Quantum computers promise to perform some computations exponentially faster: Computations that may take a classical computer 10,000 years may run within hours on a quantum computer. Yet, building universal quantum computers turned out to be notoriously difficult. Current noisy intermediate-scale quantum computers are limited by the number of processing units, so-called qubits, and by noise – every step in a quantum computation adds a little error that accumulates until no calculation results can be obtained anymore. This severely limits the number of steps one can perform. The startup Kipu Quantum found a way to compress quantum algorithms, i.e. to arrive at the same result with fewer steps.

Google Cloud is bringing its expertise in vision-based artificial intelligence to the healthcare industry with the launch of its new Medical Imaging Suite today. Vision AI has the potential to make a massive impact in healthcare. Medical imaging is one of the most critical tools used by hospitals to diagnose patients, and each year billions of images are made by clinicians to help them understand why people are sick. Google said medical images are so important that they account for about 90% of all healthcare data. The problem doctors have is that medical images are complex, as it takes expertise to look at one and understand if the patient is inflicted with some kind of disease. Until now, that has always meant human expertise.

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Built on top of scikit-learn, it allows you to rapidly create active learning workflows with nearly complete freedom. What is more, you can easily replace parts with your custom built solutions, allowing you to design novel algorithms with ease. With the recent explosion of available data, you have can have millions of unlabelled examples with a high cost to obtain labels. For instance, when trying to predict the sentiment of tweets, obtaining a training set can require immense manual labour. But worry not, active learning comes to the rescue!

Discovering meaningful molecules in the vast combinatorial chemical space has been a long-standing challenge in many fields from materials science to drug discovery. Recent advances in machine learning, especially generative models, have made remarkable progress and demonstrate considerable promise for automated molecule design. Nevertheless, most molecule generative models remain black-box systems, whose utility is limited by a lack of interpretability and human participation in the generation process. In this work we propose Chemical Space Explorer (ChemSpacE), a simple yet effective method for exploring the chemical space with pre-trained deep generative models. It enables users to interact with existing generative models and inform the molecule generation process. We demonstrate the efficacy of ChemSpacE on the molecule optimization task and the molecule manipulation task in single property and multi-property settings.

While a few firms--the digital giants--are winning big from digital technology, many large firms are investing heavily in digital, by recruiting IT specialists and spending on digital technology--including artificial intelligence, cloud computing, machine learning, and algorithmic decision making, yet getting disappointing returns. As noted in a previous article, 'How To Understand Your Digital Transformation" firms must realize is that digital transformation is only partly to do with technology. The chief obstacle is the fundamental change in the way the firm is managed--from industrial-era management to digital age management. Thus, over the last two decades, it has become increasingly apparent that the most successful firms at digital transformation are being run very differently from industrial-era. They have a different set of principles, including a goal of co-creating value for customers, a business model that generates profits as a result; team-based and network structures. These principles are supported by processes that are quite different from those of industrial-era management.

This report reveals that households across the country are still grappling with the financial ramifications of COVID-19, and women are among the hardest hit. And, while alarmingly few employees seek out financial advice, those who do fared better during the pandemic and are more confident in their ability to reach their financial goals. Download this report and see new emerging trends and learn how to take a more proactive role in your employees' financial wellness.

AI startups are one of the most popular and influential upcoming sectors in the market nowadays. From sales to software development to the medical sector, artificial intelligence has the power to revolutionize a wide range of businesses. Let's look at some of the most innovative AI startups that have become popular recently and are based in Germany. Agile Robots is creating systems that deliver cutting-edge full-body force sensitivity and unrivaled vision intelligence to close the gap between AI and robotics. It offers deep learning-based machine vision technology that enables autonomous robots to perceive their environment.

SaaS companies SiteZeus and Spatial.ai have teamed up to offer growing brands a cutting-edge customer segmentation platform: SiteZeus Market, powered by PersonaLive. Equipped with unprecedented insights, marketers across all industries can now craft highly tailored campaigns and deploy them efficiently. The end result: an increased return on every marketing dollar spent. This new technology empowers companies to truly understand their customers by transcending the limitations of traditional demographic data and surveys. A brand can now identify, analyze, and target its top customer groups based on their real-time social media activity, online behaviors, and in-store visitation patterns.

Training a machine–learning model on an intelligent edge device allows it to adapt to new data and make better predictions.