OpenAI shifts from nonprofit to ‘capped-profit’ to attract capital

OpenAI may not be quite so open going forward. The former nonprofit announced today that it is restructuring as a “capped-profit” company that cuts returns from investments past a certain point. But some worry that this move — or rather the way they made it — may result in making the innovative company no different from the other AI startups out there.
From now on, profits from any investment in the OpenAI LP (limited partnership, not limited profit) will be passed on to an overarching nonprofit company, which will disperse them as it sees fit. Profits in excess of a 100x return, that is.
In simplified terms, if you invested $10 million today, the profit cap will come into play only after that $10 million has generated $1 billion in returns. You can see why some people are concerned that this structure is “limited” in name only.
In a blog post, OpenAI explained


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Facebook open sources PyText NLP framework

Facebook AI Research is open sourcing some of the conversational AI tech it is using to power its Portal video chat display and M suggestions on Facebook Messenger.
The company announced today that its PyTorch-based PyText NLP framework is now available to developers.
Natural language processing deals with how systems parse human language and are able to make decisions and derive insights. The PyText framework, which the company sees as a conduit for AI researchers to move more quickly between experimentation and deployment will be particularly useful for tasks like document classification, sequence tagging, semantic parsing and multitask modeling, among others, Facebook says.
The company has built the framework to fit pretty seamlessly into research and production workflows with an emphasis on robustness and low-latency to meet the company’s real-time NLP needs. The product is responsible for models powering more than a billion daily predictions at Facebook.

Another big highlight is the framework’s modularity, allowing it


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Amazon Comprehend adds customized language lists to machine learning tool

Last year Amazon announced Comprehend, a natural language processing tool to help companies extract common words and phrases from a corpus of information. Today, a week ahead of its Re:invent customer conference, Amazon announced an enhancement to Comprehend that allows developers to build lists of specialized words and phrases without machine learning domain knowledge.
“Today we are excited to bring new customization features to Comprehend, which allow developers to extend Comprehend to identify natural language terms and classify text which is specialized to their team, business or industry,” Matt Wood, GM for deep learning and AI wrote in a blog post announcing the enhancement.
The key aspect of this is that Amazon is handling all of the complexity, allowing developers to add customized lists without having deep machine learning or natural language processing background. “Under the hood, Comprehend will do the heavy lifting to build, train, and host the customized machine learning


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Document editor Coda adds third-party integrations with G Suite, Slack, Twilio and more

Coda, the smart collaborative document editor that breaks down the barriers between documents, spreadsheets, databases and presentations, is today launching one of its most important updates since its launch in 2017. With this update, users will be able to pull in data from third-party sources and send out messages to their teams on Slack or by SMS and email. With this, the company’s take on building living documents that are essentially small apps is now really taking shape.
“Coda is a new type of documents,” Coda co-founder and CEO Shishir Mehrotra told me. “It combines the best of documents, spreadsheets, presentations, applications into a new surface. The goal is to allow anybody to build a doc as powerful as an app.” That means you can use your inventory spreadsheet to build a small inventory management app, for example, that lives entirely in a tabbed Coda document. Mehrotra noted that many businesses essentially run on documents


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Google Kubeflow, machine learning for Kubernetes, begins to take shape

Ever since Google created Kubernetes as an open source container orchestration tool, it has seen it blossom in ways it might never have imagined. As the project gains in popularity, we are seeing many adjunct programs develop. Today, Google announced the release of version 0.1 of the Kubeflow open source tool, which is designed to bring machine learning to Kubernetes containers.
While Google has long since moved Kubernetes into the Cloud Native Computing Foundation, it continues to be actively involved, and Kubeflow is one manifestation of that. The project was only first announced at the end of last year at Kubecon in Austin, but it is beginning to gain some momentum.
David Aronchick, who runs Kubeflow for Google, led the Kubernetes team for 2.5 years before moving to Kubeflow. He says the idea behind the project is to enable data scientists to take advantage of running machine learning jobs on Kubernetes clusters.


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Facebook announces PyTorch 1.0, a more unified AI framework

Though Facebook’s focus on day 1 of its F8 conference centered on the company’s recent struggles and their relationship with the phrase “taking broader responsibility,” day 2 shifted most of the pizazz to the technical advances its giant team has made over the past year.
Today, the company announced PyTorch 1.0, a new iteration of the framework that merges Python-based PyTorch with Caffe2 allowing developers to move from research to production in a more frictionless way without having to deal with migration.
At Facebook, the company’s AI efforts are split between two teams, the Facebook AI Research group (FAIR) and the company’s Applied Machine Learning team (AML). The distinction ultimately boils down to one division researching AI with seemingly limitless computational resources at their disposal and the other looking to implement lightweight machine learning models more suited for consumers. In the past, the former mission has been better-suited for the research-optimized PyTorch


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Cisco shakes up collaboration efforts; morphs Spark into Webex

Cisco’s collaboration business is evolving, with new leadership poised to take over and a new emphasis on artificial intelligence.The company recently announced that its Spark team messaging app will be rebranded and rolled into its Webex platform. And this week, it said Rowan Trollope – who has led Cisco’s collaboration efforts since 2012 – is leaving and will be replaced by Amy Chang, founder and CEO of Accompany, the AI business intelligence startup acquired for $270 million on Tuesday. [ Further reading: Chat happens: Your guide to 11 group-chat services ]Chang, a former Google employee, will resign from Cisco’s board to become senior vice president in charge of the Collaboration Technology Group. To read this article in full, please click here


Original URL: https://www.computerworld.com/article/3269581/collaboration/cisco-shakes-up-collaboration-efforts-morphs-spark-into-webex.html#tk.rss_all

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Voicera scoops up AI note-taking app Wrappup

Voicera wants to be the company that eliminates the need for human note taking once and for all. Their vision is an AI-driven voice recognition system that not only takes notes, but identifies speakers and summarizes key points and action items. Today, the company announced it had acquired a similar startup, Wrappup, an AI-fueled note taking app that fits in nicely with that vision.
The Wrappup team is joining Voicera immediately. Terms were not disclosed.
Voicera CEO Omar Tawakol certainly saw the fit. “Both companies approached the problem with meetings in synergistic ways. Wrappup’s mobile-first, in-person meeting product complements and extends Voicera’s initial focus on conference calls,” he said in a statement.
Wrappup’s special strength it turns out it is identifying the salient points in a meeting in a mobile context. To that end, the company also announced the launch of a new mobile app. Chances are this combining of these two companies


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Juro grabs $2M to take the hassle out of contracts

UK startup Juro, which is applying a “design centric approach” and machine learning tech to help businesses speed up the authoring and management of sales contracts, has closed $2m in seed funding led by Point Nine Capital.
Prior investor Seedcamp also contributed to the round. Juro is announcing Taavet Hinrikus (TransferWise’s co-founder) as an investor now too, as well as Michael Pennington (Gumtree co-founder) and the family office of Paul Forster (co-founder of Indeed.com).
Back in January 2017 the London-based startup closed a $750,000 (£615k) seed round, though CEO and co-founder Richard Mabey tells us that was really better classed as an angel round — with Point Nine Capital only joining “late” in the day.
“We actually could have strung it out to Series A,” he says of the funding that’s being announced now. “But we had multiple offers come in and there is so much of an explosion in demand for the [machine learning] that


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