a common-sense approach of contrasting two basic positions: On the one hand, the assumption that dataset features may be taken as reflecting the underlying concepts ML practitioners are interested in; on the other, that there inevitably is a gap between concept and measurement, a gap that may be bigger or smaller depending on what is being measured. In contrasting these fundamental views, we bring together concepts from ML, legal science, and political philosophy.

Whether it's searching for information online, using social media, or chatting with customer services, artificial intelligence is already a big part of our daily lives. Pioneers in the real estate and construction industries have already harnessed artificial intelligence to automate routines, improve the sustainability of operations, and create new services. Drawing on this experience, your organization can also begin to benefit from artificial intelligence.

With this in mind, a government-run maritime and coastguard agency plans to introduce a computer vision-based automated system that will identify ship type only from photographs captured by survey boats. You've been employed as a consultant to help this project develop an efficient model. The Data is provided by Kaggle, which comes in two files one for an images file and another is a .csv There are 6252 images in train and 2680 images in test data.

Civil society has been poring over the detail of the European Commission's proposal for a risk-based framework for regulating applications of artificial intelligence which was proposed by the EU's executive back in April. The verdict of over a hundred civil society organizations is that the draft legislation falls far short of protecting fundamental rights from AI-fuelled harms like scaled discrimination and blackbox bias -- and they've published a call for major revisions. "We specifically recognise that AI systems exacerbate structural imbalances of power, with harms often falling on the most marginalised in society. As such, this collective statement sets out the call of 11[5] civil society organisations towards an Artificial Intelligence Act that foregrounds fundamental rights," they write, going on to identify nine "goals" (each with a variety of suggested revisions) in the full statement of recommendations. The Commission, which drafted the legislation, billed the AI regulation as a framework for "trustworthy", "human-centric" artificial intelligence.

For any business, seamless deployment of ML models into production is the key to success of its live analytics use cases. In this article, we will learn about deploying ML models on AWS (Amazon Web Services) using MLflow and also look at different ways to productionize them. Subsequently, we will explore the same process on the two other popular platforms: Azure and GCP. An Identity and Access Management execution role defined that grants SageMaker access to the S3 buckets. Once the above steps are done with, here's how we proceed with the deployment process on AWS - Before any model can actually be deployed on SageMaker, Amazon workspace needs to be set up.

Random Forest is probably considered by most the silver bullet in supervised prediction tasks. For sure, any data scientist involved in standard machine learning applications is used to fit and benchmark a Random Forest. Random Forest is a well-known algorithm in literature and is proven to reach satisfactory results in both regression and classification contexts. It enjoys the ability to learn complex data relationships with low effort. There are a lot of open-sourced efficient implementations which are available to all of us (the one provided by scikit-learn is for sure the most famous).

Reading: Voice-enabled reading tools can help diagnose reading challenges, including dyslexia, at an earlier stage before a child even learns to read or recognize letters or letter sounds (phonics). Then, as a child starts down their reading journey, voice-enabled reading apps can listen, prompt, correct, and encourage a child as their reading progresses, just as a helpful adult would do. Immediate and accurate feedback from the voice-enabled reading app empowers a child to progress autonomously, practice regularly, and assess their own reading ability and areas for improvement. Voice-enabled reading assessments also provide educators and parents with immediate and granular insights into where a kid is struggling and help them to support the child with more personalized and individual approaches to achieving their reading goals. Language learning: Vice-enabled tools can listen while a child reads aloud and immediately return pronunciation scores and encouraging feedback--just as a supportive adult or tutor would.