With AI being expected to be the soul of the world's largest companies, the idea that eg Nvidia's CEO Jensen Huang designs his conpany to track (even friend) 18000 start ups may sound weir. In truth the ai ecosystem needs (probably more) seeds than ay other ecosyem - brain tools, channels to specific contxts, and in some cases eg climate ai and life scoeces ai is effectively the future of most of that market's innovation
take 2024 top 50 strtup compiled by forbes and originally tracked with Seqoia - of 50 startups early 2024 https://www.forbes.com/lists/ai50/?sh=3585c09f290f less than 10 claim fundinf over hald a billion - of course its true that by time a startup is nearing unicirn status it may well end strtup life beung ipo'd or aquired
but here are the mainly american big fish in AI start up pool of early 2024
OPEN AI - one off case a musk funded n go that turned profit -and became llm chat gpt4 producer- seems to have too many partners to be ipo'd unless microsoft buys it - valued at over 11 billion (when it comes to llms its an unique valuation league - it is said that while the first llm cost about 1000 $ , to launch a new one as a potential world leadwr would cost over 100 billion; of course llms leverage really big computing so the bigget digital companies may well see company and main llm arcitectire as insperable
Adunil Ai Defence 2.8 bn
Anthropic 7.2 bn - another llm with an odd stoiry- probably forst funded by nft notoriety banker-freeman; timely enough toi build an llm but seems to have found mixed partner to leverage computing caacity with eg amazon
cerebras 720 million chips manufacturer
Databricks -stata strage and analytics 4 bn dollar (get this field right and you emerge with data warehousing's Snowflake
Here is some commentary from Sequoia 2023 which clarifies:
When we launched the AI 50 almost five years ago, I wrote, “Although artificial general intelligence (AGI)… gets a lot of attention in film, that field is a long way off.” Today, that sci-fi future feels much closer.
The biggest change has been the rise of generative AI, and particularly the use of transformers (a type of neural network) for everything from text and image generation to protein folding and computational chemistry. Generative AI was in the background on last year’s list but in the foreground now.
The History of Generative AI
Generative AI, which refers to AI that creates an output on demand, is not new. The famous ELIZA chatbot in the 1960s enabled users to type in questions for a simulated therapist, but the chatbot’s seemingly novel answers were actually based on a rules-based lookup table. A major leap was Google researcher Ian Goodfellow’s generative adversarial networks (GANs) from 2014 that generated plausible low resolution images by pitting two networks against each other in a zero sum game. Over the coming years the blurry faces became more photorealistic but GANs remained difficult to train and scale.
In 2017, another group at Google released the famous Transformers paper, “Attention Is All You Need,” to improve the performance of text translation. In this case, attention refers to mechanisms that provide context based on the position of words in text, which vary from language to language. The researchers observed that the best performing models all have these attention mechanisms, and proposed to do away with other means of gleaning patterns from text in favor of attention.
The eventual implications for both performance and training efficiency turned out to be huge. 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 newly revived Bing. These models were trained on very large collections of human language, and are known as Large Language Models (LLMs).
Although transformers are effective for computer vision applications, another method called latent (or stable) diffusion now produces some of the most stunning high-resolution images through products from startups Stability and Midjourney. These diffusion models marry the best elements of GANs and transformers. The smaller size and open source availability of some of these models has made them a fount of innovation for people who want to experiment.
As does this visual on the top 50 at 2023
our trends in this year’s list
Generative AI Infrastructure: OpenAI made a big splash last year with the launch of ChatGPT and again this year with the launch of GPT-4, but their big bet on scale and a technique called Reinforcement Learning with Human Feedback (RLHF) is only one of many directions LLMs are taking. Anthropic and their chatbot Claude use a different approach called Reinforcement Learning Constitutional AI (RL-CAI). The CAI part encodes a set of human-friendly principles designed to limit abuse and hallucination in the outputs. Meanwhile Inflection, a secretive startup founded by DeepMind’s Mustafa Suleyman and Greylock’s Reid Hoffman, is focusing on consumer applications.
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