The ability to phenotype cells is fundamentally important in biological research and medicine. Current methods rely primarily on fluorescence labeling of specific markers. However, there are many situations where this approach is unavailable or undesirable. Machine learning has been used for image cytometry but has been limited by cell agglomeration and it is currently unclear if this approach can reliably phenotype cells that are difficult to distinguish by the human eye. Here, we show disaggregated single cells can be phenotyped with a high degree of accuracy using low-resolution bright-field and non-specific fluorescence images of the nucleus, cytoplasm, and cytoskeleton. Specifically, we trained a convolutional neural network using automatically segmented images of cells from eight standard cancer cell-lines. These cells could be identified with an average F1-score of 95.3%, tested using separately acquired images. Our results demonstrate the potential to develop an “electronic eye” to phenotype cells directly from microscopy images. Berryman et al demonstrate that disaggregated cells can be phenotyped with a high degree of accuracy from bright-field and non-specifically stained microscopy images using a trained convolutional neural network (CNN). This approach allows for the identification of cell types without the need for specific markers.

Single-particle imaging by using coherent X-ray diffraction was proposed more than a decade ago by the work of Fienup (1978), Miao et al. (1999), Robinson et al. (2001), Chao et al. (2005) and Sakdinawat & Attwood (2010). As a method of determining the inside complex structure of an individual particle, it records the diffracted coherent X-ray intensity by the particle in reciprocal space, where the phase information of the corresponding intensity is lost during the measurement (Williams et al., 2003; Chapman et al., 2006; Pfeifer et al., 2006). To provide this missing phase, one crucial step in an X-ray single-particle-imaging experiment, either by forward-scattering X-ray coherent diffraction imaging (Xu et al., 2014) or Bragg coherent diffraction imaging (BCDI) (Newton et al., 2010; Yang et al., 2013), is the reconstruction of the real-space complex information of the particle from its measured X-ray diffraction-pattern intensity. Because of the loss of the phase information of the recorded X-ray intensity, iterative phase-retrieval algorithms are widely applied to reconstruct the complex structure information of the measured particle. As shown originally by Bates (1982), this process, known as phase retrieval, depends on the diffraction data being oversampled by at least a factor of two with respect to the Shannon–Nyquist frequency.

Here you can find a list of the AI-related seminars that are scheduled to take place between now and the end of December 2020. We've also listed recent past seminars that are available for you to watch. All events detailed here are free and open for anyone to attend virtually. This list includes forthcoming seminars scheduled to take place between 13 November and 30 December. Privacy and Artificial Intelligence: a regulatory sandbox – dialogue meeting Speaker: Kari Laumann Organised by: Norwegian University of Science and Technology To attend, register via this link.

When to use Transfer Learning? In this approach, the last few fully connected layers of the pre-trained model are removed and replaced with a shallow neural network. The layers of the pre-trained model are frozen, and only the shallow neural network is trained with the available target dataset. The features extracted by the pre-trained model help the shallow to learn and perform well on the target task. The benefit of this approach is the low chance of overfitting, as we are only training the last few layers of the model, keeping the initial layers fixed.

AI took center stage in recently-announced updates to the Alexa virtual voice assistant, and in the charges this week from the European Commission that Amazon is breaking EU competition rules. During Amazon's Alexa Live event held in July, the company announced a major update to Alexa's developer toolkit that brings AI improvements. Since launching in 2014, Amazon's voice assistant has shipped hundreds of millions of units, which are targeted by a sizable developer community offering voice apps, called Skills, that extend the Alexa default feature set. Just as the Android and iOS large selections of third party applications differentiate those operating systems, so Skill plays an important role in Amazon's growth strategy for Alexa, according to a recent account in siliconAngle. Amazon added deep learning models for natural language understanding that the company said will enable Skills to recognize users' voice commands with 15% higher accuracy on average.

Growing with the remarkable development of digital data of images, videos, and speech from sources, for example, online media and the internet-of-things is driving the requirement for analytics to make that information justifiable and noteworthy. Data analytics frequently depend on machine learning (ML) algorithms. Among ML algorithms, deep convolutional neural networks (DNNs) offer cutting edge precision for significant image classification errands and are getting widely adopted. The renewed interest in artificial intelligence in the previous decade has been a boon for the graphics cards industry. Organizations like Nvidia and AMD have seen an immense lift to their stock prices as their GPUs have demonstrated to be effective for training and running deep learning models.

Microsoft and OpenAI, the AI research lab in which Microsoft has invested over $1 billion, today submitted a document to the U.S. government describing how a "digitally transformed" export controls system might work and the benefits it could provide. The organizations suggest that their proposed solutions could bring commercial benefits to users, as well as a more powerful, dynamic, and targeted method for controlling U.S. exports of fundamental technologies. Following a mandate in the Export Control Reform Act of 2018, the U.S. Department of Commerce's Bureau of Industry and Security (BIS) undertook efforts to identify and control exports of "emerging" or "foundational" technologies ostensibly vital to national security. In comment periods ending in January 2019 and earlier this week, BIS solicited comment from the industry on how to identify and approach control of these technologies. Microsoft and OpenAI take issue with the restrictions promulgated via traditional export control approaches.

This branch of artificial intelligence curates your social media and serves your Google search results. Soon, deep learning could also check your vitals or set your thermostat. MIT researchers have developed a system that could bring deep learning neural networks to new -- and much smaller -- places, like the tiny computer chips in wearable medical devices, household appliances, and the 250 billion other objects that constitute the "internet of things" (IoT). The system, called MCUNet, designs compact neural networks that deliver unprecedented speed and accuracy for deep learning on IoT devices, despite limited memory and processing power. The technology could facilitate the expansion of the IoT universe while saving energy and improving data security.

Over the last several years, deep learning -- a subset of machine learning in which artificial neural networks imitate the inner workings of the human brain to process data, create patterns and inform decision-making -- has been responsible for significant advancements in the field of artificial intelligence. Building on what is possible with the human brain, deep learning is now capable of unsupervised learning from data that is unstructured or unlabeled. This data, often referred to as big data, can be drawn from various sources such as social media, internet history and e-commerce platforms, among others. These sources of data are so vast that it could take decades for humans to comprehend it and extract relevant information, but interpreting this data through deep learning allows models to detect objects, recognize speech, translate language and make decisions at remarkable speeds. Many companies realize the incredible potential that can result from unraveling this wealth of information and are increasingly adopting AI systems driven by deep learning to gain a competitive advantage through data and automation.

Subsets of artificial intelligence such as machine learning and deep learning can help organizations sift through their data and tackle real-world …