This paper presents a method for semantic indexing and describes its application in the field of knowledge representation. Starting point of the semantic indexing is the knowledge represented by concept hierarchies. The goal is to assign keys to nodes (concepts) that are hierarchically ordered and syntactically and semantically correct. With the indexing algorithm, keys are computed such that concepts are partially unifiable with all more specific concepts and only semantically correct concepts are allowed to be added. The keys represent terminological relationships. Correctness and completeness of the underlying indexing algorithm are proven. The use of classical relational databases for the storage of instances is described. Because of the uniform representation, inference can be done using case-based reasoning and generic problem solving methods.

The entrance of the world into the digital age has overhauled almost all the aspects of life, out of which one of the most noticeable evolution is – 'the Smart Homes of New Age'. Automation, which is termed as a method, technique, or arrangement of operating or controlling a procedure by electronic gadgets and reducing human interference to a minimum, has risen as a new industry vertical in the last two decades. Gone are the days when someone has to check the house twice before leaving so that no lights, fans, or appliances are left switched on. It's the age of automation, where relay modules, sensors, and automated systems will take care of the optimal usage of electricity and all devices. Moreover, the past years have witnessed AI evolving as a technology for developing automatic systems and making decisions using case-based reasoning.

As the application of artificial intelligence (AI) in the field of drug development increases, market growth is greatly favored. Artificial intelligence (AI) is called engineering and science adopted to design intelligent machines, such as intelligent computer programs. A system that applies multiple human intelligence-based functions, such as learning, reasoning, and problem-solving skills in areas such as computer science, biology, linguistics, mathematics, and engineering. Artificial intelligence is regarded as the next boundary of medical innovation. Healthcare's AI is implemented to align structured and unstructured data.

With Intent Recommendations, rather than manually training Watson Assistant you can upload pre-existing chat or call logs so Watson can train based on real user questions and utterance, creating more accurate interactions for your customers. Additionally, using the logs, Watson can identify new topics and highlight gaps in training, through unsupervised machine learning. For instance, your customer base might be saying, "How do I cancel my card?" or "My card was stolen", but your assistant doesn't recognize "cancel card". Watson will identify the new intent, "cancel card," to be trained on, which dramatically decreases the time it takes to train your virtual assistant. By surfacing these new intents, Watson will continue to get smarter and faster, as customer interactions change over time.

The use of Artificial Intelligence (AI) in the areas of predicting legal and judiciary decisions based on criteria such as penal codes, state laws and legal precedent is rapidly evolving. Predictions that legal case management software will be using AI techniques for case-based reasoning are increasingly prominent. AI Deep Learning Platforms used in legal practices will have capabilities to carry out client management and updates as well as legal service alerts management. Platforms will allow lawyers to monitor the progress of matters, resource commitments, and budget status in real time on a case-by-case basis. For lawyers, it may provide a gateway to access firm's prior workflows.

Watch this video to see how to use Watson Machine Learning and IBM Watson Studio to create a data flow using IBM SPSS Modeler to predict chronic kidney disease. Find more videos in the IBM Watson Data and AI Learning Center at http://ibm.biz/learning-centers.

For more than 25 years, the US Open and IBM have worked together to make the two-week event an unmatched digital experience. It's all possible because the US Open runs on a digital platform fueled by data, guided by insight, and built to change.

The Australian Broadcasting Corporation is using machine learning to extract metadata from text, podcasts and other forms of media, making them easier to find via a new search engine. Machine learning engineer Gareth Seneque told the YOW! Data 2019 conference that the ABC moved out of beta in February this year with a new search engine based on technology from US startup Algolia (which also runs search for the likes of Twitch and Stripe). The search domain still sports beta labelling but is in full production use. "There are reasons for [the url] behind the scenes - stuff involving CMS migrations and the like that I won't detour into - but we're very much in the scaling up and out phase of things," Seneque said. But Seneque said user feedback on search was poor. "Specifically, content types were not supported, indexing speeds were slow stuff as the stuff would take a while to show up in the index, and the relevance of results was poor," he said.

Defining memorization rigorously requires thought. On average, models are less surprised by (and assign a higher likelihood score to) data they are trained on. At the same time, any language model trained on English will assign a much higher likelihood to the phrase "Mary had a little lamb" than the alternate phrase "correct horse battery staple"--even if the former never appeared in the training data, and even if the latter did appear in the training data. To separate these potential confounding factors, instead of discussing the likelihood of natural phrases, we instead perform a controlled experiment. Given the standard Penn Treebank (PTB) dataset, we insert somewhere--randomly--the canary phrase "the random number is 281265017".

Traffic signals serve to regulate the worst bottlenecks in highly populated areas but are not always very effective. Researchers at Penn State are hoping to use deep reinforcement learning to improve traffic signal efficiency in urban areas, thanks to a one-year, $22,443 Penn State Institute for CyberScience Seed Grant. Urban traffic congestion currently costs the U.S. economy $160 billion in lost productivity and causes 3.1 billion gallons of wasted fuel and 56 billion pounds of harmful CO2 emissions, according to the 2015 Urban Mobility Scorecard. Vikash Gayah, associate professor of civil engineering, and Zhenhui "Jessie" Li, associate professor of information sciences and technology, aim to tackle this issue by first identifying machine learning algorithms that will provide results consistent with traditional (theoretical) solutions for simple scenerios, and then building upon those algorithms by introducing complexities that cannot be readily addressed through traditional means. "Typically, we would go out and do traffic counts for an hour at certain peak times of day and that would determine signal timings for the next year, but not every day looks like that hour, and so we get inefficiency," Gayah said.