Watson: IBM question-answering machine capable of responding to queries addressed in natural language. Powers lots of commercially-available AI applications.

Is marked as a LEXICAL CONSTRAINT which indicates that it ought to be used to select the solution but shouldn’t be section of the answer.
Like Question Classes, these are identified by a group of handcrafted rules.
An open-source ontology named YAGO is available online that maps words to concepts, and the IBM Watson team used YAGO to map the LAT word onto YAGO concepts.
There is also an open-source mapping from Wikipedia to YAGO named DBPedia, and DeepQA used this to map possible answers to YAGO concepts.
Identifying entities and relations in questions and linking them to Wikipedia titles and other stored documents was a key capability for the IBM Watson DeepQA system.

While the resources had a need to train such models can be immense, and largely only available to major corporations, once trained the energy had a need to run these models is considerably less.
However, as demand for services based on these models grows, power consumption and the resulting environmental impact again becomes an issue.
With researchers pursuing a goal of 99% accuracy, expect talking with computers to become increasingly common alongside more traditional forms of human-machine interaction.
Relying heavily on voice recognition and natural-language processing and needing an immense corpus to draw upon to answer queries, a huge amount of tech switches into developing these assistants.
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Ibm Watson: A Cheat Sheet

Finally, a social concern is that the rise of VPSAs will lead for further social inequalities.

It leads to a narrowly overlaid AI adoption strategy as they mainly focus on using AI to change the way they provide the services without understanding why to they have to change it.
Because of this, the AI trend in applications in this scenario does not respond adequately to rapidly evolving intelligence capability.
It will negatively affect their knowledge of the broader AI trends on the horizon and their future AI preparations.
Conversational artificial intelligence refers to technologies, likechatbotsorvirtual agents, which users can speak to.
They use large volumes of data,machine learning, andnatural language processingto help imitate human interactions, recognizing speech and text inputs and translating their meanings across various languages.
Another similar study was done by Bohus et al. at the Microsoft Research Center at Richmond using a direction providing NAO robot which spanned over a week.

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Wealthier people, who will be more likely to afford a far more expensive and capable agent, will have even more usage of the best deals, the most current information, and the most efficient consumption processes.
Despite the possible economic benefits of VPSAs, an unwise application of the technology has the potential to further deepen the digital divide, also to increase the inequalities that plague society.
Further research might uncover ways that such an outcome can be avoided.
From the consumers’ perspective, the emergence of a virtual personal shopping assistant which can learn, predict and serve their tastes, needs and desires and optimize their product/service purchases is really a welcome development.
This virtual personal shopping concierge will be able to instantly match an immediate or imminent need against all accessible products that meet a consumer’s expectations and price points.
The savings with time will be significant, considering that consumers are hard pressed to keep up with all the latest trends, specials and sales.
They are, and remain, one source of useful measures to understand progress in AI.

• In corporate finance, AI helps to better predict and assess loan risks.
• We understand human mental processes only slightly better than a fish understands swimming.
• AI could