Cognitive imagination is a type of imagination that plays a key role in human thinking. It is not a ``picture-in-the-head'' imagination. It is a faculty to mentally visualize coherent and holistic systems of concepts and causal links that serve as semantic contexts for reasoning, decision making and prediction. Our position is that the role of cognitive imagination is still greatly underestimated, and this creates numerous problems and diminishes the current capabilities of AI. For instance, when reasoning, humans rely on imaginary contexts to retrieve background info. They also constantly return to the context for semantic verification that their reasoning is still reasonable. Thus, reasoning without imagination is blind. This paper is a call for greater attention to cognitive imagination as the next promising breakthrough in artificial intelligence. As an instrument for simulating cognitive imagination, we propose semantic models -- a new approach to mathematical models that can learn, like neural networks, and are based on probabilistic causal relationships. Semantic models can simulate cognitive imagination because they ensure the consistency of imaginary contexts and implement a glass-box approach that allows the context to be manipulated as a holistic and coherent system of interrelated facts glued together with causal relations.

In natural language understanding (NLU) production systems, users' evolving needs necessitate the addition of new features over time, indexed by new symbols added to the meaning representation space. This requires additional training data and results in ever-growing datasets. We present the first systematic investigation of this incremental symbol learning scenario. Our analysis reveals a troubling quirk in building broad-coverage NLU systems: as the training dataset grows, performance on the new symbol often decreases if we do not accordingly increase its training data. This suggests that it becomes more difficult to learn new symbols with a larger training dataset. We show that this trend holds for multiple mainstream models on two common NLU tasks: intent recognition and semantic parsing. Rejecting class imbalance as the sole culprit, we reveal that the trend is closely associated with an effect we call source signal dilution, where strong lexical cues for the new symbol become diluted as the training dataset grows. Selectively dropping training examples to prevent dilution often reverses the trend, showing the over-reliance of mainstream neural NLU models on simple lexical cues. Code, models, and data are available at https://aka.ms/nlu-incremental-symbol-learning

These four words reflect the promise of conversational AI. It takes just seconds to ask When are Megan and I both free? Indeed, almost everything we do with technology can feel like a long path to a short goal. At Microsoft Semantic Machines, we're working to bridge this gap--to build conversational AI experiences where you can focus on saying what you want and the system will worry about how to get it done. You should be able to speak as you speak to a friend: naturally, contextually, and collaboratively. A truly powerful conversational AI needs to do more than deeply understand language.

Industries are adopting artificial intelligence and machine learning on a grand scale. Tech companies both big and small are working on artificial intelligence projects that will shape the future of industries such as healthcare, banking, business, education and more. We're not quite at the point where everything is automated and machine-run, but we're getting there. These technologies are all around us, quietly running in the background and keeping operations chugging along. AI is silently reshaping our society by affecting how we get things done, how we vote, how we purchase goods, and the choices we make.

Today's intelligent assistants are full of skills. They can check the weather, traffic and sports scores. They can play music, translate words and send text messages. They can even do math, tell jokes and read stories. But, when it comes to conversations that lead somewhere grander, the wheels fall off.

According to IDC, global spending on Artificial Intelligence (AI) and cognitive systems will reach $19 billion by 2018. This is an increase by approximately 54% over the total amount consumed in 2017. Mergers and acquisitions are constantly taking place. We all know that AI is creating new opportunities in every sector be it healthcare or travel. So, companies all around the world are enchasing on such opportunities to offer much-improved products or services to consumers through mergers and acquisitions.

Due to its nearly limitless potential, artificial intelligence is at the forefront of much of this research, and Microsoft has been making headlines with new technologies, major acquisitions, and innovative ideas. The tech giant has long been moving toward a cloud-based future, and investment in AI is helping solidify its path toward becoming the AI leader in a number of fields. Here are a few technologies Microsoft has invested in recently and the potential impact they'll have on the company's future and society as a whole. Traditional computer hardware can perform complex tasks quickly. However, most hardware is tuned primarily for general-purpose performance, and systems that demand effective real-time performance often rely on specialized hardware, as milliseconds saved can be critical in certain scenarios.

"Conversational AI" is a hot topic right now, what with the tech giants' virtual assistants all competing to be the most human-esque, and Microsoft (msft) just gave itself a boost by purchasing a startup called Semantic Machines. The move could improve Microsoft's Cortana assistant, as well as the "social chatbots" that it is deploying on social networks. Semantic Machines is a company that's been developing technology for voice-enabled agents that can speak like a person and understand what others tell it. The use cases on its website echo those recently demonstrated by Google with its Duplex app, for example interacting with real people to make appointments on behalf of the user. In a blog post, Microsoft noted that the Berkeley, Calif.-based company comes with some well-known talents in the field, such as UC Berkeley professor Dan Klein and Stanford's Percy Liang.

Microsoft is acquiring conversational AI startup Semantic Machines in an effort to make bots and intelligent assistants like Cortana sound and respond more like humans. Founded in 2014, Semantic Machines uses machine learning to make bots respond in a more natural way to queries. Semantic Machines is led by UC Berkeley professor Dan Klein and former Apple chief speech scientist Larry Gillick. Both are considered pioneers in conversational AI. Microsoft's acquisition will boost the company's Cortana digital assistant, as well as the company's Azure Bot Service that's used by 300,000 developers.

Microsoft is acquiring conversational AI startup Semantic Machines in an effort to make bots and intelligent assistants like Cortana sound and respond more like humans. Founded in 2014, Semantic Machines uses machine learning to make bots respond in a more natural way to queries. Semantic Machines is led by UC Berkeley professor Dan Klein and former Apple chief speech scientist Larry Gillick. Both are considered pioneers in conversational AI. Microsoft's acquisition will boost the company's Cortana digital assistant, as well as the company's Azure Bot Service that's used by 300,000 developers.