Google CEO Sundar Pichai announced Tuesday the company is updating its news service. Google/Business Insider

Intel Capital, the investment arm of the computer processor giant, is today announcing $72 million in funding for the 12 newest startups to enter its portfolio, bringing the total invested so far this year to $115 million. Announced at the company’s global summit currently underway in southern California, investments in this latest tranche cover artificial intelligence, Internet of Things, cloud services, and silicon. A detailed list is below.

Other notable news from the event included a new deal between the NBA and Intel Capital to work on more collaborations in delivering sports content, an area where Intel has already been working for years; and the news that Intel has now invested $125 million in startups headed by minorities, women and other under-represented groups as part of its Diversity Initiative. The mark was reached 2.5 years ahead of schedule, it said.

The range of categories of the startups that Intel is investing in is a mark of how the company continues to back ideas that it views as central to its future business — and specifically where it hopes its processors will play a central role, such as AI, IoT and cloud. Investing in silicon startups, meanwhile, is a sign of how Intel is also focusing on businesses that are working in an area that’s close to the company’s own DNA.

It’s hasn’t been a completely smooth road. Intel became a huge presence in the world of IT and early rise of desktop and laptop computers many years ago with its advances in PC processors, but its fortunes changed with the shift to mobile, which saw the emergence of a new wave of chip companies and designs for smaller and faster devices. Mobile is area that Intel itself acknowledged it largely missed out.

Later years have seen still other issues hit the company. For example, the Spectre security flaw (fixes for which are still being rolled out). And some of the business lines where Intel was hoping to make a mark have not panned out as it hoped they would. Just last month, Intel shut down development of its Vaunt smart glasses and reportedly the entirety of its new devices group.

The investments that Intel Capital makes, in contrast, are a fresher and more optimistic aspect of the company’s operations: they represent hopes and possibilities that still have everything to play for. And given that, on balance, things like AI and cloud services still have a long way to go before being truly ubiquitous, there remains a lot of opportunity for Intel.

“These innovative companies reflect Intel’s strategic focus as a data leader,” said Wendell Brooks, Intel senior vice president and president of Intel Capital, in a statement. “They’re helping shape the future of artificial intelligence, the future of the cloud and the Internet of Things, and the future of silicon. These are critical areas of technology as the world becomes increasingly connected and smart.”

Intel Capital since 1991 has put $12.3 billion into 1,530 companies covering everything from autonomous driving to virtual reality and e-commerce and says that more than 660 of these startups have gone public or been acquired. Intel has organised its investment announcements thematically before: last October, it announced $60 million in 15 big data startups.

Here’s a rundown of the investments getting announced today. Unless otherwise noted, the startups are based around Silicon Valley:

Avaamo is a deep learning startup that builds conversational interfaces based on neural networks to address problems in enterprises — part of the wave of startups that are focusing on non-consumer conversational AI solutions.

Fictiv has built a “virtual manufacturing platform” to design, develop and deliver physical products, linking companies that want to build products with manufacturers who can help them. This is a problem that has foxed many a startup (notable failures have included Factorli out of Las Vegas), and it will be interesting to see if newer advances will make the challenges here surmoutable.

Gamalon from Cambridge, MA, says it has built a machine learning platform to “teaches computers actual ideas.” Its so-called Idea Learning technology is able to order free-form data like chat transcripts and surveys into something that a computer can read, making the data more actionable. More from Ron here.

Reconova out of Xiamen, China is focusing on problems in visual perception in areas like retail, smart home and intelligent security.

Syntiant is an Irvine, CA-based AI semiconductor company that is working on ways of placing neural decision making on chips themselves to speed up processing and reduce battery consumption — a key challenge as computing devices move more information to the cloud and keep getting smaller. Target devices include mobile phones, wearable devices, smart sensors and drones.

Alauda out of China is a container-based cloud services provider focusing on enterprise platform-as-a-service solutions. “Alauda serves organizations undergoing digital transformation across a number of industries, including financial services, manufacturing, aviation, energy and automotive,” Intel said.

CloudGenix is a software-defined wide-area network startup, addressing an important area as more businesses take their networks and data into the cloud and look for cost savings. Intel says its customers use its broadband solutions to run unified communications and data center applications to remote offices, cutting costs by 70 percent and seeing big speed and reliability improvements.

Espressif Systems, also based in China, is a fabless semiconductor company, with its system-on-a-chip focused on IoT solutions.

VenueNext is a “smart venue” platform to deliver various services to visitors’ smartphones, providing analytics and more to the facility providing the services. Hospitals, sports stadiums and others are among its customers.

Lyncean Technologies is nearly 18 years old (founded in 2001) and has been working on something called Compact Light Source (CLS), which Intel describes as a miniature synchrotron X-ray source, which can be used for either extremely detailed large X-rays or very microscopic ones. This has both medical and security applications, making it a very timely business.

Movellus “develops semiconductor technologies that enable digital tools to automatically create and implement functionality previously achievable only with custom analog design.” Its main focus is creating more efficient approaches to designing analog circuits for systems on chips, needed for AI and other applications.

SiFive makes “market-ready processor core IP based on the RISC-V instruction set architecture,” founded by the inventors of RISC-V and led by a team of industry veterans.

Gamalon wants to change the game when it comes to understanding text-based customer communications. Instead of using neural networks to learn about a vast corpus of information, the startup takes a different approach, putting the text in a database and building decision trees to very rapidly train the data to arrive at the required information. Today, it announced a $20 million Series A investment led by Intel Capital.

Other participants in the round included .406 Ventures and Omidyar Technology Ventures along with existing investors Boston Seed Capital, Felicis Ventures and Rivas Capital. Today’s investment brings the total raised by the company since inception in 2013 to $32 million including backing from DARPA in earlier rounds.

Gamalon CEO Ben Vigoda says they developed a new approach to analyzing customer interactions because the state of the art in AI and machine learning was too much of a black box.

His company wants to change that by making the whole process much more interactive. To that end Gamalon also released a new tool called Idea Studio, a product that can automatically build learning trees to help users arrive at answers extremely fast or allow a business analyst or data scientists to simply enter a series of queries and build a decision tree on the fly based on the text. With neural networks, Vigoda says, the user has no control over the end result, but with Idea Studio you can edit the trees and refine the results immediately.

The product still needs a way to review all of the text-based content, of course, but instead of having humans categorize it all manually, with Gamalon you import your data into a database, do analytics on it and then make it available for rapid categorization and response.

This could have multiple utilities, whether for customer service agents to find answers very quickly or customers to interact with bots and find answers much faster. Analysts could use it to locate answers to business issues, and it’s sophisticated enough for data scientists to build machine learning projects based on a large corpus of data.

Naveen Rao, corporate vice president and general manager in the Artificial Intelligence Products Group at Intel Corporation says they like how Gamalon puts machine learning into hands of many different employees around the customer information use case. “We want enterprises of all levels of AI capability to take full advantage of this growing volume and complexity of data. Gamalon’s unique approach can help users better understand billions of customer communications, customize individual responses, and take action to better serve those customers,” Rao explained in a statement.

The company is based in Cambridge, MA and has 23 employees. They have six large customers including Avaya.

Facebook CEO Mark Zuckerberg speaks during the F8 Facebook Developers conference on May 1, 2018 in San Jose, California. Justin Sullivan/Getty Images

Android’s development studio is getting a new update as Google rolls out Android Studio 3.2 Canary, adding new tools for visual navigation editing and Jetpack.

The new release includes build tools for the new Android App Bundle format, Snapshots, a new optimizer for smaller app code and a new way to measure an app’s impact on battery life. The Snapshots tool is baked into the Android Emulator and is geared toward getting the emulator up and running in two seconds. All this is geared toward making Android app development easier as the company looks to woo developers — especially potentially early ones — into an environment that’s built around creating Android apps.

The visual navigation editing looks a bit like a flow chart, where users can move screens around and connect them. You can add new screens, position them in your flow, and under covers will help you manage the whole stack in the background. Google has increasingly worked to abstract away a lot of the complex elements of building applications, whether that’s making its machine learning framework TensorFlow more palatable by letting developers create tools using their preferred languages or trying to make it easier to build an app quickly. Visual navigation is one way to further abstract out the complex process of programming in different activities within an app.

As competition continues to exist between Apple and Google, it’s important that Google ensures that the apps are launching on Google Play in order to continue to drive Android device adoption. The sped-up emulator, in particular, may solve a pain point for developers that want to rapidly test parts of their apps and see how they may operate in the wild without having to wait for the app to load in an emulator or on a test device.

Sramana Mitra: One thing that I find very annoying of what venture capital allows entrepreneurs to do is ignore fundamentals. I think there is a very bad habit in venture-funded companies of ignoring...

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As Slack looks to woo larger and larger companies with the prospect of a simpler workplace collaboration tool, the company said it has now hit 8 million daily active users.

The company said it also has 3 million paid users. A darling in Silicon Valley, Slack was initially able to capitalize on pent-up demand for workplace communications tools that were much simpler and easy to use. Companies like Yammer, Microsoft, and others looked to remake internal communications in ways that looked more like consumer tools in the Web 2.0 era, but Slack came out with an approach that was initially just a slick chat and team communications tool. That helped it rocket to a $5.1 billion valuation and drive its initial adoption among smaller companies and startups.

Slack in September said it had around 6 million daily active users, 50,000 teams and 2 million paid users, and around $200 million in annual recurring revenue. So it’s a pretty significant jump over the past nine months or so, though the company still has to break from the perception that it’s a tool that’s just good for startups and smaller companies. The larger enterprise deals are the ones that tend to drive larger contracts — and additional revenue — as it looks to build a robust business. More than half of Slack’s users are outside the U.S., a signal that it looks to continue to expand into new regions that may demand tools like Slack beyond just domestic markets.

Slack has been trying to roll out additional tools to support those larger companies, rather than just operate as a chat tool that can get out of control when companies have thousands of employees. The company has invested heavily in machine learning tools to make it easier to search for answers that may already exist in some Slack channel or direct message. Slack also rolled out threads, a long-awaited feature that users often demanded though it wasn’t clear how that would exist in Slack’s simpler interface.

There are already startups looking to pick away at niches that the company might not necessarily fill, too. Slite, a startup looking to build a simpler notes tool that would create a smarter internal wiki of sorts, raised $4.4 million last month. There’s also Atlassian’s Stride, which opened up to developers in February this year. And Microsoft has its own Slack competitor, Teams, that continues to get pretty big updates. Slack clearly exposed a lot of pent-up demand for similar tools, and now faces a lot of competition going forward.

Slack started the Slack Fund as a way to woo developers to build tools for Slack, and early last year invested in 11 new companies. The company has been trying to create a robust ecosystem where developers can fill the niches that the company might be missing, but has looks to focus on its core products. The company says there are now more than 1,500 apps in the Slack directory.

In 2016, Walmart announced that it would begin testing grocery delivery in conjunction with Uber and Lyft.

Today, however, Reuters reports that those partnerships have come to an end, which was confirmed by Walmart and Uber.

TechCrunch first reported in 2015 about Uber’s plans to launch a merchant delivery system, wherein goods from retailers would be delivered (via the trunk) and Uber users would be transported to their destination simultaneously.

The deal with Walmart, alongside rival services like Lyft and Deliv, marked massive progress for this merchant delivery system. But things haven’t panned out long term.

“It is incredibly hard to deliver people and packages together,” said one of Reuters’ sources with a delivery company that works with Walmart and has direct knowledge of the matter. “They are two completely different business models.”

Walmart has a number of other channels through which it can offer delivery.

It has partnered with Postmates and DoorDash, but has excluded Instacart from its delivery partners list. According to Re/Code, Instacart was excluded from the partnership opportunity because Instacart wanted Walmart to list its retail items within the Instacart app, whereas Walmart wanted to use Instacart as a delivery partner while exclusively selling items on its own digital property.

This obviously comes at a time where the grocery delivery game is heating up. Amazon’s acquisition of Whole Foods has put pressure on incumbent grocery retailers to step up their digital presence and delivery capabilities.

Target acquired Alabama-based Shipt for $550 million in December of 2017. Meanwhile, Instacart has raised another $150 million this year, and recently announced a partnership with Walmart-owned Sam’s Club.

We’ve reached out to Uber and will update if/when we hear back.

Update: Uber has responded and provided the following statement:

UberRUSH fell under the team called “UberEverything” which is a collection of big bets, and the beginning of Uber as a platform. Recently we’ve been leveraging our platform into new products like Uber Eats, Uber Health or our aquisition with Jump Bikes. When we launched UberRUSH in 2014, we followed the same thinking, and wanted apply the model to help large and small businesses quickly and reliably move their goods.

After analyzing what made the most sense for our broader efforts, we decided to sunset UberRUSH worldwide on June 30th. Walmart has been an incredible partner for Uber these past few years and we have enjoyed serving Walmart’s customers and delivering their groceries through the UberRUSH platform. We are coordinating with Walmart to make this change as seamless as possible.

We’re already applying a lot of the lessons we learned together to our Uber Eats food delivery business. Since launching Eats two years ago, we’ve seen the business take off, growing through new markets, and forming new partnerships with restaurant owners around the world.

Sramana Mitra: A few trends questions. How do you process the current investment climate where capital is moving further and further upstream? How does a seed investor mitigate the Series A gap?...

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Between Microsoft Build and Google I/O, there are probably more people saying “AI” this week than any previous week in history. But the AI those companies deploy tends to live off in a cloud somewhere — XNOR puts it on devices that may not even be capable of an internet connection. The startup has just pulled in $12 million to continue its pursuit of bringing AI to the edge.

I wrote about the company when it spun off of Seattle-based, Paul Allen-backed AI2; its product is essentially a proprietary method of rendering machine learning models in terms of operations that can be performed quickly by nearly any processor. The speed, memory and power savings are huge, enabling devices with bargain-bin CPUs to perform serious tasks like real-time object recognition and tracking that normally take serious processing chops to achieve.

Since its debut it took $2.6 million in seed funding and has now filled up its A round, led by Madrona Venture Group, along with NGP Capital, Autotech Ventures and Catapult Ventures.

“AI has done great,” co-founder Ali Farhadi told me, “but for it to become revolutionary it needs to scale beyond where it is right now.”

The fundamental problem, he said, is that AI is too expensive — both in terms of processing time and in money required.

Nearly all major “AI” products do their magic by means of huge banks of computers in the cloud. You send your image or voice snippet or whatever, it does the processing with a machine learning model hosted in some data center, then sends the results back.

For a lot of stuff, that’s fine. It’s okay if Alexa responds in a second or two, or if your images get enhanced with metadata over a period of hours while you’re not paying attention. But if you need a result not just in a second, but in a hundredth of a second, there’s no time for the cloud. And increasingly, there’s no need.

XNOR’s technique allows things like computer vision and voice recognition to be stored and run on devices with extremely limited processing power and RAM. And we’re talking Raspberry Pi Zero here, not just like an older iPhone.

If you wanted to have a camera or smart home type device in every room of your home, monitoring for voices, responding to commands, sending its video feed in to watch for unauthorized visitors or emergency situations — that constant pipe to the cloud starts getting crowded real fast. Better not to send it at all.

This has the pleasant byproduct of not requiring what might be personal data to some cloud server, where you have to trust that it won’t be stored or used against your will. If the data is processed entirely on the device, it’s never shared with third parties. That’s an increasingly attractive proposition.

Developing a model for edge computing isn’t cheap, though. Although AI developers are multiplying, comparatively few are trying to run on resource-limited devices like old phones or cheap security cameras.

XNOR’s model lets a developer or manufacturer plug in a few basic attributes and get a model pre-trained for their needs.

Say you’re the cheap security camera maker; you need to recognize people and pets and fires, but not cars or boats or plants, you’re using such and such ARM core and camera and you need to render at five frames per second but only have 128 MB of RAM to work with. Ding — here’s your model.

Or say you’re a parking lot company and you need to recognize empty spots, license plates and people lurking suspiciously. You’ve got such and such a setup. Ding — here’s your model.

These AI agents can be dropped into various code bases fairly easily and never need to phone home or have their data audited or updated, they’ll just run like greased lightning on the platform. Farhadi told me they’ve established the most common use cases and devices through research and feedback, and many customers should be able to grab an “off the shelf” model just like that. That’s Phase 1, as he called it, and should be launching this fall.

Phase 2 (in early 2019) will allow for more customization, so for example if your parking lot model becomes a police parking lot model and needs to recognize a specific set of cars and people, or you’re using proprietary hardware not on the list. New models will be able to be trained up on demand.

And Phase 3 is taking models that normally run on cloud infrastructure and adapting and “XNORifying” them for edge deployment. No timeline on that one.

Although the technology lends itself in some ways to the needs of self-driving cars, Farhadi told me they aren’t going after that sector — yet. It’s still essentially in the prototype phase, he said, and creators of autonomous vehicles are currently trying to prove the idea works fundamentally, not trying to optimize and deliver it at lower cost.

Edge-based AI models will surely be increasingly important as the efficiency of algorithms improves, the power of devices rises and the demand for quick-turnaround applications grows. XNOR seems to be among the vanguard in this emerging area of the field, but you can almost certainly expect competition to expand along with the market.

True friends share everything, from purses to lawnmowers to the limitless energy they pull from the sun, wind, and deep underground. Green Power Exchange aims to make one of those sharing scenarios a bit simpler.

Green Power Exchange or GPX is running an ICO to create a coin that lets you trade energy. While there are a number of efforts on this front, GPX is fairly far along and already has 62 solar sites signed up to try the tech when it launches. Founder Christian Wentzel sees the project as a solution to the energy monopolies.

“The utility and power company either is a direct or quasi-monopoly, leaving no customer choice,” he said. “We want to change that and save consumers a ton of money while improving the environment.”

“Solar and wind power is cheap, so we want people to use more of it.”

The system essentially greases the wheels of energy trading. Whole current solutions require complex transactions and contracts, this solution lets energy generators sell their excess energy to other people with a minimum of fuss. The team plans to launch in 2019. The have created two coins, the GPX and the GET. The GPX is a liquid coin that can be used to buy and sell power while the GET is tied to the actual generation of energy. You would buy and sell GPX, for example, and manage your energy purchases using the GET token.

GPX’s goals are noble and these sorts of markets are definitely headed toward the blockchain. ” One of the largest barriers to renewable power development in these markets is the complexity of PPA negotiations and the rigidity of the agreements,” said Wentzel. It’s at least clear that something like GPX could help.

As we plunge into our baffling future, it is believed that, at some point, we will be trading in cryptographically secure kittens, monsters, and playing cards. While it is unclear why this will happen, Rare Bits and their new service, Fan Bits, is ready for the oncoming rush.

Co-founded by Dave Pekar, Amitt Mahajan and Danny Lee (who met after selling their gaming startups to Zynga) and Payom Dousti (formerly of fintech VC fund 1/0 Capital), the company trades in digital goods and has built a blockchain-based solution for buying and selling digital collectables. Lee brought in a team of ex-Zynga and other digital platform creators to build a blockchain-based solution for buying and selling digital collectables. For example, on Rare Bits you can buy this monster and battle it against other monsters on the blockchain. Further, with their new platform called Fan Bits, you can buy actual collectables that are tied to the blockchain. For example, you can sell collectible cards and give some of the proceeds to charity. If the new owner resells those cards then some of the resell price also goes to charity, an interesting if slightly intrusive use of smart contracts.

The team has raised $6 million in Series A. Fan Bits launches on May 17.

“To date, collectible content has only been created by developers for their own dapps – which I suppose could be considered our competition,” said Lee. “Fan Bits is the first to let anyone, especially people who are not technical, to create collectibles. It will create an abundance of supply that didn’t exist before.”

“We started Rare Bits to let people buy, sell, and discover crypto assets. We believe that assets on the blockchain mark a fundamental shift in how we own and exchange property. Our overall mission is to enable the worldwide exchange of online and offline property on the blockchain,” he said.

Lee sees this as a Trojan horse of sorts, allowing non tech-savvy creators sell digital art and designs online without having to understand the vagaries of blockchain.

“For creators, it’s a DIY platform to turn their content into unique collectibles and earn Ethereum on every sale,” he said. “For the first time, a creator can go from idea to published cryptocollectible on a live marketplace without having to have any technical knowledge.”

Given the popularity of other digital collectables – including in-game gear for many multi-player games – things look like they’re going to get pretty interesting in the next few years.

According to Transparency Market Research, the global sales performance management market grew 17.1% annually from $2.3 billion in 2017 to $8.1 billion by the end of 2025. Utah-based InsideSales.com...

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There are times where voice-driven systems don’t work all that well because of background noise or other voices. That’s because it’s hard for machines (and humans) to pull out a particular voice when there are many others speaking. This is sometimes called ‘the cocktail party problem.’ Yobe was created out of research at MIT on how to solve this issue, and today it announced $1.8 million in seed funding.

The investment comes from Clique Capital Partners, a $100 million fund created specifically to fund innovative voice technology. Yobe had previously received $790,000 in the form of a National Science Foundation SBIR grant in 2016.

Company co-founder and CEO Ken Sutton says Yobe is solving an entrenched problem identifying a particular voice in noise. That means for instance if you are at a party and you want Alexa to play a Spotify playlist, you could (in theory at least), say the wake word from across a crowded room, give the playlist command and the device would execute it in spite of the noise. That’s because Yobe can pinpoint a voice based on biometric markers, aggressively enhance the volume and then use AI to smooth it out.

As Sutton says, most of these voice interface technologies fail in this situation because they can’t distinguish your voice from the background noise, but Yobe is supposed to solve this.

Sutton made clear the research phase is done and the funding is about getting ready to go to market. “The capital raised is not to continue R&D. The capital raised is to streamline and optimize [the technology] for deployment. We will be in market with a product to sell in 30 days, and all of the usual suspects are lined up and waiting for us to call with a live demo,” Sutton told TechCrunch.

Ultimately the company hopes to license its technology to chip or phone manufacturers and others in a scenario not unlike Dolby. Sutton acknowledges there are many use cases for a  solution that could identify a specific voice in noise or among other voices such as law enforcement, hearing aid manufacturers and meeting transcription services. You could even use voice as a biometric marker for authentication purposes. But the company has decided to focus its early efforts on voice-driven devices as an initial go-to market strategy.

The company was founded by Sutton and Dr. S. Hamid Nawab, an MIT PhD and researcher, whose work has focused on applying AI to signal processing. Nawab is the company’s Chief Scientist.

You can watch this video demo to see how Yobe works:

Dreamlines, which claims to be Europe’s largest online travel agency specialising in cruise-related travel, is disclosing that it has raised €45 million in Series E funding. The round is led by Princeville Global, with participation from existing investors that include Holtzbrinck Ventures, Target Global, Dimaventures, Hasso Plattner Ventures, TruVenturo, and Rocket Internet’s Global Founders Capital.

Founded by Felix Schneider in 2012, Hamburg-based Dreamlines can be thought of as a Booking.com or Expedia but for cruise holidays and other cruise line type travel. The company connects customers to what it says is the largest portfolio of cruises around the world, including holiday packages exclusive to Dreamlines.

Meanwhile, unlike other forms of holiday and travel, the cruise industry is only more recently being digitised, a sentiment echoed by Emmanuel DeSousa, Managing Partner of Princeville Global, who joins the Dreamlines board.

“The cruise industry is the last sizable, global travel segment to be disrupted by a tech-focused online booking platform,” he says. “Under the leadership of its visionary founders, Dreamlines is uniquely positioned to continue transforming the cruise industry to an online model, leading in Europe and expanding around the world”.

To that end, Dreamlines says the investment will support its continued growth and international expansion. The company currently operates in 10 countries, partnering with over 100 cruise operators, and has raised around €110 million to date.

Adds Christian Saller, General Partner at HV Holtzbrinck Ventures and the Dreamlines chairman: “As an early investor, HV Holtzbrinck Ventures has seen Dreamlines grow by a factor ten since its initial investment into the European market leader. The new investment will allow Dreamlines to continue this success story”.