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.

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.

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.

Common sense is what differentiates humans from machines. For years, scientists and researchers have been looking for ways to bridge the gap and make Artificial Intelligence (AI) more capable of interacting with the human world. However, the process is more complicated than it sounds. Artificial intelligence researchers have been unsuccessful in giving intelligent agents the common-sense knowledge they need to reason about the world. Common sense is considered as something that will pull artificial intelligence closer to humankind.

An international team of researchers has developed a way to use artificial intelligence to predict the risk of a patient developing cardiovascular disease. In their paper published in the journal Nature Biological Engineering, the group describes using retinal blood vessel scans as a data-source for a deep learning system to teach it to recognize the signs of cardiovascular disease in people. For over 100 years, doctors have peered into the eyes of patients looking for changes in retinal vasculature--blood vessels in the retina that can reflect the impact of high blood pressure over a period of time. Such an impact can be an indicator of impending cardiovascular disease. Over time, medical scientists have developed instruments that allow eye doctors to get a better look at the parts of the eye most susceptible to damage from hypertension and have used them as a part of a process to diagnose patients that are likely to develop the disease.

With the advent of new deep learning approaches based on transformer architecture, natural language processing (NLP) techniques have undergone a revolution in performance and capabilities. Cutting-edge NLP models are becoming the core of modern search engines, voice assistants, chatbots, and more. Modern NLP models can synthesize human-like text and answer questions posed in natural language. As DeepMind research scientist Sebastian Ruder says, NLP's ImageNet moment has arrived. While NLP use has grown in mainstream use cases, it still is not widely adopted in healthcare, clinical applications, and scientific research.

In a recent article, I discussed the relevance of the machine learning techniques powering the famous OpenAI's GPT-3 could have for the crypto market. GPT-3 – which can answer questions, perform language analysis and generate text – might be the most famous achievements in recent years of the deep learning space. But, by no means, is it the most applicable to the crypto space. In this article, I would like to discuss some novel areas of deep learning that can have a near immediate impact in the quant models applied to crypto. Jesus Rodriguez is the CEO of IntoTheBlock, a market intelligence platform for crypto assets.

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.

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.

Recently, the developers at Google detailed the methods and ways they have been using artificial intelligence and machine learning in order to improve its search experience. The announcements were made during the Search On 2020 event, where the tech giant unveiled several enhancements in AI that will help to get search results in the coming years. In 2018, the tech giant introduced the neural network-based technique for natural language processing (NLP) pre-training called Bidirectional Encoder Representations from Transformers or simply, BERT. Last year, the company introduced how BERT language understanding systems are helping to deliver more relevant results in Google Search. Since then, there have been enhancements in a lot of areas including the language understanding capabilities of the engine, search queries and more.