谁是赢家

by Terren Peterson

由Terren Peterson

人工智能竞赛正在进行中。 这是赢家。 (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，肯塔基州路易斯维尔举办了第143场肯塔基德比大赛 。 超过15万人亲自观看的奇观。 电视和流媒体上还有数以百万计的关注。 获胜者将获得140万美元的奖金，并有机会在今年的以后比赛中赢得更多奖金。

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.

谁可以将机器学习作为服务商品化，因此在技术领域内，一场激烈的竞赛正在展开。 预建的机器学习模型价值数十亿美元。 这场竞争使全球最大的技术公司陷入困境。

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.

诸如肯塔基德比之类的活动实际上在同一天进行着许多比赛。 主导机器学习的竞赛是相同的。 在本文中，我将只关注图像识别竞赛的发展趋势。

云竞争者 (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.

现在，每个主要的公共云供应商都提供了一些选择。 亚马逊，谷歌和微软基于它们的存储托管服务而处于领先地位。 他们的产品将决定市场方向。 图像识别可能会成为内置在大型基于云的图像存储系统中的功能。 此举将消除预建模型作为单独的产品。

测试当前产品 (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.

为了使提供程序彼此“竞争”，我使用了Wikipedia的以下照片。 为了使文章更具可读性，我将下面每个回答的精度降低到三位数。

亚马孙 (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.

亚马逊拥有业内最大的公共云资源。 六个月前，他们发布了Rekognition的MVP 。 该服务在集成到S3和Lambda的云平台上构建。 这是他们的模型根据比赛照片确定的。

[{’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拥有庞大的Cloud业务，包括对象存储。 他们在搜索中具有图像识别的历史也是一个巨大的优势。 使用他们的Cloud Vision API，可以对比赛图像提供全面的响应。

[{ "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进一步增加了文本识别功能。 扫描图像时，它会翻译记分板上的文本。 请参见下图左上方的黄色框。

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

Google会将这些信息转换为机器可读格式(JSON)。 这是其他人尚未提供的强大功能。

微软 (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.

微软还拥有大型云和搜索业务的组合。 他们的产品已经投放市场一年多了。 他们的Cloud Vision API可以识别图像，并提供以下结果。

[ { “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 }]

长时间射击 (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.

与三大主要公有云提供商相比，该竞赛的参与者更多。 IBM具有Watson，并具有强大的AI功能。 他们在BlueMix中启用了此功能。 这是我尝试使用带有照片的公开演示时得到的信息。

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.

此服务存在限制，因为存在大小限制。 这可能会阻止用户使用可用性。 我在Wikipedia上发现了一张 2MB阈值以内的类似照片 。 识别的质量与其他类似。

[ { "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.

初创企业在这场比赛中提供了创新的选择。 一个例子就是Clarifai ，它去年筹集了3000万美元 。 他们的API使用与技术巨头相同的图像强调了强大的识别能力。

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.

这凸显了新人打入这场比赛的潜力。 该初创公司可以利用现有云托管提供商的优势，从而实现规模经济。

谁是赢家？ (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.

比赛非常激烈，Google目前处于领先地位。 将图像识别集成到其数字产品中的软件开发人员也是赢家。 我最近制作了一个Alexa游戏，用它玩寻宝游戏。 只需执行几行代码，就无需训练模型。

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!

当前的价格点约为$ 1 /千张图片。 在此级别上，图像识别将被集成到许多不同的产品中。 成为最消耗服务的竞赛正在进行中！

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

谁是赢家