AI: It's Here

简介:

ab875c1d46132f281740e531d9f7e3755625fe25


AI: It's Here

According to IDC, there will be 44 zettabytes[1] of big data created by 2020. With the huge volume of data now being generated, a new world of value lies in its analysis and utilization. This is where artificial intelligence comes into play.

We’re in an era where big data and analytics are used to automate processes and replace humans for some tasks, and we’re now beginning to see significant advances in the application of artificial intelligence. Below are just a few examples of how the technology is being applied creatively.

 

You beauty: Can AI really judge looks?

AI starts to appear everywhere. Beauty.ai 2.0 was the first international beauty contest judged entirely by artificial intelligence. What was notable was not so much its content, but rather its judges: the “human beauty” was judged by complex algorithms.

Programmers submitted algorithms for machine beauty detection. With a dataset of 6,000 entries from over 100 countries, people’s selfies were evaluated in terms of complexion, wrinkles, youthfulness, symmetry and appearance relative to databases of models and actors with five algorithms.

Despite the controversial contest results, the goal of the contest was to explore a comprehensive rating program that could teach machines to evaluate humans and also to understand the ways humans act. Machine intelligence capabilities have been steadily growing, but still have some way to go before they can identify the next Miss World.

 

DeepMind: Play like a pro

A program that plays video games might seem gimmicky. But last year DeepMind made big news when it mastered one of the most complicated games Go in the world and beat one if its best players Lee Sedol.

DeepMind watched its own gameplay and learned how to win all by itself. The program had already learned how to play 49 different Atari 2600 games. While it did “give up” one some games due to perceived difficulty, artificial curiosity and reward systems were programmed into its core in order to encourage it keep trying.

By utilizing the two techniques – deep learning and deep reinforcement learning – it is expected to build a general-learning algorithm that is applicable to many other tasks. For example, researchers have said if AI can successfully play car racing games, the technology might be useful for the development of autonomous vehicles.

 

Sorting cucumbers: Driving away tediousness

Artificial intelligence not only solves big scale problems, but has wide applications throughout society and industry alter tedious jobs that no one wants to do. For example, when former systems designer Makoto Koike returned home to help out on his family cucumber farm, he quickly realized that sorting nine categories of spiky cucumbers could be overcome through automation.


Koike built a machine that uses a set of algorithms to control the sorting decisions, and move the cucumbers into one of the nine groups. Koike spent three months taking and labelling more than 7,000 images of cucumbers before the machine actually could work. Deep learning is critical to classifying all sorts of images with high accuracy as the machine needs a wide variety of examples to learn how to recognize the most defining cucumber features. Workers are freed from sorting cucumbers to more productive work like overall farm production.

 

Prisma: More than just discoloration

Prisma, the Russian photo-editing app, has achieved phenomenal success since it was launched in mid-2016. Given the large variety of photo retouching apps in the market, the app uses deep learning to give photos a hyper-realistic makeover in the style of classical painters and other famous forms.

Instead of merely applying filters to images, Prisma uses artificial intelligence to take the art styles of famous artists to create the “masterpiece” feeling by combining the photos. Starting with a blank canvas, the algorithm generates a final image using different data inputs that specify the style chosen.

What is fascinating about Prisma is that artificial intelligence has been able to handle tasks only humans were previously capable of – separating content with artistic style.

 

With the insights extracted from big data, AI is presenting lots of possibilities in our lives –predicting outcomes accurately, gaining competitiveness and improving productivity. While the marriage of big data and AI will provide opportunities and surprises, it will also drive innovation across different industries.



[1] 1 zettabyte = 1 billion terabytes

目录
相关文章
|
8月前
|
人工智能 算法 安全
AI未来
计算机算法的不断迭代,越来越大的训练模型以及越来越优越的算法,对于算力的需求也是逐渐增加。
57 0
|
6月前
|
机器学习/深度学习 人工智能 自然语言处理
《AI在文学创作中的应用》
《AI在文学创作中的应用》
295 0
|
6月前
|
机器学习/深度学习 人工智能 搜索推荐
《AI在音乐创作中的应用》
《AI在音乐创作中的应用》
218 0
|
8月前
|
人工智能
AI动画教程
AI动画教程
177 0
|
9月前
|
人工智能
2023年AI大课
2023年AI大课直播预约,7月8日——9日混沌APP线上直播宣传海报。
73 1
|
10月前
|
机器学习/深度学习 人工智能 自然语言处理
你真的懂AI吗?其实我们一直在与AI接触!
一、什么是ChatGPT ChatGPT全称为Chat Generative Pre-trained Transformer,Chat是聊天的意思,GPT是生成型预训练变换模型,可以翻译为聊天生成预训练转换器或简称优化对话的语言模型。 ChatGPT由美国人工智能公司OpenAI 开发的ChatGPT两个月时间内用户已超1个亿。 作为一款建立在云计算、海量数据库、人工智能算法架构和深度神经网络基础之上开发的聊天机器人程序,ChatGPT不像传统的搜索引擎一样复制、粘贴、拼凑网上已有的信息给你。它的回答是有逻辑的、生动的,与上下文有关联的。
69 0
|
人工智能 自然语言处理 开发者
真正的AI需要实际的需求
AI不应该只停留在实验室、或论文中,而是应该用来解决实际问题的。 我们在研究AI问题时,总是喜欢将问题分类,比如视觉、自然语言等类型,然后在已有的数据集中,设计模型结构,一点点调优,最后追求那一点点的模型指标上的提升。
84 0
真正的AI需要实际的需求
|
人工智能 自然语言处理 算法
他们用AI,让大山里的孩子也能「说好」普通话
他们用 AI 帮助千千万万个「丁真」学习普通话,走出大山,走向更好的未来。
185 0
他们用AI,让大山里的孩子也能「说好」普通话
|
机器学习/深度学习 人工智能 算法
AI香水来了,你会买吗?
“A woman who doesn’t wear perfume has no future.”—from Coco Chanel
AI香水来了,你会买吗?
|
人工智能 人机交互 数据中心
蜷缩在你耳膜边的AI
"多希望我可以触碰你。" 西奥多躺在床上,静静地说。他的生活中充满了沉默与拒绝,但这一次,塞曼莎温柔地问:“你想怎样触碰我呢?”
蜷缩在你耳膜边的AI

热门文章

最新文章