Text-to-image generation is a hot topic in the AI domain, mainly thanks to the open-source release of stable-diffusion. Do you want to see an image of "a teddy bear sleeping in a medieval bed drawn in Van Gogh style"? You can pass a prompt with details, and the stable-diffusion AI will generate a realistic image for you. The X-to-Y generation madness using diffusion models is not just limited to images. You can go from text-to-image, text-to-speech, image-to-image, and the list goes on.

This study showed that the machine learning-based model has a higher predictive ability than the conventional logistic regression model and may be …

Machine learning is gradually being widely used in predicting acute pancreatitis. No study has classified or summarised various prediction tasks for acute pancreatitis. The performance of models in different studies and the problems associated with model construction remain unclear. Machine learning-based models have great predictive performance, and outperform conventional statistical models and clinical scores in some prediction tasks for acute pancreatitis. The IJMEDI checklist is a new quality assessment tool, and scores can be attempted to be associated with it to evaluate the effects and reliability of machine learning-based models.

Google has announced it is updating its attribution model for marketers. The company will no longer rely on last-click attribution, but will shift to what it's calling "data-driven attribution," per a blog post published earlier today by Google Ads' vice president and general manager of buying, analytics and measurement Vidhya Srinivasan. While last-click attribution measures which touchpoint a consumer engaged with last before making a purchase, Google's new framework employs machine learning to gauge everything from how conversions are measured to how to improve automated bidding in the media buying process. While Google's ads business already offers this data-driven attribution model, it was not previously accessible to all advertisers, due to minimum data requirements as well as some limitations on types of conversion. Per the company's announcement today, minimum data rules will be dropped and data-driven attribution will be made available to all advertisers in Google Ads beginning in October.

Shilpi Bhabhra, head of the Analytics and Data Sciences at Acko General Insurance, was one of the guest speakers on the day 1 of the Deep Learning DevCon 2020 (DLDC 2020). Like the topic of her talk, 'Who Priced My Insurance', the content was equally engaging. Through her talk, she shed light on how digital insurance was quickly changing the way risk is calculated and how the insurance amount ranged from known factors such as age and experience to colour of the car and the brand of the customer's phone. The speaker's talk was broadly divided into three main parts: Insurance, as we know, is the contract between a customer and the insurance company where the former pays a premium to the latter on a regular basis. In case of a loss, this amount is reimbursed to the customer.

Using the expression of 17 key genes (messenger RNAs) it is now possible to distinguish primary and metastatic cutaneous melanoma, which is the most common type of skin cancer. While 11 of the 17 genes have already been reported by other studies for cutaneous melanoma, it is for the first time that the potential role of remaining six genomic signatures in classifying samples as either primary or metastatic skin cutaneous cancer has been made. The 17 genomic signatures, which were identified by a team led by Prof. Gajendra P.S. Raghava from the Indraprastha Institute of Information Technology (IIIT), New Delhi, have high accuracy -- over 89% -- in discriminating metastatic from primary skin melanoma. These signatures also have high sensitivity (in case tumour is metastatic), and high specificity (in case the tumour is primary). The results were published in the journal Scientific Reports.

Six machine learning models were used to study and validate the genomic signatures.