40m Additiondillettechcrunch: The working class of the United States doesn’t get many breaks these days. It’s not just a function of low pay and long hours, but also the incredible uncertainty of income and expenses that makes surviving week-to-week so challenging.
One in five Americans have a negative net wealth, even in an economy where the unemployment rate is the lowest in almost two decades. Banks, meanwhile, are actively dissuading the working class from banking with them, creating a permanent class of unbanked and underbanked citizens.
For Jon Schlossberg, CEO and co-founder of Even.com, improving the plight of ordinary Americans and their finances is a deeply personal and professional mission. And now that mission has a huge new bucket of capital behind it, with Keith Rabois of Khosla Ventures leading a $40 million Series B round into the Oakland-based startup.
Rabois is a return investor, having previously backed the company in its late 2014 seed round. With this latest round of capital, Even.com has now raised $50.5 million.
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Lightning AI, the startup behind the open-source PyTorch Lightning framework, today announced that it raised $40 million in a Series B round led by Coatue with participation from Index Ventures, Bain, the Chainsmokers’ Mantis VC and First Minute Capital.
CEO William Falcon told TechCrunch that the new money will be used to expand Lightning AI’s 60-person team while supporting the community around PyTorch Lightning development.
Lightning AI, formerly Grid.ai, is the culmination of work that began in 2018 at the New York University Computational Intelligence, Learning, Vision, and Robotics (NYU CILVR) Lab and Facebook AI Research (now Meta AI Research).
After Falcon started developing PyTorch Lightning as an undergrad at Columbia in 2015, he founded Lightning AI in 2019 with Luis Capelo, the former head of data products at Forbes. While working on his PhD at NYU and Facebook AI Research, Falcon open-sourced PyTorch Lightning and — according to him — the project quickly gained traction.
“[W]e realized that the biggest challenge holding back AI adoption at scale was fragmentation of the AI ecosystem,” Falcon said. “I first noticed the impact of the fragmented AI ecosystem back in 2019.
Meta-owned messaging app WhatsApp is stepping into the film business. Earlier this week, WhatsApp announced its first original short film “Naija Odyssey,” which tells the story of NBA player Giannis Antetokounmpo, who was born in Athens, Greece to Nigerian parents.
“Naija Odyssey” will premiere on Prime Video on September 21, 2022.
The short film signifies WhatsApp’s foray into entertainment, an unusual venture for a social messaging app. However, unlike tech giant Apple’s original content, “Naija Odyssey” appears to be a way to promote the brand given that Antetokounmpo recently signed an endorsement deal with WhatsApp, another first for the app.
Vivian Odior, WhatsApp’s global head of marketing, told Variety, “‘Naija Odyssey’ is a story that reinforces how WhatsApp helps us embrace our multifaceted lives. In navigating relationships, identity, and even adversity, WhatsApp is there — enabling you to embrace all sides of you by connecting you to those who matter most.”
Hugging Face has raised a $15 million funding round led by Lux Capital. The company first built a mobile app that let you chat with an artificial BFF, a sort of chatbot for bored teenagers. More recently, the startup released an open-source library for natural language processing applications. And that library has been massively successful.
A.Capital, Betaworks, Richard Socher, Greg Brockman, Kevin Durant and others are also participating in today’s funding round.
Hugging Face launched its original chatbot app back in early 2017. After months of work, the startup wanted to prove that chatbots don’t have to be a glorified command line interface for customer support.
With the app, you could generate a digital friend and text back and forth with your companion. And it wasn’t just about understanding what you meant — the app tried to detect your emotions to adapt answers based on your feelings.
Code-generating systems like DeepMind’s AlphaCode, Amazon’s CodeWhisperer and OpenAI’s Codex, which powers GitHub’s Copilot service, provide a tantalizing look at what’s possible with AI today within the realm of computer programming.
But so far, only a handful of such AI systems have been made freely available to the public and open sourced — reflecting the commercial incentives of the companies building them.
In a bid to change that, AI startup Hugging Face and ServiceNow Research, ServiceNow’s R&D division, today launched BigCode, a new project that aims to develop “state-of-the-art” AI systems for code in an “open and responsible” way.
The goal is to eventually release a dataset large enough to train a code-generating system, which will then be used to create a prototype — a 15-billion-parameter model, larger in size than Codex (12 billion parameters) but smaller than AlphaCode (~41.4 billion parameters) — using ServiceNow’s in-house graphics card cluster.
In machine learning, parameters are the parts of an AI system learn from historical training data and essentially define the skill of the system on a problem, such as generating code.
Hugging Face also offers hosted services, such as the Inference API that lets you use thousands of models via a programming interface, and the ability to “AutoTrain” your model.
With today’s funding round, the company plans to do more of the same; 10,000 companies are now using Hugging Face in one way or another, so it’s not time to pivot again.
Essentially, Hugging Face is building the GitHub of machine learning. It’s a community-driven platform with a ton of repositories. Developers can create, discover and collaborate on ML models, datasets and ML apps.
Wikipedia dataset containing cleaned articles of all languages. The datasets are built from the Wikipedia dump (https://dumps.wikimedia.org/) with one split per language. Each example contains the content of one full Wikipedia article with cleaning to strip markdown and unwanted sections (references, etc.).
As artificial intelligence (AI) has become more prevalent, one segment has become increasingly important: natural language processing (NLP). From 2020 to 2021, 60% of tech leaders increased their NLP budgets by at least 10%, with almost a fifth of them doubling it.
While the relevance of the technology has exploded, it has remained somewhat inaccessible. Hugging Face is focus on improving accessibility.
Hugging Face is an open-source hosting platform for natural language processing (NLP) and other machine learning (ML) domains, such as computer vision and reinforcement learning.
Large tech companies, like Google, Facebook, and Microsoft, were traditionally the only companies developing and using large NLP models because of their required cost and processing resources. The cost of training one large language model can be as high as $1.6 million.
Progress in AI has been nothing short of amazing, to the point where some people are now seriously debating whether AI models may be better than humans at certain tasks.
However, that progress has not at all been even: to a machine learner from several decades ago, modern hardware and algorithms might look incredible, as might the sheer quantity of data and compute at our disposal, but the way we evaluate these models has stayed roughly the same.
However, it is no exaggeration to say that modern AI is in an evaluation crisis. Proper evaluation these days involves measuring many models, often on many datasets and with multiple metrics. But doing so is unnecessarily cumbersome.
This is especially the case if we care about reproducibility, since self-reported results may have suffered from inadvertent bugs, subtle differences in implementation, or worse.
Due to the success of this libary, Hugging Face quickly became the main repository for all things related to machine learning models — not just natural language processing. On the company’s website, you can browse thousands of pre-trained machine-learning models, participate in the developer community with your own model, download datasets and more.
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