A Sequoia Capital-Backed AI-Video Startup Raises Nearly $20 Million; Eight Startups Taking on Nvidia

Instead of processing a string of text word by word, as previous natural language methods had, transformers can analyze an entire string all at once. This allows transformer models to be trained in parallel, making much larger models viable, such as the generative pretrained transformers, the GPTs, that now power ChatGPT, GitHub Copilot and Microsoft’s
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newly revived Bing. These models were trained on very large collections of human language, and are known as Large Language Models (LLMs). Mistral is without doubt some of the hottest property in European AI. Fresh off a monster €105m seed round led by Lightspeed, the company is now setting to work building its own LLMs as a European alternative to the likes of OpenAI’s GPT-4, aimed at European enterprises. We’ve mapped out the most noteworthy models-as-a-service startups based in the continent to compare how their businesses are shaping up.

They’ll be empowered to prioritize products that not only fulfill their hard-set requirements, but those that provide an intuitive and seamless experience. In April the company launched a new GenAI offering to businesses based on LLMs, building on the services it’s built using other types of AI models over a number of years. Polu’s cofounder Gabriel Hubert previously developed products at payment provider Stripe and healthtech Alan, giving the founding team a strong mix of experience. Dust tells Sifted that it’s getting a lot of inbound requests from companies that want to use its services, and that there’s a clear route for its current design partners to become paying customers.

Judge Zia Faruqui is trying to teach you crypto, one ‘SNL’ reference at a time

Gan.ai’s journey illustrates that India has been a force to be reckoned with in the AI space for a while now. We believe the recent flurry of attention on India’s emerging LLMs is a spark for what is already a gathering storm of energy and ideas. We would love to hear from folks who want to build truly foundational tech in AI in the service of moving the state of art forward. Now at this point, we had already partnered with a couple of early Generative AI startups, so we were not noobs to the potential of this space.

Second-order capabilities are some of the more dynamic use cases or applications of the first-order capabilities, including chat, Q&A, translation, parsing data, and classification, such as sentiment analysis. Third-order capabilities refer to the concept of ‘embedding’ – using the result of a language model as an input to other machine learning algorithms to create even more complex use cases. This opens up more advanced abilities, such as smart search and recommendation engines. For example – with generative text, marketers can use it to create contextually relevant blog posts based on some keywords to save time. With generative images, interior designers can use the technology to generate design ideas for their clients to augment their own creativity. With generative speech, newsletter writers can convert a written post into a podcast, unlocking additional distribution channels.

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They include copywriting tools Copy.ai, Contenda, Copysmith, Hypotenuse, text editor Get Writer, coding tools GitHub and Replit, and image generators Stability AI, Craiyon, Lexica. It is interesting, and I will say somewhat surprising to me, how much basic capabilities, such as price performance of compute, are still absolutely vital to our customers. Part of that is because of the size of datasets and because of the machine learning capabilities which are now being created. They require vast amounts of compute, but nobody will be able to do that compute unless we keep dramatically improving the price performance.

It’s a foundational principle that many tech firms leverage to defend their market position. Lighton researchers contributed to the development of Bloom — one of the most popular open source LLMs — and, as far as Sifted knows, is the first European company to have built a GenAI model in Arabic, for a client in the UAE. Foundational models, though nascent, are already inspiring a flurry of activity downstream in the UK startup ecosystem. GitHub’s star history provides a window into developer interest and adoption. This chart illustrates both the fast growth in adoption of these platforms and the increasing interest in new models from Stability and Nomic.

Yakov Livshits
Founder of the DevEducation project
A prolific businessman and investor, and the founder of several large companies in Israel, the USA and the UAE, Yakov’s corporation comprises over 2,000 employees all over the world. He graduated from the University of Oxford in the UK and Technion in Israel, before moving on to study complex systems science at NECSI in the USA. Yakov has a Masters in Software Development.

Over the past decade, many new healthcare software companies confronted unfavorable market dynamics. Providers operate with razor thin margins and are often unwilling to spend on the promises of long-term cost efficiencies. Payors also suffer low margins and are a concentrated buyer group, with the top 5 players commanding more than 50% market share. These organizations can be slow moving and sales cycles can be incredibly long, creating roadblocks for upstarts.

During a rapid emergence, Generative AI startups have attracted huge funding from investors, with over $22B in funding in the last five years. Runway AI provides a powerful platform for video editing based on generative AI. Their API processes terabytes of audio data daily with over 99.9% uptime and success rate and meets SOC 2 Type 2 requirements. AssemblyAI develops AI solutions for processing audio data, extracting needed information, and converting it to other useful formats. Tabnine aims to simplify and expedite the coding process for developers and make it more efficient and error-free by offering tools that automatically suggest code based on the context in which the developer is working.

A surge in generative AI startup investment.

With companies like Jasper, we are just starting to see what is possible with generative technology. We have reached a point where not only are the models getting really good, but they are also accessible and affordable. With just a few lines of code and some training data, you can start unlocking the potential of generative AI. The key now is to find real problems that can be solved with generative technology. Now a San Francisco-headquartered startup called Gan.ai has also attracted the attention of investors. The outfit — which helps brands create tailored videos for their customers with an AI-powered video creation platform — just closed on $5.2 million in seed funding led by Surge, Sequoia Capital’s rapid scale-up program.

You can replace or add certain elements to an image from a text caption. Productivity tools including writing assistants such as Jasper and Regie.ai likewise have drawn millions of dollars in funding. These companies won business helping blog writers and salespeople do their jobs faster. But now, Big Tech has previewed upgrades for Google Yakov Livshits Docs and Microsoft Word that can draft marketing copy. You.com, a search engine company founded in 2020 and backed by Salesforce’s CEO Marc Benioff, has found new life from incorporating generative AI technology. It has attracted more attention from s and investors, handling millions of searches per day, the company told Reuters.

Sure enough, where there’s an untapped market worth $418+ billion, there will be countless companies racing at break-neck speeds to get there first. It took private equity investment from Altor Equity Partners in 2022, but until then had largely been bootstrapped. That can be in the form of asking a chatbot the kinds of things you’d normally ask HR, creating onboarding training programmes or surfacing valuable information from large troves of internal documents. Bhooshan said that they are eager to expand their operations using the new funds, particularly aiming to enlarge their engineering team and scale sales across the U.S. and India. Gan.ai’s founder and CEO, Suvrat Bhooshan, forecasts that by the end of 2023, the majority of the company’s revenues will originate from the U.S. High-profile clients, such as Mumbai Indians and Swiggy, also use Gan.ai to develop personalised video landing pages.

ChatGPT’s human-like responses to any query made observers predict AI could disrupt search-engine technology, along with Google’s market dominance. Two months after its launch, Microsoft upgraded its search competitor Bing with a chatbot powered Yakov Livshits by OpenAI’s technology. “Now you are getting this herd mentality” among venture capitalists, said Kaul. That means lackluster companies “will get funded,” then “fail and give the entire sector, which is very promising, a black eye.”

• Discover the key players who are actively investing in generative AI and paving the way for groundbreaking advancements in fields like art, music, language, and beyond.
• Receive data science tips and tutorials from leading Data Science leaders, right to your inbox.
• Generative AI is inherently creative, so it’s natural to see a lot of innovation in other creative fields.
• When people can easily switch to another company and bring their financial history with them, that presents real competition to legacy services and forces everyone to improve, with positive results for consumers.
• Among the companies that secured the largest investments this year are OpenAI, with $10 billion; Inflection specializing in human-computer interfaces with $1.3 billion; and Anthropic, an AI model developer with $850 million.