You can learn anything in just four steps.
Massachusetts Governor Charlie Baker has returned a police reform bill back to the state legislature, asking lawmakers to strike out several provisions — including one for a statewide ban on police and public authorities using facial recognition technology, the first of its kind in the United States.
The bill, which also banned police from using rubber bullets and tear gas, was passed on December 1 by both the state’s House and Senate after senior lawmakers overcame months of deadlock to reach a consensus. Lawmakers brought the bill to the state legislature in the wake of the killing of George Floyd, an …
AI startup RealityEngines.AI changed its name to Abacus.AI in July. At the same time, it announced a $13 million Series A round. Today, only a few months later, it is not changing its name again, but it is announcing a $22 million Series B round, led by Coatue, with Decibel Ventures and Index Partners participating as well. With this, the company, which was co-founded by former AWS and Google exec Bindu Reddy, has now raised a total of $40.3 million.
In addition to the new funding, Abacus.AI is also launching a new product today, which it calls Abacus.AI Deconstructed. Originally, the idea behind RealityEngines/Abacus.AI was to provide its users with a platform that would simplify building AI models by using AI to automatically train and optimize them. That hasn’t changed, but as it turns out, a lot of (potential) customers had already invested into their own workflows for building and training deep learning models but were looking for help in putting them into production and managing them throughout their lifecycle.
“One of the big pain points [businesses] had was, ‘look, I have data scientists and I have my models that I’ve built in-house. My data scientists have built them on laptops, but I don’t know how to push them to production. I don’t know how to maintain and keep models in production.’ I think pretty much every startup now is thinking of that problem,” Reddy said.
Since Abacus.AI had already built those tools anyway, the company decided to now also break its service down into three parts that users can adapt without relying on the full platform. That means you can now bring your model to the service and have the company host and monitor the model for you, for example. The service will manage the model in production and, for example, monitor for model drift.
Another area Abacus.AI has long focused on is model explainability and de-biasing, so it’s making that available as a module as well, as well as its real-time machine learning feature store that helps organizations create, store and share their machine learning features and deploy them into production.
As for the funding, Reddy tells me the company didn’t really have to raise a new round at this point. After the company announced its first round earlier this year, there was quite a lot of interest from others to also invest. “So we decided that we may as well raise the next round because we were seeing adoption, we felt we were ready product-wise. But we didn’t have a large enough sales team. And raising a little early made sense to build up the sales team,” she said.
Reddy also stressed that unlike some of the company’s competitors, Abacus.AI is trying to build a full-stack self-service solution that can essentially compete with the offerings of the big cloud vendors. That — and the engineering talent to build it — doesn’t come cheap.
It’s no surprise then that Abacus.AI plans to use the new funding to increase its R&D team, but it will also increase its go-to-market team from two to ten in the coming months. While the company is betting on a self-service model — and is seeing good traction with small- and medium-sized companies — you still need a sales team to work with large enterprises.
Come January, the company also plans to launch support for more languages and more machine vision use cases.
“We are proud to be leading the Series B investment in Abacus.AI, because we think that Abacus.AI’s unique cloud service now makes state-of-the-art AI easily accessible for organizations of all sizes, including start-ups,” Yanda Erlich, a p artner at Coatue Ventures told me. “Abacus.AI’s end-to-end autonomous AI service powered by their Neural Architecture Search invention helps organizations with no ML expertise easily deploy deep learning systems in production.”
Seldon is a U.K. startup that specializes in the rarified world of development tools to optimize machine learning. What does this mean? Well, dear reader, it means that the “AI” that companies are so fond of trumpeting does actually end up working.
It has now raised a £7.1 million Series A round co-led by AlbionVC and Cambridge Innovation Capital . The round also includes significant participation from existing investors Amadeus Capital Partners and Global Brain, with follow-on investment from other existing shareholders. The £7.1 million funding will be used to accelerate R&D and drive commercial expansion, take Seldon Deploy — a new enterprise solution — to market and double the size of the team over the next 18 months.
Key to its success is that its open-source project Seldon Core has more than 700,000 models deployed to date, drastically reducing friction for users deploying ML models. The startup says its customers are getting productivity gains of as much as 92% as a result of utilizing Seldon’s product portfolio.
Alex Housley, CEO and founder of Seldon speaking to TechCrunch explained that companies are using machine learning across thousands of use cases today, “but the model actually only generates real value when it’s actually running inside a real-world application.”
“So what we’ve seen emerge over these last few years are companies that specialize in specific parts of the machine learning pipeline, such as training version control features. And in our case we’re focusing on deployment. So what this means is that organizations can now build a fully bespoke AI platform that suits their needs, so they can gain a competitive advantage,” he said.
In addition, he said Seldon’s open-source model means that companies are not locked-in: “They want to avoid locking as well they want to use tools from various different vendors. So this kind of intersection between machine learning, DevOps and cloud-native tooling is really accelerating a lot of innovation across enterprise and also within startups and growth-stage companies.”
Nadine Torbey, an investor at AlbionVC, added: “Seldon is at the forefront of the next wave of tech innovation, and the leadership team are true visionaries. Seldon has been able to build an impressive open-source community and add immediate productivity value to some of the world’s leading companies.”
Vin Lingathoti, partner at Cambridge Innovation Capital, said: “Machine learning has rapidly shifted from a nice-to-have to a must-have for enterprises across all industries. Seldon’s open-source platform operationalizes ML model development and accelerates the time-to-market by eliminating the pain points involved in developing, deploying and monitoring machine learning models at scale.”
Campuswire was in a fortuitous spot when colleges and universities across the world shut down on short notice because of the threat of coronavirus. Founded by Tade Oyerinde in 2018, Campuswire is a virtual solution for any teacher who wants to digitize their internal classroom communications, from Q&A time to the lecture itself.
The strategy, for the most part, has worked. Campuswire is now used at more than 300 universities among 200,000 students, Oyerinde tells me.
While Campuswire’s pitch was set to boom overnight, the founder instead saw a bigger challenge approaching: more competition. As professors moved online, lectures moved to Zoom or tools built atop of Zoom. Microsoft Teams and Google Hangouts filled in the gap for classrooms that couldn’t afford fancy licenses. Campuswire’s key monetization strategy, which was selling pro licenses for its online class software, felt threatened by alternatives.
So, after months of iterating, Campuswire has adapted its monetization strategy and today announced that it is launching live courses taught by professors. Instead of solely working with professors to streamline internal class communications, Campuswire will now help teachers produce classes that students can then take for a fee. The tuition revenue will be split between the teacher and Campuswire.
Campuswire courses kick off with an angel investing class taught by Charles Hudson, the founder and general partner of Precursor Ventures. Hudson lectures at Stanford occasionally, and working with Campuswire allows him to teach a broader set of students.
Meanwhile, Campuswire software will be free to use starting in January 2021.
The move marks Campuswire’s further dive into synchronous learning. Campuswire’s model is built on how existing classrooms work in universities and colleges. Classes on Campuswire are capped at 500 to promote conversation, and large lectures are supplemented with teacher assistant (TA) classes to hammer home confusing concepts.
Meanwhile, it’s clear amid the pandemic that asynchronous learning has its perks (students can learn on their own schedule, while educators are able to work more flexible hours). Still, Oyerinde thinks a pre-recorded format is not effective for pedagogy purposes.
“This is kind of the hill we’re going to die on,” he said. “Real, lasting learning has to be synchronous for the majority of people.”
In other words, while there’s a small group of gifted-and-talented students who can watch a one-hour lecture and absorb every factoid and nuance, the majority of students need engagement, interaction and motivation to understand a topic, he argues. It’s the reason why MOOCs, or massive open online course providers, only have a 2-3% completion rate on their courses, he argues.
At its core, Campuswire has evolved from a platform trying to compete with Zoom to a platform that is trying to compete with these MOOCS through engaging content taught by experienced professors. Its main differentiation from MOOCs is that it’s live and has teacher assistants.
There are a number of startups that are trying to create engaging, celebrity professor-taught classes through hybrid plays. MasterClass, which just raised $100 million a few months ago, sells entertainment and education in one go, offering cooking classes from Gordon Ramsay and tennis lessons from Serena Williams. While you can’t interact with Ramsay or Williams, you can chat with fellow classmates.
BookClub connects readers to the authors they are reading, giving bookworms an opportunity to ask about cliffhangers and character development. The upstart is still in its early stages, but founder David Blake says that readers could talk directly to authors down the road. There’s also Teachable, which got acquired by Hotmart earlier this year. Teachable helps any expert who wants to create a business around their expertise do so with a virtual course. Arlan Hamilton, a seed-stage investor, has a course on the platform.
Today’s pivot signals the founder’s mindset that, in order to grow to the billion-dollar business mark in edtech, you need to sell more than software that Google and Microsoft will always give away for free.
“Online learning can be 100 times bigger than it is today,” Oyerinde said. “Once you actually support synchronicity, you actually support people getting to actually interact with UCLA/Princeton/Cornell professors, not just watching them on pre-recorded videos.”