May
07
2018
--

Microsoft and DJI team up to bring smarter drones to the enterprise

At the Microsoft Build developer conference today, Microsoft and Chinese drone manufacturer DJI announced a new partnership that aims to bring more of Microsoft’s machine learning smarts to commercial drones. Given Microsoft’s current focus on bringing intelligence to the edge, this is almost a logical partnership, given that drones are essentially semi-autonomous edge computing devices.

DJI also today announced that Azure is now its preferred cloud computing partner and that it will use the platform to analyze video data, for example. The two companies also plan to offer new commercial drone solutions using Azure IoT Edge and related AI technologies for verticals like agriculture, construction and public safety. Indeed, the companies are already working together on Microsoft’s FarmBeats solution, an AI and IoT platform for farmers.

As part of this partnership, DJI is launching a software development kit (SDK) for Windows that will allow Windows developers to build native apps to control DJI drones. Using the SDK, developers can also integrate third-party tools for managing payloads or accessing sensors and robotics components on their drones. DJI already offers a Windows-based ground station.

“DJI is excited to form this unique partnership with Microsoft to bring the power of DJI aerial platforms to the Microsoft developer ecosystem,” said Roger Luo, DJI president, in today’s announcement. “Using our new SDK, Windows developers will soon be able to employ drones, AI and machine learning technologies to create intelligent flying robots that will save businesses time and money and help make drone technology a mainstay in the workplace.”

Interestingly, Microsoft also stresses that this partnership gives DJI access to its Azure IP Advantage program. “For Microsoft, the partnership is an example of the important role IP plays in ensuring a healthy and vibrant technology ecosystem and builds upon existing partnerships in emerging sectors such as connected cars and personal wearables,” the company notes in today’s announcement.

May
07
2018
--

Microsoft brings more AI smarts to the edge

At its Build developer conference this week, Microsoft is putting a lot of emphasis on artificial intelligence and edge computing. To a large degree, that means bringing many of the existing Azure services to machines that sit at the edge, no matter whether that’s a large industrial machine in a warehouse or a remote oil-drilling platform. The service that brings all of this together is Azure IoT Edge, which is getting quite a few updates today. IoT Edge is a collection of tools that brings AI, Azure services and custom apps to IoT devices.

As Microsoft announced today, Azure IoT Edge, which sits on top of Microsoft’s IoT Hub service, is now getting support for Microsoft’s Cognitive Services APIs, for example, as well as support for Event Grid and Kubernetes containers. In addition, Microsoft is also open sourcing the Azure IoT Edge runtime, which will allow developers to customize their edge deployments as needed.

The highlight here is support for Cognitive Services for edge deployments. Right now, this is a bit of a limited service as it actually only supports the Custom Vision service, but over time, the company plans to bring other Cognitive Services to the edge as well. The appeal of this service is pretty obvious, too, as it will allow industrial equipment or even drones to use these machine learning models without internet connectivity so they can take action even when they are offline.

As far as AI goes, Microsoft also today announced that it will bring its new Brainwave deep neural network acceleration platform for real-time AI to the edge.

The company has also teamed up with Qualcomm to launch an AI developer kit for on-device inferencing on the edge. The focus of the first version of this kit will be on camera-based solutions, which doesn’t come as a major surprise given that Qualcomm recently launched its own vision intelligence platform.

IoT Edge is also getting a number of other updates that don’t directly involve machine learning. Kubernetes support is an obvious one and a smart addition, given that it will allow developers to build Kubernetes clusters that can span both the edge and a more centralized cloud.

The appeal of running Event Grid, Microsoft’s event routing service, at the edge is also pretty obvious, given that it’ll allow developers to connect services with far lower latency than if all the data had to run through a remote data center.

Other IoT Edge updates include the planned launch of a marketplace that will allow Microsoft partners and developers to share and monetize their edge modules, as well as a new certification program for hardware manufacturers to ensure that their devices are compatible with Microsoft’s platform. IoT Edge, as well as Windows 10 IoT and Azure Machine Learning, will also soon support hardware-accelerated model evaluation with DirextX 12 GPU, which is available in virtually every modern Windows PC.

May
04
2018
--

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. Kubeflow lets machine learning teams take existing jobs and simply attach them to a cluster without a lot of adapting.

With today’s announcement, the project begins to move ahead, and according to a blog post announcing the milestone, brings a new level of stability, while adding a slew of new features that the community has been requesting. These include Jupyter Hub for collaborative and interactive training on machine learning jobs and Tensorflow training and hosting support, among other elements.

Aronchick emphasizes that as an open source project you can bring whatever tools you like, and you are not limited to Tensorflow, despite the fact that this early version release does include support for Google’s machine learning tools. You can expect additional tool support as the project develops further.

In just over 4 months since the original announcement, the community has grown quickly with over 70 contributors, over 20 contributing organizations along with over 700 commits in 15 repositories. You can expect the next version, 0.2, sometime this summer.

Apr
09
2018
--

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 it made sense to do a round now rather than wait for the A. The whole legal industry is undergoing radical change and we want to be leading it.”

Juro’s SaaS product is an integrated contracts workflow that combines contract creation, e-signing and commenting capabilities with AI-powered contract analytics.

Its general focus is on customers that have to manage a high volume of contacts — such as marketplaces.

The 2016-founded startup is not breaking out any customer numbers yet but says its client list includes the likes of Estee Lauder, Deliveroo and Nested. And Mabey adds that “most” of its demand is coming from enterprise at this point, noting it has “several tech unicorns and Fortune 500 companies in trial”.

While design is clearly a major focus — with the startup deploying clean-looking templates and visual cues to offer a user-friendly ‘upgrade’ on traditional legal processes — the machine learning component is its scalable, value-added differentiator to serve the target b2b users by helping them identify recurring sticking points in contract negotiations and keep on top of contract renewals.

Mabey tells TechCrunch the new funding will be used to double down on development of the machine learning component of the product.

“We’re not the first to market in contract management by about 25 years,” he says with a smilie. “So we have always needed to prove out our vision of why the incumbents are failing. One part of this is clunky UX and we’ve succeeded so far in replacing legacy providers through better design (e.g. we replace DocuSign at 80% of our customers).

“But the thing we and our investors are really excited about is not just helping businesses with contract workflow but helping them understand their contract data, auto-tag contracts, see pattens in negotiations and red flag unusual contract terms.”

While this machine learning element is where he sees Juro cutting out a competitive edge in an existing and established market, Mabey concedes it takes “quite a lot of capital to do well”. Hence taking more funding now.

“We need a level of predictive accuracy in our models that risk averse lawyers can get comfortable with and that’s a big ask!” he says.

Specifically, Juro will be using the funding to hire data scientists and machine learning engineers — building out the team at both its London and Riga offices. “We’re doing it like crazy,” adds Mabey. “For example, we just hired from the UK government Digital Service the data scientist who delivered the first ML model used by the UK government (on the gov.uk website).

“There is a huge opportunity here but great execution is key and we’re building a world class team to do it. It’s a big bet to grow revenue as quickly as we are and do this kind of R&D but that’s just what the market is demanding.”

Juro’s HQ remains in London for now, though Mabey notes its entire engineering team is based in the EU — between Riga, Amsterdam and Barcelona — “in part to avoid ‘Brexit risk’”.

“Only 27% of the team is British and we have customers operating in 12 countries — something I’m quite proud of — but it does leave us rather exposed. We’re very open minded about where we will be based in the future and are waiting to hear from the government on the final terms of Brexit,” he says when asked whether the startup has any plans to Brexit to Berlin.

“We always look beyond the UK for talent: if the government cannot provide certainty to our Romanian product designer (ex Kalo, Entrepreneur First) that she can stay in the UK post Brexit without risking a visa application, tbh it makes me less bullish on London!”

Mar
19
2018
--

Apple, IBM add machine learning to partnership with Watson-Core ML coupling

Apple and IBM may seem like an odd couple, but the two companies have been working closely together for several years now. That has involved IBM sharing its enterprise expertise with Apple and Apple sharing its design sense with IBM. The companies have actually built hundreds of enterprise apps running on iOS devices. Today, they took that friendship a step further when they announced they were providing a way to combine IBM Watson machine learning with Apple Core ML to make the business apps running on Apple devices all the more intelligent.

The way it works is that a customer builds a machine learning model using Watson, taking advantage of data in an enterprise repository to train the model. For instance, a company may want to help field service techs point their iPhone camera at a machine and identify the make and model to order the correct parts. You could potentially train a model to recognize all the different machines using Watson’s image recognition capability.

The next step is to convert that model into Core ML and include it in your custom app. Apple introduced Core ML at the Worldwide Developers Conference last June as a way to make it easy for developers to move machine learning models from popular model building tools like TensorFlow, Caffe or IBM Watson to apps running on iOS devices.

After creating the model, you run it through the Core ML converter tools and insert it in your Apple app. The agreement with IBM makes it easier to do this using IBM Watson as the model building part of the equation. This allows the two partners to make the apps created under the partnership even smarter with machine learning.

“Apple developers need a way to quickly and easily build these apps and leverage the cloud where it’s delivered. [The partnership] lets developers take advantage of the Core ML integration,” Mahmoud Naghshineh, general manager for IBM Partnerships and Alliances explained.

To make it even easier, IBM also announced a cloud console to simplify the connection between the Watson model building process and inserting that model in the application running on the Apple device.

Over time, the app can share data back with Watson and improve the machine learning algorithm running on the edge device in a classic device-cloud partnership. “That’s the beauty of this combination. As you run the application, it’s real time and you don’t need to be connected to Watson, but as you classify different parts [on the device], that data gets collected and when you’re connected to Watson on a lower [bandwidth] interaction basis, you can feed it back to train your machine learning model and make it even better,” Naghshineh said.

The point of the partnership has always been to use data and analytics to build new business processes, by taking existing approaches and reengineering them for a touch screen.

“This adds a level of machine learning to that original goal moving it forward to take advantage of the latest tech. “We are taking this to the next level through machine learning. We are very much on that path and bringing improved accelerated capabilities and providing better insight to [give users] a much greater experience,” Naghshineh said.

Mar
06
2018
--

Intelligo is using AI to make background checks relevant again

To realize that the background check industry needs an overhaul look no further than the backlog of 700,000 background checks faced by the federal agency that handles all background checks for sensitive government positions. This backlog has essentially rendered background checks useless, as many agencies are able to give security clearances on a temporary basis before a background check is even started.

Intelligo is an Israeli company trying to make background checks relevant again by using AI and machine learning to not only speed up and automate the process, but also run more thorough checks.

Launching out of beta today, the company has raised $6.8M to date – a seed round of $1.1M and a Series A of $5.7M. They boast investors like Eileen Murray (Co-CEO of Bridgewater Associates) and advisors like the former director of the NSA Michael McConnell and former Managing Director of the Israel Ministry of Defense Pinhas Buchris.

Currently most serious background checks are done manually. This means that when an analyst creating a report comes across a new data source they need to decide if it’s worth taking the time to parse it and add it to the report. Consequently, many important sources like social media pages and news sites are left out of reports. It also means that background checks can take up to a week or longer, which is frustrating for the company and applicant.

Alternatively, Intelligo’s solution is primarily driven by an automated machine learning platform that can indiscriminately look at all thousands of data sources without concern for how much manual labor it will take. Reports are also provided in a user-friendly interactive dashboard, which is a stark contrast to the dozens of typed pages that an old-school background check will be.

Automating the process also dramatically costs down on cost – Intelligo says their prices are half of the average market price, which is allowing small and midsize businesses to now get the benefit of a high-level background check that typically would only be used by a larger corporation.

The startup also offers an ongoing monitoring product designed for the investment world. Funds often want the ability to monitor their portfolio companies and management teams even after the initial due diligence process, and by using an automated platform Intelligo can let let funds know of management issues long before a human would find the source of the issue.

Mar
06
2018
--

Intelligo is using AI to make background checks relevant again

 To realize that the background check industry needs an overhaul look no further than the backlog of 700,000 background checks faced by the federal agency that handles all background checks for sensitive government positions. This backlog has essentially rendered background checks useless, as many agencies are able to give security clearances on a temporary basis before a background check is… Read More

Feb
27
2018
--

IBM Watson CTO Rob High on bias and other challenges in machine learning

 For IBM Watson CTO Rob High, the biggest technological challenge in machine learning right now is figuring out how to train models with less data. “It’s a challenge, it’s a goal and there’s certainly reason to believe that it’s possible,” High told me during an interview at the annual Mobile World Congress in Barcelona. Read More

Feb
22
2018
--

Feature Labs launches out of MIT to accelerate the development of machine learning algorithms

 Feature Labs, a startup with roots in research begun at MIT, officially launched today with a set of tools to help data scientists build machine learning algorithms more quickly. Co-founder and CEO Max Kanter says the company has developed a way to automate “feature engineering,” which is often a time consuming and manual process for data scientists. “Feature Labs helps… Read More

Feb
12
2018
--

Oracle to expand automation capabilities across developer cloud services

Larry Ellison, chairman of Oracle Corp. Last fall at Oracle OpenWorld, chairman Larry Ellison showed he was a man of the people by comparing the company’s new autonomous database service to auto-pilot on his private plane. Regardless, those autonomous capabilities were pretty advanced, providing customers with a self-provisioning, self-tuning and self-repairing database. Today, Oracle announced it was expanding that… Read More

Powered by WordPress | Theme: Aeros 2.0 by TheBuckmaker.com