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Alchemab Therapeutics Ltd hiring Machine Learning Scientist in Babraham, England, United Kingdom

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The candidate will work within the technology team to develop, apply, and design novel machine learning (ML) algorithms with the ultimate aim of discovering therapeutic antibodies from next-generation sequencing (NGS) datasets. The candidate will be involved in multiple projects spanning our oncology, neuroscience, and infectious disease programmes. You will be responsible for the growth and development of our ML product roadmap. This will initially focus on exploiting methods in natural language processing for antibody discovery and patient stratification, and exploring the latest advances in ML (in areas such as self-supervised learning) to extend our capabilities. You will contribute new algorithms and strategies to increase accuracy, explainability, and/or automation of our technology platform.


Ticking bomb of Warning

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I can tell that every next year the hot temperature getting hotter and hotter. You know that in the upcoming year climate change will be the reason for the collapse of human civilisation. We are just like dinosaurs who were wiped out of this earth. This is you can say a law or cycle of life and death of species who are the majority on this planet earth. We as humans will be the reason to kill ourselves like cutting legs with our axe.


Thought Leaders in Artificial Intelligence: Florian Quarre, Chief Revenue Officer, Exponential AI (Part 1)

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In this segment of TLAI, we discuss use cases in life sciences, healthcare, and retail. Sramana Mitra: Let's start by introducing our audience to yourself as well as to Exponential Machines. Florian Quarre: I operate primarily in the field of healthcare technology. I have quite recently joined Exponential AI. Exponential AI is an AI platform focused on accompanying our clients through their digital transformation journey. What we see is, there're three prongs that we believe help our clients. Large companies can then go through the discovery of what AI is about and how they can use it within their environment. The first one that is core to our business is our platform. The purpose of our platform is to bring together development and production of AI models so that we can evolve from a problem that you are tinkering on and have the ability to bring it all the way to production with the scale and strength of a business that runs millions of analysis. The second part that


AI solves complex physics problems; Amazon 'creepy' AI cameras; Is DeepMind's AI really a human-level intelligence?

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I hope that you enjoy the latest AI news and insights, don't forget to comment with your feedback. Make sure to check the Web3 section at the end! But is Gato truly intelligent – having AGI? Google AI took on the challenge: The first iteration of the AI-generated script was completed by November 2021. The script was interesting, but there was also a lot of gibberish. A second version aims to dial a new gate address for a more involved and engaging Stargate script.


Suicidal Text Analysis Using NLP

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It is estimated that each year many people, most of whom are teenagers and young adults die by suicide worldwide. Suicide receives special attention with many countries developing national strategies for prevention. It is found that, social media is one of the most powerful tool from where we can analyze the text and estimate the chances of suicidal thoughts. Using nlp we can analyze twitter and reddit texts monitor the actions of that person. The most difficult part to prevent suicide is to detect and understand the complex risk factors and warning signs that may lead to suicide.


Microsoft AI news: Making AI easier, simpler, more responsible

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We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Today is a big day for AI announcements from Microsoft, both from this week's Build conference and beyond. But one common theme bubbles over consistently: For AI to become more useful for business applications, it needs to be easier, simpler, more explainable, more accessible and, most of all, responsible. Responsible AI is actually at the heart of a lot of today's Build news, John Montgomery, corporate vice president of Azure AI, told VentureBeat. Most notable is Azure Machine Learning's preview of a responsible AI dashboard, which brings together capabilities in use over the past 18 months, such as data explorer, model interpretability, error analysis, counterfactual and causal inference analysis, into a single view.


How AI makes developers' lives easier, and helps everybody learn to develop software

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Ever since Ada Lovelace, a polymath often considered the first computer programmer, proposed in 1843 using holes punched into cards to solve mathematical equations on a never-built mechanical computer, software developers have been translating their solutions to problems into step-by-step instructions that computers can understand. Today, AI-powered software development tools are allowing people to build software solutions using the same language that they use when they talk to other people. These AI-powered tools translate natural language into the programming languages that computers understand. "That allows you, as a developer, to have an intent to accomplish something in your head that you can express in natural language and this technology translates it into code that achieves the intent you have," Scott said. "That's a fundamentally different way of thinking about development than we've had since the beginning of software."


Development and internal validation of a machine-learning-developed model for predicting 1-year mortality after fragility hip fracture - BMC Geriatrics

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Fragility hip fracture increases morbidity and mortality in older adult patients, especially within the first year. Identification of patients at high risk of death facilitates modification of associated perioperative factors that can reduce mortality. Various machine learning algorithms have been developed and are widely used in healthcare research, particularly for mortality prediction. This study aimed to develop and internally validate 7 machine learning models to predict 1-year mortality after fragility hip fracture. This retrospective study included patients with fragility hip fractures from a single center (Siriraj Hospital, Bangkok, Thailand) from July 2016 to October 2018. A total of 492 patients were enrolled. They were randomly categorized into a training group (344 cases, 70%) or a testing group (148 cases, 30%). Various machine learning techniques were used: the Gradient Boosting Classifier (GB), Random Forests Classifier (RF), Artificial Neural Network Classifier (ANN), Logistic Regression Classifier (LR), Naive Bayes Classifier (NB), Support Vector Machine Classifier (SVM), and K-Nearest Neighbors Classifier (KNN). All models were internally validated by evaluating their performance and the area under a receiver operating characteristic curve (AUC). For the testing dataset, the accuracies were GB model = 0.93, RF model = 0.95, ANN model = 0.94, LR model = 0.91, NB model = 0.89, SVM model = 0.90, and KNN model = 0.90. All models achieved high AUCs that ranged between 0.81 and 0.99. The RF model also provided a negative predictive value of 0.96, a positive predictive value of 0.93, a specificity of 0.99, and a sensitivity of 0.68. Our machine learning approach facilitated the successful development of an accurate model to predict 1-year mortality after fragility hip fracture. Several machine learning algorithms (eg, Gradient Boosting and Random Forest) had the potential to provide high predictive performance based on the clinical parameters of each patient. The web application is available at www.hipprediction.com . External validation in a larger group of patients or in different hospital settings is warranted to evaluate the clinical utility of this tool. Thai Clinical Trials Registry (22 February 2021; reg. no. TCTR20210222003 ).


Pentagon names new chief of responsible artificial intelligence

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The Pentagon has tapped artificial intelligence ethics and research expert Diane Staheli to lead the Responsible AI (RAI) Division of its new Chief Digital and AI Office (CDAO), FedScoop confirmed on Tuesday. In this role, Staheli will help steer the Defense Department's development and application of policies, practices, standards and metrics for buying and building AI that is trustworthy and accountable. She enters the position nearly nine months after DOD's first AI ethics lead exited the Joint Artificial Intelligence Center (JAIC), and in the midst of a broad restructuring of the Pentagon's main AI-associated components under the CDAO. "[Staheli] has significant experience in military-oriented research and development environments, and is a contributing member of the Office of the Director of National Intelligence AI Assurance working group," Sarah Flaherty, CDAO's public affairs officer, told FedScoop. Advanced computer-driven systems use AI to perform tasks that generally require some human intelligence.


combining-developer-knowledge-with-artificial-intelligence-to-improve-software-maintenance

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In our increasingly digitized world, enterprise software applications can better serve your business and your customers. By connecting every department within a company, enterprise systems allow companies to improve productivity and efficiency. These systems are generally created with specific goals in mind and serve many users at the same time, typically over a computer network instead of an end-user application. From payment processing and online shopping to automated billing, interactive product catalogs, business process management, content management, security, and more, the services such software systems provide are built to satisfy the needs of businesses, schools, clubs, charities, and government organizations alike. These systems are frequently enhanced to meet the changing needs and opportunities of the particular entity for which they were written.