CJ 2016 This company claims to be made 3 months experience near Vive products

Hypereal founder Huang Chaiming

"Why startups? "

"Because I don't want to find a job. "

This is a small series of dialogues with Hypereal founder Huang Chaiming, hearing the answers make an awkward smile.

During this year's MWC in Shanghai, I heard that a domestic venture companies do a VR Head, team of luxury, first-class products, so attracted to visits, experiences and found really quite close to Vive (make himself Vive users).

Hoang Hypereal split the Chinese name of the company name is clearly derived from the founder's name. Huang Chaiming speaks bluntly, made no secret of his talent and teams confidence, conversation shrugged off the current VR Head Sanda. In fact, it's no wonder, he said that their team a 3-month period, invested a total of 10 people, had made his HYPEREAL Pano (HyPano for short) this product, comes with its own research and development of space positioning system and handles, base station called HyBeacon, the handle called HyFeel.

This product is officially unveiled at the ChinaJoy 2016, at first glance, the entire product like HTC Vive helmet with Lighthouse base station and Oculus Touch handles.

Parameters it also snap and Vive and Rift, official parameters are:

2 k OLED screen;

110 degree field of view; 

11-millisecond delay;

90Hz refresh rate;

Location and space could be extended.

HyPano support Open VR compatible with VR games on Steam, but also has its own SDK. Huang Chaiming stressed that this product's hardware and software are developed. Though its spatial location and Lighthouse are the same, but he said before the Lighthouse have many related research in this area, and they are based on these studies, the HyBeacon, is not faux-Vive products. Cath Kidston iPhone 6 cases

HiBeacon base station

Small series of two experienced HyPano think of this experience has been approaching Vive, did not reach the level of Vive, because the two case experience, position jitter. In this connection, an interview with Huang Chaiming later explained that it was because base stations with motor problems, later switched to a HTC Vive's motor supplier's products, will be able to solve this problem.

This announcement and did not disclose key information such as cost and time to market, and later of Lei Feng Huang Chaiming network, products by the end of this year (November, December) look to buy, prices would be relatively high level, but many were not disclosed.

User experience, handle or are development versions

Hardware is not focused

Hypereal as a company was founded only last year, employs more than 100 people, startups, in VR products, hardware, software, and content. But hardware is not the focus of 10 human inputs (5 people do spatial orientation) to illustrate this point, in addition to Huang Chaiming said they do not do mass production HyPano, but doing a few of the high-end products (prices are high) and will be licensed to other manufacturers to produce VR head. In addition, small team also found that VR peripherals has played Hypereal space positioning system. Cath Kidston iPhone

Then the company as much as the rest of us are doing? He said studying technology in graphic images, say a little too generic. Lei feng's network (search for "Lei feng's network", public interest) under questioning, he revealed that company's products under development include a professional graphics design software, will be around December this year coupled with first launched; there are also image processing chip and can be used for fields such as video compression, transmission, but these studies are still in the early stages.

The company's team has a number of experts in graphics technology, including Chief Scientist Xu Dong, Zhejiang University PhD in graphic, former AUTODECK software development supervisor, responsible for AUTOCAD graphics; graphics technical director Chen Lu, also in Zhejiang University PhD in graphic.

Hypereal in CJ also launched two own game, one is of the Zombies Never Die, is a first-person zombie Tower Defense game; there is also one of the HyBuilder, it can make you like stacking wood in the virtual world to create a whole block.

Whether it is the game or application, the company does not intend to put their own platform, but will make cross-platform products.

Importance of content is more than hardware

After experiencing the HyPano, small and VR industry insiders talking about this product. Someone said that VR hardware, the threshold is not high, actually quite a lot of such companies, but their channels are not too wide.

He said, making a set of products similar to HTC Vive is not difficult, including Lighthouse. But that HTC sure is one of the strongest of the supply chain, domestic manufacturers can do a similar experience, but to true mass production requires a lot of investment to burn.

"In fact, VR technology barrier is not high, now none of the VR device is marked black technology, have the potential to cross. "He went on. He believed that these manufacturers will not have a particularly large impact on industry, supply chain, content marketing, the sale of these big companies advantage is clear.

But people in the industry believe that now have no need to pay much attention to VR hardware, hardware structure has been formed, is the focus of attention, no hardware is useless.

In small's view, if the future supply chain maturity, resulting in decreased quality VR equipment prices, could be most affected HTC Vive such high-end VR Head producer. But HTC is also trying to develop ecological and avoid the risks of hardware only.

Hypereal, this hardware product has proven its strength is more important in the future is to look at his house graphic design software and image products.

Learning how to apply to the financial sector

P2P company last year after a cold, in transition says it will use the data, machine learning technology services such as financial, transformed into modern financial technology companies in Fintech. But it wasn't easy to machines instead of people to handle the data to judge, in the domestic financial sector has just started.

Contact CreditX krypton is Lei feng, a used machine learning to do venture capital company, in the financial sector has a lot of practical experience and reflection. Krypton founder Zhu Mingjie Randy China Summit recently gave a speech talking about applying machine learning to financial difficulties, as well as how to improve the interpretability of the model. Below, edit content, delete.

I made more than 10 years of machine learning, with machines instead of people to deal with data to make decisions and judgments. Machine learning over the past more than 10 years, successful application is available on the Internet, search, advertising, testimonials, can said Internet first to achieve data age. Financial innovation in this area, how to achieve the level of machine learning and artificial intelligence of the Internet, everyone just started today, I would like to talk about our CreditX machine learning practices in the financial arena of the Internet level experience and reflection.

Financial risk management pain points Guerlain iPhone 5 Case

I've been thinking, "scientific and technological progress is forced out by business requirements." We rely on algorithms in the Internet industry and machinery, are forced, why, because the amount of data is so large, you want to go to Taobao search phone case, Ali's schoolmates human flesh from the hundreds of millions of products to help you figure out like most appropriate, that's out of the question. Traditional financial scene, a loan of 1 million of the wind people and relationships, it is possible and to the Bank's credit card Center, backlog audited, weekly overtime approval personnel, are endless.

Now more inclusive Internet facing financial scenarios, such as hundreds of dollars a phone credit, shop by manpower must be impracticable. Therefore, this issue is not merely improving operational efficiency, but had to be put down to the machine, allow machines to learn risk experience, robot into the wind-control expert.

Application of machine learning and artificial intelligence in the financial sector difficulties Guerlain iPhone 5 Case

The first problem is that too few data. Because financial data is very sparse, and many forms of financial products that did not happen today, not more than 10 years of accumulated data. In other words, the lack of training data, which is also known as a cold boot, missing data. In addition, have turned sour in the financial sector situation at least a few months more than a month, accumulating data have to wait for a long time, by contrast, can quickly get Internet search click on the feedback, the difference is great. Data loss is a huge barrier to impede a machine to study the human experience.

The second is too much data. Here refers to the data dimensionality more than people can handle. Characteristics of traditional financial only more than 10 dimensions variable, manual adjustment formula to deal with. But is now facing such a multidimensional data, we think a lot of good vision, and discusses a lot of data can be used. But why not? Question is is there any way we can have a very strong ability to express these very primitive, and can also be called a weak variable data to use. Weak data are combined, linked with the result, one visual experience can be understood, risk expert feedback.

On the financial scene, like the Internet as machine learning is not a black box, throw a bunch of data, results feedback from iteration. Financial scene, with particular emphasis on the interpretability of the model, in order to spread the risk experience and intuitive data correlate performance results. On this basis, we can say that the experience of people involved to use machine learning data modeling operations. Feature to be able to go back home, especially in the financial report has to wait a long time, need someone capable of rapid intervention feedback.

How to deal with financial risk control cold start problems

Too few data

Too little and too slow for the data problems, cold startup issue is a typical case. We often face the problem of missing data in the Internet industry, has accumulated a mature experience, was added to the human factors in machine learning. When we do search ads, they invite people to label data, then the annotation data's experts to guide the tuning algorithm algorithm engineer, improved sorting results. On the financial scene, there are many ready-made experience and our experienced risk management personnel, these experts have a strong risk control knowledge.

In theory, if there are hundreds of risk management experts, without wages, we do cell phone credit can do so, but the reality is that we have to rely on machines to learn that they controlled experience. So our semi-supervised learning approach, risk control business experts and actual results in online learning, doing a combination of credit. In this process, the risk officer can intervene in real time, constantly make some adjustments based on output, and real time feedback to the model training iterations of upgrade process.

This says that we pay special attention to the human factor. Everyone is now talking about artificial intelligence, artificial intelligence is the essence of what? My understanding is to get experience for machine learning. We used to rely on a few experienced risk management staff, now we can make machines learn human experience, and let the machines do an automatic decision.

Results of financial operations and sample very precious. For example, I have some samples in the mortgage business, before, then change to a new consumer credit business, or switch from a consumer credit business to another new business. These precious sample data cannot be lost, but how to use it? We can make use of their experience and knowledge as much as possible, to separate the generic risks of core models, and domain knowledge, according to business information, as well as scenes of combining prior knowledge, learning and reuse across fields on the basis of this knowledge across the scene, and the accumulation of knowledge.

Depth too much difficulty to learn engineering technology characteristics data

Next, we'll look at "too much data". I put this question in two parts.

First is the dimensionality of the data a lot. All we care about is how the data and financial risk linked to the issue of, there really needs to be very powerful feature processing and presentation skills. This is traditional linear regression modeling method is hard to do. Our approach has a lot of, this included all have a high heat now "deep learning". Deep learning by learning of data characteristics is the essence of human knowledge and the way that data. In order to solve the problem of too much data, so that people can see through the vastness of the raw data, in front of the model, we tried different depth feature coding methods, unsupervised learning methods to preprocess raw data, in order to achieve the dimensionality of the feature, the vastness of the raw data and the final result is linked to.

Model interpretability

Followed by model interpretability. Model interpretability of particular interest to financial experts. This has two meanings:

If credit scoring results, if not explained, it's hard and the applicant to communicate;

In addition, we are facing a very complex environment, if the risk result is still a black box black box, risks are difficult to control and estimation.

If the model is wrong, we can't afford the risk caused. In the context of rapid growth of the Internet financial services, company's business most likely did not go down. Therefore, Internet black boxes into a black box will not apply to the financial scene, need explanation of a local model to do so. Our experience is, using LIME to capture key variables from the results or partial results and control experts were quick to catch the wind is what lead to changes in the results.

Krypton effect

We use the technology of the Internet experience, do a difficult attempt in the financial scene, and got some practical experience, including from the very beginning the data acquisition process, participation of people involved, intervention process of complex models, resulting eventually in our practice.

From efficiency, said, one of our partners to get a good result. They made a financial credit scenario, deployment run on the system and model of krypton, just 3-4 a business risk and operations personnel, risk to do most of the work to the machine.

In effect, we use DNN model to a result, you can see the results than traditional LR model KS from 0.19 to 0.43. Model of the human figure and the result is that we do the most direct answer, there is no concept of what can speak.

All data prior to high expectations and repeated disappointment, for data technology, now is a good time. Because we really need to be able to have the ability to use data, using machines to solve financial problems, this is the opportunity of our time, and air, but also a new beginning.

Next month, 12, 13th, Lei feng's network (search for "Lei feng's network", public interest) in Shenzhen will hold an unprecedented summit of artificial intelligence and robots, we will publish "artificial intelligence and robot Top25 list of innovative enterprises" list, we collect and confirm that AI, robotics, autonomous, unmanned, and several high quality projects in the area. If your project is related to the field, and technical barriers, sufficient enough to have growth potential, please contact 2020@leiphone.com.

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The world s largest aircraft flying butt will start her maiden voyage

According to the daily mail, the world's largest aircraft Airlander 10 scheduled to start its first test flight in late July. Former Airlander 10 from the United States military plans LEMV program.

American LEMV project plan

But in the United States military tide forced reductions in 2013, sold to United Kingdom Hybrid Air Vehicles (hybrid air airships of companies, referred to as HAV), put the LEMV HAV company shipped back to the United Kingdom to continue research and development and later stopped due to lack of development, and moored at Bell Ford County. 2015 through the United Kingdom authorities funded and HAV all the chips, renovation continues. And launch two product series Airlander 10 Airlander 50.

Airlander 10 airship filled with 1.3 million cubic feet of helium, these gases enough to fill 15 Olympic swimming pool. HAV said its test flight and technical details, said Airlander 10 will be in the United Kingdom test over Kart parts of Bedfordshire, including controlled tests, electrical system, calibrated airspeed, as well as other aspects of performance.

Second phase of testing will require regulatory approval, and test Airlander 10 at a maximum speed of 144 kilometers and maximum elevation 10000 feet to sail 60 nautical miles per hour.

HAV's Chief test pilot David Burns (David Burns) said: "Although we have flights on the ground for hours, but still need to collect flight data to prove its performance. During the tests, the team on the ground will receive data from more than 1000 different sensor, display and monitor its performance and behavior in real time. On the first flight data analysis and report writing, and eventually submitted to the supervision authority in order to test for the second time. "

Airlander 10 is the world's largest aircraft, Airlander 10 long and 92 metres, width of 44 meters high, 26 m, weight up to 10,000 kg. More huge than the Airbus A380 size, but lower than Germany in the 1930 of the 20th century developed the "Hindenburg" airship should be small, "the Hindenburg", 245 meters long, weight 206 tons. On May 6, 1937, the "Hindenburg" due to static electric spark caused a hydrogen explosion during landing, killing 35 people. After Britain, the United States also has many large airships crashed one after another, unable to resolve security issues ended the hydrogen airship technology lines, since the development of airships was halted.

"The Hindenburg" disaster

With the advancement of technology, became safer helium instead of hydrogen gas chapters of modern airship, airship began to get people's attention, countries in the world have again started to develop airships.

Airlander 10 belonged to the air-hybrid vehicle, buoyancy of lighter than air gases 60%, 40% air power provided by the wing-like device, it can 25% of the rotary engine to provide additional impetus to push up or down. This means that Airlander 10 can hover over any surface, including snow and ice, desert and water quality. Airlander 10 can fly in the air for 2 weeks. DVF lips iPhone case

Airlander 10 successfully suspended testing, but flight testing the specific date has not been announced. After a successful test, the first based on the prototype aircraft will begin production. Diane Von Furstenberg iPhone 5 case

HAV Nike·aoerman Project Manager (Nick Allman) said: "according to weather conditions, we will begin the final phase of ground tests, including 4 engines running full power tests, since similar tests could not be conducted in the hangar for security reasons. In the past few months, we've made remarkable progress, which excited about the aviation sector as a whole, will completely change the game rules for air travel. "

It is interesting, for Airlander 10 unique styling, raised on the social networking site users spoof.

Kardashian Airlander 10