Projected to impact 1.6 million people in the UK by 2040 and costing {\pounds}25 billion annually, dementia presents a growing challenge to society. This study, a pioneering effort to predict the translational potential of dementia research using machine learning, hopes to address the slow translation of fundamental discoveries into practical applications despite dementia's significant societal and economic impact. We used the Dimensions database to extract data from 43,091 UK dementia research publications between the years 1990-2023, specifically metadata (authors, publication year etc.), concepts mentioned in the paper, and the paper abstract. To prepare the data for machine learning we applied methods such as one hot encoding and/or word embeddings. We trained a CatBoost Classifier to predict if a publication will be cited in a future patent or clinical trial. We trained several model variations. The model combining metadata, concept, and abstract embeddings yielded the highest performance: for patent predictions, an Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.84 and 77.17% accuracy; for clinical trial predictions, an AUROC of 0.81 and 75.11% accuracy. The results demonstrate that integrating machine learning within current research methodologies can uncover overlooked publications, expediting the identification of promising research and potentially transforming dementia research by predicting real-world impact and guiding translational strategies.

Google has pledged not to use its powerful artificial intelligence (AI) to create weapons of war, illegal surveillance or to cause "overall harm". On Thursday, Sundar Pichai, CEO for Alphabet Inc.'s Google, set out a series of principles about AI at the company. The announcement follows more than 4,500 Google employees having written a letter in April, calling on the company to get out of the "business of war" and cancel Pentagon work. Pichai said in Thursday's post that Google recognizes that its powerful technology "raises equally powerful questions about its use". AI is being used for good, he said, citing use cases such as machine-learning sensors being built by higher schoolers to predict the risk of wildfires; farmers using it to monitor their cows' health; and doctors who are using it to diagnose breast cancer and to prevent blindness.

Imagine a future where you are regularly stopped and searched by the police, based simply on bad information fed into a computer. That is the fear of one authority on the subject, who is concerned that human biases and errors are being programmed into machine learning systems. Studies have already shown that AI experiments, including predictive policing, demonstrate exaggerated versions of human biases when put into practice. Experts are worried that this could lead to a'toxic' future where machines make bad decisions on our behalf, unless something is done now to prevent it. Imagine a future where you are regularly stopped and searched by the police, based simply on bad information fed into a computer.

Can you really predict if someone is going to commit a crime? Some authorities are using facial recognition, predictive analytics and machine learning to predict who will commit a crime. Even if you can use an algorithm to deduce the likelihood of an individual's future, to apprehend a suspect before a crime is even committed surely cannot lead to a conviction as no offence will have actually taken place. Yes, this is all very Minority Report. Nevertheless, companies are currently working on these technologies to catch the bad guys before they even strike.

Keith Kirkpatrick is principal of 4K Research & Consulting, LLC, based in Lynbrook, NY. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and full citation on the first page.