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RealityEngines launches its autonomous AI service
RealityEngines.AI, an AI and machine learning startup founded by a number of former Google executives and engineers, is coming out of stealth today and announcing its first set of products.
When the company first announced its $5.25 million seed round last year, CEO Bindu Reddy wasn’t quite ready to disclose RealityEngines’ mission beyond saying that it planned to make machine learning easier for enterprises. With today’s launch, the team is putting this into practice by launching a set of tools that specifically tackle a number of standard enterprise use cases for ML, including user churn predictions, fraud detection, sales lead forecasting, security threat detection and cloud spend optimization. For use cases that don’t fit neatly into these buckets, the service also offers a more general predictive modeling service.
Before co-founding RealiyEngines, Reddy was the head of product for Google Apps and general manager for AI verticals at AWS. Her co-founders are Arvind Sundararajan (formerly at Google and Uber) and Siddartha Naidu (who founded BigQuery at Google). Investors in the company include Eric Schmidt, Ram Shriram, Khosla Ventures and Paul Buchheit.
As Reddy noted, the idea behind this first set of products from RealityEngines is to give businesses an easy entry into machine learning, even if they don’t have data scientists on staff.
Besides talent, another issue that businesses often face is that they don’t always have massive amounts of data to train their networks effectively. That has long been a roadblock for many companies that want to see what AI can do for them but that didn’t have the right resources to do so. RealityEngines overcomes this by creating realistic synthetic data that it can then use to augment a company’s existing data. In its tests, this creates models that are up to 15% more accurate than models that were trained without the synthetic data.
“The most prominent use of generative adversarial networks — GANS — has been to create deepfakes,” said Reddy. “Deepfakes have captured the public’s imagination by highlighting how easy it to spread misinformation with these doctored videos and images. However, GANS can also be applied to productive and good use. They can be used to create synthetic data sets which when then be combined with the original data, to produce robust AI models even when a business doesn’t have much training data.”
RealityEngines currently has about 20 employees, most of whom have a deep background in ML/AI, both as researchers and practitioners.
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VMware acquires ML acceleration startup Bitfusion
VMware today announced that it has acquired Bitfusion, a former participant in our Startup Battlefield competition. Bitfusion was one of the earliest companies to help businesses accelerate their complex computing workloads on GPUs, FPGAs and ASICs. In its earliest iteration, over four years ago, the company’s focus was less on AI and machine learning and more on other areas of high-performance computing, but, unsurprisingly, that shifted as the interested in AI and ML increased in recent years.
VMware will use Bitfusion’s technology, which is vendor- and hardware-agnostic, to bring similar capabilities to its customers. Specifically, it plans to integrate Bitfusion into its vSphere platform.
“Once closed, the acquisition of Bitfusion will bolster VMware’s strategy of supporting AI- and ML-based workloads by virtualizing hardware accelerators,” writes Krish Prasad, senior vice president and general manager of VMware’s Cloud Platform Business Unit. “Multi-vendor hardware accelerators and the ecosystem around them are key components for delivering modern applications. These accelerators can be used regardless of location in the environment – on-premises and/or in the cloud.”
Prasad also notes that to get the most out of hardware accelerators like GPUs, most enterprises deploy them on bare metal. VMware, however, argues that this leads to poor utilization and poor efficiencies (as it would, of course, given that it is in the business of virtualization). “This provides a perfect opportunity to virtualize them—providing increased sharing of resources and lowering costs,” writes Prasad.
The two companies did not disclose the price of the acquisition. Bitfusion had raised $5 million in 2017 and a smaller, strategic investment from Samsung Ventures in 2018.
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Twitter bags deep learning talent behind London startup, Fabula AI
Twitter has just announced it has picked up London-based Fabula AI. The deep learning startup has been developing technology to try to identify online disinformation by looking at patterns in how fake stuff vs genuine news spreads online — making it an obvious fit for the rumor-riled social network.
Social media giants remain under increasing political pressure to get a handle on online disinformation to ensure that manipulative messages don’t, for example, get a free pass to fiddle with democratic processes.
Twitter says the acquisition of Fabula will help it build out its internal machine learning capabilities — writing that the UK startup’s “world-class team of machine learning researchers” will feed an internal research group it’s building out, led by Sandeep Pandey, its head of ML/AI engineering.
This research group will focus on “a few key strategic areas such as natural language processing, reinforcement learning, ML ethics, recommendation systems, and graph deep learning” — now with Fabula co-founder and chief scientist, Michael Bronstein, as a leading light within it.
Bronstein is chair in machine learning & pattern recognition at Imperial College, London — a position he will remain while leading graph deep learning research at Twitter.
Fabula’s chief technologist, Federico Monti — another co-founder, who began the collaboration that underpin’s the patented technology with Bronstein while at the University of Lugano, Switzerland — is also joining Twitter.
“We are really excited to join the ML research team at Twitter, and work together to grow their team and capabilities. Specifically, we are looking forward to applying our graph deep learning techniques to improving the health of the conversation across the service,” said Bronstein in a statement.
“This strategic investment in graph deep learning research, technology and talent will be a key driver as we work to help people feel safe on Twitter and help them see relevant information,” Twitter added. “Specifically, by studying and understanding the Twitter graph, comprised of the millions of Tweets, Retweets and Likes shared on Twitter every day, we will be able to improve the health of the conversation, as well as products including the timeline, recommendations, the explore tab and the onboarding experience.”
Terms of the acquisition have not been disclosed.
We covered Fabula’s technology and business plan back in February when it announced its “new class” of machine learning algorithms for detecting what it colloquially badged ‘fake news’.
Its approach to the problem of online disinformation looks at how it spreads on social networks — and therefore who is spreading it — rather than focusing on the content itself, as some other approaches do.
Fabula has patented algorithms that use the emergent field of “Geometric Deep Learning” to detect online disinformation — where the datasets in question are so large and complex that traditional machine learning techniques struggle to find purchase. Which does really sound like a patent designed with big tech in mind.
Fabula likens how ‘fake news’ spreads on social media vs real news as akin to “a very simplified model of how a disease spreads on the network”.
One advantage of the approach is it looks to be language agnostic (at least barring any cultural differences which might also impact how fake news spread).
Back in February the startup told us it was aiming to build an open, decentralised “truth-risk scoring platform” — akin to a credit referencing agency, just focused on content not cash.
It’s not clear from Twitter’s blog post whether the core technologies it will be acquiring with Fabula will now stay locked up within its internal research department — or be shared more widely, to help other platforms grappling with online disinformation challenges.
The startup had intended to offer an API for platforms and publishers later this year.
But of course building a platform is a major undertaking. And, in the meanwhile, Twitter — with its pressing need to better understand the stuff its network spreads — came calling.
A source close to the matter told us that Fabula’s founders decided that selling to Twitter instead of pushing for momentum behind a vision of a decentralized, open platform because the exit offered them more opportunity to have “real and deep impact, at scale”.
Though it is also still not certain what Twitter will end up doing with the technology it’s acquiring. And it at least remains possible that Twitter could choose to make it made open across platforms.
“That’ll be for the team to figure out with Twitter down the line,” our source added.
A spokesman for Twitter did not respond directly when we asked about its plans for the patented technology but he told us: “There’s more to come on how we will integrate Fabula’s technology where it makes sense to strengthen our systems and operations in the coming months. It will likely take us some time to be able to integrate their graph deep learning algorithms into our ML platform. We’re bringing Fabula in for the team, tech and mission, which are all aligned with our top priority: Health.”
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Algorithmia raises $25M Series B for its AI automation platform
Algorithmia, a Seattle-based startup that offers a cloud-agnostic AI automation platform for enterprises, today announced a $25 million Series B funding round led by Norwest Partners. Madrona, Gradient Ventures, Work-Bench, Osage University Partners and Rakuten Ventures also participated in this round.
While the company started out five years ago as a marketplace for algorithms, it now mostly focuses on machine learning and helping enterprises take their models into production.
“It’s actually really hard to productionize machine learning models,” Algorithmia CEO Diego Oppenheimer told me. “It’s hard to help data scientists to not deal with data infrastructure but really being able to build out their machine learning and AI muscle.”
To help them, Algorithmia essentially built out a machine learning DevOps platform that allows data scientists to train their models on the platform and with the framework of their choice, bring it to Algorithmia — a platform that has already been blessed by their IT departments — and take it into production.
“Every Fortune 500 CIO has an AI initiative but they are bogged down by the difficulty of managing and deploying ML models,” said Rama Sekhar, a partner at Norwest Venture Partners, who has now joined the company’s board. “Algorithmia is the clear leader in building the tools to manage the complete machine learning life cycle and helping customers unlock value from their R&D investments.”
With the new funding, the company will double down on this focus by investing in product development to solve these issues, but also by building out its team, with a plan to double its headcount over the next year. A year from now, Oppenheimer told me, he hopes that Algorithmia will be a household name for data scientists and, maybe more importantly, their platform of choice for putting their models into production.
“How does Algorithmia succeed? Algorithmia succeeds when our customers are able to deploy AI and ML applications,” Oppenheimer said. “And although there is a ton of excitement around doing this, the fact is that it’s really difficult for companies to do so.”
The company previously raised a $10.5 million Series A round led by Google’s AI fund. It’s customers now include the United Nations, a number of U.S. intelligence agencies and Fortune 500 companies. In total, more than 90,000 engineers and data scientists are now on the platform.
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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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The right way to do AI in security
Artificial intelligence applied to information security can engender images of a benevolent Skynet, sagely analyzing more data than imaginable and making decisions at lightspeed, saving organizations from devastating attacks. In such a world, humans are barely needed to run security programs, their jobs largely automated out of existence, relegating them to a role as the button-pusher on particularly critical changes proposed by the otherwise omnipotent AI.
Such a vision is still in the realm of science fiction. AI in information security is more like an eager, callow puppy attempting to learn new tricks – minus the disappointment written on their faces when they consistently fail. No one’s job is in danger of being replaced by security AI; if anything, a larger staff is required to ensure security AI stays firmly leashed.
Arguably, AI’s highest use case currently is to add futuristic sheen to traditional security tools, rebranding timeworn approaches as trailblazing sorcery that will revolutionize enterprise cybersecurity as we know it. The current hype cycle for AI appears to be the roaring, ferocious crest at the end of a decade that began with bubbly excitement around the promise of “big data” in information security.
But what lies beneath the marketing gloss and quixotic lust for an AI revolution in security? How did AL ascend to supplant the lustrous zest around machine learning (“ML”) that dominated headlines in recent years? Where is there true potential to enrich information security strategy for the better – and where is it simply an entrancing distraction from more useful goals? And, naturally, how will attackers plot to circumvent security AI to continue their nefarious schemes?
How did AI grow out of this stony rubbish?
The year AI debuted as the “It Girl” in information security was 2017. The year prior, MIT completed their study showing “human-in-the-loop” AI out-performed AI and humans individually in attack detection. Likewise, DARPA conducted the Cyber Grand Challenge, a battle testing AI systems’ offensive and defensive capabilities. Until this point, security AI was imprisoned in the contrived halls of academia and government. Yet, the history of two vendors exhibits how enthusiasm surrounding security AI was driven more by growth marketing than user needs.
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Vizion.ai launches its managed Elasticsearch service
Setting up Elasticsearch, the open-source system that many companies large and small use to power their distributed search and analytics engines, isn’t the hardest thing. What is very hard, though, is to provision the right amount of resources to run the service, especially when your users’ demand comes in spikes, without overpaying for unused capacity. Vizion.ai’s new Elasticsearch Service does away with all of this by essentially offering Elasticsearch as a service and only charging its customers for the infrastructure they use.
Vizion.ai’s service automatically scales up and down as needed. It’s a managed service and delivered as a SaaS platform that can support deployments on both private and public clouds, with full API compatibility with the standard Elastic stack that typically includes tools like Kibana for visualizing data, Beats for sending data to the service and Logstash for transforming the incoming data and setting up data pipelines. Users can easily create several stacks for testing and development, too, for example.
Vizion.ai GM and VP Geoff Tudor
“When you go into the AWS Elasticsearch service, you’re going to be looking at dozens or hundreds of permutations for trying to build your own cluster,” Vision.ai’s VP and GM Geoff Tudor told me. “Which instance size? How many instances? Do I want geographical redundancy? What’s my networking? What’s my security? And if you choose wrong, then that’s going to impact the overall performance. […] We do balancing dynamically behind that infrastructure layer.” To do this, the service looks at the utilization patterns of a given user and then allocates resources to optimize for the specific use case.
What VVizion.ai hasdone here is take some of the work from its parent company Panzura, a multi-cloud storage service for enterprises that has plenty of patents around data caching, and applied it to this new Elasticsearch service.
There are obviously other companies that offer commercial Elasticsearch platforms already. Tudor acknowledges this, but argues that his company’s platform is different. With other products, he argues, you have to decide on the size of your block storage for your metadata upfront, for example, and you typically want SSDs for better performance, which can quickly get expensive. Thanks to Panzura’s IP, Vizion.ai is able to bring down the cost by caching recent data on SSDs and keeping the rest in cheaper object storage pools.
He also noted that the company is positioning the overall Vizion.ai service, with the Elasticsearch service as one of the earliest components, as a platform for running AI and ML workloads. Support for TensorFlow, PredictionIO (which plays nicely with Elasticsearch) and other tools is also in the works. “We want to make this an easy serverless ML/AI consumption in a multi-cloud fashion, where not only can you leverage the compute, but you can also have your storage of record at a very cost-effective price point.”
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Workato raises $25M for its integration platform
Workato, a startup that offers an integration and automation platform for businesses that competes with the likes of MuleSoft, SnapLogic and Microsoft’s Logic Apps, today announced that it has raised a $25 million Series B funding round from Battery Ventures, Storm Ventures, ServiceNow and Workday Ventures. Combined with its previous rounds, the company has now received investments from some of the largest SaaS players, including Salesforce, which participated in an earlier round.
At its core, Workato’s service isn’t that different from other integration services (you can think of them as IFTTT for the enterprise), in that it helps you to connect disparate systems and services, set up triggers to kick off certain actions (if somebody signs a contract on DocuSign, send a message to Slack and create an invoice). Like its competitors, it connects to virtually any SaaS tool that a company would use, no matter whether that’s Marketo and Salesforce, or Slack and Twitter. And like some of its competitors, all of this can be done with a drag-and-drop interface.
What’s different, Workato founder and CEO Vijay Tella tells me, is that the service was built for business users, not IT admins. “Other enterprise integration platforms require people who are technical to build and manage them,” he said. “With the explosion in SaaS with lines of business buying them — the IT team gets backlogged with the various integration needs. Further, they are not able to handle all the workflow automation needs that businesses require to streamline and innovate on the operations.”
Battery Ventures’ general partner Neeraj Agrawal also echoed this. “As we’ve all seen, the number of SaaS applications run by companies is growing at a very rapid clip,” he said. “This has created a huge need to engage team members with less technical skill-sets in integrating all these applications. These types of users are closer to the actual business workflows that are ripe for automation, and we found Workato’s ability to empower everyday business users super compelling.”
Tella also stressed that Workato makes extensive use of AI/ML to make building integrations and automations easier. The company calls this Recipe Q. “Leveraging the tens of billions of events processed, hundreds of millions of metadata elements inspected and hundreds of thousands of automations that people have built on our platform — we leverage ML to guide users to build the most effective integration/automation by recommending next steps as they build these automations,” he explained. “It recommends the next set of actions to take, fields to map, auto-validates mappings, etc. The great thing with this is that as people build more automations — it learns from them and continues to make the automation smarter.”
The AI/ML system also handles errors and offers features like sentiment analysis to analyze emails and detect their intent, with the ability to route them depending on the results of that analysis.
As part of today’s announcement, the company is also launching a new AI-enabled feature: Automation Editions for sales, marketing and HR (with editions for finance and support coming in the future). The idea here is to give those departments a kit with pre-built workflows that helps them to get started with the service without having to bring in IT.
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