AI Funding Frenzy Escalates The New York Times

But machines are just starting to get good at creating sensical and beautiful things. This new category is called “Generative AI,” meaning the machine is generating something new rather than analyzing something that already exists. Generative AI has the ability to generate new data instances in various types, not just text. While most commercial SBOM analysis vendors use some form of machine learning or AI models to digest data, most have not added generative AI yet. Instead, recent blog posts from two SBOM vendors warned about the security hazards of generative AI. Almost no one is saying that the current generation of AI is ready for prime time.

Emad Mostaque, the founder and chief executive of Stability AI, has pushed back on the idea of content restrictions. He argues that radical freedom is necessary to achieve his vision of a democratized A.I. Project has created as much buzz — or as much controversy — as Stable Diffusion. That much became clear Monday night at the San Francisco Exploratorium, where Stability AI, the start-up behind the popular Stable Diffusion image-generating algorithm, gave a party that felt a lot like a return to prepandemic exuberance.

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“If something is so exciting, that means it comes with a lot of security [implications] behind the scenes. … It could be used for both positive and negative.” “An overwhelming majority are using generative AI today … an incredible (even historic) rate of adoption and organizational effort to establish processes, though some report feeling pressured to incorporate the technology despite concerns about security risks.” ChatGPT was recently super-charged by GPT-4, the latest language-writing model from OpenAI’s labs. Paying ChatGPT users have access to GPT-4, which can write more naturally and fluently than the model that previously powered ChatGPT. In addition to GPT-4, OpenAI recently connected ChatGPT to the internet with plugins available in alpha to users and developers on the waitlist.

There is however no true Notion-like competitor in the spreadsheet space for the hard core spreadsheet user. If you’re embarrassed to touch a mouse when in a spreadsheet (guilty as charged!), Airtable or Rows won’t do it for you. But automating quantitative work in spreadsheets should be a slam dunk application for this new era.

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There is also a growing focus on making generative AI more controllable and ethically responsible. The late 2010s saw the rise of transformer-based models, particularly in the domain of Natural Language Processing (NLP). Models like generative pre-training transformers (GPT) and Bidirectional Encoder Representations from Transformers (BERT) revolutionized NLP with an ability to understand and generate human-like text. In 2014, the generative adversarial network (GAN) was introduced, demonstrating an impressive ability to generate realistic data, especially images. Around the same time, the variational autoencoder (VAE) was introduced, offering a probabilistic approach to autoencoders that supported a more principled framework for generating data. “I’m not very sold on security and generative AI at this point of time,” said Chitra Elango, senior director of DevSecOps, vulnerability management and red team at financial services company Fannie Mae, based in Washington D.C., during the discussion.

Intuit had MLops systems in place before a lot of vendors sold products for managing machine learning, said Brett Hollman, Intuit’s director of engineering and product development in machine learning. “That is the biggest gap in the tech industry right now,” said Nicola Morini Bianzino, global chief client technology officer at EY. The auditing firm has thousands of models in deployment that are used for its customers’ tax returns and other purposes, but has not come across a suitable system for managing various MLops modules, he said.

Major brands are experimenting with it, using the AI to generate ad and marketing copy, for example. ChatGPT, OpenAI’s text-generating AI chatbot, has taken the world by storm. It’s able to write essays, code and more given short text prompts, hyper-charging productivity.

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.

This background provides him with a unique perspective on what comes next with AI. “A lot of these places that are attempting to do this are just not tech-native or tech-first companies,” BCG’s Gupta said. For one thing, smaller companies are competing for talent against big tech firms that offer higher salaries and better resources.

We have seen this distribution strategy pay off in other market categories, like consumer/social. Form Factor Today, Generative AI apps largely exist as plugins in existing software ecosystems. Code completions happen in your IDE; image generations happen in Figma or Photoshop; even Discord bots are the vessel to inject generative AI into digital/social communities. Despite all the fundamental research progress, these models are not widespread. They are large and difficult to run (requiring GPU orchestration), not broadly accessible (unavailable or closed beta only), and expensive to use as a cloud service. Despite these limitations, the earliest Generative AI applications begin to enter the fray.

• We spoke with them two months ago and last week to capture the pace of change.
• Open source and custom model training and tuning also seem to be on the rise.
• They will likely go into specific problem spaces (e.g., code, design, gaming) rather than trying to be everything to everyone.
• “We try to be very long term on investing in technology trends as a firm,” said Buhler.

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.

Both the free version of ChatGPT and the paid ChatGPT Plus are regularly updated with new GPT models. A week after ChatGPT was released into the wild, two developers — Steven Tey and Dom Eccleston — made a Chrome extension called ShareGPT to make it easier to capture and share the AI’s answers with the world. Other companies beyond Microsoft joined in on the AI craze by implementing ChatGPT, including OkCupid, Kaito, Snapchat and Discord — putting the pressure on Big Tech’s AI initiatives, like Google. OpenAI makes another move toward monetization by launching a paid API for ChatGPT.

It wasn’t just a joke that the article was co-written with GPT-3; it actually was. And then I’d be like, “Specifically for image generation, you can think of it as ….” That human-machine iteration loop I hadn’t experienced before, and it was very much how we created both the blog post and landscape. Generative AI, which refers to AI that processes huge amounts of data in order to create something completely original, 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
GOOG
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.

Given the steep computing costs, some said big players like Microsoft and Google’s parent company, Alphabet, had too much of an advantage. Lab DeepMind, which is developing a wide range of technologies as an Alphabet subsidiary, in 2014 for $650 million. He declined to comment on his company’s fund-raising ahead of Y Combinator’s Yakov Livshits demo day, when companies pitch investors, in April. A lot of tech analysts like to talk about “emerging AI.” However, when experts from Sequoia make a list, the industry pays attention. As a Silicon Valley capital firm behind such unicorns as DoorDash, Zoom, Snowflake, and Vanta, Sequoia knows what to look for in tech trends.

Per a story in MIT Technology Review, “Ernie Bot” from Baidu reached 1 million users in the 19 hours following its recent public launch. Since then, at least four additional Chinese companies have made their large language model (LLM) chatbot products available. Intuit also has constructed its own systems for building and monitoring the immense number of ML models it has in production, including models that are customized for each of its QuickBooks software customers. Sometimes the distinctions in each model are minimal — one company might label certain types of purchases as “office supplies” while another categorizes them with the name of their office retailer of choice, for instance.

Fintech puts American consumers at the center of their finances and helps them manage their money responsibly. From payment apps to budgeting and investing tools and alternative credit options, fintech makes it easier for consumers to pay for their purchases and build better financial habits. Biz Carson (
@bizcarson) is a San Francisco-based reporter at Protocol, covering Silicon Valley with a focus on startups and venture capital. Previously, she reported for Forbes and was co-editor of Forbes Next Billion-Dollar Startups list.