Dec
13
2018
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Chorus.ai rings up $33M for its platform that analyses sales calls to close more deals

Chorus.ai, a service that listens to sales calls in real time, and then transcribes and analyses them to give helpful tips to the salesperson, has raised $33 million to double down on the current demand for more AI-based tools in the enterprise.

The Series B is being led by Georgian Partners, with participation also from Redpoint Ventures and Emergence Capital, previous investors that backed Israeli-founded, SF-based Chorus.ai in its $16 million Series A two years ago.

In the gap between then and now, the startup has seen strong growth, listening in to some 5 million calls, and performing hundreds of thousands of hours of transcriptions for around 200 customers, including Adobe, Zoom, and Outreach (among others that it will not name).

Micha Breakstone, the co-founder (who has a pretty long history in conversational AI, heading up R&D at Ginger Software and then Intel after it acquired the startup; and before that building the tech that eventually became Summly and got acquired by Yahoo, among other roles), says that while the platform gives information and updates to salespeople in real time, much of the focus today is on providing information to users post-conversation, based on both audio and video calls.

One of its big areas is “smart themes” — patterns and rules Chorus has learned through all those calls. For example, it has identified what kind of language the most successful sales people are using and in turn prompts those who are less successful to use it more. Two general tips Breakstone told me about: using more collaborative terms like we and us; and giving more backstory to clients, although there will be more specific themes and approaches based on Chorus’s specific customers and products.

“I’d say we are super attuned to our customers and what they need and want,” Breakstone said. Which makes sense given the whole premise of Chorus.

It also creates smart “playlists” for managers who will almost certainly never have the time to review hundreds of hours of calls but might want to hear instructive highlights or ‘red alert’ moments where a more senior person might need to step in to save or close a deal.

There are currently what seems like dozens of startups and larger businesses that are currently tackling the opportunity to provide “conversational intelligence” to sales teams, using advances in natural language processing, voice recognition, machine learning and big data to help turn every sales person into a Jerry Maguire (yes, I know he’s an agent, but still, he needs to close deals, and he’s a salesman). They include TalkIQ (which has now been acquired by Dialpad), People.AI, Gong, Voicera, VoiceOps, and I’m pulling from a long list.

“We were among the very first to start this, no one knew what conversational intelligence was before us,” Breakstone says. He describes most of what was out in the market at the time as “Nineties technology” and adds that “our tech is superior because we built it in the correct way from the ground up, with nothing sent to a third party.”

He says that this is one reason why the company has negative churn — it essentially wins customers and hasn’t lost any. And having the tech all in-house not only means the platform is smarter and more accurate, but that helps with compliance around regulations like GDPR, which also has been a boost to its business. It’s also scored well on metrics around reps hitting targets better with its tools (the company claims its products lead to 50 percent greater quota attainment and ‘ramp time’ up by 30 percent for new sales people who use it).

Chorus.ai has helped us become a smarter sales organization as we’ve scaled. We have visibility into our sales conversations and what is working across all of our offices”, said Greg Holmes, Head of Sales for Zoom Video Communications, in a statement. “We’ve seen a drastic reduction in new hire ramp times and higher sales productivity with even more reps hitting quota. Chorus.ai is a game changer.”

Chorus has raised $55 million to date and Breakstone said he would not disclose its valuation — despite my best attempts to use some of those sales tips to winkle the information out of him. But I understand it to be “significantly higher” than in its last round, and definitely in the hundreds of millions.

As a point of reference, after its Series A two years ago, it was only valued at around $33 million post-money according to PitchBook.

“Maintaining high-quality sales conversations as you scale a sales organization is hard for many companies, but key to delivering predictable revenue growth. Chorus.ai’s Conversation Intelligence platform solves that challenge with a market-leading solution that is easy-to-use and delivers best-in-class results.” said Simon Chong, Managing Partner at Georgian Partners, in a statement. (Chong is joining the board with this round.) “Chorus.ai works with some of the best sales teams in the world and they love the product. We are very excited to partner with Chorus.ai on their next phase of growth as they help world class sales teams reach higher quota attainment and efficiency.”

Dec
11
2018
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TechSee nabs $16M for its customer support solution built on computer vision and AR

Chatbots and other AI-based tools have firmly found footing in the world of customer service, used either to augment or completely replace the role of a human responding to questions and complaints, or (sometimes, annoyingly, at the same time as the previous two functions) sell more products to users.

Today, an Israeli startup called TechSee is announcing $16 million in funding to help build out its own twist on that innovation: an AI-based video service, which uses computer vision, augmented reality and a customer’s own smartphone camera to provide tech support to customers, either alongside assistance from live agents, or as part of a standalone customer service “bot.”

Led by Scale Venture Partners — the storied investor that has been behind some of the bigger enterprise plays of the last several years (including Box, Chef, Cloudhealth, DataStax, Demandbase, DocuSign, ExactTarget, HubSpot, JFrog and fellow Israeli AI assistance startup WalkMe), the Series B also includes participation from Planven Investments, OurCrowd, Comdata Group and Salesforce Ventures. (Salesforce was actually announced as a backer in October.)

The funding will be used both to expand the company’s current business as well as move into new product areas like sales.

Eitan Cohen, the CEO and co-founder, said that the company today provides tools to some 15,000 customer service agents and counts companies like Samsung and Vodafone among its customers across verticals like financial services, tech, telecoms and insurance.

The potential opportunity is big: Cohen estimates there are about 2 million customer service agents in the U.S., and about 14 million globally.

TechSee is not disclosing its valuation. It has raised around $23 million to date.

While TechSee provides support for software and apps, its sweet spot up to now has been providing video-based assistance to customers calling with questions about the long tail of hardware out in the world, used for example in a broadband home Wi-Fi service.

In fact, Cohen said he came up with the idea for the service when his parents phoned him up to help them get their cable service back up, and he found himself challenged to do it without being able to see the set-top box to talk them through what to do.

So he thought about all the how-to videos that are on platforms like YouTube and decided there was an opportunity to harness that in a more organised way for the companies providing an increasing array of kit that may never get the vlogger treatment.

“We are trying to bring that YouTube experience for all hardware,” he said in an interview.

The thinking is that this will become a bigger opportunity over time as more services get digitised, the cost of components continues to come down and everything becomes “hardware.”

“Tech may become more of a commodity, but customer service does not,” he added. “Solutions like ours allow companies to provide low-cost technology without having to hire more people to solve issues [that might arise with it.]”

The product today is sold along two main trajectories: assisting customer reps; and providing unmanned video assistance to replace some of the easier and more common questions that get asked.

In cases where live video support is provided, the customer opts in for the service, similar to how she or he might for a support service that “takes over” the device in question to diagnose and try to fix an issue. Here, the camera for the service becomes a customer’s own phone.

Over time, that live assistance is used in two ways that are directly linked to TechSee’s artificial intelligence play. First, it helps to build up TechSee’s larger back catalogue of videos, where all identifying characteristics are removed with the focus solely on the device or problem in question. Second, the experience in the video is also used to build TechSee’s algorithms for future interactions. Cohen said there are now “millions” of media files — images and videos — in the company’s catalogue.

The effectiveness of its system so far has been pretty impressive. TechSee’s customers — the companies running the customer support — say they have on average seen a 40 percent increase in customer satisfaction (NPS scores), a 17 percent decrease in technician dispatches and between 20 and 30 percent increase in first-call resolutions, depending on the industry.

TechSee is not the only company that has built a video-based customer engagement platform: others include Stryng, CallVU and Vee24. And you could imagine companies like Amazon — which is already dabbling in providing advice to customers based on what its Echo Look can see — might be interested in providing such services to users across the millions of products that it sells, as well as provide that as a service to third parties.

According to Cohen, what TechSee has going for it compared to those startups, and also the potential entry of companies like Microsoft or Amazon into the mix, is a head start on raw data and a vision of how it will be used by the startup’s AI to build the business.

“We believe that anyone who wants to build this would have a challenge making it from scratch,” he said. “This is where we have strong content, millions of images, down to specific model numbers, where we can provide assistance and instructions on the spot.”

Salesforce’s interest in the company, he said, is a natural progression of where that data and customer relationship can take a business beyond responsive support into areas like quick warranty verification (for all those times people have neglected to do a product registration), snapping fender benders for insurance claims and of course upselling to other products and services.

“Salesforce sees the synergies between the sales cloud and the service cloud,” Cohen said.

“TechSee recognized the great potential for combining computer vision AI with augmented reality in customer engagement,” said Andy Vitus, partner at Scale Venture Partners, who joins the board with this round. “Electronic devices become more complex with every generation, making their adoption a perennial challenge. TechSee is solving a massive problem for brands with a technology solution that simplifies the customer experience via visual and interactive guidance.”

Nov
14
2018
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‘Software robot’ startup UiPath expands Series C to $265M at a $3B valuation

UiPath, a startup that works in the growing area of RPA, or robotic process automation — where AI-based software is used to help businesses run repetitive or mundane back-office tasks, to free up humans to tackle more sophisticated work — has raised money for the third time this year. The company is today announcing that it has closed out its Series C at $265 million — $40 million higher than the amount it said it was aiming for two months ago.

UiPath is now disclosing new investors in the round — namely, IVP, Madrona Venture Group and Meritech Capital — plus secondary sales for employees to give them liquidity, which made up the difference. The company has confirmed to me that the transactions were done at the same valuation as the rest of the Series C, at $3 billion. The Series C is still led by CapitalG and Sequoia Capital as before.

For some context, earlier this year, the company also raised a Series B of $153 million at a $1.1 billion valuation.

UiPath’s strong valuation hike and the rapid pace of its funding come at a time when both the company and its rivals are all growing quickly, as enterprises rush to capitalise on the rise of artificial intelligence in the workplace. In the case of RPA, the promise is that it will help bring down the cost of doing business and improve organizations’ efficiency. UiPath’s mantra is to provide “one robot for every person,” essentially doubling a company’s workforce without the need to hire more people.

UiPath says that its current annual run rate is now $150 million, up from a $100 million ARR figure it put out just two months ago, with customers now numbering at 2,100 and including the US Army, Defense Logistics Agency, GSA, IRS, NASA, Navy, and the Department of Veterans Affairs. One source at the company tells me that it’s getting approached “almost daily” for more funding at the moment.

At the same time, the competitive landscape is most definitely heating up. We’ve heard that Automation Anywhere, which also just raised money — $250 million — earlier this year, may also be looking to raise more (we’re looking into it). And just earlier this week, we reported that another RPA player, Kofax, acquired a division of Nuance for $400 million to ramp up its image processing business.

“I am honored to have IVP, Madrona Venture Group and Meritech Capital as new investors in UiPath. Their leadership and guidance will no doubt help us continue to define and lead the Automation First era for customers everywhere. UiPath has had many funding options and I believe we have selected the investors that align best with our culture and beliefs. I am humbled as the syndicate of unquestionably top-tier venture capital firms who believe in UiPath and support our future,” said UiPath CEO and co- founder Daniel Dines said in a statement. “Additionally, it is a core UiPath principle to share the success of the company in a meaningful way with our hard-working and long-time employees and we were excited to be able to extend the opportunity, at their personal choice, to realize partial liquidity in this round.”

Updated with clarification about the employee liquidity sales and new investor names.

Oct
23
2018
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Customer service ‘behavioral pairing’ startup Afiniti quietly raised $130M at a $1.6B valuation

Artificial intelligence touches just about every aspect of the tech world these days, aiming to provide new ways of making old processes work better. Now, a startup that has built an AI platform that tackles the ever-present, but never-perfect, business of customer service has quietly raised a large round of funding as it gears up for its next act, an IPO. Afiniti, which uses machine learning and behavioral science to better match customers with customer service agents — “behavioral pairing” is how it describes the process — has closed a $130 million round of funding ($75 million cash, $60 million debt) — a Series D that Afiniti CEO Zia Chishti says values his company at $1.6 billion.

If you are not familiar with the name Afiniti, you might not be alone. The company has been relatively under the radar, in part because it has never made much of an effort to publicise itself, and in part because the funding that it has raised up to now has largely been from outside the hive of VCs that swarm around many other startup deals that push those startups into the limelight.

At the same time, its backers make for a pretty illustrious list. This latest round includes former Verizon CEO Ivan SeidenbergFred Ryan, the CEO and publisher of the Washington Post; and investors Global Asset ManagementThe Resource Group (which Chishti helped found), Zeke Capitalas well as unnamed Australian investors.

The previous Series C round of $26.5 million, also has an interesting list of backers and also was not widely reported. They included McKinsey & Company, Elisabeth Murdoch, former Thomson Reuters CEO Tom Glocer, and former BP CEO John Browne, alongside Global Asset Management, The Resource Group, Seidenberg and Ryan.

That Series C was at a $100 million valuation, meaning that Afiniti’s valuation has increased more than 10 times in the last year on the back of 100 percent revenue growth each year over the last five.

That momentum led the company also to file confidentially for an IPO — although ultimately Chishti told TechCrunch that the company decided to raise privately at the potential IPO valuation since the money was easy to come by. (It’s also been one of the reasons he said he’s also rebuffed acquisitions, although at least one of the companies that’s approached him, McKinsey, now an investor.)

Now, Chishti — who is a repeat entrepreneur, with his previous company, Align Technology (which makes teeth alignment alternatives to braces), now at a $24 billion market cap — said that Afiniti has started to tip into profitability, so it seems the prospect of an IPO might be back on the table. That is possibly one reason that the company has started to speak to the press more and to make itself more visible.

Chishti and Afiniti are based out of the US, but it has roots into a range of local businesses globally in part by way of its well-connected team of advisors and local leaders. Among them, Princess Beatrice (or Beatrice York), currently 8th in line to the throne to succeed Queen Elizabeth, is the company’s vice president of partnerships. Alonso Aznar, the son of the former prime minister of Spain, runs Afiniti’s operations in Madrid.

The company itself sits in the general area of CRM, and specifically among that wave of startups that are trying to build tools using AI and other new technology to improve on the old ways of getting things done (it’s not alone: just today we noted that People.ai raised $30 million for its own AI-based CRM tools).

Afiniti on one hand calls itself a traditional AI company, but on the other, its CEO laments how overused and hackneyed the term has become. “AI is just a bubble,” he said in an interview. “The intensity of interest in AI is unwarranted because nothing has changed. It’s the same algorithms and software, and we just have faster hardware now.”

In actual fact, what Afiniti does is supply an AI layer to a process that is otherwise “ninety-nine percent human”, in the words of Chishti. The company uses AI to analyse sales people’s performance with specific types of calls and situations, and also to analyse customers in terms of their previous interactions with a company. It then matches up customer service reps who it believes will be most compatible with specific customers.

Afiniti’s pricing model has been an important lever for getting its foot in the door with companies. The company does not price its service per-seat or even per-month, but on a calculation between how well the company does when its call routing and running through Afiniti, versus how much is sold when it does not.

“We run systems on for 15 minutes, off for 5 minutes, and we do that perpetually,” Chishti said. It integrates with a company’s CRM, sales and telephony systems at the back end, in order both to route calls but also to track when those calls result in a sale. “We count the revenues, calculate the delta, and we get a share of that delta.”

If that sounds like a tricky measure, it doesn’t to customers, it seems. The zero-cost-to-try-it model is how it has surmounted the hurdle of getting used by a number of large, often slow-moving carriers and other large incumbents. “It means we have to continuously prove our value,” Chishti added.

As one example of how this works out, he used the example of Verizon (which is the owner of TechCrunch, by way of Oath). “Say Verizon makes $120 billion in revenues in a year,” he said, “and $30 billion of that is in phone-based sales. Afiniti would make $600 million on that.” Times that by dozens of customers in 22 countries, and that may point to how the company has quietly reached the valuation that it has.

Beyond its core product, the company has dozens of patents and more in the application phase in the US and other jurisdictions.

Sep
19
2018
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IBM launches cloud tool to detect AI bias and explain automated decisions

IBM has launched a software service that scans AI systems as they work in order to detect bias and provide explanations for the automated decisions being made — a degree of transparency that may be necessary for compliance purposes not just a company’s own due diligence.

The new trust and transparency system runs on the IBM cloud and works with models built from what IBM bills as a wide variety of popular machine learning frameworks and AI-build environments — including its own Watson tech, as well as Tensorflow, SparkML, AWS SageMaker, and AzureML.

It says the service can be customized to specific organizational needs via programming to take account of the “unique decision factors of any business workflow”.

The fully automated SaaS explains decision-making and detects bias in AI models at runtime — so as decisions are being made — which means it’s capturing “potentially unfair outcomes as they occur”, as IBM puts it.

It will also automatically recommend data to add to the model to help mitigate any bias that has been detected.

Explanations of AI decisions include showing which factors weighted the decision in one direction vs another; the confidence in the recommendation; and the factors behind that confidence.

IBM also says the software keeps records of the AI model’s accuracy, performance and fairness, along with the lineage of the AI systems — meaning they can be “easily traced and recalled for customer service, regulatory or compliance reasons”.

For one example on the compliance front, the EU’s GDPR privacy framework references automated decision making, and includes a right for people to be given detailed explanations of how algorithms work in certain scenarios — meaning businesses may need to be able to audit their AIs.

The IBM AI scanner tool provides a breakdown of automated decisions via visual dashboards — an approach it bills as reducing dependency on “specialized AI skills”.

However it is also intending its own professional services staff to work with businesses to use the new software service. So it will be both selling AI, ‘a fix’ for AI’s imperfections, and experts to help smooth any wrinkles when enterprises are trying to fix their AIs… Which suggests that while AI will indeed remove some jobs, automation will be busy creating other types of work.

Nor is IBM the first professional services firm to spot a business opportunity around AI bias. A few months ago Accenture outed a fairness tool for identifying and fixing unfair AIs.

So with a major push towards automation across multiple industries there also looks to be a pretty sizeable scramble to set up and sell services to patch any problems that arise as a result of increasing use of AI.

And, indeed, to encourage more businesses to feel confident about jumping in and automating more. (On that front IBM cites research it conducted which found that while 82% of enterprises are considering AI deployments, 60% fear liability issues and 63% lack the in-house talent to confidently manage the technology.)

In additional to launching its own (paid for) AI auditing tool, IBM says its research division will be open sourcing an AI bias detection and mitigation toolkit — with the aim of encouraging “global collaboration around addressing bias in AI”.

“IBM led the industry in establishing trust and transparency principles for the development of new AI technologies. It’s time to translate principles into practice,” said David Kenny, SVP of cognitive solutions at IBM, commenting in a statement. “We are giving new transparency and control to the businesses who use AI and face the most potential risk from any flawed decision making.”

Sep
18
2018
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Microsoft launches new AI applications for customer service and sales

Like virtually every other major tech company, Microsoft is currently on a mission to bring machine learning to all of its applications. It’s no surprise then that it’s also bringing ‘AI’ to its highly profitable Dynamics 365 CRM products. A year ago, the company introduced its first Dynamics 365 AI solutions and today it’s expanding this portfolio with the launch of three new products: Dynamics 365 AI for Sales, Customer Service and Market Insights.

“Many people, when they talk about CRM, or ERP of old, they referred to them as systems of oppression, they captured data,” said Alysa Taylor, Microsoft corporate VP for business applications and industry. “But they didn’t provide any value back to the end user — and what that end user really needs is a system of empowerment, not oppression.”

It’s no secret that few people love their CRM systems (except for maybe a handful of Dreamforce attendees), but ‘system of oppression’ is far from the ideal choice of words here. Yet Taylor is right that early systems often kept data siloed. Unsurprisingly, Microsoft argues that Dynamics 365 does not do that, allowing it to now use all of this data to build machine learning-driven experiences for specific tasks.

Dynamics 365 AI for Sales, unsurprisingly, is meant to help sales teams get deeper insights into their prospects using sentiment analysis. That’s obviously among the most basic of machine learning applications these days, but AI for Sales also helps these salespeople understand what actions they should take next and which prospects to prioritize. It’ll also help managers coach their individual sellers on the actions they should take.

Similarly, the Customer Service app focuses on using natural language understanding to understand and predict customer service problems and leverage virtual agents to lower costs. Taylor used this part of the announcement to throw some shade at Microsoft’s competitor Salesforce. “Many, many vendors offer this, but they offer it in a way that is very cumbersome for organizations to adopt,” she said. “Again, it requires a large services engagement, Salesforce partners with IBM Watson to be able to deliver on this. We are now out of the box.”

Finally, Dynamics 365 AI for Market Insights does just what the name implies: it provides teams with data about social sentiment, but this, too, goes a bit deeper. “This allows organizations to harness the vast amounts of social sentiment, be able to analyze it, and then take action on how to use these insights to increase brand loyalty, as well as understand what newsworthy events will help provide different brand affinities across an organization,” Taylor said. So the next time you see a company try to gin up some news, maybe it did so based on recommendations from Office 365 AI for Market Insights.

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.

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