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RealityEngines.AI raises $5.25M seed round to make ML easier for enterprises
RealityEngines.AI, a research startup that wants to help enterprises make better use of AI, even when they only have incomplete data, today announced that it has raised a $5.25 million seed funding round. The round was led by former Google CEO and Chairman Eric Schmidt and Google founding board member Ram Shriram. Khosla Ventures, Paul Buchheit, Deepchand Nishar, Elad Gil, Keval Desai, Don Burnette and others also participated in this round.
The fact that the service was able to raise from this rather prominent group of investors clearly shows that its overall thesis resonates. The company, which doesn’t have a product yet, tells me that it specifically wants to help enterprises make better use of the smaller and noisier data sets they have and provide them with state-of-the-art machine learning and AI systems that they can quickly take into production. It also aims to provide its customers with systems that can explain their predictions and are free of various forms of bias, something that’s hard to do when the system is essentially a black box.
As RealityEngines CEO Bindu Reddy, who was previously the head of products for Google Apps, told me, the company plans to use the funding to build out its research and development team. The company, after all, is tackling some of the most fundamental and hardest problems in machine learning right now — and that costs money. Some, like working with smaller data sets, already have some available solutions like generative adversarial networks that can augment existing data sets and that RealityEngines expects to innovate on.
Reddy is also betting on reinforcement learning as one of the core machine learning techniques for the platform.
Once it has its product in place, the plan is to make it available as a pay-as-you-go managed service that will make machine learning more accessible to large enterprise, but also to small and medium businesses, which also increasingly need access to these tools to remain competitive.
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Microsoft launches a drag-and-drop machine learning tool
Microsoft today announced three new services that all aim to simplify the process of machine learning. These range from a new interface for a tool that completely automates the process of creating models, to a new no-code visual interface for building, training and deploying models, all the way to hosted Jupyter-style notebooks for advanced users.
Getting started with machine learning is hard. Even to run the most basic of experiments takes a good amount of expertise. All of these new tools greatly simplify this process by hiding away the code or giving those who want to write their own code a pre-configured platform for doing so.
The new interface for Azure’s automated machine learning tool makes creating a model as easy as importing a data set and then telling the service which value to predict. Users don’t need to write a single line of code, while in the backend, this updated version now supports a number of new algorithms and optimizations that should result in more accurate models. While most of this is automated, Microsoft stresses that the service provides “complete transparency into algorithms, so developers and data scientists can manually override and control the process.”
For those who want a bit more control from the get-go, Microsoft also today launched into preview a visual interface for its Azure Machine Learning service that will allow developers to build, train and deploy machine learning models without having to touch any code.
This tool, the Azure Machine Learning visual interface, looks suspiciously like the existing Azure ML Studio, Microsoft’s first stab at building a visual machine learning tool. Indeed, the two services look identical. The company never really pushed this service, though, and almost seemed to have forgotten about it despite the fact that it always seemed like a really useful tool for getting started with machine learning.
Microsoft says this new version combines the best of Azure ML Studio with the Azure Machine Learning service. In practice, this means that while the interface is almost identical, the Azure Machine Learning visual interface extends what was possible with ML Studio by running on top of the Azure Machine Learning service and adding that services’ security, deployment and life cycle management capabilities.
The service provides an easy interface for cleaning up your data, training models with the help of different algorithms, evaluating them and, finally, putting them into production.
While these first two services clearly target novices, the new hosted notebooks in Azure Machine Learning are clearly geared toward the more experienced machine learning practitioner. The notebooks come pre-packaged with support for the Azure Machine Learning Python SDK and run in what the company describes as a “secure, enterprise-ready environment.” While using these notebooks isn’t trivial either, this new feature allows developers to quickly get started without the hassle of setting up a new development environment with all the necessary cloud resources.
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3D-printed heads let hackers – and cops – unlock your phone
There’s a lot you can make with a 3D printer: from prosthetics, corneas, and firearms — even an Olympic-standard luge.
You can even 3D print a life-size replica of a human head — and not just for Hollywood. Forbes reporter Thomas Brewster commissioned a 3D printed model of his own head to test the face unlocking systems on a range of phones — four Android models and an iPhone X.
Bad news if you’re an Android user: only the iPhone X defended against the attack.
Gone, it seems, are the days of the trusty passcode, which many still find cumbersome, fiddly, and inconvenient — especially when you unlock your phone dozens of times a day. Phone makers are taking to the more convenient unlock methods. Even if Google’s latest Pixel 3 shunned facial recognition, many Android models — including popular Samsung devices — are relying more on your facial biometrics. In its latest models, Apple effectively killed its fingerprint-reading Touch ID in favor of its newer Face ID.
But that poses a problem for your data if a mere 3D-printed model can trick your phone into giving up your secrets. That makes life much easier for hackers, who have no rulebook to go from. But what about the police or the feds, who do?
It’s no secret that biometrics — your fingerprints and your face — aren’t protected under the Fifth Amendment. That means police can’t compel you to give up your passcode, but they can forcibly depress your fingerprint to unlock your phone, or hold it to your face while you’re looking at it. And the police know it — it happens more often than you might realize.
But there’s also little in the way of stopping police from 3D printing or replicating a set of biometrics to break into a phone.
“Legally, it’s no different from using fingerprints to unlock a device,” said Orin Kerr, professor at USC Gould School of Law, in an email. “The government needs to get the biometric unlocking information somehow,” by either the finger pattern shape or the head shape, he said.
Although a warrant “wouldn’t necessarily be a requirement” to get the biometric data, one would be needed to use the data to unlock a device, he said.
Jake Laperruque, senior counsel at the Project On Government Oversight, said it was doable but isn’t the most practical or cost-effective way for cops to get access to phone data.
“A situation where you couldn’t get the actual person but could use a 3D print model may exist,” he said. “I think the big threat is that a system where anyone — cops or criminals — can get into your phone by holding your face up to it is a system with serious security limits.”
The FBI alone has thousands of devices in its custody — even after admitting the number of encrypted devices is far lower than first reported. With the ubiquitous nature of surveillance, now even more powerful with high-resolution cameras and facial recognition software, it’s easier than ever for police to obtain our biometric data as we go about our everyday lives.
Those cheering on the “death of the password” might want to think again. They’re still the only thing that’s keeping your data safe from the law.
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SnapType makes it easy for kids with learning disabilities to do their homework
Sometimes the simplest ideas make the biggest difference. Take SnapType, for example. Created by a husband and wife team – Ben and Amberlynn Slavin – this app lets kids take pictures of their homework and simply type in answers instead of having to hand-write them. Amberlynn, a pediatric occupational therapist, works with kids with ADHD, Autism, Down Syndrome, and dyslexia. Many… Read More
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Clara Labs nabs $7M Series A as it positions its AI assistant to meet the needs of enterprise teams
Clara Labs is announcing a $7 million Series A led by Basis Set Ventures. Slack Fund also joined in the round, alongside existing investors Sequoia and First Round. The startup will be looking to further differentiate within the crowded field of email-centric personal assistants by building in features and integrations to address the needs of enterprise teams. Read More
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MateLabs mixes machine learning with IFTTT
If you’ve ever wanted to train a machine learning model and integrate it with IFTTT, you now can with a new offering from MateLabs. MateVerse, a platform where novices can spin out machine learning models, now works with IFTTT so that you can automatically set up models to run based on conditional statements. If you’re not familiar with IFTTT, it’s an automation tool for… Read More
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Deepgram open sources Kur to make DIY deep learning less painful
Deepgram, a YC backed startup using machine learning to analyze audio data for businesses, is open sourcing an internal deep learning tool called Kur. The release should further help those interested in the space get their ideas off the ground more easily. The startup is also including 10 hours of transcribed audio, spliced into 10 second increments, to expedite the training process. Similar… Read More
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How To Use A Book Club To Turn Your Startup Into A Learning Machine
For me and my co-founders, building our first tech company from the ground up has been both an exhilarating and humbling process. Coming from a corporate background, we had limited experience in scaling a business — and there was a lot of learning to do. As the team grew, the challenges multiplied; many of us were adjusting to the shifting needs of a startup. There was an anxiety to… Read More
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LendEDU Is Making Student Loan Refinancing Easier
When 21-year-old college students Nate Matherson and Matt Lenhard started their first business for tutors, they didn’t really know that they would soon embark on a journey to help solve the $1.2 trillion student loan problem in the U.S. Launched out of their University of Delaware dorm room, their first business was designed to help college tutors. The two even made it into an… Read More
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