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To Operationalize AI, Invest in Humans - InformationWeek

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IT leaders and business executives around the world recognize the strategic importance of operationalizing AI, yet surprisingly few have moved beyond experimentation. A recent Capgemini survey finds that only 13% of companies have moved beyond proofs of concept (POC) to scaling AI across the enterprise. The struggle to operationalize AI is painful because it represents lost time and resources and unrealized potential. Articles abound full of suggestions, frameworks and manifestos, shared with the intent of closing the gap between AI concept and enterprise delivery (including one proposal to eliminate the POC altogether). Many of these are smart and worthwhile.


Rethinking stress and nutrition with smart tech

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Personalised nutrition start-up myAir has unveiled its nutritional solution for better management of stress. The company developed a series of plant-based nutrition bars with a personalised edge. Each formulation contains a botanical blend designed to deliver a specific stress-relief effect. The herbal extract blends are based on profiling machine learning technology, and are customised to the consumer's stress profile and cognitive needs.


How can startups make machine learning models production-ready?

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Today, every technology startup needs to embrace AI and machine learning models to stay relevant in their business. Machine learning (ML), if implemented well, can have a direct impact on a company's ability to succeed and raise the next round of funding. However, the path to implementing ML solutions comes with some specific hurdles for start-ups. Let's discuss the top considerations for getting ML models production-ready and the best approaches for a startup. An ML model is only as good as the data used to train it.


Machine Learning Algorithms: Deepen your Python ML knowledge – IAM Network

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This article is part of "AI education", a series of posts that review and explore educational content on data science and machine learning. Teaching yourself Python machine learning can be a daunting task if you don't know where to start. Fortunately, there are plenty of good introductory books and online courses that teach you the basics. It is the advanced books, however, that teach you the skills you need to decide which algorithm better solves a problem and which direction to take when tuning hyperparameters. A while ago, I was introduced to Machine Learning Algorithms, Second Edition by Giuseppe Bonaccorso, a book that almost falls into the latter category. While the title sounds like another introductory book on machine learning algorithms, the content is anything but.


Making AI, Machine Learning Work for You!

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Most data organisations hold is not labeled, and labeled data is the foundation of AI jobs and AI projects. "Labeled data, means marking up or annotating your data for the target model so it can predict. In general, data labeling includes data tagging, annotation, moderation, classification, transcription, and processing." Particular features are highlighted by labeled data and the classification of those attributes maybe be analysed by models for patterns in order to predict the new targets. An example would be labelling images as cancerous and benign or non-cancerous for a set of medical images that a Convolutional Neural Network (CNN) computer vision algorithm may then classify unseen images of the same class of data in the future. Niti Sharma also notes some key points to consider.


The first AI model that translates 100 languages without relying on English data

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Facebook AI is introducing, M2M-100 the first multilingual machine translation (MMT) model that translates between any pair of 100 languages without relying on English data. When translating, say, Chinese to French, previous best multilingual models train on Chinese to English and English to French, because English training data is the most widely available. Our model directly trains on Chinese to French data to better preserve meaning. It outperforms English-centric systems by 10 points on the widely used BLEU metric for evaluating machine translations. M2M-100 is trained on a total of 2,200 language directions -- or 10x more than previous best, English-centric multilingual models.


Learn Python machine learning with these essential books and online courses

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Teaching yourself Python machine learning can be a daunting task if you don't know where to start. Fortunately, there are plenty of good introductory books and online courses that teach you the basics. It is the advanced books, however, that teach you the skills you need to decide which algorithm better solves a problem and which direction to take when tuning hyperparameters. A while ago, I was introduced to Machine Learning Algorithms, Second Edition by Giuseppe Bonaccorso, a book that almost falls into the latter category. While the title sounds like another introductory book on machine learning algorithms, the content is anything but.


How to put machine learning models into production

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It is very complex because most of the time there is no simple rule such as a threshold on the confidence score of the prediction. In practice it might be more like, "if the user has more than 7 items in their cart and if the user is not a returning customer that filled out personal data and the value of their cart is greater than $100 and they have not put a new item in the cart for 2 minutes, and the confidence score of the predictor is less than 0.4, THEN don't show the next recommended item, just display a checkout link."


Making AI, Machine Learning Work for You!

#artificialintelligence

Most data organisations hold is not labeled, and labeled data is the foundation of AI jobs and AI projects. "Labeled data, means marking up or annotating your data for the target model so it can predict. In general, data labeling includes data tagging, annotation, moderation, classification, transcription, and processing." Particular features are highlighted by labeled data and the classification of those attributes maybe be analysed by models for patterns in order to predict the new targets. An example would be labelling images as cancerous and benign or non-cancerous for a set of medical images that a Convolutional Neural Network (CNN) computer vision algorithm may then classify unseen images of the same class of data in the future. Niti Sharma also notes some key points to consider.


How I'd study machine learning -- if I'd be starting out today

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I'm underground, back where it all started. Sitting at the hidden cafe where I first met Mike. I'd been studying in my bedroom for the past 9-months and decided to step out of the cave. Half of me was concerned about having to pay $19 for breakfast (unless it's Christmas, driving Uber on the weekends isn't very lucrative), the other half about whether any of this study I'd been doing online meant anything. In 2017, I left Apple, tried to build a web startup, failed, discovered machine learning, fell in love, signed up to a deep learning course with zero coding experience, emailed the support team asking what the refund policy was, didn't get a refund, spent the next 3-months handing in the assignments four to six days late, somehow passed, decided to keep going and created my own AI Masters Degree.