Disney’s first streaming service, ESPN+ launched five months ago.
Source: CNET
The investment arms of BMW and the Chinese search technology giant, Baidu, along with a large original equipment manufacturer for the auto industry and a slew of technology investors have all come together to back Lunewave, a startup developing new sensor technologies for autonomous vehicles.
The $5 million seed round which the company just closed will serve as a launching pad to get its novel radar technology, based on the concept of a Luneburg antenna, to market.
First developed in the 1940s, Lunewave’s spin the antenna technology involves leveraging 3D printing to create new architectures that enable more powerful antennas with greater range and accuracy than the sensing technologies currently on the market, according to the company’s chief executive John Xin.
Lunewave was co-founded by brothers John and Hao Xin and is based off of research that Hao had been conducting as a professor at the University of Arizona. Hao previously spent years working in the defense community for companies like Raytheon and Rockwell Scientific after graduating with a doctorate from the Massachusetts Institute of Technology in 2000.
Younger brother John took a more entrepreneurial approach, working in consulting and financial services for companies like PriceWaterhouseCoopers and Liberty Mutual.
Lunewave represents the culmination of nine years of research the elder Xin spent at the University of Arizona applying 3D printing to boost the power of the Luneburg antenna. With so much intellectual firepower behind it, Hao was able to convince his younger brother to join him on the entrepreneurial journey.
“He has a strong desire to commercialize his inventions,” John Xin said of his older brother. “He wants to see it in everyday life.”
Image courtesy of Driving-Tests.org
Now the company has $5 million in new funding to take the technology that Hao Xin has dedicated so much time and effort to develop and bring it to market.
“With a single 3D printer in the laboratory version we can produce 100 per day,” John Xin told me. “With an industrial printer you can print 1000 per day.”
The first market for the company’s new technology will be autonomous vehicles — and more specifically autonomous cars.
Lunewave is focused on the eyes of the vehicle, says John Xin. Currently, autonomous technologies rely on a few different sensing systems. There are LIDAR technologies which use lasers to illuminate a target and measure the reflected pulses with a sensor; camera technologies which rely on — well — camera technologies; and radar which uses electromagnetic waves to detect objects.
Startups developing and refining these technologies have raised hundreds of millions of dollars to tackle the autonomous vehicle market. In June, the camera sensing technology developer Light raised over $120 million from SoftBank. Meanwhile, LIDAR technology developers like Quanergy and Leddartech have raised $134 million and $117 million respectively and some studies have claimed that the market for LIDAR technologies was already a $5.2 billion last year alone.
Most companies working with autonomous cars these days use some combination of these technologies, but the existing products on the market have significant limitations, according to Lunewave’s chief executive.
John Xin argues that the Lunewave technology can detect more objects in a wider field of view and at greater distances than existing products thanks to the unique properties of the Luneburg antenna.
Think of the antenna as a giant golf ball with a 360 field of “view” that can detect objects at greater distances than existing Lidar technologies because of the distance constraints on laser technologies.
Xin with a Lunewave prototype
“LIDAR right now is at the end of the day because of its short wavelength. It does not function as well in poor weather conditions. Penetration of shorter wave lengths would be very difficult in poor weather conditions,” Xin said. “Our radar technology has the ability to function across all weather conditions. Our hardware architecture of our Lunenberg antenna has the best distance and the spherical nature of the device has the 360 detection capacity.”
The company came out with its minimum viable product in 2017 — the same year that it launched. It was one of the early companies in the UrbanX accelerator — a collaboration between Mini and Urban.us — and is part of BMW’s startup garage program.
The company raised $5 million in two structures. Its seed financing was a $3.75 million equity round led by the automotive investment specialist McCombs Fraser with participation from Ekistic Ventures, Urban.us, Plug and Play, Shanda Capital, Lighthouse Ventures, Baidu Ventures and BMW iVentures. But a portion of its capital came in the form of a $1.25 million non-dilutive government grant through the National Science Foundation . “In late 2016 that’s what helped us to jumpstart the company,” said Xin.
Now, the company just needs to fulfill Hao Xin’s dream of taking the product to market.
“We have the product,” John Xin said. “It’s not just taking in money. Now it’s about [proof of concepts] and pilots.”
Source: TechCrunch
Scientists have demonstrated a promising technique for measuring and mapping your exposome—the flecks of animate and inanimate stuff that surround you at all times.
Source: Wired
With the Apple Watch Series 4, Apple introduced a new, larger display. It now has rounded edges and thinner bezels. And the company took advantage of that display to introduce new fire, water, liquid metal and vapor faces. Apple didn’t use CGI to create those faces — they shot those faces in a studio.
Many companies would have rendered those effects on a computer given the size of the display. But those are actual videos shot with a camera.
Cool Hunting shared a video of the actual process, and it’s insane:
As you can see, Apple used a flamethrower against a transparent surface, exploded a balloon at the top of a basin of water, made a color powder explosion in a cylinder and rotated a small puddle of metallic liquid.
It says a lot about Apple’s design culture — they don’t take shortcuts and they have a lot of money.
Here’s the introduction video for the new Apple Watch:
Source: TechCrunch
Graph databases have always been useful to help find connections across a vast data set, and it turns out that capability is quite handy in artificial intelligence and machine learning too. Today, Neo4j, the makers of the open source and commercial graph database platform, announced the release of Neo4j 3.5, which has a number of new features aimed specifically at AI and machine learning.
Neo4j founder and CEO Emil Eifrem says he had recognized the connection between AI and machine learning and graph databases for awhile, but he says that it has taken some time for the market to catch up to the idea.
“There has been a lot momentum around AI and graphs…Graphs are very fundamental to AI. At the same time we were seeing some early use cases, but not really broad adoption, and that’s what we’re seeing right now,” he explained.
AI graph uses cases. Graphic: Neo4j
To help advance AI uses cases, today’s release includes a new full text search capability, which Eifrem says has been one of the most requested features. This is important because when you are making connections between entities, you have to be able to find all of the examples regardless of how it’s worded — for example, human versus humans versus people.
Part of that was building their own indexing engine to increase indexing speed, which becomes essential with ever more data to process. “Another really important piece of functionality is that we have improved our data ingestion very significantly. We have 5x end-to-end performance improvements when it comes to importing data. And this is really important for connected feature extraction, where obviously, you need a lot of data to be able to train the machine learning,” he said. That also means faster sorting of data too.
Other features in the new release include improvements to the company’s own Cypher database query language and better visualization of the graphs to give more visibility, which is useful for visualizing how machine learning algorithms work, which is known as AI explainability. They also announced support for the Go language and increased security.
Graph databases are growing increasingly important as we look to find connections between data. The most common use case is the knowledge graph, which is what lets us see connections in a huge data sets. Common examples include who we are connected to on a social network like Facebook, or if we bought one item, we might like similar items on an ecommerce site.
Other use cases include connected feature extraction, a common machine learning training techniques that can look at a lot of data and extract the connections, the context and the relationships for a particular piece of data, such as suspects in a criminal case and the people connected to them.
Neo4j has over 300 large enterprise customers including Adobe, Microsoft, Walmart, UBS and NASA. The company launched in 2007 and has raised $80 million. The last round was $36 million in November 2016.
Source: TechCrunch
Bird just announced 10 million scooter rides since launching about one year ago. If this story sounds familiar to you, it’s probably because Bird competitor Lime earlier today announced it surpassed 11.5 million rides across its shared bikes and scooters.
Bird, which launched last September in Santa Monica, Calif., currently operates in 100 cities and has over two million unique riders, Bird founder and CEO Travis VanderZanden told TechCrunch. But Bird’s first year of operations has been full of ups and downs.
Many of the downs have been around regulatory issues. Bird faced, and overcame them, in Santa Monica but failed in San Francisco.
“I think anytime you’re doing something new that the cities haven’t contemplated before, there always seems to be gray area on where you fit in in the regulatory environment,” VanderZanden said. “Cities hadn’t thought about electric scooters and electric scooter sharing. We collaborated very closely with the cities we’re in now.”
Although San Francisco did not grant an operating permit to Bird — the city gave them to Scoot and Skip — VanderZanden stressed that “San Francisco is one city. We’re in 100 cities.”
He also said Bird is not looking to appeal the decision in San Francisco. Lime, however, is in engaging in the appeals process.
As Bird enters its second year of operations, the name of the game is to double down on its efforts with cities and building out its government tech platform. Bird is also looking into manufacturing its own scooters to provide more durability to its customers and differentiate itself from other scooters on the market.
“We’ve been investing heavily in that area,” VanderZanden said. “You’ll start to see new vehicles coming from us soon.”
He added, “we want to keep building vehicles that are more ruggedized but also vehicles that have new features for the riders as well.”
And Bird definitely has the funds to do that. To date, Bird has raised $415 million in funding for shared electric scooters.
Source: TechCrunch
Nvidia’s RTX 2080 Ti is already the most powerful graphics card ever released, but with liquid nitrogen cooling overclocker Kingpin was able to push the card to new heights and break a bunch of records in the process.
The post Heavily overclocked RTX 2080 Ti steals every 3DMark record appeared first on Digital Trends.
Source: Digital trends
Stabilized action-cam footage has gotten a whole lot better.
Source: Wired
Whether you’re pounding the pavement or cheering your friends on, we have the gear for you.
Source: Wired
Inflatable graspers don’t have to be taught how to hold something, the way robo-hands do.
Source: Wired