誰是贏家

by Terren Peterson

由Terren Peterson

人工智能競賽正在進(jìn)行中。 這是贏家。 (The race is on for artificial intelligence. Here’s who is winning.)

On Saturday, Louisville, Kentucky hosted the 143rd running of the Kentucky Derby. It was a spectacle where more than 150k people watched in person. Millions more followed on television and streaming media. The winner received a $1.4 million prize, and the opportunity for more winnings in later races this year.

星期六，肯塔基州路易斯維爾舉辦了第143場肯塔基德比大賽 。 超過15萬人親自觀看的奇觀。 電視和流媒體上還有數(shù)以百萬計(jì)的關(guān)注。 獲勝者將獲得140萬美元的獎(jiǎng)金，并有機(jī)會(huì)在今年的以后比賽中贏得更多獎(jiǎng)金。

A bigger race is raging within the technology sector around who can commoditize machine learning as a service. Prebuilt machine learning models are worth billions of dollars. This competition pits the largest technology companies on the planet.

誰可以將機(jī)器學(xué)習(xí)作為服務(wù)商品化，因此在技術(shù)領(lǐng)域內(nèi)，一場激烈的競賽正在展開。 預(yù)建的機(jī)器學(xué)習(xí)模型價(jià)值數(shù)十億美元。 這場競爭使全球最大的技術(shù)公司陷入困境。

Events such as the Kentucky Derby actually have many races going on during the same day. The race to dominate machine learning is the same. For this article, I’m going to just focus on how the race for image recognition is shaping up.

諸如肯塔基德比之類的活動(dòng)實(shí)際上在同一天進(jìn)行著許多比賽。 主導(dǎo)機(jī)器學(xué)習(xí)的競賽是相同的。 在本文中，我將只關(guān)注圖像識(shí)別競賽的發(fā)展趨勢(shì)。

云競爭者 (The Cloud Contenders)

Right now there are options from each of the major Public Cloud vendors. Amazon, Google, and Microsoft get a prime position based on their storage hosting services. Their offerings will determine the market direction. Image recognition may become a feature built into big cloud-based image storage systems. This move would eliminate prebuilt models as a separate product.

現(xiàn)在，每個(gè)主要的公共云供應(yīng)商都提供了一些選擇。 亞馬遜，谷歌和微軟基于它們的存儲(chǔ)托管服務(wù)而處于領(lǐng)先地位。 他們的產(chǎn)品將決定市場方向。 圖像識(shí)別可能會(huì)成為內(nèi)置在大型基于云的圖像存儲(chǔ)系統(tǒng)中的功能。 此舉將消除預(yù)建模型作為單獨(dú)的產(chǎn)品。

測試當(dāng)前產(chǎn)品 (Testing out the current offerings)

To “race” the providers against one another, I used the photo below from Wikipedia. To make the article more readable, I reduced the precision on each of the responses below to three digits.

為了使提供程序彼此“競爭”，我使用了Wikipedia的以下照片。 為了使文章更具可讀性，我將下面每個(gè)回答的精度降低到三位數(shù)。

亞馬孫 (Amazon)

Amazon has the largest Public Cloud footprint in the industry. Six months ago they released their MVP of Rekognition. This service builds on their Cloud platform as it integrates into S3 and Lambda. Here is what their models determine from the race photo.

亞馬遜擁有業(yè)內(nèi)最大的公共云資源。 六個(gè)月前，他們發(fā)布了Rekognition的MVP 。 該服務(wù)在集成到S3和Lambda的云平臺(tái)上構(gòu)建。 這是他們的模型根據(jù)比賽照片確定的。

[{’Confidence’: 98.0, ’Name’: ’Animal’},{’Confidence’: 98.0, ’Name’: ’Horse’},{’Confidence’: 98.0, ’Name’: ’Mammal’},{’Confidence’: 90.8, ’Name’: ’Equestrian’},{’Confidence’: 90.8, ’Name’: ’Person’},{’Confidence’: 52.7, ’Name’: ’Colt Horse’}]

谷歌 (Google)

Google has a large Cloud business, including object storage. Their history with image recognition in search is also a massive advantage. Using their Cloud Vision API provides a thorough response on the race image.

Google擁有龐大的Cloud業(yè)務(wù)，包括對(duì)象存儲(chǔ)。 他們?cè)谒阉髦芯哂袌D像識(shí)別的歷史也是一個(gè)巨大的優(yōu)勢(shì)。 使用他們的Cloud Vision API，可以對(duì)比賽圖像提供全面的響應(yīng)。

[{ "description": "horse", "score": 0.937 },{ "description": "western riding", "score": 0.889 },{ "description": "jockey", "score": 0.881 },{ "description": "racing", "score": 0.861 },{ "description": "stallion", "score": 0.810},{ "description": "mare", "score": 0.810 },{ "description": "western pleasure", "score": 0.806 },{ "description": "sports", "score": 0.776 },{ "description": "horse racing", "score": 0.775 },{ "description": "english riding", "score": 0.731 },{ "description": "horse trainer", "score": 0.722 },{ "description": "equestrian sport", "score": 0.708 },{ "description": "equestrianism", "score": 0.705 },{ "description": "animal sports", "score": 0.685 },{ "description": "barrel racing", "score": 0.648},{ "description": "eventing", "score": 0.614},{ "description": "horse like mammal", "score": 0.590},{ "description": "reining", "score": 0.546 }]

Google goes even further by adding in text recognition. When scanning the image, it translated the text in the scoreboard. See the yellow boxes in the top left of the image below.

Google進(jìn)一步增加了文本識(shí)別功能。 掃描圖像時(shí)，它會(huì)翻譯記分板上的文本。 請(qǐng)參見下圖左上方的黃色框。

Google translates this information into a machine readable format (JSON). This is a powerful feature that others don’t offer yet.

Google會(huì)將這些信息轉(zhuǎn)換為機(jī)器可讀格式(JSON)。 這是其他人尚未提供的強(qiáng)大功能。

微軟 (Microsoft)

Microsoft also has the combination of a large Cloud and Search business. Their offering has been on the market for more than a year. Their Cloud Vision API recognized the image, and provided the following results.

微軟還擁有大型云和搜索業(yè)務(wù)的組合。 他們的產(chǎn)品已經(jīng)投放市場一年多了。 他們的Cloud Vision API可以識(shí)別圖像，并提供以下結(jié)果。

[ { “name”: “grass”, “confidence”: 0.999 },{ “name”: “fence”, “confidence”: 0.999 },{ “name”: “outdoor”, “confidence”: 0.995 },{ “name”: “horse”, “confidence”: 0.985 },{ “name”: “ground”, “confidence”: 0.974 },{ “name”: “sport”, “confidence”: 0.821 },{ “name”: “horse racing”, “confidence”: 0.519 }]

長時(shí)間射擊 (The Long-Shots)

This race has more entrants than the three major Public Cloud providers. IBM has Watson, and strong capabilities in AI. They have enabled this capability within BlueMix. Here’s what I got when attempting to use the public demo using the photo.

與三大主要公有云提供商相比，該競賽的參與者更多。 IBM具有Watson，并具有強(qiáng)大的AI功能。 他們?cè)贐lueMix中啟用了此功能。 這是我嘗試使用帶有照片的公開演示時(shí)得到的信息。

There are limitations with this service as there are restrictions on size. This may be a usability gap the deters customers. I found a similar photo on Wikipedia that was within the 2MB threshold. The quality of the recognition was similar to the others.

此服務(wù)存在限制，因?yàn)榇嬖诖笮∠拗啤?這可能會(huì)阻止用戶使用可用性。 我在Wikipedia上發(fā)現(xiàn)了一張 2MB閾值以內(nèi)的類似照片 。 識(shí)別的質(zhì)量與其他類似。

[ { "class": "horse racing", "score": 0.922 },{ "class": "racing", "score": 0.928 },{ "class": "sport", "score": 0.928 },{ "class": "jockey (horse rider)", "score": 0.622 },{ "class": "traveler", "score": 0.622 },{ "class": "person", "score": 0.622 },{ "class": "racehorse", "score": 0.53 },{ "class": "mammal", "score": 0.53 },{ "class": "animal", "score": 0.53 },{ "class": "green color", "score": 0.876 }]

Start-ups provide creative alternatives in this race. An example is Clarifai that raised $30M last year. Their API highlighted strong recognition using the same image as the tech giants.

初創(chuàng)企業(yè)在這場比賽中提供了創(chuàng)新的選擇。 一個(gè)例子就是Clarifai ，它去年籌集了3000萬美元 。 他們的API使用與技術(shù)巨頭相同的圖像強(qiáng)調(diào)了強(qiáng)大的識(shí)別能力。

horse, 0.999equine, 0.992race, 0.990track, 0.989fast, 0.984jockey, 0.983thoroughbred, 0.981competition, 0.966gambling, 0.951filly, 0.942mare, 0.936turf, 0.924whip, 0.902best, 0.897stallion, 0.882athlete, 0.869saddle, 0.865racehorse, 0.864rider, 0.864blinker, 0.858

This highlights the potential for a newcomer to break into this race. The startup could ride the rails of an existing Cloud hosting provider, giving it economies of scale.

這凸顯了新人打入這場比賽的潛力。 該初創(chuàng)公司可以利用現(xiàn)有云托管提供商的優(yōu)勢(shì)，從而實(shí)現(xiàn)規(guī)模經(jīng)濟(jì)。

誰是贏家？ (Who is the winner?)

The race is very competitive, with Google currently in the lead. Software developers integrating image recognition into their digital products are also winners. I recently built an Alexa game that uses it to play scavenger hunt. This was done with just a few lines of code, and no effort to train models.

比賽非常激烈，Google目前處于領(lǐng)先地位。 將圖像識(shí)別集成到其數(shù)字產(chǎn)品中的軟件開發(fā)人員也是贏家。 我最近制作了一個(gè)Alexa游戲，用它玩尋寶游戲。 只需執(zhí)行幾行代碼，就無需訓(xùn)練模型。

The current price point is around $1/thousand images. At this level, image recognition will be incorporated into many different products. The race to become the most consumed service is on!

當(dāng)前的價(jià)格點(diǎn)約為$ 1 /千張圖片。 在此級(jí)別上，圖像識(shí)別將被集成到許多不同的產(chǎn)品中。 成為最消耗服務(wù)的競賽正在進(jìn)行中！

翻譯自: https://www.freecodecamp.org/news/the-race-is-on-for-artificial-intelligence-heres-who-is-winning-f7dad96f1d33/

誰是贏家

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