NeuralSpace Recognized as a Sample Vendor in 2022 Gartner Hype Cycle for Natural Language Technology

The 2022 Gartner Hype Cycle for Natural Language Technologies, “will assist IT leaders in assessing how and where these new opportunities and methods can best be applied.”

We’re thrilled to announce that NeuralSpace has been identified as one of the Sample Vendors listed in the Multilingual Models category alongside tech giants such as Kore.ai, Cognigy, AppTek, and Salesforce.

We are especially happy that we as a startup with under $2M in funding and less than 25 employees have been included in the report in which the median company size is 102 employees with a median funding of $20M. Only 16% of all Sample Vendors have a company size of less than 25 employees and only two have received less funding than NeuralSpace, according to publicly available information.

We think that NeuralSpace’s acknowledgment strengthens our market leadership, innovation, and credibility. This, in our opinion, indicates that technology has a lot to offer to both enterprises and society at large.

Here’s what you need to know about Multilingual Models and how they are only in the early days of mainstream adoption.

Multilingual Models explained by Gartner:

According to Gartner, “Multilingual models are transformer-based neural machine learning models trained over large, multilingual corpora. Such ML models allow the NLUs and other language applications to operate more efficiently in multiple language outputs via the common underlying multilingual model.”

An important element of Multilingual Models is that they can significantly reduce costs and increase the effectiveness of multilingual NLP initiatives. Adding to the importance of multilingual models, Gartner mentions, “thanks to their transfer-learning capabilities, they are especially recommended for use cases covering low-resource languages or multilingual datasets. Their general applicability to multilingual scenarios, usually handled via individual monolingual models, seems promising with anecdotal evidence of successful deployments or customer stories.”

When it comes to mainstream adoption, Gartner writes that Multilingual Models have 1%-5% target market penetration and a maturity listed as “emerging.”

Challenges to a broader market adaptation will include difficulty in product-wide implementation and deployment, catching up with monolingual approaches in terms of accuracy and assisting effectively in human-in-the-loop scenarios.

Although businesses are just now starting to realize the benefits multilingual models can have on their operations, this decade should see the technology take off and become widely used.

We are honored to be mentioned in this study as a Sample Vendor as we pursue our goal of bridging the massive language gap by enabling people to access the Internet in a language and mode of their choice.

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