Neuropsychological studies suggest that co-operative activities among different brain functional areas drive high-level cognitive processes. To learn the brain activities within and among different functional areas of the brain, we propose LGGNet, a novel neurologically inspired graph neural network, to learn local-global-graph representations of electroencephalography (EEG) for Brain-Computer Interface (BCI). The input layer of LGGNet comprises a series of temporal convolutions with multi-scale 1D convolutional kernels and kernel-level attentive fusion. It captures temporal dynamics of EEG which then serves as input to the proposed local and global graph-filtering layers. Using a defined neurophysiologically meaningful set of local and global graphs, LGGNet models the complex relations within and among functional areas of the brain. Under the robust nested cross-validation settings, the proposed method is evaluated on three publicly available datasets for four types of cognitive classification tasks, namely, the attention, fatigue, emotion, and preference classification tasks. LGGNet is compared with state-of-the-art methods, such as DeepConvNet, EEGNet, R2G-STNN, TSception, RGNN, AMCNN-DGCN, HRNN and GraphNet. The results show that LGGNet outperforms these methods, and the improvements are statistically significant (p<0.05) in most cases. The results show that bringing neuroscience prior knowledge into neural network design yields an improvement of classification performance. The source code can be found at https://github.com/yi-ding-cs/LGG

Companies are constantly faced with obstacles and hurdles that impact the efficiency of their business processes and operations. To successfully manage these obstacles, organizations must seek technology solutions that adequately address their data and turn information into usable knowledge. Doing so will allow companies to have healthy growth with partners, customers, and their workforce. To grow in these areas, companies should lean on technology solutions that are easily scalable across various business areas – for example, an insight solution specializing in information insight. One of the most prominent areas that make insight solutions "masters of scale" is the ability to automate repetitive and monotonous tasks.

Artificial intelligence (AI) is currently at the forefront of technological innovation in a variety of industries, and certain industry leaders are leading the charge. Continue reading to learn more. With the pandemic necessitating automation, Artificial Intelligence (AI) plays a critical part in bolstering enterprises' efforts to stay afloat and survive in the market. It was also during this time that many businesses understood the importance and use of AI in their operations and increased their investments in the technology. Because of breakthroughs in the area and its subdivisions such as Machine Learning, Robotics, Natural Language Processing, Deep Learning, and others, Artificial Intelligence has now become the backbone of various companies. AI TECH WILL BE SEEN AT THE FOREFRONT OF DIGITAL TRANSFORMATION FOR MANY ENTERPRISES, FROM GAINING A COMPETITIVE EDGE TO REFORMING ORGANIZATIONAL FUNCTIONALITIES.

When it comes to artificial intelligence, don't try to go it alone. IT departments, no matter how skilled and ready, can only go so far past proofs of concept. Industry experts say that AI initiatives need everyone across the enterprise on board. "A copious amount of training data and elastic compute power are not the cornerstones for successful AI implementations," says Sreedhar Bhagavatheeswaran, global head of Mindtree Consulting. That cornerstone of AI success is people -- not only people with AI skills, but also those from all disciplines, from marketing to supply chain management.

As Artificial Intelligence (AI) continues to democratize and scale toward many functional areas in enterprise organizations, solutions like Insight Engines prove their utility and make great strides toward massive adoption. They give business users better search results and help departments save time and all functional areas of an organization to be more efficient. They also get the ability to search for, access quickly, and analyze data stored in various locations across their enterprise. This rapid digitization of information and delivery of insight has transformed the way businesses find and consume information. How do you keep up with this pace of change to stay ahead of the competition? The answer is by using advanced tools like AI-driven Insight Engines.

Daniel Fallmann is Founder and CEO of Mindbreeze, a leader in enterprise search, applied artificial intelligence and knowledge management. CEOs and line-of-business leaders are smart people. But they don't know what their enterprise knows. As a result, their organizations miss out on a multitude of valuable opportunities. Organizations possess a wealth of data. However, the data is all over the place, held in siloed systems and various formats.

Most consumer packaged goods companies (CPGs) have struggled to find solid footing in a turbulent industry. Bain research has found that 34 of the world's top 50 consumer goods companies experienced a decline in revenues, profits or both in recent years,1 forcing CPG executives to find new ways to compete. Emerging technologies, including AI, have given a sharp advantage to firms in other sectors. Companies at the forefront of AI are household names, known for changing the playing field and reinventing their industries: Amazon, Facebook, Microsoft, Apple. To stay ahead, these leaders are investing heavily in technology. Amazon, for example, ranks No. 1 in R&D, spending more than 10% of its revenues on IT while retailers manage 1% to 2%.

Most CPG companies (CPGs) have struggled to find solid footing in a turbulent industry. Bain research has found that 34 of the world's top 50 consumer goods companies experienced a decline in revenues, profits or both in recent years,1 forcing CPG executives to find new ways to compete. Emerging technologies, including AI, have given a sharp advantage to firms in other sectors. Companies at the forefront of AI are household names, known for changing the playing field and reinventing their industries: Amazon, Facebook, Microsoft, Apple. To stay ahead, these leaders are investing heavily in technology. Amazon, for example, ranks No. 1 in R&D, spending more than 10% of its revenues on IT while retailers manage 1% to 2%.

Different functional areas of the human brain play different roles in brain activity, which has not been paid sufficient research attention in the brain-computer interface (BCI) field. This paper presents a new approach for electroencephalography (EEG) classification that applies attention-based transfer learning. Our approach considers the importance of different brain functional areas to improve the accuracy of EEG classification, and provides an additional way to automatically identify brain functional areas associated with new activities without the involvement of a medical professional. We demonstrate empirically that our approach out-performs state-of-the-art approaches in the task of EEG classification, and the results of visualization indicate that our approach can detect brain functional areas related to a certain task.

The Health and Human Services Department sees value in integrating automation and artificial intelligence technologies into its workflows and is building a contract vehicle to help other agencies, as well. The Program Support Center, a shared services agency within Health and Human Services, released a request for proposals Thursday for the Intelligent Automation/Artificial Intelligence, or IAAI, contract. The five-year, $49 million contract vehicle will offer a host of automation and AI technologies and support services, including robotic process automation, machine and supervised learning and machine vision. "PSC believes that IAAI solutions will be doing everything from reducing backlog and cutting costs to performing functions, such as predicting fraudulent transactions and identifying critical suspects via facial recognition, which are considered difficult for an individual to complete on their own," contracting officers wrote in the RFP. The center's efforts fall in line with the administration's policy of shifting federal employees from low-value to high-value work by automating rote processes that can be digitized.