In the near future, we should see the value of AI-generated NFTs to expand beyond generative art into more generic NFT utility categories providing a natural vehicle for leveraging the latest deep learning techniques. An example of this value proposition can be seen in digital artists like Refik Anadol who are already experimenting with cutting edge deep learning methods for the creation of NFTs. Anadol's studio have been a pioneer in using techniques such as GANs, and even dabbling into quantum computing, trained models in hundreds of millions images and audio clips to create astonishing visuals. NFTs have been one of the recent delivery mechanisms explored by Anadol.
The ability to identify antigenic determinants of pathogens, or epitopes, is fundamental to guide rational vaccine development and immunotherapies, which are particularly relevant for rapid pandemic response. A range of computational tools has been developed over the past two decades to assist in epitope prediction; however, they have presented limited performance and generalization, particularly for the identification of conformational B-cell epitopes. Here, we present epitope3D, a novel scalable machine learning method capable of accurately identifying conformational epitopes trained and evaluated on the largest curated epitope data set to date. Our method uses the concept of graph-based signatures to model epitope and non-epitope regions as graphs and extract distance patterns that are used as evidence to train and test predictive models. We show epitope3D outperforms available alternative approaches, achieving Mathew's Correlation Coefficient and F1-scores of 0.55 and 0.57 on cross-validation and 0.45 and 0.36 during independent blind tests, respectively.
Clustering time series data before fitting can improve accuracy by 33% -- src. In 2021, researchers at UCLA developed a method that can improve model fit on many different time series'. By aggregating similarly structured data and fitting a model to each group, our models can specialize. While fairly straightforward to implement, as with any other complex deep learning method, we are often computationally limited by large data sets. However, all of the methods listed have support in both R and python, so development on smaller datasets should be pretty "simple."
Generative AI, distributed enterprise and cloud-native platforms are amongst the top strategic technology trends for 2022, Gartner has predicted. David Groombridge, research vice president at Gartner, says with CEOs and boards striving to find growth through direct digital connections with customers, the priorities of a CIO must reflect the same business imperatives, which run through each of Gartner's top strategic tech trends for 2022. "CIOs must find the IT force multipliers to enable growth and innovation, and create scalable, resilient technical foundations whose scalability will free cash for digital investments," Groombridge says. "These imperatives form the three themes of this year's trends: engineering trust, sculpting change and accelerating growth." Gartner says one of the most visible and powerful AI techniques coming to market is generative AI – machine learning methods that learn about content or objects from their data, and use it to generate brand-new, completely original, realistic artefacts.
Package Name: Small Cap Forecast Recommended Positions: Long Forecast Length: 3 Months (7/20/21 – 10/20/21) I Know First Average: 21.48% During the 3 Months forecasted period several picks in the Small Cap Forecast Package saw significant returns. The algorithm had correctly predicted 10 out of 10 returns. The top-performing prediction in this forecast was IKNX, which registered a return of 59.72%. Other notable stocks were MDP and DDS with a return of 44.68% and 34.32%.
Amazon Web Services are looking for a passionate and talented Applied Scientist who will collaborate with other scientists and engineers to develop computer vision and machine learning methods and algorithms to address real-world customer use-cases. You'll design and run experiments, research and develop new algorithms, and put your algorithms and models into practice to help solve our customers' most challenging problems. This role resides in AWS Professional Services, a unique consulting team where we pride ourselves on being customer obsessed and highly focused on the AI enablement of our customers. If you do not live in a market where we have an open Applied Scientist position, please feel free to apply. Our Applied Scientists can live in any location (D.C, Maryland, Virginia, Illinois, Pennsylvania, New York, New Jersey, Denver) where we have a WWPS Professional Service office.
According to Kaggle's 2020 edition of the State of Machine Learning and Data Science report -- which includes insights gathered from a survey of 20,036 Kaggle members -- more than 55 per cent of data scientists have less than three years of experience, and six per cent of professionals pursuing data science have been using machine learning for more than a decade. The study further revealed that machine learning has become more rooted in the companies where Kaggle scientists work. Nearly 31 %of data scientists claimed well-established machine learning methods, up from 28% in 2019 and 25 % in 2018. Though Kaggle competitions are great to practice data science skills, are they really that different from real-world data science and machine learning work? This article will unveil the difference between the two, especially when solving machine learning problems on Kaggle vs real life.
A team of scientists used a machine learning method called a deep neural network to discern the signal created by the spin orientation of electrons on quantum dots. Researchers led by the Institute of Scientific and Industrial Research (SANKEN) at Osaka University have trained a deep neural network to correctly determine the output state of quantum bits, despite environmental noise. The team's novel approach may allow quantum computers to become much more widely used. Modern computers are based on binary logic, in which each bit is constrained to be either a 1 or a 0. But thanks to the weird rules of quantum mechanics, new experimental systems can achieve increased computing power by allowing quantum bits, also called qubits, to be in "superpositions" of 1 and 0. For example, the spins of electrons confined to tiny islands called quantum dots can be oriented both up and down simultaneously. However, when the final state of a bit is read out, it reverts to the classical behavior of being one orientation or the other.
Researchers in China and the United States recently explored how an attention-based deep neural network (ABNN) could help improve sonar systems. The research was published in the Journal of the Acoustical Society of America by the Acoustical Society of America through AIP Publishing. Qunyan Ren is co-author of the research. “We found the ABNN was […]
Machine learning is a hot topic in research and industry, with new methodologies developed all the time. The speed and complexity of the field makes keeping up with new techniques difficult even for experts -- and potentially overwhelming for beginners. To demystify machine learning and to offer a learning path for those who are new to the core concepts, let's look at ten different methods, including simple descriptions, visualizations, and examples for each one. A machine learning algorithm, also called model, is a mathematical expression that represents data in the context of a problem, often a business problem. The aim is to go from data to insight.