AI programs are constructed within a complex framework that includes a computer's hardware and operating system, programming languages, and often general frameworks for representing and reasoning.
The Cyc project (initially planned from 1984 to 1994) is the world's longest-lived AI project. The idea was to create a machine with "common sense," and it was predicted that about 10 years should suffice to see significant results. That didn't quite work out, and today, after 35 years, the project is still going on -- although by now very few experts still believe in the promises made by Cyc's developers. Common sense is more than just explaining the meaning of words. For example, we have already seen how "sibling" or "daughter" can be explained in Prolog with a dictionary-like definition.
The growing use of AI will increase data usage exponentially. As part of Singapore's smart nation initiative, the government has planned to invest up to S$150m from the National Research Foundation on AI over five years through the AI Singapore programme. While first-generation AI architectures have historically been centralised, Equinix predicts that enterprises will enter the realm of distributed AI architectures, where AI model building and model inferencing will take place at the edge, physically closer to the origin source of the data. To access more external data sources for accurate predictions, enterprises will turn to secure data transaction marketplaces. They will also strive to leverage AI innovation in multiple public clouds without getting locked into a single cloud, further decentralising AI architectures.
Abstract: Automatic neural architecture design has shown its potential in discovering powerful neural network architectures. Existing methods, no matter based on reinforcement learning or evolutionary algorithms (EA), conduct architecture search in a discrete space, which is highly inefficient. We call this new approach neural architecture optimization (NAO). There are three key components in our proposed approach: (1) An encoder embeds/maps neural network architectures into a continuous space. The performance predictor and the encoder enable us to perform gradient based optimization in the continuous space to find the embedding of a new architecture with potentially better accuracy.
Syntactic search relies on keywords contained in a query to find suitable documents. So, documents that do not contain the keywords but contain information related to the query are not retrieved. Spreading activation is an algorithm for finding latent information in a query by exploiting relations between nodes in an associative network or semantic network. However, the classical spreading activation algorithm uses all relations of a node in the network that will add unsuitable information into the query. In this paper, we propose a novel approach for semantic text search, called query-oriented-constrained spreading activation that only uses relations relating to the content of the query to find really related information. Experiments on a benchmark dataset show that, in terms of the MAP measure, our search engine is 18.9% and 43.8% respectively better than the syntactic search and the search using the classical constrained spreading activation. KEYWORDS: Information Retrieval, Ontology, Semantic Search, Spreading Activation
Alexa-like voice services traditionally have supported small numbers of well-separated domains, such as calendar or weather. In an effort to extend the capabilities of Alexa, Amazon in 2015 released the Alexa Skills Kit, so third-party developers could add to Alexa's voice-driven capabilities. We refer to new third-party capabilities as skills, and Alexa currently has more than 40,000. Four out of five Alexa customers with an Echo device have used a third-party skill, but we are always looking for ways to make it easier for customers to find and engage with skills. For example, we recently announced we are moving toward skill invocation that doesn't require mentioning a skill by name.
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This is a very important consideration that is often overlooked by many in the field of Artificial Intelligence (AI). I suspect there are very few academic researchers who understand this aspect. The work performed in academe is distinctly different from the work required to make a product that is sustainable and economically viable. It is the difference between computer code that is written to demonstrate a new discovery and code that is written to support the operations of a company. The former kind turns to be exploratory and throwaway while the the latter kind tends to be exploitive and requires sustainability.
Training unprecedentedly large networks with'codistillation': …New technique makes it easier to train very large, distributed AI systems, without adding too much complexity… When it comes to applied AI, bigger can frequently be better; access to more data, more compute, and (occasionally) more complex infrastructures can frequently allow people to obtain better performance at lower cost. One limit is in the ability for people to parallelize the computation of a single neural network during training. To deal with that, researchers at places like Google have introduced techniques like'ensemble distillation' which let you train multiple networks in parallel and use these to train a single'student' network that benefits from the aggregated learnings of its many parents. Though this technique has shown to be effective it is also quite fiddly and introduces additional complexity which can make people less keen to use it. New research from Google simplifies this idea via a technique they call'codistillaiton'.