The process of manually searching for relevant instances in, and extracting information from, clinical databases underpin a multitude of clinical tasks. Such tasks include disease diagnosis, clinical trial recruitment, and continuing medical education. This manual search-and-extract process, however, has been hampered by the growth of large-scale clinical databases and the increased prevalence of unlabelled instances. To address this challenge, we propose a supervised contrastive learning framework, CROCS, where representations of cardiac signals associated with a set of patient-specific attributes (e.g., disease class, sex, age) are attracted to learnable embeddings entitled clinical prototypes. We exploit such prototypes for both the clustering and retrieval of unlabelled cardiac signals based on multiple patient attributes. We show that CROCS outperforms the state-of-the-art method, DTC, when clustering and also retrieves relevant cardiac signals from a large database. We also show that clinical prototypes adopt a semantically meaningful arrangement based on patient attributes and thus confer a high degree of interpretability.

Hyperparameters of Deep Learning (DL) pipelines are crucial for their downstream performance. While a large number of methods for Hyperparameter Optimization (HPO) have been developed, their incurred costs are often untenable for modern DL.Consequently, manual experimentation is still the most prevalent approach to optimize hyperparameters, relying on the researcher's intuition, domain knowledge, and cheap preliminary explorations.To resolve this misalignment between HPO algorithms and DL researchers, we propose PriorBand, an HPO algorithm tailored to DL, able to utilize both expert beliefs and cheap proxy tasks. Empirically, we demonstrate PriorBand's efficiency across a range of DL benchmarks and show its gains under informative expert input and robustness against poor expert beliefs.

The stunning capabilities of ChatGPT, the chatbot from startup OpenAI, has triggered a surge of new interest and investment in artificial intelligence. But late last week, OpenAI's CEO warned that the research strategy that birthed the bot is played out. It's unclear exactly where future advances will come from. OpenAI has delivered a series of impressive advances in AI that works with language in recent years by taking existing machine-learning algorithms and scaling them up to previously unimagined size. GPT-4, the latest of those projects, was likely trained using trillions of words of text and many thousands of powerful computer chips.

Art is standing on the Precipice of Revolution never before seen in human history. From cave painting to the creation of paints from natural material, to Illuminated Manuscripts, to the Gutenberg Printing Press, to Renaissance painters and sculptors, to synthetic paints, to photography, videography, and spherography, to music composition, to digital representation and storage, all guided by the creativity of the human mind. We are accelerating into the Age of Artificial (General) Intelligence Artists. Optionally combined with advances in robotics, AI Artists will discover and use new creative insights. They will optionally place bush to canvas, or chisel to marble, or digit to piano key, or become a photographer, videographer, or spherographer, and compete with -- and eventually eclipse-- human artists.

On May 1-4th, 2018, data scientists from around the world gathered in Boston, Massachusetts for the fourth annual Open Data Science Conference East. With over 4,500 attendees hailing from academia, business, nonprofit sectors, and government, the conference marked one of ODSC's largest and most diverse gatherings to date. The 4-day conference hosted over 200 speakers and presentations as well as numerous workshops on topics ranging from specific concepts in machine learning and artificial intelligence to the application of data science principles in medical research, for-profit enterprise, and humanitarian causes. Dozens of core contributors to the most prominent open source data science tools, libraries, and languages gave practical demonstrations and shared their expertise including Dr. Drew Conway, Professor Gary Marcus and Professor Alex "Sandy" Pentland. Of the hundreds of talks, a series of talks was available via live stream to the global ODSC community.

Kai-Fu Lee said the 50% of all current jobs will be replaced by AI in 10 years and warned about AI oligopoly by big companies and propose minimal stipend for unemployed. He is optimistic that China will rise to the top in the field of AI. His Columbia Commencement Speech on AI 2017 is here.

Robot co-workers and artificial intelligence assistants are becoming more common in the workplace. Could they edge human employees out? Still haven't subscribed to WIRED on YouTube? http://wrd.cm/15fP7B7 CONNECT WITH WIRED Web: http://wired.com ABOUT WIRED WIRED is where tomorrow is realized.

I've had robots on my mind recently, and not in a "Wow, robots will make my life so much easier!" Robots are coming to take the jobs of people in their 50s and 60s!" The alarm bells rang furiously at a Milken Institute Global Conference panel I recently saw in Los Angeles ("I, Worker: Employment in the Age of Robots). And I just received a new book with the foreboding title, The Robots Are Coming, by John Pugliano, founder and money manager of Investable Wealth. A World Without Work As We Know It? Speaking at the "I, Worker" panel, Anne-Marie Slaughter said: "We need to get ready for a world without work as we know it now. Change is happening much faster than we thought."

Jerry Kaplan does for the future what Jared Diamond did for the past: He pulls together our human (or humanoid) fate in sparkling,often hilarious, prose. Kaplan begins by offering the non scientific reader (me) a clear overview of the AI advances that are poised to make human workers obsolete--offering eye popping examples explaining how the pace of technology is destined to overwhelm the human landscape of life and work. He then charts the changes that span FAR more than driverless cars. Mechanical robots (or what Kaplan calls "forged intelligences") will be more adept (and. of course, far more cost effective) than humans at performing every routine job from collecting our garbage to stocking our grocery shelves (and make those physical stores quaint relics of the past). "Synthetic intelligences" (machines that think and analyze information) will outwit humans at making complex diagnoses or writing legal briefs--automating out many of the hapless law school or medical students spending decades accumulating those mountainous student debts .

In the past year, an AI replaced a design agency (Albert), an AI-powered app made headlines with its ability to turn your boring photos into artistic masterpieces (Prisma), and an AI platform was created to turn your ugly doodles into professional drawings (Autodraw). Many have thought low wage jobs like burger flipping would be the first to be automated, but it's becoming apparent that white collar jobs are just as likely to be replaced. These innovations signal a new existential challenge to the creative industry. But rather than panicking, designers should consider how they could become more empowered with the rise of AI-assisted design. Being a successful designer today means having the ability to pick up dozens of tools and skills, and having a comprehensive understanding of visual design, interaction design, information architecture, motion, and strategy.