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Gong is an impressive business, with incredible revenue growth and a long list of blue-chip customers. Yet by most accounts, the core NLP in Gong’s product offering is not particularly advanced. There are few applications of language AI that can more directly affect a company’s top line.
- The company raised a Series A and a Series B in quick succession last year from top venture capitalists.
- Next-generation NLP promises to transform how humans write, reconceptualizing one of civilization’s most basic and vital activities.
- Relevant clinical and pharmaceutical information is typically free-form, poorly organized and spread across disparate data sources, from siloed EHRs to difficult-to-edit PDFs.
- There are few applications of language AI that can more directly affect a company’s top line.
- The most well-funded of these competitors is Ada Support, a Toronto-based startup that has raised close to $200 million from blue-chip venture capitalists.
Machine translation has been a central goal of artificial intelligence researchers dating back to the very beginnings of the field of AI in the 1950s. Automated language translation products have been available since the dawn of the commercial internet in the 1990s. Yet machine translation has proven to be a fiendishly difficult challenge. AI-based translation tools have historically been deeply flawed (as anyone who remembers using AltaVista’s Babel Fish service in their younger days can attest). To temper expectations, we should not expect that today’s NLP will immediately take over all writing from humans. Some forms of writing—brief formulaic content like marketing copy or social media posts—will yield more naturally to these new AI tools than will others.
AI-driven audio cloning startup gives voice to Einstein chatbot
A few weeks later, direct competitor Cresta announced an $80 million fundraise led by Tiger Global at a $1.6 billion valuation. These fundraises have made these two startups among today’s first NLP unicorns. Expect VC dollars to continue pouring into this space given the outsize market opportunity in play. Founded in 2009, Grammarly has admirably remained abreast of the latest NLP technologies over the years. The company raised funding late last year at a whopping $13 billion valuation. Grammarly’s product provides automated recommendations for improved spelling, grammar, diction and phrasing in real-time as users write.
The pandemic has driven rapid growth for AI Rudder, whose revenue quadrupled last year. The company’s AI system can not only speak a wide range of different languages but can also adopt the appropriate regional accent depending on the caller. The leading player in this category is Moveworks, which raised a $200 million Series C from Tiger Global last year.
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Yet as anyone who has experienced writer’s block can attest, writing can be a frustrating experience. The act of translating inchoate thoughts into well-crafted language—of finding the right words—can be time-consuming and unsystematic. In today’s information-based economy, perhaps no skill matters more than effective writing.
MetaDialog has been a tremendous help to our team, It’s saving our customers 3600 hours per month with instant answers. AI Engine connects to your website and any other content you have, and automatically reads everything, and within an hour it is ready to answer the questions. AI Engine does not get tired or sick, it is always there to answer your customers’ questions, no matter what the situation is. MetaDialog`s AI Engine transforms large amounts of textual data into a knowledge base, and handles any conversation better than a human could do. Ultimately, by deciphering the “language” of nucleic acids, genes, amino acids and cells, today’s language AI will give us a deeper understanding of how life itself works. A handful of young startups have popped up that are nipping at Gong’s heels, though none have yet broken out.
Any platform that features user-generated content of any kind—from gaming companies to dating apps—is susceptible to the proliferation of toxic language. At scale, it becomes impossible for companies to rely on humans alone to monitor and moderate all this content. A less common approach is to develop contact center AI purpose-built for a particular vertical. This is the approach taken by BirchAI, a young startup that recently spun out of the Allen Institute for AI . BirchAI has built a cutting-edge NLP solution focused on contact centers in healthcare.
Conversational Voice Assistants
While opportunities for vertical-specific NLP applications do exist in some other industries, for instance financial services and law, no sector offers a greater breadth of language AI use cases than healthcare. 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 aidriven audio startup voice to chatbot and at the same time more accurate than using AI alone. In a different corner of the healthcare universe, Infinitus is another fast-growing startup to keep an eye on. Infinitus offers voice AI technology—what the company has termed “VoiceRPA”—to automate routine phone calls for providers, insurers and pharmacies. Infinitus’ product is directly comparable to players like Replicant and AI Rudder, discussed above in the “Conversational Voice Assistants” section, except that it is built specifically for healthcare.
The most basic way that humans use natural language to interface with machines is through search. It is the primary means by which we access and navigate digital information; it lies at the heart of the modern internet experience. Your customers are being addressed in real time, AI Engine answers their questions and helps them with anything they need through a chat conversation. A related application is chatbots for mental health, a use case that has seen tremendous growth during the pandemic. These “AI therapists” are freely available and immediately responsive via mobile app for individuals to discuss their lives and problems with.
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The company’s target customers include health insurers, pharmaceutical companies and medical device companies. All four of the companies mentioned so far use AI primarily to provide recommendations and insights on existing text that humans have already written. The next frontier in AI-augmented writing will be for the AI to generate novel written content itself based on guidance from the human user. Building a state-of-the-art NLP model today is incredibly resource-intensive and technically challenging. As a result, very few companies or researchers actually build their own NLP models from scratch.
The company recently made headlines when its technology was used to reproduce Andy Warhol’s voice for an upcoming Netflix documentary. Its AI platform takes a video with spoken dialogue in one language and applies AI to quickly reproduce that video with the dialogue in another language, doing so in a way that the speakers’ lip movements continue to look natural. Think of it as sophisticated dubbing, except that it can be carried out automatically and at scale. Founded by Richard Socher, former Chief Scientist at Salesforce and one of the world’s most widely cited NLP 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.
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. Based in the U.K., Logically focuses on misinformation and disinformation. (The latter is the subset of the former that is spread to deceive intentionally.) Its platform relies on a large team of expert human reviewers working in tandem with its AI system. Many of Logically’s clients are governments, which use its technology for issues including national security, election integrity and COVID-19 misinformation.
More profoundly, the inability for people around the world to understand one another inhibits the advancement of grand global goals and species-level harmony. But in a polyglot world like ours , language barriers have always been an unavoidable reality. Language barriers are a fundamental impediment to international business and travel, costing untold billions in lost productivity every year. Given the caliber of the company’s founders and backers, expect Inflection AI to make waves in the world of language AI before long.
Original, analytical, creative work—say, op-eds, thought pieces or investigative journalism—will resist automation for the time being. Leveraging the latest transformer-based techniques, ZIR is seeking to develop search technology with true semantic comprehension (as opposed to keyword-based matching) and more sophisticated multilingual capabilities. Like You.com, ZIR has a pedigreed founding team that includes former Cloudera CTO/cofounder Amr Awadallah. While some clinicians and patients are uneasy about the idea of a machine providing mental health support, the fact is that we face a critical shortage of trained therapists and affordable mental healthcare today. The average wait time to see a mental health professional in the United States is nearly 2 months; last year, almost 60% of those with mental health issues did not receive any treatment. Given this reality, these AI-powered conversational agents may have an important role to play providing patients with support in an accessible, scalable way.
But thanks to recent breakthroughs in AI, opportunities now exist for startups to build search tools for data modalities beyond text—and no new modality represents a bigger opportunity than video. 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. DigitalOwl is an Israeli startup applying machine learning to enable health insurers to automate the review of medical records, allowing these insurers to process claims more efficiently and accurately. DigitalOwl claims that its technology can analyze and summarize a typical medical case in 3-5 minutes, compared to 3-4 hours for a human reviewer, while identifying twice as many medically relevant datapoints. Co-founded by AI legend Sebastian Thrun (the creator of Google X and Google’s self-driving car program) and two of his Stanford PhD students, Cresta is the most pedigreed competitor in this category.
Contact centers are an unglamorous back-office function that happen to also be a staggeringly massive market—an estimated $340 billion in 2020, on its way to $500 billion by 2027. But thanks to the remarkable advances underway in language AI, reliable and high-quality machine translation is fast becoming a reality. Textio, LitLingo, and Writer are three newer entrants using next-generation language AI to build advanced Grammarly-like solutions for more targeted use cases. Textio focuses on hiring and recruiting, LitLingo on business compliance and risk management, and Writer on company-wide style and brand consistency. Search has been dominated by a single player for so long that it is often seen as an unpromising or even irrelevant category for startups. But there is also tremendous opportunity in this category for younger startups.
Relevant clinical and pharmaceutical information is typically free-form, poorly organized and spread across disparate data sources, from siloed EHRs to difficult-to-edit PDFs. Extracting insights from this data manually is time-consuming and costly. Another important pain point that NLP can help solve is navigating the vast troves of unstructured data in healthcare. There is no one particular NLP “killer app” in healthcare; rather, startups have identified a wide range of different use cases to which language AI can be valuably applied.