• 神码ai人工智能写作机器人One could be excused for confusing the Investor’s list on the Covariant.ai website with the all-star lineup for a top AI conference. The names include 2018 Turing Award ...

神码ai人工智能写作机器人

One could be excused for confusing the Investor’s list on the Covariant.ai website with the all-star lineup for a top AI conference. The names include 2018 Turing Award winners Geoffrey Hinton and Yann LeCun, Google AI lead Jeff Dean, Director of the Stanford Artificial Intelligence Lab Fei-Fei Li and Berkeley Artificial Intelligence Research (BAIR) Lab Founding Co-Director Trevor Darrell.
可能会因为将Covariant.ai网站上的投资者名单与顶级AI会议的全明星阵容混淆而被原谅。 这些名字包括2018年图灵奖的获得者Geoffrey Hinton和Yann LeCun，谷歌AI主管Jeff Dean，斯坦福人工智能实验室主任李飞飞和伯克利人工智能研究(BAIR)实验室创始联合主任Trevor Darrell。
Covariant last month secured a US$40 million Series B funding round led by Index Ventures to push its total funding to US$67 million. The ever-astute Professor Hinton recently tweeted he wishes he’d invested a hundred times more.
Covariant上个月获得了由Index Ventures牵头的4000万美元B轮融资，将其总资金推至6700万美元。 这位永远精明的教授欣顿最近在推特上发文说，他希望自己能再投资一百倍。
从学术界到现实世界 (From academia to real world)
Berkeley-based Covariant is building a universal AI designed to enable robots to see, reason, and act in the real world. “Covariant was founded with a very strong research DNA. And in a sense, you can say the company is on a quest to solve the hot research challenge of how do you build general AI for robotics,” Chief Executive and Co-founder Peter Chen told Synced. Initially known as Embodied Intelligence, the company was co-founded in 2017 by esteemed UC Berkeley professor Pieter Abbeel and colleagues Peter Chen, Rocky Duan and Tianhao Zhangfrom UC Berkeley and OpenAI.
基于伯克利的Covariant正在构建一种通用AI，旨在使机器人能够在现实世界中看到，推理和行动。 “ Covariant成立时有着非常强大的研究DNA。 从某种意义上讲，您可以说该公司正在寻求解决如何构建用于机器人技术的通用AI的热门研究难题， ”首席执行官兼联合创始人Peter Chen告诉Synced 。 该公司最初被称为Embodied Intelligence，由知名的加州大学伯克利分校教授Pieter Abbeel及其同事Peter Chen，Rocky Duan和来自加州大学伯克利分校和OpenAI的Zhang Tianhao于2017年共同创立。
The launch came with a mandate both ambiguous and ambitious, based on the team’s extensive expertise in artificial intelligence, deep learning, and robotics: “Traditional robot programming required substantial time and expertise,” explained Abbeel in a seed founding round press release. “What we will provide is an AI layer that can be added to any existing robot, enabling robots to learn new skills rather than requiring explicit programming.”
基于团队在人工智能，深度学习和机器人领域的广泛专业知识，此次发布的任务既含糊又雄心勃勃：“传统的机器人编程需要大量的时间和专业知识，” Abbeel在种子创始回合新闻稿中解释道。 “ 我们将提供的AI层可以添加到任何现有的机器人中，从而使机器人能够学习新技能，而无需进行明确的编程。 ”
Three years later, Covariant’s marque product is its Covariant Brain solution, which delivers “universal AI for robots that can be applied to any use case or customer environment. Covariant robots learn general abilities such as robust 3D perception, physical affordances of objects, few-shot learning and real-time motion planning, which enables them to quickly learn to manipulate objects without being told what to do.”
三年后，Covariant的标志产品是Covariant Brain解决方案，该解决方案为可应用于任何用例或客户环境的机器人提供了“ 通用AI ” 。 协变机器人学习通用能力，例如强大的3D感知能力，物体的实际承受能力，几次射击学习和实时运动计划，这使它们能够快速学习操纵物体而无需告知要做的事情。”
From advanced research techniques to robust applications in the warehouse, Covariant has developed and rolled out its solutions with impressive speed and success. In a 2019 global competition organized by Swiss-based ABB Robotics, 20 AI startups were tasked with designing software for ABB robot arms, with the efforts evaluated on 26 real-world picking, packing, and sorting tasks. Covariant won the global competition and entered into a partnership with ABB to co-develop AI solutions to assist autonomous materials handling. The first such deployment was at Active Ants, a leading provider of e-commerce services for web businesses in the Netherlands.
从先进的研究技术到仓库中强大的应用程序，Covariant均以惊人的速度和成功开发并推出了其解决方案。 在总部位于瑞士的ABB Robotics举办的2019年全球竞赛中，有20家AI初创公司受命为ABB机械臂设计软件，并评估了26种现实世界中的拣选，包装和分类任务。 Covariant赢得了全球竞争，并 与ABB建立 了 合作伙伴关系 ，共同开发AI解决方案，以协助自动化物料搬运。 此类首次部署是在Active Ants上进行的，Active Ants是荷兰网络企业的领先电子商务服务提供商。

KNAPP, a world market leader in warehouse logistics and automation, has also been eyeing Covariant Brain. This March, KNAPP announced a partnership with Covariant to deploy and bring to market advanced AI Robotics solutions. Their first joint deployment was the Pick-It-Easy Robot at Obeta, a German electrical supply wholesaler located near Berlin. Jusuf Buzimkic, SVP of Engineering at KNAPP, enthusiastically endorsed the partnership: “We looked at every solution on the market, and Covariant was the clear winner. It can handle unlimited SKU types and works on challenging objects, including polybags, banded-apparel, transparent objects and blister packs. It also learns to pick new objects it’s never seen before and improves over time.” The New York Times reports the Pick-It-Easy Robot arm can handle more than 10,000 different items with better than 99 percent accuracy.
仓库物流和自动化领域的全球市场领导者KNAPP也一直在关注Covariant Brain。 今年3月，KNAPP宣布与Covariant 合作 ，以部署先进的AI机器人技术并将其推向市场。 他们的第一个联合部署是位于柏林附近的德国电力供应批发商Obeta的Pick-It-Easy机器人。 KNAPP工程高级副总裁Jusuf Buzimkic热情地支持了这一合作伙伴关系：“我们研究了市场上的每种解决方案，而Covariant无疑是赢家。 它可以处理无限制的SKU类型，并且可以处理 具有挑战性的物体，包括塑料袋，带状服装，透明物体和泡罩包装 。 它还学习选择前所未有的新对象，并随着时间的流逝而不断改进。 《 纽约时报》报道，Pick-It-Easy机器人手臂可以处理10,000多种不同的物品，准确性超过99％。

解决物流难题 (Tackling the tough part of logistics)
How have Covariant-powered robot arms so successfully positioned themselves in terms of logistics and funding? The phrase “pragmatic research” surfaced repeatedly in our interview with CEO Chen.
用协变驱动的机器人手臂如何在物流和资金方面成功地定位自己？ 在我们对首席执行官陈的采访中，“务实研究”一词反复出现。
“There are no textbook answers to a lot of things that we are trying to build. You need to embrace the fact that you need to solve unsolved problems. Then you need to assemble a team that could do that cutting edge research and advance over the state-of-the-art.”
“对于我们试图构建的许多东西，没有教科书的答案。 您需要接受一个事实，那就是您需要解决未解决的问题。 然后，您需要组建一支可以进行前沿研究并超越最新技术的团队。 ”
“Much amazing research talent is concentrated in places like Google, Facebook, DeepMind and OpenAI. They tend to focus much more on long term fundamental research. So this is typically a very long term problem that they’re aiming towards. What we try to do is be very pragmatic. Be very specific about the real-world problems that we want to solve, and get very deep into the problems, get our hands dirty in order to understand. You can say it’s a strategy, but it’s more of a culture, more of a mindset that we have to build up as a company.”
Chen told Synced that Covariant heard from the market that although many have approached picking problems in logistics and offered automated solutions, there as yet aren’t many large-scale AI systems available. He stresses that the new era of AI robotics is different from traditional industrial automation: “Now, your robot needs to deal with variability. It needs to deal with randomness. It needs to deal with changes in your environment.”
Chen告诉Synced ，Covariant从市场上听说，尽管许多人已经解决了物流中的拣货问题并提供了自动化解决方案，但目前尚没有很多大型AI系统可用。 他强调说，人工智能机器人技术的新时代不同于传统的工业自动化： “现在，您的机器人需要处理可变性。 它需要处理随机性。 它需要应对环境的变化。 ”
It wasn’t a coincidence that Covariant chose to tackle the tougher part of logistics first. Abbeel told IEEE Spectrum that Covariant spent almost a year “talking with literally hundreds of different companies [in electronics manufacturing, car manufacturing, textiles, bio labs, construction, farming, hotels, eldercare, etc.] about how smarter robots could potentially make a difference for them. Over time, it became clear to us that manufacturing and logistics are the two spaces where there’s most demand now, and logistics especially is just hurting really hard for more automation.”
Covariant选择首先解决物流中更困难的部分并不是巧合。 Abbeel告诉IEEE Spectrum ，Covariant花费了将近一年的时间 “ 与数以百计的不同公司(在电子制造，汽车制造，纺织品，生物实验室，建筑，农业，酒店，养老院等)进行了交谈，探讨了更智能的机器人如何制造机器人。对他们来说有所不同。 随着时间的流逝，对我们来说很明显，制造和物流是目前需求最大的两个领域，尤其是物流对于真正实现更高的自动化正造成极大的伤害 。”
According to Statista, retail e-commerce sales worldwide have reached US$3.53 trillion and are expected to grow to US$6.54 trillion by 2022. Accelerating global growth in the e-commerce sector has seen industrial leaders shift focus to AI-powered robotics solutions across a wide range of applications, including logistics, warehousing, and package sorting.
根据Statista的数据 ，全球零售电子商务销售额已达到3.53万亿美元，预计到2022年将增长到6.54万亿美元。电子商务领域的全球增长加速，行业领导者已将重点转移到了人工智能驱动的机器人解决方案上。广泛的应用程序，包括物流，仓储和包裹分拣。
The Covariant Brain solution has so far provided use cases in various warehouse operations, where robotic arm applications mainly include depalletizing, picking, and sorting. Depalletizer systems unload and handle palletized products of different shapes and forms — such as boxes, trays, cases, sheets, bags, pails, and pallets. Warehouse picking meanwhile is where items are picked from a fulfillment facility to complete customer orders, and is one of the most expensive and labour-heavy processes in warehouses. Sorting is another essential warehouse operation, involving identifying items and sending them to the correct bin or storage area. Warehouse sorting robots are typically equipped with conveyors, arms, cameras and sensors; and rely on specialized algorithms.
到目前为止，协变大脑解决方案已在各种仓库操作中提供了用例，其中机械手应用主要包括卸垛，拣选和分类 。 卸垛机系统卸载并处理不同形状和形式的托盘产品，例如箱子，托盘，箱子，薄片，袋子，桶和托盘。 同时，仓库拣货是从履行设施中拣选物品以完成客户订单的过程，并且是仓库中最昂贵且劳动最重的过程之一。 分类是仓库的另一项重要操作，涉及识别物品并将其发送到正确的垃圾箱或存储区域。 仓库分拣机器人通常配备有输送机，手臂，摄像机和传感器； 并依靠专门的算法
机器人通用AI平台 (Universal AI platform for robots)
Covariant’s mission is to build a universal AI platform that enables AI robotics work autonomously in the real world. Rather than customizing entire AI systems to fit in different use cases in different environments, Chen says what’s important is a system’s ability to generalize to something new. “The world is constantly changing. Once you deploy a system into the real world, you need to face a lot of that.”
Covariant的任务是建立一个通用的AI平台，使AI机器人技术能够在现实世界中自主工作。 Chen说，与其定制整个AI系统以使其适合不同环境中的不同用例，不如说重要的是系统具有将其推广到新事物的能力。 “世界在不断变化。 将系统部署到现实世界后，您需要面对很多。”
Covariant has built its business model to reflect its mission “A robotics system is not just the AI but also the robot itself, the surrounding, mechatronics equipment that you need to go with them. So the Covariant go-to-market model is what we call a development partner model. We will expose our Covariant Brain, this unified AI, as a platform, and our partners will develop on top of it.”
Covariant建立了自己的业务模型以反映其使命：“机器人系统不仅是AI，而且还包括机器人本身，与之配套的周围机电一体化设备。 因此，我们将协变推向市场模型称为开发合作伙伴模型。 我们将把Covariant Brain这种统一的AI作为平台公开，我们的合作伙伴将在此之上发展。”
Chen says Covariant’s partnership with industry leaders such as Knapp and ABB also points to where a universal AI for robots might go next. “How do we as an AI software company help scaling these kind of applications quickly? This is not a well-known path. You could say there are obstacles, but maybe, in a sense, they are challenges that you necessarily need to face when you create a new category.”
Chen说Covariant与Knapp和ABB等行业领导者的合作伙伴关系还指出了下一步可能会出现面向机器人的通用AI。 “作为一家AI软件公司，我们如何帮助快速扩展此类应用程序？ 这不是一条众所周知的路径。 您可以说存在障碍，但从某种意义上说，也许是当您创建新类别时必须要面对的挑战。”
Covariant plans to continue expanding into industries where robots are needed for repetitive tasks, such as food, healthcare, retail, parcels and manufacturing.
Covariant计划继续扩展到需要机器人来执行重复性任务的行业，例如食品，医疗保健，零售，包裹和制造业。

Journalist: Fangyu Cai | Editor: Michael Sarazen
记者 ：蔡芳玉| 编辑 ：迈克尔·萨拉森(Michael Sarazen)

We know you don’t want to miss any story. Subscribe to our popular Synced Global AI Weekly to get weekly AI updates.
我们知道您不想错过任何故事。 订阅我们流行的 Synced Global AI Weekly， 以获取每周的AI更新。

Thinking of contributing to Synced Review? Synced’s new column Share My Research welcomes scholars to share their own research breakthroughs with global AI enthusiasts.
想为“同步审核”做出贡献？ 同步的新栏   分享我的研究   欢迎学者与全球AI爱好者分享他们自己的研究突破。

Need a comprehensive review of the past, present and future of modern AI research development? Trends of AI Technology Development Report is out!
需要全面回顾现代AI研究发展的过去，现在和将来吗？ 人工智能技术发展趋势报告   出来了！

2018 Fortune Global 500 Public Company AI Adaptivity Report is out!Purchase a Kindle-formatted report on Amazon.Apply for Insight Partner Program to get a complimentary full PDF report.
2018年《财富》全球500强上市公司AI适应性报告   已结束！在Amazon上购买Kindle格式的报告。 申请Insight合作伙伴计划可获得免费的完整PDF报告。

翻译自: https://medium.com/syncedreview/ai-startup-covariant-ai-building-universal-ai-for-robots-ee0aa4e118d5

神码ai人工智能写作机器人

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• 神码ai人工智能写作机器人 机器学习指南 (MACHINE LEARNING GUIDE) Half of this crazy year is behind us and summer is here. Over the years, we machine learning engineers at Ximilar have gathered a lot of ...

神码ai人工智能写作机器人

机器学习指南 (MACHINE LEARNING GUIDE)
Half of this crazy year is behind us and summer is here. Over the years, we machine learning engineers at Ximilar have gathered a lot of interesting ML/AI material from which we draw. I have chosen the best ones from podcasts to online courses that I recommend to listen to, read, and check. Some of them are introductory, others more advanced. However, all of them are high-quality ones made by the best people in the field and they are worth checking. If you are interested in the current progress of AI or you are just curious about what will be the future then you are on the right page. AI will change all possible fields whether it is physics, law, or retail and one should be prepared for what is to come…
这个疯狂的一年h的ALF已经过去，夏天就在这里。 多年来，我们Ximilar的机器学习工程师收集了许多有趣的ML / AI材料，我们从中汲取了经验。 我选择了从播客到在线课程中最好的课程，建议您收听，阅读和检查。 其中一些是介绍性的，其他则更高级。 但是，它们都是由该领域最好的人制造的高质量的，值得一试。 如果您对AI的当前进展感兴趣，或者只是对未来会感到好奇，那么您来对地方了。 人工智能将改变所有可能的领域，无论是物理学 ，法律还是零售，都应该为即将发生的事情做好准备……
播客 (Podcasts)
If there is one medium that has become popular in recent years, it must be podcasts. Everyone is doing it right now — there are podcasts about sex, politics, tech, healthcare, brains, bicycles,… and AI is not missing. But one of them stands out. It is a podcast by Lex Fridman. This MIT alumni is doing an incredible job by interviewing top people from the field, famous people included (like Garry Kasparov or Elon Musk). Some episodes are more about science, physics, mind, startups, and the future of humanity. The ideas presented in the podcast are just mind-blowing. The talks are deep but clever and it will take you some time to get through them.
如果有一种媒体近年来变得流行，那么它一定是播客。 每个人都在做这件事-关于性，政治，技术，医疗保健，大脑，自行车等的播客，而且人工智能并没有丢失。 但是其中之一脱颖而出。 这是Lex Fridman的播客 。 这位麻省理工学院的毕业生通过采访该领域的顶尖人物(包括加里•卡斯帕罗夫(Garry Kasparov)或伊隆•马斯克(Elon Musk))来取得令人称奇的工作。 有些情节更多地涉及科学，物理学，思维，创业和人类的未来。 播客中提出的想法令人赞叹。 会谈虽然很深，但是很巧妙，您将需要一些时间来完成它们。

The Turing test is a recursive test. The Turing test is a test on us. It is a test of whether people are intelligent enough to understand themselves.
图灵测试是一个递归测试。 图灵测试是对我们的测试。 这是对人们是否足够聪明以了解自己的考验。

Another great podcast is Brain Inspired by Paul Middlebrooks with interesting guests. It shows and discusses topics from Neuroscience and AI and how these fields are connected together.
另一个很棒的播客是Paul Middlebrooks带来的Brain灵感与有趣的嘉宾。 它显示并讨论了来自Neuroscience和AI的主题，以及这些领域是如何连接在一起的。
图书 (Books)
Life 3.0 by Max Tegmark — How will AI change healthcare, jobs, justice, or war? Max Tegmark is a professor at MIT who has written this provocative and engaging book about the future. He tries to answer a lot of questions like What is intelligence? Can a machine have a consciousness? Can we control AI? … This is a great introduction even for non-technical people.
Max Tegmark撰写的 Life 3.0 —人工智能将如何改变医疗保健，工作，正义或战争？ 马克斯·泰格马克(Max Tegmark)是麻省理工学院的教授，他写了这本关于未来的极具吸引力的书。 他试图回答很多问题，例如什么是智力？ 机器可以有意识吗？ 我们可以控制AI吗？ …即使是非技术人员，这也是很棒的介绍。
AI Superpowers: China, Silicon Valley, and the New World Order by Kai-Fu Lee — Book is about incredible progress in AI in China.
AI超级大国：中国，硅谷和李开复的新世界秩序 -这本书讲述了中国AI的惊人发展。
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron — Do you know how to code and would you like to start with some experiments? This book is not only about one of the most popular programming framework TensorFlow but also about modern techniques in machine learning and neural networks. You will code your first image recognition model and learn how to preprocess and analyze text.
AurélienGéron的Scikit-Learn，Keras和TensorFlow进行动手机器学习 -您知道如何编码，并且想从一些实验开始吗？ 本书不仅涉及最受欢迎的编程框架TensorFlow之一，而且涉及机器学习和神经网络中的现代技术。 您将编写第一个图像识别模型的代码，并学习如何预处理和分析文本。
Deep Learning for Coders with fastai and PyTorch by Jeremy Howard and Sylvain Gugger — Another great book for coders. Code examples are in the PyTorch framework. Jeremy Howard is a famous researcher and developer in the AI community. His fastai project helps millions of people to get into deep learning.
杰西 ·霍华德(Jeremy Howard)和席尔文·古格(Sylvain Gugger) 撰写的Fastai和PyTorch进行的《面向程序员的深度学习》，这是另一本针对程序员的好书。 代码示例在PyTorch框架中。 杰里米·霍华德(Jeremy Howard)是AI社区的著名研究人员和开发人员。 他的fastai项目帮助数以百万计的人进行深度学习。
Looking for more hardcore books with math equations? Then try Deep Learning by MIT Press. Are you interested in classic approaches, then many university students will remember preparing for exams with Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig or the Bishop’s Pattern Recognition and Machine Learning. (These two are a bit advanced and many topics are for a master or even Ph.D. level).
寻找更多具有数学方程式的硬核书籍吗？ 然后尝试由MIT Press进行深度学习 。 您是否对经典方法感兴趣，那么许多大学生会记得准备为人工智能考试做准备： Stuart Russell和Peter Norvig撰写的《 现代方法》或Bishop的模式识别与机器学习。 (这两个有点高级，许多主题是针对硕士甚至博士学位的。)
Magazines
杂志
MIT Technology Review is a great magazine with the latest news and trends in technology and future innovations. The magazine covers also other interesting topics as biotechnology, blockchain, space, climate change, and more. There is a print or digital access option for you.
麻省理工学院技术评论是一本很棒的杂志，其中包含技术和未来创新的最新新闻和趋势。 该杂志还涵盖了其他有趣的话题，如生物技术，区块链，空间，气候变化等。 有适合您的打印或数字访问选项。
热门视频和频道 (Popular videos & channels)
Tesla AI, Tesla Autopilot, PyTorch at Tesla by Andrej Karpathy — is simply an amazing look under the hood of how Tesla is building their autopilot. 特斯拉AI ， 特斯拉自动驾驶仪和安德烈·卡帕蒂(Andrej Karpathy)的特斯拉 ( PyTorch)，在特斯拉如何构建其自动驾驶仪的背后，简直是惊人的外观。 Machine Learning Zero to Hero — this short lecture by Google is great, especially for people who can code. 机器学习零到英雄-Google的这次简短演讲非常棒，特别是对于那些会编码的人。 AlphaGo — The Movie — the documentary about the first system which was able to beat top players in Go game. First Chess and now Go — what’s next? AlphaGo —电影 —关于第一个能够在Go游戏中击败顶尖玩家的系统的纪录片。 首先是国际象棋，现在是棋—接下来是什么？ Yannic Kilcher — this YouTube channel explains the latest research techniques and news in a simple and accessible way. Yannic Kilcher-此YouTube频道以简单易用的方式介绍了最新的研究技术和新闻。 Two Minute Papers — are you busy and don’t have time to look at all the new stuff? Then this youtube channel is for you… 两分钟的论文 -您是否很忙，没有时间查看所有新内容？ 那么这个YouTube频道适合您...
跟随的人 (People to Follow)
There are a lot of famous Scientists & Engineers & Entrepreneurs to follow. For example often mentioned Jeremy Howard (fast.ai), Andrej Karpathy (Tesla AI), Yann LeCun (Facebook AI), Rachel Thomas (fast.ai, data ethics), Francois Chollet (Google), Fei-Fei Li (Stanford), Anima Anandkumar (Nvidia AI), Demis Hassabis (DeepMind), Geoffrey Hinton (Google) and more …
有很多著名的科学家与工程师和企业家。 例如经常提到杰里米·霍华德 (fast.ai)， 安德烈Karpathy (特斯拉AI)， 亚·莱卡 (脸谱AI)， 雷切尔·托马斯 (fast.ai，数据伦理)， 弗朗索瓦CHOLLET (谷歌)， 斐翡丽 (斯坦福大学) ， Anima Anandkumar (Nvidia AI)， Demis Hassabis (DeepMind)， Geoffrey Hinton (Google)等…
讲座和在线课程 (Lectures & Online courses)
So you’ve read some books and articles and now you want to start digging a little deeper? Or you want to become a Machine Learning Specialist? Then start with some online courses. Of course, you will need to learn a little bit about math before and get some basic programming skills. Online courses are a great option if you can’t study at university or you want to get knowledge at your own pace. Here are some of the courses that can serve you as the start point:
因此，您已经阅读了一些书籍和文章，现在想开始更深入地研究吗？ 或者您想成为机器学习专家？ 然后从一些在线课程开始。 当然，您需要先学习一些数学知识 ，并掌握一些基本的编程技能 。 如果您不能在大学学习或希望以自己的速度获取知识，那么在线课程是一个不错的选择。 以下是一些可以作为起点的课程：
Machine Learning course from Andrew Ng — this one is a classic and most popular one for a number of reasons, it’s great introductory material. Ng的机器学习课程-这是一门经典且最受欢迎的课程，由于多种原因，它是很棒的入门资料。 To learn more math we can recommend Mathematics for Machine Learning. 要学习更多数学，我们可以推荐机器学习数学 。 Deep Learning specialization is more about modern approaches of neural networks. 深度学习专业化更多地是关于神经网络的现代方法。 There are a lot of great specializations on Udacity by top companies and engineers from various fields like Healthcare or Automotive. 来自Udacity的很多专业公司都来自医疗保健或汽车等各个领域的顶尖公司和工程师。 CS231n and CS224N are great Stanford courses for computer vision and natural language processing (NLP), including video lectures, slides, and materials. It’s FREE! CS231n和CS224N是斯坦福大学针对计算机视觉和自然语言处理(NLP)开设的一流课程，包括视频讲座，幻灯片和材料。 免费！ 6.034 and 6.S191 — lectures for AI and Deep Learning by MIT on YouTube. 6.034和6.S191 -MIT在YouTube上进行的AI和深度学习讲座。 Practical Deep Learning for Coders by fast.ai — Jeremy Howard is doing a great job here by explaining concepts, ideas and showing the code in Jupiter notebooks. fast.ai的《面向程序员的实用深度学习》 —杰里米·霍华德(Jeremy Howard)通过解释概念，想法并在Jupiter笔记本中显示代码，在此方面做得很好。 PyImageSearch — offers great introductory tutorials in the computer vision field PyImageSearch —在计算机视觉领域提供出色的入门教程
研究博客 (Research blogs)
You know how to code and you even know how to build your CNN? Or are you just simply interested in what is the future of the field and how the companies are using AI? Check out some of the latest trends and SOTA approaches from the top research groups in the world. There are several giants like Facebook, Google pushing the AI boundaries:
很棒的文章 (Great articles)
We are always looking for high-quality content that is why some of the following articles can be a bit longer. AI is a complex field which is disrupting the way we live and do business:
我们一直在寻找高质量的内容，这就是以下某些文章可能会更长的原因。 人工智能是一个复杂的领域，正在扰乱我们的生活和经商方式：
The New Business of AI article by Andreessen Horowitz. Andreessen Horowitz撰写 的《 AI的新业务》一文。 The AI Revolution: The road to superintelligence article by Tim Urban. 蒂姆·厄本(Tim Urban)撰写的《人工智能革命：通往超级智能之路》 。 The Global AI Index — which country is most innovative and which country is investing the most resources? Right now the USA is still dominating but China is catching up rapidly. 全球AI指数 -哪个国家最具创新性，哪个国家投资最多的资源？ 目前，美国仍占主导地位，但中国正在Swift追赶。 AI and Efficiency — algorithmic progress has yielded more gains than classical hardware efficiency. 人工智能和效率 -算法的进步比传统的硬件效率产生了更多的收益。 Reflecting on a year of making machine learning actually useful — iterating over dataset is much more important than the latest model architectures. 回顾使机器学习真正有用的一年 -在数据集上进行迭代比最新的模型架构重要得多。
Data Science Weekly and Deep Learning Weekly — as the names suggest this is every week news from data science and machine learning. 数据科学周刊和深度学习周刊 -顾名思义，这是每周来自数据科学和机器学习的新闻。 The Algorithm — a newsletter released by MIT. 算法 -麻省理工学院发布的新闻通讯。 The Batch — a newsletter by deeplearning.ai. The Batch — deeplearning.ai的新闻通讯。 Alignment — a newsletter by Rohin Shah. 对齐 — Rohin Shah的新闻通讯。
趋势与问题 (Trends & Problems)
Ethics & Transparency & Safety — Should countries ban the usage of face recognition technology? [source][source] Is ethical to scrape the data from the internet to build your face search startup? [source] What is an unethical use of AI? [source] What about autonomous weapons for defensive purposes? Are social media polarizing people with their clever algorithms optimized for more clicks/likes/…? [source] 道德与透明度与安全-各国应禁止使用面部识别技术吗？ [ 来源 ] [ 来源 ]从互联网上收集数据来构建您的人脸搜索启动程序是否合乎道德？ [ 来源 ]什么是不道德的AI使用？ [ 来源 ]出于防御目的的自动武器呢？ 社交媒体是否通过针对更多点击/喜欢/…进行了优化的聪明算法使人们两极分化？ [ 来源 ] Jobs replacement — Will AI replace all manufacturing and basic jobs? Or will the research in AI create even more job opportunities? What is going to do countries that are heavily dependent on manual work labor? [source] Will one day companies using robots/clever algorithms pay AI Tax? 职位替换-AI是否会替换所有制造业和基本职位？ 还是人工智能的研究会创造更多的工作机会？ 严重依赖体力劳动的国家怎么办？ [ 来源 ]有一天，使用机器人/智能算法的公司会缴纳AI税吗？ Interpretability & Explainability — Why did the deep learning model predict X and not Y? What the neural network has actually learned? How can we fool the model with adversarial attacks to make it the wrong prediction? 解释性和Explainability -为什么深度学习模型预测X，而不是Y' 神经网络实际上学到了什么？ 我们如何通过对抗性攻击来愚弄模型以做出错误的预测？ Racial bias in datasets and models — a big issue mostly in Face recognition, Insurance, and Healthcare. [source] 数据集和模型中的种族偏见-主要在人脸识别，保险和医疗保健方面是一个大问题。 [ 来源 ] GANs and Deep Fakes — GANs are incredible technology which brings also challenges, … Have you heard about Deep Fakes videos? One day the Deep Fakes will be unrecognizable from genuine content. This could create new problems in politics, business, or our personal lives … GAN和Deep Fakes-GAN是令人难以置信的技术，它也带来了挑战，…您听说过Deep Fakes视频吗？ 有一天，从真实内容中将无法识别“深造假”。 这可能会在政治，商业或我们的个人生活中产生新的问题…… Big and Small models — bigger models can lead to incredible results in NLP [source]. On the other hand, there is also more research to make models lighter and faster with binarization or pruning techniques. 大，小车型-更大的车型可能会导致不可思议的结果在NLP [ 来源 ]。 另一方面，还有更多的研究通过二值化或修剪技术使模型更轻，更快。 Self-supervised learning — high-quality datasets lead to better results, but building such datasets are expensive and requires a lot of manual labeling work. Maybe one day the AI models will be able to create better internal representations without labels. 自我监督的学习-高质量的数据集可以带来更好的结果，但是构建这样的数据集非常昂贵，并且需要大量的手动标记工作。 也许有一天，AI模型将能够创建没有标签的更好的内部表示形式。
That is all for now. There are other great resource lists like the one from DeepMind, from which we got inspired. The list is divided by the level of the target audience — introductory, intermediate, and advanced. We will try to keep this post updated and if we find a gem it will appear here. There is much more material from which you can learn but now it’s up to you to start your own machine learning journey.
到此为止。 还有其他很棒的资源列表，例如DeepMind的资源列表，我们从中得到了启发。 该列表按目标受众的级别划分-入门级，中级和高级。 我们将尝试使该帖子保持最新状态，如果我们发现一个宝石，它将显示在此处。 您可以从中学习更多的材料，但是现在取决于您自己开始机器学习的旅程。

翻译自: https://medium.com/swlh/the-best-resources-on-artificial-intelligence-and-machine-learning-2231011488bf

神码ai人工智能写作机器人

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• 神码ai人工智能写作机器人“Is there a true AI?” This is one question that a lot of experts in the industry have tried to give answers to in different ways, but there seems to be conflicting response ...

神码ai人工智能写作机器人

“Is there a true AI?” This is one question that a lot of experts in the industry have tried to give answers to in different ways, but there seems to be conflicting response nonetheless. The concept of AI is increasingly being adopted in various spheres of life, which further suggests that it’s high time an answer is provided for the question.
“真的有AI吗？” 这个问题很多业内专家试图以不同的方式给出答案，但是似乎仍然存在矛盾的回答。 人工智能的概念正越来越多地应用于生活的各个领域，这进一步表明，现在是时候为这个问题提供答案了。
In this article, we’ll be exploring the future of AI and its ability to learn and evolve as proposed by experts and leading corporations in the industry.
在本文中，我们将探索AI的未来，以及该行业的专家和领先公司提出的AI的学习和发展能力。
什么是人工智能(AI)？ (What Is Artificial Intelligence (AI)?)
Artificial Intelligence is an extensive field of science that deals with Machine learning and the creation of smart machines that can act with little or no human interference. These machines have been supposedly tagged the future of technology since they will supposedly be able to think and act somewhat like humans.
人工智能是一门广泛的科学领域，涉及机器学习和创建可以在很少或没有人为干预的情况下运行的智能机器。 据说这些机器被标记为技术的未来，因为它们将能够像人类一样思考和行动。
A lot of big corporations are already diving into this field of technology, and one of the most common AI today is Apple’s Siri. Siri can do a whole lot of things, including calling your contacts, sending of SMS, show locations, play music, and a whole lot of other things.
许多大公司已经开始涉足这一技术领域，而当今最常见的AI之一是Apple的Siri。 Siri可以做很多事情，包括给您的联系人打电话，发送短信，显示位置，播放音乐以及很多其他事情。
The major question, however, is the possibility of having a true and independent AI. By true AI, I mean an AI that can completely think on its own without any form of human support. One of the foremost persons to raise this subject of this ability for an AI was the English mathematician, Alan Turing.
然而，主要的问题是拥有真正独立的AI的可能性。 真正的AI是指无需任何形式的人工支持就可以完全独立思考的AI。 英国数学家艾伦·图灵(Alan Turing)是提高人工智能这一能力的最重要人物之一。
History recognizes Turing for breaking the Nazi encryption machine, Enigma, during World War II. About a decade later, he posed the simple question that got the world thinking, “Can machines think?”
历史承认图灵在第二次世界大战期间破坏了纳粹加密机Enigma。 大约十年后，他提出了一个让世界思考的简单问题：“ 机器可以思考吗？ ”
In his 1950 paper, Turing charted a new course in the study of AI, which the world is still exploring today. While many believe that AI can be further developed to attain the full thinking dimension by man, others opine that it’s a futile dream.
图灵在其1950年的论文中绘制了AI研究的新课程，当今世界仍在探索这一课程。 尽管许多人认为可以进一步发展人工智能以实现人的全部思维能力，但其他人则认为这是徒劳的梦想。
真正的AI存在吗？ (Does True AI Exist?)
According to Yann LeCun (Professor of Computer Science at NYU and VP and Chief AI Scientist at Facebook), the technology of Generative Adversarial Networks (GANs) is one of the most “promising directions” in machine learning. LeCun is an authority in the field and certainly knows the depth of research that has been put in to the industry by different people and corporations.
However, I am an outlier on this topic as I don’t believe that true AI exists. Judging from his previous lectures, Professor LeCun also doesn’t believe that true AI exists, which is why I am perplexed by his comments on GANs being promising.
但是，由于我不相信真正的AI，所以在这个话题上我离奇。 从他以前的演讲来看， 勒昆教授也不相信真正的AI存在 ，这就是为什么我对他对GAN很有希望的评论感到困惑。
This idea of two machines facing off to sharpen each other skills sounds interesting indeed, but I think it ignores some of the staples of ML and Data Science. First, the tools don’t have inherent value on their own, meaning outside of human direction/guidance.
两台机器相互面对以提高彼此技能的想法听起来确实很有趣，但我认为它忽略了ML和Data Science的某些主要功能。 首先，这些工具本身并没有内在价值，这意味着超出了人类的指导/指导范围。
You cannot teach a machine to play like a master chess player by feeding it basic chess rules and facing it off against another machine that is a beginner chess player. I can see slight time savings by giving a GAN remedial work, but all the valuable ML we have now is always sourced and verified by human intelligence.
您不能通过提供基本的国际象棋规则并使它与另一台是初学者的象棋游戏机对峙的方式来教导一台象棋手一样的机器。 通过进行GAN补救工作，我可以节省一些时间，但是我们现在拥有的所有有价值的ML始终是由人类情报提供和验证的。
Even with all of the choices and all the data, “machines” still cannot predict, discern, qualify, or create much past the level of a 5-year-old child. This should not be surprising, it’s very much analogous to the way the study of thermodynamics and non-equilibrium thermodynamics are related, but the rules of the first seldom hold in the latter.
即使拥有所有选择和所有数据，“机器”仍然无法预测，辨别，鉴定或创造超过5岁孩子的水平。 这并不奇怪，它与热力学和非平衡热力学研究之间的联系非常相似，但后一种很少遵循第一个规则。
Simulations of intelligence have a long way to go, and pitting them against one another in GAN as a way of discovering something new, is at best a McNamara fallacy (quantitative fallacy): “making a decision based solely on quantitative observations and ignoring all others.” Having all the data is not the only factor in making a ‘good’ decision.
智力模拟还有很长的路要走，在GAN中将它们相互竞争，以此作为发现新事物的方式，充其量是McNamara谬论(定量谬论) ：“仅基于定量观察而忽略所有其他因素来做出决定。” 拥有所有数据并不是做出“良好”决策的唯一因素。
使用机器学习能否打破标准AES块密码加密？ (Is it Possible to Break Standard AES Block Cipher Encryption Using Machine Learning?)
My colleague once sought my opinion on the feasibility of training a machine learning system that could break standard AES block cipher encryption. The idea was to train an ML system with a set of AES ciphertexts labelled with the corresponding plaintexts (assume for simplicity that the encryption key is fixed). The hope was that it would generate an ML model that is able to invert AES, that is, decrypt ciphertexts into the corresponding plaintexts.
我的同事曾经就培训机器学习系统的可行性征询我的意见，该系统可能会破坏标准的AES块密码加密。 这个想法是用一组带有相应明文标记的AES密文来训练ML系统(为简单起见，假定加密密钥是固定的)。 希望它会生成一个能够反转AES的ML模型，即将密文解密为相应的明文。
If this works, the model will successfully guess future unobserved plaintexts by processing ciphertexts through the learned function, thus breaking all modern secure communication and storage solutions. Brilliant idea, it seemed.
如果可行，该模型将通过学习的功能来处理密文，从而成功猜测出未来未观察到的明文，从而打破所有现代的安全通信和存储解决方案。 似乎很棒的主意。
But, sadly, this idea won’t work because there is no pattern for the machine to learn as assumed by the statement: “let’s train an ML system with a set of AES ciphertexts labelled with the corresponding plaintexts.” The colleague is falsely assuming that ML can “find” key by looking at pairs of ciphertexts and plain texts.
但是，可悲的是，这个想法行不通，因为机器没有按照以下语句所假设的学习模式：“让我们训练带有一组标有相应明文的AES密文的ML系统。” 同事错误地认为ML可以通过查看成对的密文和纯文本来“查找”密钥。
However, there is no magic algorithm to undo a one-way trapdoor function. Of course, there is always brute force, trying all the combinations until you find the right one. But there is no way to “learn” from such a small dataset.
但是，没有魔术算法可以撤销单向陷门功能。 当然，总会有蛮力，尝试所有组合，直到找到合适的组合为止。 但是没有办法从如此小的数据集中“学习”。
Machine learning is about analyzing large data sets, training, and finding the rules that exist to tie things together with math. In this case, however, the math used to relate the ciphertext and the plain text is designed to be irreversible, and the variables can only be discovered with brute force.
机器学习是关于分析大型数据集，训练并找到将数学与事物联系在一起的规则。 但是，在这种情况下，用于将密文和明文联系起来的数学是不可逆的，并且只能用蛮力来发现变量。
如何重新训练Chatbot机器学习系统以更好地与用户互动 (How To Retrain A Chatbot Machine Learning System To Interact Better With Users)
Typical examples of chatbots are those used in banking sites for automated customer service. Usually, such bots are trained by looking at user chat records and discovering the highest mode among customer questions.
聊天机器人的典型示例是银行站点中用于自动客户服务的聊天机器人。 通常，通过查看用户聊天记录并发现客户问题中的最高模式来训练此类漫游器。
When we find the first-order mode for an array of questions, we can use the question as a first-order intent and use natural language processing to record all the possible ways a user can ask the same question.
当我们找到一系列问题的一阶模式时，我们可以将问题用作一阶意图，并使用自然语言处理来记录用户可以问相同问题的所有可能方式。
We can look and see if there is a high correlation between the satisfaction of the user and the agent providing anyone answer or solution. If there is a high correlation between the users, this would be the algorithmic way of determining the most common question and the most satisfying answer.
我们可以看看用户的满意度与提供答案或解决方案的代理商之间是否存在高度相关性。 如果用户之间的相关性很高，这将是确定最常见问题和最令人满意答案的算法方法。
Retraining such a chatbot would recommend a Supervised Learning Model (Discrete Variable Prediction) with a Decision Tree Classifier that is powered by a rich set of curated user intents.
对这样的聊天机器人进行再培训将建议使用带有决策树分类器的监督学习模型(离散变量预测)，该决策树由一组丰富的策划用户意图提供支持。
Retraining the bot based on unedited chat transcripts is fine (and is probably best) as long as the models are preprocessed to dealing with missing data, handle data imputation, and handle categorical data.
只要对模型进行预处理以处理丢失的数据，处理数据估算和分类数据，就可以基于未编辑的聊天记录对机器人进行再培训是很好的(并且可能是最好的)。
The model should also be trained to encode class labels for classification problems, induce features to transform the data, and deal with dimensionality reduction, such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA).
还应该训练模型以对分类问题的类标签进行编码，引入特征以转换数据以及处理降维，例如主成分分析(PCA)和线性判别分析(LDA)。
结论 (Conclusion)
Artificial intelligence can certainly do several things, but it has limitations. More so, its evolution still subjects it to the dependence on humans. True AI does not exist, at least for now. More so, the industry needs more and more research if further advancements can be achieved.
人工智能当然可以做几件事，但是它有局限性。 更重要的是，它的进化仍然使它依赖于人类。 真正的AI至少在现在还不存在。 因此，如果要实现进一步的发展，行业需要越来越多的研究。

翻译自: https://medium.com/@brianrusseldavis/the-future-of-true-ai-and-machine-learning-688d76eb44bc

神码ai人工智能写作机器人

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• 神码ai人工智能写作机器人Here you will get list of best books for Machine Learning and Artificial Intelligence that are useful for beginners and intermediates. 在这里，您将获得有关机器学习和人工智能的...


神码ai人工智能写作机器人
Here you will get list of best books for Machine Learning and Artificial Intelligence that are useful for beginners and intermediates.
在这里，您将获得有关机器学习和人工智能的最佳书籍清单，这些书籍对初学者和中级者很有用。
Hope you all are doing good. We are again here in front you all with another successive post on Machine Learning. We almost have covered the theoretical portion of the course and will be doing the hands-on practical soon. We all know that there are plenty of resources on the internet that we can use to study and learn almost anything. But again availability of the contents in such a humongous amount haunts the learners that where to start their journey and very often a learner ends up confused and irritated. Great scholars suggest reading books, ain’t they? So why don’t we take the easier path? While the internet is full of plenty of choices that seem very confusing for a novice, we would suggest to start the journey with conventional steps, of course books.
希望大家都做的很好。 我们再次在此面前着另一篇有关机器学习的文章。 我们几乎涵盖了该课程的理论部分，并将很快进行动手实践。 我们都知道，互联网上有很多资源可用于学习和学习几乎所有东西。 但是，如此大量的内容又一次困扰着学习者，使他们从何处开始旅程，而且常常使学习者感到困惑和烦恼。 伟大的学者建议读书，不是吗？ 那么，为什么我们不走更简单的道路呢？ 尽管互联网上有很多选择，对于新手来说似乎很令人困惑，但我们建议您从传统步骤开始学习，当然也要包括书本。
Again you guys do not really worry or need to wander here and there in search of books neither you have to ask someone else’s suggestion for what book to have. Here we have complied a list of some useful books that will give a kick-start to your effort towards data sciences and analytics also on the other hand are interesting to read. Moreover keeping in mind our readers convenience, we’ve also provided the links from where you can order books of your choice without even stepping out of the comfort of your home.  So without talking much let’s get started and step toward the list we have compiled for you.
你们再一次不必担心或到处乱逛寻找书籍，也不必问别人对买什么书的建议。 在这里，我们整理了一些有用的书的清单，这些书将为您在数据科学和分析方面的工作提供一个开端，另一方面，它们也很有趣。 此外，为了方便读者阅读，我们还提供了一些链接，您可以在其中订购所需的书籍，而不必离开家中的舒适环境。 因此，不用多说，让我们开始吧，迈向我们为您编制的清单。
最佳机器学习书籍(ML) (Best Books for Machine Learning (ML))
统计学习的要素 (The Elements of Statistical Learning)

As the name itself suggests, this book aims at explaining the algorithms of machine learning mathematically with a tint of statistics. The three authors are Trevor Hastie, Robert Tibshirani and Jerome Friedman has emphasized on explaining the logic behind the machine learning algorithms with the help of mathematical derivations.
顾名思义，这本书的目的是在数学上用统计色彩解释机器学习的算法。 这三位作者是Trevor Hastie，Robert Tibshirani和Jerome Friedman强调了借助数学推导来解释机器学习算法背后的逻辑。
Note: If you have a good grasp of linear algebra, we would suggest to go with this book.
注意 ：如果您对线性代数有很好的了解，我们建议您阅读本书。
Python机器学习实例 (Python Machine Learning by Example)

Instead you can buy this book written by Yuxi (Hayden) Liu. With this book you will be able to learn the fundamentals of machine learning and would be able to build your own intelligent applications.
相反，您可以购买由Yuxi(Hayden)Liu撰写的这本书。 通过这本书，您将能够学习机器学习的基础知识，并能够构建自己的智能应用程序。
Note: Please note there is no pre-requisite to start with this book. Even a person with zero knowledge about machine learning can easily get a grasp over the course.
注意 ：请注意，本书没有先决条件。 即使是一个对机器学习的知识为零的人也可以很容易地掌握整个课程。
从数据中学习 (Learning from Data)

This very books provide a simplified understanding of the complex areas of machine learning. Instead of lengthy explanations, small and to-the point explanation is being provided by Yaser Abu Mostafa, Malik Magdon Ismail and Hsuan-Tien Lin. We would suggest this book as a good means to learn and apply the principles of machine learning for the beginners.
这些书对机器学习的复杂领域提供了简化的理解。 Yaser Abu Mostafa，Malik Magdon Ismail和Hsuan-Tien Lin不再提供冗长的解释，而是提供小而精要的解释。 我们建议这本书是初学者学习和应用机器学习原理的好方法。
Moreover in addition to the book reading you can also refer to online tutorials by Yaser Abu Mostafa.
此外，除了阅读书之外，您还可以参考Yaser Abu Mostafa的在线教程 。
编程集体智慧 (Programming Collective Intelligence)

This book popularly known as PCI in the world of machine learning is said to have all that requires to start learning machine learning. It is believed that this book was written long before the evolution of machine learning as we see it today, but to our surprise, the topics and chapters discussed entirely relate to the version of machine learning we have today.
据说这本书在机器学习领域广为人知，具有开始学习机器学习所需的全部知识。 可以相信，这本书是在我们今天看到的机器学习发展之前就写的，但是令人惊讶的是，所讨论的主题和章节完全与我们今天拥有的机器学习版本有关。
We strongly recommend this book to every aspiring data scientist, ml enthusiast and even folks who are into machine learning since quite a few time. We bet you won’t regret giving this book a try.
我们强烈推荐这本书给每位有抱负的数据科学家，ml爱好者以及甚至很多时间以来从事机器学习的人们。 我们打赌您不会后悔尝试这本书。
Tom Mitchell的机器学习 (Machine Learning by Tom Mitchell)

After reading the book mentioned just above, we would recommend you to give this too a try. Tom has tried to make his readers understand the concept of machine learning with the help of pseudocodes and case studies. You will also find some interesting basic examples to understand the algorithms with ease.
阅读完上述书籍后，我们建议您也尝试一下。 汤姆(Tom)试图借助伪代码和案例研究使读者理解机器学习的概念。 您还将找到一些有趣的基本示例，以轻松理解算法。
人工智能最佳书籍(AI) (Best Books for Artificial Intelligence (AI))
人工智能：一种现代方法 (Artificial Intelligence: A Modern Approach)

This book is considered as the holy book for understanding the immense field of AI. Peter Norvig and Stuart Russell worked together to make this art happen. This book is suited to the people new to AI. Not only this provides an overview about AI but also covers some advanced topics like search algorithms, working with logic, machine learning, language processing, etc.
这本书被视为了解AI广阔领域的圣书。 彼得·诺维格 ( Peter Norvig)和斯图尔特·罗素 ( Stuart Russell )共同努力，使这种艺术得以实现。 这本书适合AI新手。 这不仅概述了AI，还涵盖了一些高级主题，例如搜索算法，逻辑处理，机器学习，语言处理等。
人工智能编程范式 (Paradigm of Artificial Intelligence Programming)

This book too is written by Peter Norvig. This book primarily aims at teaching its readers the common lisp techniques to build robust AI systems. Instead of just teaching theory, in this book Norvig has put more emphasis on the practical part to let his readers develop programs and systems at their own. If a personnel want to make his/her career in the AI domain, this book is worth giving a shot.
这本书也是彼得·诺维格 ( Peter Norvig )写的。 本书的主要目的是教给读者常见的Lisp技术以构建强大的AI系统。 在本书中，Norvig不仅仅是教授理论，还更加注重实践部分，以使读者可以自己开发程序和系统。 如果人员想在AI领域从事自己的职业，那本书值得一试。
人类人工智能 (Artificial Intelligence for Humans)

Jeff Heaton, the author of this book aims to teach his readers the basic AI algorithms like clustering, error calculation, linear regression, etc. This book is well equipped with good examples and relevant test cases. Moreover this book demands good grasp on mathematics in order to understand the equations described.
本书的作者杰夫·希顿 ( Jeff Heaton)旨在教给读者一些基本的AI算法，例如聚类，误差计算，线性回归等。本书配备了很好的示例和相关测试案例。 此外，本书要求对数学有很好的掌握，以便理解所描述的方程式。
人工智能第一门课程 (A First Course in Artificial Intelligence)

This book is an introductory step towards AI and written by Deepak Khemani. This book is written in such a manner that a person from non-programming background can also understand the concepts easily. Although the advanced topics are not explained into depth, but the overall structure of the book is acceptable. The books explains the classical methods and the updated concepts as well.
本书是Deepak Khemani撰写的迈向AI的入门课程。 本书的编写方式使非编程背景的人也可以轻松理解这些概念。 尽管没有深入解释高级主题，但是本书的总体结构是可以接受的。 这些书解释了经典方法和更新的概念。
Any doubts or suggestions are welcomed in the comment section below. Also let us know if there is any other best books for machine learning and artificial intelligence you have read and is worth mentioning in the list.
如有任何疑问或建议，请访问下面的评论部分。 还请让我们知道您是否已经阅读过其他值得一提的关于机器学习和人工智能的最佳书籍。

翻译自: https://www.thecrazyprogrammer.com/2018/03/best-books-for-machine-learning-and-artificial-intelligence.html

神码ai人工智能写作机器人

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