Given language’s ubiquity throughout the economy, few areas of technology will have a more far-reaching impact in the years ahead than NLP.
The first category of language AI startups worth discussing is those players that develop and make available core general-purpose NLP technology for other organizations to apply across industries and use cases.
While Cohere does produce generative models along the lines of GPT-3, the company is increasingly focused on models that analyze existing text rather than generate novel text. These classification models have myriad commercial use cases: from customer support to content moderation, from market analysis to search.
Primer is an older competitor in this space, founded two years before the invention of the transformer. The company primarily serves clients in government and defense. The new entrant taking on Google most directly is You.com. Founded by Richard Socher, former Chief Scientist at Salesforce and one of the world’sNLP researchers, You.com is reconceptualizing the search engine from the ground up. Its product vision includes a horizontal layout, an emphasis on content summarization, and above all, a commitment to user data privacy.
One final enterprise search startup worth keeping an eye on is Hebbia, which is building an AI research platform to enable companies to extract insights from their private unstructured data. its seed financing earlier this month. Twelve Labs fuses cutting-edge NLP and computer vision to enable precise semantic search within videos. “Multimodal AI” like this—that is, AI that ingests and synthesizes data from multiple informational modalities at once, like image and audio—will play a central role in AI’s future.
This technology will transform writing from an act of solo creation to a collaboration between human and machine: one in which the human provides some initial language, the AI suggests edits or follow-up sentences, the human iterates based on the AI’s feedback, and so forth. The skillset required for good writing may accordingly expand to include an understanding of how to get the most out of the AI—how to best guide and coax it into producing the desired language.
Trained on millions of writing samples, Textio’s AI can give users nuanced insights about their job postings and other hiring-related content: for instance, that a certain phrase will resonate more with male than with female candidates, that a given word suggests a fixed mindset over a growth mindset, that a particular metaphor may come across as exclusionary to applicants.
But make no mistake: in the years ahead, whether we like it or not, NLP will fundamentally change how humans produce the written word. Ten years from now, writing one’s own content from scratch may well be considered an artisanal craft, with the vast majority of the world’s written text produced or at least augmented by AI.Language barriers are a fundamental impediment to international business and travel, costing untold billions in lost productivity every year.
But significant opportunities also exist for startups in the fast-changing world of language translation. Thus, Lilt offers a hybrid model that combines cutting-edge AI with “humans in the loop” to translate written content for global organizations, from marketing to mobile apps to technical documentation. This partially automated approach enables Lilt to provide translation that is cheaper than using human translators and at the same time more accurate than using AI alone.
Is a rep spending the right amount of time on the right topics in sales calls, from product to pricing to small talk? Is she letting the customer ask enough questions? Has she engaged the right senior stakeholders at the customer organization at the right times over the course of the sales process? Is she following up with prospects on the right cadence?
Gong is an impressive business, with incredible revenue growth and a long list of blue-chip customers. The company seems destined to debut on public markets before long. Yet by most accounts, the core NLP in Gong’s product offering is not particularly advanced. These AI-powered conversational interfaces are commonly known as chatbots—though some startups today prefer to avoid that terminology and its mixed connotations, givenNotwithstanding earlier false starts, chatbots today have begun to gain real market adoption, thanks to improvements in the underlying NLP as well as in companies’ understanding of how to best productize and deploy these bots.
A promising group of startups has emerged to provide the technology and infrastructure for companies across industries to create and operationalize chatbots.
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