May
07
2018
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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
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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.

Apr
02
2018
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Apple, in a very Apple move, is reportedly working on its own Mac chips

Apple is planning to use its own chips for its Mac devices, which could replace the Intel chips currently running on its desktop and laptop hardware, according to a report from Bloomberg.

Apple already designs a lot of custom silicon, including its chipsets like the W-series for its Bluetooth headphones, the S-series in its watches, its A-series iPhone chips, as well as customized GPU for the new iPhones. In that sense, Apple has in a lot of ways built its own internal fabless chip firm, which makes sense as it looks for its devices to tackle more and more specific use cases and remove some of its reliance on third parties for their equipment. Apple is already in the middle of in a very public spat with Qualcomm over royalties, and while the Mac is sort of a tertiary product in its lineup, it still contributes a significant portion of revenue to the company.

Creating an entire suite of custom silicon could do a lot of things for Apple, the least of which bringing in the Mac into a system where the devices can talk to each other more efficiently. Apple already has a lot of tools to shift user activities between all its devices, but making that more seamless means it’s easier to lock users into the Apple ecosystem. If you’ve ever compared connecting headphones with a W1 chip to the iPhone and just typical Bluetooth headphones, you’ve probably seen the difference, and that could be even more robust with its own chipset. Bloomberg reports that Apple may implement the chips as soon as 2020.

Intel may be the clear loser here, and the market is reflecting that. Intel’s stock is down nearly 8% after the report came out, as it would be a clear shift away from the company’s typical architecture where it has long held its ground as Apple moves on from traditional silicon to its own custom designs. Apple, too, is not the only company looking to design its own silicon, with Amazon looking into building its own AI chips for Alexa in another move to create a lock-in for the Amazon ecosystem. And while the biggest players are looking at their own architecture, there’s an entire suite of startups getting a lot of funding building custom silicon geared toward AI.

Apple declined to comment.

Mar
26
2018
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The Linux Foundation launches a deep learning foundation

Despite its name, the Linux Foundation has long been about more than just Linux. These days, it’s a foundation that provides support to other open source foundations and projects like Cloud Foundry, the Automotive Grade Linux initiative and the Cloud Native Computing Foundation. Today, the Linux Foundation is adding yet another foundation to its stable: the LF Deep Learning Foundation.

The idea behind the LF Deep Learning Foundation is to “support and sustain open source innovation in artificial intelligence, machine learning, and deep learning while striving to make these critical new technologies available to developers and data scientists everywhere.”

The founding members of the new foundation include Amdocs, AT&T, B.Yond, Baidu, Huawei, Nokia, Tech Mahindra, Tencent, Univa and ZTE. Others will likely join in the future.

“We are excited to offer a deep learning foundation that can drive long-term strategy and support for a host of projects in the AI, machine learning, and deep learning ecosystems,” said Jim Zemlin, executive director of The Linux Foundation.

The foundation’s first official project is the Acumos AI Project, a collaboration between AT&T and Tech Mahindra that was already hosted by the Linux Foundation. Acumos AI is a platform for developing, discovering and sharing AI models and workflows.

Like similar Linux Foundation-based organizations, the LF Deep Learning Foundation will offer different membership levels for companies that want to support the project, as well as a membership level for non-profits. All LF Deep Learning members have to be Linux Foundation members, too.

Mar
21
2018
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Clari raises $35M for its AI-based sales platform, expands into marketing and supply chain management

Clari — a startup that has built a predictive sales tool that provides just-in-time assistance for sales people close deals and for those who work in the bigger chain of command to monitor the progress of the sales operation — is capitalising on the big boom in interest for all things AI in the business world. The company is today announcing that it has closed a Series B round of $35 million, funding that it will be using to build out its own sales and marketing team and expand its platform capabilities.

The round was led by Tenaya Capital, the VC fund that started its life as a part of Lehman Brothers, along with participation from other new investors Thomvest Ventures and Blue Cloud Ventures, and previous investors Sequoia Capital, Bain Capital Ventures and Northgate Capital. It brings the total raised by Clari to $61 million.

Andy Byrne, the founder and CEO who is a repeat entrepreneur and has been involved in several exits, said the funding closed “definitely at an upround, and much bigger than we thought it was going to be,” but declined to give a number. For some context, Clari, according to Pitchbook, had a relatively modest post-money valuation of $83.5 million in its last round in 2014, so my guess is that it’s now comfortably into hundred-million territory, once you add in this latest $35 million.

The funding comes at an interesting time for AI startups, particularly those aimed at enterprise IT.

When Clari first emerged from stealth in April 2014, the idea of applying AI to solve pain points for non-technical people in organizations was a fairly nascent and still-novel concept.

Fast forward to today, things have moved very fast, as is often the case in the tech world. Now, you can’t seem to move for all the enterprise IT startups that are either using or claiming to use AI in their solutions. There are so many startup hopefuls, and so many organizations looking for the best way to use AI to improve their business and operations, that there are even startups being founded to manage that opportunity of connecting the two pieces together, such as Element AI.

“I’m not saying we were clairvoyant for targeting the idea of using AI for sales in 2013,” Byrne said. “There has been a large macro trend and if you happen to be a small company that is along for the ride. When we first launched, we had this thesis about AI for sales. Now it’s not the number three or two priority for sales teams, it’s number one. It’s everywhere. Businesses want to invest and spend more money on AI and making things more efficient.”

Clari says that its customer base has tripled in the last year, with customers including Adobe, Audi, Check Point Software, Equinix, Epicor Software Corporation, GE, and PerkinElmer.

Clari’s approach for using AI for the sales team comes in two main areas. First, the company’s system is aimed to reduce some of the busywork that salespeople have in maintaining and updating files on people, by bringing in a number of different data sources and using them to provide composite pictures of target companies that salespeople might have had to otherwise compile with more manual means. Second, Clari puts a lot of focus on its “Opportunity-to-Close (OTC) solutions” — a type of risk-analysis for salespeople and their managers to help them figure out which leads and strategic directly would be the most likely to produce sales.

“Working with Clari since inception, we have been impressed with its growth and strong execution,” said Aaref Hilaly, Partner at Sequoia Capital, in a statement. “Clari has fast become indispensable to many of the most successful sales teams, giving them visibility into their most important metrics: rep productivity, pipeline health, and forecast accuracy.”

Indeed, risk and outcome is a smart area to be in: using AI to help model this is a key area of focus in enterprise IT at the moment, according to feedback I’ve had from a number of others in the enterprise world.

“If you have 150 opportunities presented to you as a salesperson, how do you choose 10 where you should spend your time?” Byrne asked. “A more traditional CRM platform has never showcased your risk and outcomes.”

While up to now Clari has focused on providing intelligence on what is already in a company’s account database, the next step, Byrne noted, is to draw on data from around the web, providing completely new business leads to the sales team.

When we last covered a funding round for Clari, we noted that the company’s laser focus on sales was something that made the company stand out for investors: nailing one aspect of a business’s operations without distractions from other parts of the organization and what it could be spending time solving elsewhere (in fact, when you think about it, the very goal that Clari has been aiming to achieve for salespeople through its product).

But four years on, the company is now widening that ambition. It’s applying its AI engine now to help marketeers weigh up the best opportunities for reaching out to prospective customers; and interestingly it sounds like it will also be applying its engine to product development and specifically supply chain management.

Byrne described one customer, a medical device maker, that was encountering “inefficiencies” around what they should build and when to meet market demand. “Now that they can predict and forecast order bookings and revenue targets, and what’s happened is that their supply chain has become more efficient,” he said. “It is great example of how our AI is now being expanded.”

“The Clari team has leveraged its deep AI expertise to build a unique platform that surfaces predictive insights for sales reps, managers, and execs during the opportunity-to-close process,” said Brian Paul, MD at Tenaya Capital, in a statement. “We see a massive opportunity for AI to transform how sales teams operate which is clearly validated by Clari’s customers and the impressive growth the team has achieved.”

Mar
06
2018
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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
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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
21
2018
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Vectra raises $36M for its AI-based approach to cybersecurity intrusion detection

 With the trend of growing cybercrime showing no indication of abating, a startup called Vectra that has built an artificial intelligence-based system called Cognito to detect cyberattacks and mobilise security systems to respond to them has raised $36 million to expand its R&D and business development. This Series D comes on the back of a strong year for the startup, with 181 percent growth… Read More

Feb
12
2018
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Amazon may be developing AI chips for Alexa

 The Information has a report this morning that Amazon is working on building AI chips for the Echo, which would allow Alexa to more quickly parse information and get those answers. Read More

Nov
22
2017
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AWS ramps up in AI with new consultancy services and Rekognition features

 Ahead of Amazon’s big AWS division Re:invent conference next week, the company has announced two developments in the area of artificial intelligence. AWS is opening a machine learning lab, ML Solutions Lab, to pair Amazon machine learning experts with customers. And it’s releasing new features within Amazon Rekognition, Amazon’s deep learning-based image recognition platform. Read More

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