AI used to find powerful antibiotic that can kill drug-resistant bacteria

The Japan Times

WASHINGTON – In a first, U.S. researchers have used artificial intelligence to identify a powerful new antibiotic capable of killing several drug-resistant bacteria. Antibiotics have been a cornerstone of modern medicine since the discovery of penicillin, but their effectiveness has seriously diminished in recent years as overuse has led to bacteria becoming resistant. The scientists at MIT and Harvard trained a machine-learning algorithm to analyze compounds capable of fighting infections using different mechanisms than those of existing drugs. Their findings were published in the journal Cell. "Our approach revealed this amazing molecule, which is arguably one of the more powerful antibiotics that has been discovered," said James Collins, a professor of medical engineering at MIT and one of the paper's senior authors.

Artificial Intelligence Bootcamp 44 projects Ivy League pro


Artificial Intelligence Bootcamp 44 projects Ivy League pro, Be a Machine Learning, Matplotlib, NumPy, and TensorFlow pro. I used AI to classify brain tumors. I have 11 publications on pubmed talking about that. I went to Cornell University and taught at Cornell, Amherst and UCSF. AI and Data Science are taking over the world!

Robotics Is More Than Just Automation Analytics Insight


While numerous robots are intended for automating tasks, there are others that are intended to augment human capabilities rather than automate tasks. Robots are good at performing repetitive tasks on factory floors today, however, cutting-edge robotics will go past automation use cases. The next challenge is someplace in the middle of where we need to have robots that can settle on decisions all alone independently, yet additionally, have the option to have people tuned in. Automation is obviously a top reason behind organizations considering the utilization of robotic innovation within their business processes. Nonetheless, it is essential to understand that, while numerous robots are intended for automating tasks, there are others that are designed to increase human abilities as opposed to automate tasks.

Transparent human organs allow 3-D maps at the cellular level


For the first time, researchers have managed to make intact human organs transparent. Using microscopic imaging, they revealed complex underlying structures of the transparent organs at the cellular level. The resulting organ maps can serve as templates for 3-D bioprinting technologies. In the future, this could lead to the creation of on-demand artificial organs for many patients in need. The findings have been published in Cell.

Engineering the Future of Maps at Uber


Maps are at the heart of Uber's services and core to the experience for millions of users. Our cutting-edge cartography makes it easy for drivers to locate passengers, delivery people to quickly transport meals via Uber Eats, and JUMP users to hop on the closest scooter or bike. Maps aren't always flashy (although many of our Uber Movement data visualizations are quite striking), but they're incredibly important. In fact, the nature of our maps technology means that if our users take them for granted, we're doing a good job. Underlying the graphical representation of streets and places exists a complex set of data allowing algorithms to calculate optimal routes based on traffic, speed limits, and other properties.

ACT-IAC Releases New Artificial Intelligence Playbook


The American Council for Technology and Industry Advisory Council (ACT-IAC), the premier public-private partnership dedicated to advancing government through the application of information technology, officially announced the release of the "Artificial Intelligence (AI) Playbook for the U.S. Federal Government." It was produced through a collaborative, volunteer effort by a working group of 133 leaders from government and industry plus academia and associations, hosted by the ACT-IAC Emerging Technology Community of Interest (COI). "The AI Playbook is designed to help the United States Federal Government achieve successful outcomes and reduce risk in its understanding and application of AI technologies," said David Wennergren, CEO of ACT-IAC, "and this important work directly supports the President's Management Agenda (PMA), Cross Agency Priority (CAP) Goal 6 – Shifting from Low-Value to High-Value Work." The Playbook also follows the General Service Administration's Office of Government-wide Policy Modernization and Migration Management (M3) framework used for Shared Services. AI has the power to accelerate government services in fields as diverse as medical research and disaster recovery to help save lives and improve quality of service in impactful ways.

Why Golang and Not Python? Which Language is Perfect for AI?


Golang is now becoming the mainstream programming language for machine learning and AI with millions of users worldwide. Python is awesome, but Golang is perfect for AI programming! Launched a decade back, November 2009, Golang recently turned ten. The language built by Google's developers is now making programmers more productive. These creators main goal was to create a language that would eliminate the so-called "extraneous garbage" of programming languages like C .

The Future of Healthcare


Later in the years after new technology had established itself in society, we started to witness technology become smarter. Automation started taking place wherever possible, virtual reality technologies started to emerge, and new forms of AI such as natural language processing (NLP) started to become a part of our daily lives, whether we knew it or not. Tools like Siri and Alexa have started to become more popular, and the once-believed notion of "big brother" watching over us has started to fade as youth have become more amenable to giving these technologies access to their personal data. Not only are these technologies present in our casual lives, but they have also allowed organizations to help us shop smarter and more efficiently as AI systems track our every click.

Do AI startups have worse economics than SaaS shops? – TechCrunch


A few days ago, Andreessen Horowitz's Martin Casado and Matt Bornstein published an interesting piece digging into the world of artificial intelligence (AI) startups, and, more specifically, how those companies perform as businesses. Core to the argument presented is that while founders and investors are wagering "that AI businesses will resemble traditional software companies," the well-known venture firm is "not so sure." Given that TechCrunch cares a lot about startup business fundamentals, the notion that one oft-discussed and well-funded category of venture-backed startup might sport materially less attractive economics than we expected captured our attention. The Andreessen Horowitz (a16z) perspective is straightforward, arguing that AI-focused companies have lesser gross margins than software companies due to cloud compute and human-input costs, endure issues stemming from "edge-cases" and enjoy less product differentiation from competing companies when compared to software concerns. Today, we're drilling into the gross margin point, as it's something inherently numerical that we can get other, informed market participants to weigh in on.

How to Develop an Imbalanced Classification Model to Detect Oil Spills


Many imbalanced classification tasks require a skillful model that predicts a crisp class label, where both classes are equally important. An example of an imbalanced classification problem where a class label is required and both classes are equally important is the detection of oil spills or slicks in satellite images. The detection of a spill requires mobilizing an expensive response, and missing an event is equally expensive, causing damage to the environment. One way to evaluate imbalanced classification models that predict crisp labels is to calculate the separate accuracy on the positive class and the negative class, referred to as sensitivity and specificity. These two measures can then be averaged using the geometric mean, referred to as the G-mean, that is insensitive to the skewed class distribution and correctly reports on the skill of the model on both classes. In this tutorial, you will discover how to develop a model to predict the presence of an oil spill in satellite images and evaluate it using the G-mean metric. Develop an Imbalanced Classification Model to Detect Oil Spills Photo by Lenny K Photography, some rights reserved. In this project, we will use a standard imbalanced machine learning dataset referred to as the "oil spill" dataset, "oil slicks" dataset or simply "oil."