The prototype technology brings together imaging, processing, machine learning and memory in one electronic chip, powered by light. If scientists could train artificial intelligence (AI) systems to remember the images they capture from their photodetectors and learn from them--all in one package--it would be one step closer to an artificial brain. But the huge data sets and computer power required to make sense of them usually require offloading images elsewhere for processing--unlike a natural brain. Now, researchers in Australia have developed a neuromorphic imaging chip that performs image pre-processing and recognition by itself (Adv. The optically driven chip, made of two-dimensional black phosphorus, demonstrates a way to combine AI software and imaging hardware in a brain-like package that could run autonomously.

As some of you know, we have had a Flipboard magazine that deals with Blockchain, Cryptocurrencies, and of course of Fintech for quite some time. We are known for publishing the short blog post - Crypto stories you may have missed. We have taken a leaf from our book and done the same with artificial intelligence, machine learning, and robotics. From this moment on, we will be putting out a similar blog post for AI, ML, and robotics. We intend to have this continue into the near future.

In this paper, we introduce ProtoPShare, a self-explained method that incorporates the paradigm of prototypical parts to explain its predictions. The main novelty of the ProtoPShare is its ability to efficiently share prototypical parts between the classes thanks to our data-dependent merge-pruning. Moreover, the prototypes are more consistent and the model is more robust to image perturbations than the state of the art method ProtoPNet. We verify our findings on two datasets, the CUB-200-2011 and the Stanford Cars.

Most of the existing medicine recommendation systems that are mainly based on electronic medical records (EMRs) are significantly assisting doctors to make better clinical decisions benefiting both patients and caregivers. Even though the growth of EMRs is at a lighting fast speed in the era of big data, content limitations in EMRs restrain the existed recommendation systems to reflect relevant medical facts, such as drug-drug interactions. Many medical knowledge graphs that contain drug-related information, such as DrugBank, may give hope for the recommendation systems. However, the direct use of these knowledge graphs in the systems suffers from robustness caused by the incompleteness of the graphs. To address these challenges, we stand on recent advances in graph embedding learning techniques and propose a novel framework, called Safe Medicine Recommendation (SMR), in this paper. Specifically, SMR first constructs a high-quality heterogeneous graph by bridging EMRs (MIMIC-III) and medical knowledge graphs (ICD-9 ontology and DrugBank). Then, SMR jointly embeds diseases, medicines, patients, and their corresponding relations into a shared lower dimensional space. Finally, SMR uses the embeddings to decompose the medicine recommendation into a link prediction process while considering the patient's diagnoses and adverse drug reactions. To our best knowledge, SMR is the first to learn embeddings of a patient-disease-medicine graph for medicine recommendation in the world. Extensive experiments on real datasets are conducted to evaluate the effectiveness of proposed framework.

Electricity load forecasting enables the grid operators to optimally implement the smart grid's most essential features such as demand response and energy efficiency. Electricity demand profiles can vary drastically from one region to another on diurnal, seasonal and yearly scale. Hence to devise a load forecasting technique that can yield the best estimates on diverse datasets, specially when the training data is limited, is a big challenge. This paper presents a deep learning architecture for short-term load forecasting based on bidirectional sequential models in conjunction with feature engineering that extracts the hand-crafted derived features in order to aid the model for better learning and predictions. In the proposed architecture, named as Deep Derived Feature Fusion (DeepDeFF), the raw input and hand-crafted features are trained at separate levels and then their respective outputs are combined to make the final prediction. The efficacy of the proposed methodology is evaluated on datasets from five countries with completely different patterns. The results demonstrate that the proposed technique is superior to the existing state of the art.

Can computers read and apply legal rules? It's an idea that's gaining momentum, as it promises to make laws more accessible to the public and easier to follow. But it raises a host of legal, technical and ethical questions. The OECD recently published a white paper on "Rules as Code" efforts around the world. The Australian Senate Select Committee on Financial Technology and Regulatory Technology will be accepting submissions on the subject until 11 December 2020.

The market research report compiles information from over 120 hardware and software companies to identify the key trends in the drone industry. The major players of the drone industry are compared within the areas of drone industry such as software, hardware, and analytics. This provides you with the knowledge to make informed decisions and understanding of this disruptive and fast-growing market area. These use cases include Search and Rescue, Agriculture, Delivery, Security, Mapping and Localisation.

As even the most inattentive observer of contemporary international politics will attest, technological competition – mostly, but not always, between the U.S. and its allies on one hand, and China and Russia on the other – has once again risen to the fore. Analysts, so far, have approached this issue from various angles: what it means in terms of military balances, the possibility of international cooperation, what a technological edge implies for domestic policies, and so on. The outgoing Trump administration has made technological contestation with China a cornerstone of its strategic policy, emphasizing the need for the United States to maintain its edge when it comes to artificial intelligence (AI), quantum information science, and aerospace and other critical technologies, among others. Other Indo-Pacific powers, such as Australia, India, and Japan, have also joined the fray in pushing both new and emerging tech at home as well as promoting collaboration around it between "like-minded countries." In June this year, a Global Partnership on Artificial Intelligence of 14 states along with the European Union was launched, to facilitate collective AI research as well as implementation.

Rideshare platform Ola, is using the power of artificial intelligence to help increase safety in the rideshare industry. The new safety-tech feature Guardian will be the first of its kind in New Zealand and is set to roll out in all its locations across the country early next year. The safety technology uses real-time trip information to detect irregular activity such as, a possible crash, an unusually long stop, or an unexpected deviation from the planned route. Ola's 24/7 Safety Response Team is alerted and they contact both riders and drivers to confirm they are safe and offer any assistance they might need. Because Guardian is an intelligent product built using machine learning, it is able to continuously improve its ability to predict risk signals as it keeps collecting data over time, says Ola.

A graphic illustration showing how the technology combines the core software needed to drive AI with image-capturing hardware, in a single electronic device. Prototype tech shrinks AI to deliver brain-like functionality in one powerful device. Researchers have developed artificial intelligence technology that brings together imaging, processing, machine learning, and memory in one electronic chip, powered by light. The prototype shrinks artificial intelligence technology by imitating the way that the human brain processes visual information. The nanoscale advance combines the core software needed to drive artificial intelligence with image-capturing hardware in a single electronic device.