Today, one of the biggest challenges facing the apparel market seems to be a challenge for many friends who are not familiar with the industry – this is the number of items available for selection. This situation means that customers lack loyalty to specific brands and retailers, and may also make customers unable to afford shopping.
The vast array of options and conveniences that e-commerce brings is really important to consumers, as evidenced by the rapid growth of the industry. However, its characteristics also put a burden on shoppers - it is difficult for people to find the products they like and to match them into a combination that will satisfy them. This requires us to put together the costumes from different designers and retailers, and consider what clothes are already in the wardrobe.
This seemingly infinite choice of space has brought about the difficulty of choice. Even if shoppers use filters or search features to narrow down the search results, most online merchants don’t put all the options in front of customers. Now, they realize that they have to give more and more correct options, thus reducing the buyer's purchasing burden.
Personalization is the space that artificial intelligence is best at. According to Mad Street Den, a computer vision-based artificial intelligence startup that combines computer vision and AI technology to explore visual search, based on the pictures of the goods that shoppers get online, to help users find similar products online. According to Julia Kaplan, the operating officer, retailers should not stop there. Artificial intelligence technology can be used to help employees make informed purchasing decisions after they choose a product. She explained that this is also the Mad Street Den smart retail automation system Vue. The meaning of ai. (Vue.ai uses AI technology to perform image searches, display similar products for shoppers, and combine them with each shopper's shopping history and personal preferences to provide them with a personalized shopping experience.)
Kaplan explained Vue in the interview. How ai works and how it can help retailers increase business revenue while achieving cost savings.
Kaplan said that when users can visually see the background of the use of a certain product - the "upper body" effect - whether it is worn on the model or on the customer, it can effectively increase sales. However, for e-commerce sites, providing such a background is often costly – requiring them to invest heavily in models, photographers, studios, and even stylists.
To help retailers build such a background while saving image production costs, Kaplan said Vue. Ai can use computer vision technology to scan garments that are placed on the table, and then map their attributes (such as sleeve length, neckline shape, or length of clothing) to computer-generated models.
Next, the merchant will be able to choose different poses for the virtual model, adjust the skin tone and add other accessories. Kaplan pointed out that the technology is currently unable to show the shape and size of the body, but the company is making efforts to this end.
Kaplan said that in order to provide such display effects on the website, the setup process needs to be established first. Businesses now need a complete team to handle the work manually, including creating titles, describing and using metadata to optimize search engine specific entries.
Using computer vision technology will be able to extract clothing attributes to provide uniform and rich data, improve search engine performance without having to bear expensive time and labor costs.
Of course, in terms of photography, this approach can also eliminate the expenses incurred by models and photographers while significantly saving time. Generally speaking, in the case of high efficiency, the merchant can only complete manual shooting and display of 60 to 80 items per day.
With computer vision technology, data that would otherwise need to be manually entered will be automatically generated, so employees only need to verify the data provided by the computer—basically, human employees only need to act as a quality assurance role.
Through this process automation solution, companies can effectively reduce the number of employees and improve execution efficiency. This is especially important for market and multi-brand sites, especially considering that they often need to display more apparel on the storefront platform.
Kaplan said that there are also some clothing retail related practitioners who want to use artificial intelligence to solve the size problem. They are using a combination of statistical models and body scanning to help customers accurately understand the size and version that suits them best.
Vue. Ai does not use any high-cost high-tech templates, but simply emphasizes solving problems with the lowest modeling and imaging costs. In essence, the goal is to provide the closest possible imaging results with much less overhead than real photographers plus fashion models.
Once the company has actually achieved this goal, Kaplan says it plans to offer customers more imaging options – such as the use of small-size clothing on medium-sized and even sturdy models for a more comprehensive reference. Of course, it will continue to adhere to the premise of using computer imaging technology to generate realistic images, but it will not bring high cost in real shooting scenes.
Application-Specific Integrated Circuit refers to an integrated circuit specifically designed to perform a specific computing task. It is very common to use ASIC for mining in the field of blockchain. This article will analyze the principle of ASIC mining and why it should be anti-ASIC.
For Bitcoin, mining has gone through four stages: CPU, GPU, FPGA and ASIC. GPU is naturally suitable for parallel simple operations, so the execution of SHA256 is much higher than the CPU. FPGA is a programmable hardware, because it has a certain degree of universality, so the unit price will be relatively expensive. ASIC has a large initial design investment, but the unit price will be cheaper after mass production. Therefore, if you can determine that the market size is relatively large, the use of ASIC technology will be the most cost-effective.
This is the basic principle of ASIC.
In a nutshell, mining is running complicated calculations in the search for a specific number. Whether it`s an ASIC miner or a GPU mining rig, mining hardware must run through many calculations before finding that number. In proof of work systems like Bitcoin, the first one to find that number gets a reward - at the time of writing, 12.5 Bitcoins worth around $96,850. That reward will fall to 6.25 Bitcoins in May 2020.
There are so many people and powerful computing systems trying to mine Bitcoin that miner groups form to find that number and share the profit. Even more, the faster your hardware, the more you earn. That`s why people who can afford it opt for ASIC miners because it gives them the greatest chance of earning cryptocurrency in exchange for their investment.
Each cryptocurrency has its own cryptographic hash algorithm, and ASIC miners are designed to mine using that specific algorithm. Bitcoin ASIC miners are actually designed to calculate the SHA-256 hash algorithm. In the case of Litecoin, it uses Scrypt. That means technically they could mine any other coin that`s based on the same algorithm, though typically, people who buy ASIC hardware designed for Bitcoin mine that specific digital currency.
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