Numerical reasoning skills are essential for complex question answering (CQA) over text. It requires opertaions including counting, comparison, addition and subtraction. A successful approach to CQA on text, Neural Module Networks (NMNs), follows the programmer-interpreter paradigm and leverages specialised modules to perform compositional reasoning. However, the NMNs framework does not consider the relationship between numbers and entities in both questions and paragraphs. We propose effective techniques to improve NMNs' numerical reasoning capabilities by making the interpreter question-aware and capturing the relationship between entities and numbers. On the same subset of the DROP dataset for CQA on text, experimental results show that our additions outperform the original NMNs by 3.0 points for the overall F1 score.

Last month, Apple announced that it will be introducing new child safety features in three areas, developed in collaboration with child safety experts. After backlash from various industry experts including whistleblower Edward Snowden, Apple has now decided to back down from this initiative. On Friday, Apple issued a statement saying that they have taken the feedback of customers, researchers and advocacy groups into consideration and have decided to step back for now. "We have decided to take additional time over the coming months to collect input and make improvements before releasing these critically important child safety features," read the statement. According to Snowden, no matter how well-intentioned, Apple is rolling out mass surveillance to the entire world with this.

A U.S. judge has ruled that artificial intelligence can't get a patent for its creations, ruling that such a privilege is reserved for people. District court judge Leonie Brinkema backed a decision by the U.S. patent office to turn away applications made on behalf of a "creativity machine" named DABUS. Brinkema issued a ruling saying that "the clear answer is'no'" to the question of whether an AI machine qualifies as an inventor under patent law. "As technology evolves, there may come a time when artificial intelligence reaches a level of sophistication that might satisfy accepted meanings of inventorship," Brinkema said in the ruling. "But that time has not yet arrived and, if it does, it will be up to Congress to decide how, if at all, it wants to expand the scope of patent law."

Should an artificially intelligent machine be able to patent its own inventions? For a US federal judge, the larger implications of that question were irrelevant. In April 2020, the US Patent and Trademark Office (USPTO) ruled that only "natural persons" could be credited as the inventor of a patent, and a US court decided Thursday that yes, that's what the law technically says (via Bloomberg). Not every country agrees with that direction. South Africa and Australia decided to go the other direction, granting one patent and reinstating a second patent application filed by AI researcher Steven Thaler, whose AI system DABUS reportedly came up with a flashing light and a new type of food container.

Community detection, aiming to group the graph nodes into clusters with dense inner-connection, is a fundamental graph mining task. Recently, it has been studied on the heterogeneous graph, which contains multiple types of nodes and edges, posing great challenges for modeling the high-order relationship between nodes. With the surge of graph embedding mechanism, it has also been adopted to community detection. A remarkable group of works use the meta-path to capture the high-order relationship between nodes and embed them into nodes' embedding to facilitate community detection. However, defining meaningful meta-paths requires much domain knowledge, which largely limits their applications, especially on schema-rich heterogeneous graphs like knowledge graphs. To alleviate this issue, in this paper, we propose to exploit the context path to capture the high-order relationship between nodes, and build a Context Path-based Graph Neural Network (CP-GNN) model. It recursively embeds the high-order relationship between nodes into the node embedding with attention mechanisms to discriminate the importance of different relationships. By maximizing the expectation of the co-occurrence of nodes connected by context paths, the model can learn the nodes' embeddings that both well preserve the high-order relationship between nodes and are helpful for community detection. Extensive experimental results on four real-world datasets show that CP-GNN outperforms the state-of-the-art community detection methods.

A US judge has ruled that artificial intelligence can't get a patent for its creations, ruling that such a privilege is reserved for people. District court judge Leonie Brinkema backed a decision by the US patent office to turn away applications made on behalf of a "creativity machine" named DABUS. Brinkema issued a ruling on Thursday saying that "the clear answer is'no'" to the question of whether an AI machine qualifies as an inventor under patent law. "As technology evolves, there may come a time when artificial intelligence reaches a level of sophistication that might satisfy accepted meanings of inventorship," Brinkema said in the ruling. "But that time has not yet arrived and, if it does, it will be up to Congress to decide how, if at all, it wants to expand the scope of patent law."

Sooner or later, the concept of digitization will completely take over all repetitive tasks. Today, with the help of big data, advanced technologies like automation, artificial intelligence, IoT, and machine learning are leveraging unimaginable amounts and types of information to work from. It is streamlining tedious, repetitive, and difficult tasks, which tend to slow down production and also increases the cost of operation. Owing to the evolution of technology, artificial intelligence startups are mushrooming like never before. The companies are driving the world into a new phase of digitization with a mixture of disruptive statistical methods, computational intelligence, soft computing, and traditional symbolic AI. Artificial intelligence is the combination of two amazing concepts namely science and engineering. With the infusion of disruptive trends and human intelligence, intelligent machines and intelligent computing programs are emerging. Slowly, the flare of innovations moved away from IT and entered into diverse industries including healthcare, education, finance, marketing, business, telecommunication, etc. Organizations realized that by digitizing repetitive tasks, an enterprise can cut the cost of paperwork and labor which further eliminates human error, thus boosting efficiency. Automating processes involve employing artificial intelligence solutions that can support digitization and deliver data-driven insights. Artificial intelligence startups emerge as a ready-made solution provider that supports every company's individual needs. AI startups in 2021 use big data to sophisticated AI models and leverage new solutions that could better serve customers. Analytics Insight has listed the top 100 artificial intelligence startups that are driving the next-generation development in technology. It democratizes the way investments are done by bringing sophisticated elite trading technology to laymen. Accrad is a health tech company that assists radiologists to reduce their workload with the precision of artificial intelligence. Radiologists work under different circumstances and deadlines and might find diagnosis through x-rays a bit difficult. Therefore, Accrad has come up with a futuristic solution to help with accurate and fast image diagnosis. The company has made x-ray processing more convincing and simpler. Its signature product CheXRad, a deep learning algorithm that identifies locations in the chest radiograph has the capability to predict 15 different diseases including Covid-19. Affable.ai is a data-driven influencer marketing platform where customers can find relevant and authentic influencers and manage marketing operations. By using cutting-edge computer vision algorithms on social media posts, the company delivers actionable insights about micro-influencers and their audience. Similar to how Google has sophisticated its search and promote relative ads to users, Affable.ai has also built one-click marketing at a shorter scale.

TrackMate is an automated tracking software used to analyze bioimages and distributed as a Fiji plugin. Here we introduce a new version of TrackMate rewritten to improve performance and usability, and integrating several popular machine and deep learning algorithms to improve versatility. We illustrate how these new components can be used to efficiently track objects from brightfield and fluorescence microscopy images across a wide range of bio-imaging experiments.

Researchers at the University of South Australia have designed a computer vision system that can automatically detect a tiny baby's face in a hospital bed and remotely monitor its vital signs from a digital camera with the same accuracy as an electrocardiogram machine. Using artificial intelligence-based software to detect human faces is now common with adults, but this is the first time that researchers have developed software to reliably detect a premature baby's face and skin when covered in tubes, clothing, and undergoing phototherapy. Engineering researchers and a neonatal critical care specialist from UniSA remotely monitored heart and respiratory rates of seven infants in the Neonatal Intensive Care Unit (NICU) at Flinders Medical Centre in Adelaide, using a digital camera. One of the lead researchers, UniSA Professor Javaan Chahl, stated that babies in neonatal intensive care can be extra difficult for computers to recognise because their faces and bodies are obscured by tubes and other medical equipment. Many premature babies are being treated with phototherapy for jaundice, so they are under bright blue lights, which also makes it challenging for computer vision systems.

Transformer-based pre-trained language models boost the performance of open-domain dialogue systems. Prior works leverage Transformer-based pre-trained language models to generate texts with desired attributes in two general approaches: (1) gradient-based methods: updating all latent representations of pre-trained models with gradients from attribute models; (2) weighted-decoding methods: re-ranking beam candidates from pre-trained models with attribute functions. However, gradient-based methods lead to high computation cost and can easily get overfitted on small training sets, while weighted-decoding methods are inherently constrained by the low-variance high-bias pre-trained model. In this work, we propose a novel approach to control the generation of Transformer-based pre-trained language models: the SideControl framework, which leverages a novel control attributes loss to incorporate useful control signals, and is shown to perform well with very limited training samples. We evaluate our proposed method on two benchmark open-domain dialogue datasets, and results show that the SideControl framework has better controllability, higher generation quality and better sample-efficiency than existing gradient-based and weighted-decoding baselines.