Andrew Ng's AI Efforts in Manufacturing

Revitalizing manufacturing through AI

Dear Friends, 

I am excited to announce Landing.ai, a new Artificial Intelligence company that will help other enterprises transform for the age of AI. We will initially focus on the manufacturing industry.

AI is already transforming the IT industry. In my work leading Google Brain and Baidu’s AI Group, I’ve been fortunate to play a role in the transformation of two great Internet companies, and see firsthand the benefits modern AI brings to these businesses and to their users. It is now time to build not just an AI-powered IT industry, but an AI-powered society. One in which our physical needs, health care, transportation, food, and lodging are more accessible through AI, and where every person is freed from repetitive mental drudgery. For the whole world to experience the benefits of AI, it must pervade many industries, not just the IT industry.

AI transformation is hard

Many companies are figuring out how to use AI, but this is not easy. The technology is still complex, and few teams understand AI well enough to implement it effectively. Outside the IT industry, almost no companies have enough access to AI talent.

Further, just as using IT to transform a traditional company requires more than building a website, using AI to transform a company requires much more than training a few machine learning models. The strategy of integrating AI — everything from data acquisition, to organizational structure design, to figuring out how to prioritize AI projects — is as complex as the technology, and good AI strategists are even rarer than good AI technologists.

Landing.ai will help enterprises address these challenges. We are developing a wide range of AI transformation programs, from the introduction of new technologies, to reshaping organizational structure, to employee training, and more.

The Manufacturing Industry

The IT industry has primarily shaped our digital environment. Manufacturing touches nearly every part of our society by shaping our physical environment. It is through manufacturing that human creativity goes beyond pixels on a display to become physical objects. By bringing AI to manufacturing, we will deliver a digital transformation to the physical world.

AI technology is well suited to addressing the challenges facing manufacturing, such as variable quality and yield, inflexible production line design, inability to manage capacity, and rising production costs. AI can help address these issues, and improve quality control (see our video demo), shorten design cycles, remove supply-chain bottlenecks, reduce materials and energy waste, and improve production yields.

We are also excited to announce today a strategic partnership with Foxconn. We have been collaborating with Foxconn since July, and are developing AI technologies, talent and systems that build on the core competencies of the two companies. As one of the world’s leading technology service providers and a multinational company running manufacturing in several continents, Foxconn provides Landing.ai a platform to jointly develop and deploy AI solutions and training globally.

Jobs and training/retraining

Bringing AI to manufacturing will also revitalize manufacturing jobs in the US and globally. There has been much discussion about how people will work in an AI-powered future. The next wave of manufacturing jobs will be very different than the previous one. They be higher-level and higher paying, but also require new skills. Thus, they will require large scale training or retraining.

The retraining of workers for the next generation of jobs in AI is a challenge that my team is uniquely equipped to tackle. We are dedicating time and resources to creating retraining solutions for current or displaced workers. We are discussing the deployment of skills training programs with a variety of partners, including local governments. We hope we will have more to say on this soon, and are excited to take on this challenge.

In developing economies, the AI transformation of manufacturing will accelerate the affordability of products ranging from antibiotics to bicycles to computers. It will also help small-scale producers sell products to and benefit from global supply chains. In developed economies, deeply integrating AI into manufacturing will also pave the way to power a new generation of products, devices and experiences.

If you are interested in learning more, please visit our website landing.ai or contact us at contact@landing.ai.

We look forward to sharing more about Landing.ai in the near future!

Andrew Ng

CEO, Landing.ai

What is the Internet of Things (IoT)?

The term “Internet of Things” refers to “scenarios where network connectivity and computing capability extends to objects, sensors and everyday items not normally considered computers, allowing these devices to generate, exchange and consume data with minimal human intervention.” IoT includes consumer products, durable goods, cars and trucks, industrial and utility components, sensors, and more. It presents a new way for users to interact with the network, using devices that are not limited to traditional computers, smartphones, and laptops. IoT brings unparalleled new opportunities for industrial applications and critical infrastructure, but significant challenges as well. Many of the challenges and recommendations addressed in this paper are focused on consumer-grade IoT but are also applicable to industrial and critical infrastructure applications of IoT. While local communication protocols used for IoT, such as Zigbee, LORA, Z-Wave, or Bluetooth, present interesting challenges of their own, the Internet Society’s primary focus is how IoT systems interact with, and impact, the Internet and its users.

(Source: Internet Society)

Mark Zuckerberg's Words on AI

My personal challenge for 2016 is to build a simple AI -- like Jarvis from Iron Man -- to help run my home and help me with work. 

I'm planning on writing up some thoughts every month on what I've built and what I'm learning. I'm still early in coding, so I'll start this month with a summary of the state of the AI field.

Artificial intelligence may seem like something out of science fiction, but most of us already use tools and services every day that rely on AI. When you do a voice search on your phone, put a check into an ATM, or use a fitness tracker to count your steps, you're using basic forms of pattern recognition and artificial intelligence. More sophisticated AI systems can already diagnose diseases, drive cars and search the skies for planets better than people. This is why AI is such an exciting field -- it opens up so many new possibilities for enhancing humanity's capabilities.

So what can AI do and what are its limits? What things is AI good at and what is AI bad at?

Simply put, today's AI is good at recognizing patterns and bad at what we would call "common sense".

The primary method used to train AI systems is called supervised learning. This is like when you show a picture book to a child and tell them the names of everything they see. If you show an AI thousands of pictures of dogs, you can train it to start recognizing dogs.

You can teach AIs to do a lot of things this way. For example, we can teach an AI to recognize all of your friends' faces by showing it thousands of photos, and then it can suggest tags for the photos you upload on Facebook. You can teach an AI to recognize speech by having it listen to thousands of hours of speeches throughout history while also showing it transcriptions of what was said. You can teach an AI to diagnose melanoma by showing it thousands of photos of tumors. You can even teach an AI how to drive a car and automatically brake by showing it thousands of examples of people and obstacles it might encounter on the road.

Diagnosing cancer, driving cars, transcribing speech, playing games and tagging photos may sound like very different tasks, but they're all examples of teaching an AI to recognize patterns by showing them many examples. 

Many different problems can be reduced to pattern recognition tasks that sophisticated AIs can then solve. This year, I'll teach my simple AI to recognize patterns. I'll train it to recognize my voice so I can control my home through speaking. I'll train it to recognize my face so it can open the door when I'm approaching, and so on.

But there are lots of limitations of this approach. For one, to teach a person something new, you typically don't need to tell them about it thousands of times. So the state of the art in AI is still much slower than how we learn. 

But more importantly, pattern recognition is very different from common sense -- and nobody knows how to teach an AI that yet.

Without common sense, AI systems can't use knowledge they've learned in one area and easily apply it to another situation. This means they can't effectively react to new problems or situations they haven't seen before, which is so much of we all do everyday and what we call intelligence. 

Our best guess at how to teach an AI common sense is through a method called unsupervised learning. My example of supervised learning above was showing a picture book to a child and telling them the names of everything they see. Unsupervised learning would be giving them a book and letting them figure out what to do with it. They could pick it up and by touching it learn to turn the pages. Or they could let go of it and realize it falls to the ground. 

Unsupervised learning is learning how the world works by observing and trying things out rather than being told what to do. This is how most animals learn. It's key to building systems with human-like common sense because it doesn't require a person to teach it everything they know. It gives the machine the ability to anticipate what may happen in the future and predict the effect of an action. It could help us build machines that can hold conversations or plan complex sequences of actions -- necessary components for any authentic Jarvis.

Unsupervised learning is a long term focus of our AI research team at Facebook, and it remains an important challenge for the whole AI research community.

Since no one understands how general unsupervised learning actually works, we're quite a ways off from building the general AIs you see in movies. Some people claim this is just a matter of getting more computing power -- and that as Moore's law continues and computing becomes cheaper we'll naturally have AIs that surpass human intelligence. This is incorrect. We fundamentally do not understand how general learning works. This is an unsolved problem -- maybe the most important problem of this century or even millennium. Until we solve this problem, throwing all the machine power in the world at it cannot create an AI that can do everything a person can.

We should not be afraid of AI. Instead, we should hope for the amazing amount of good it will do in the world. It will saves lives by diagnosing diseases and driving us around more safely. It will enable breakthroughs by helping us find new planets and understand Earth's climate. It will help in areas we haven't even thought of today.

Jarvis is still a long way off, and we’re not going to solve most of these engineering challenges in the next year. But I'm glad to be joining the effort and doing what I can to push the field of AI forward.

The Words from Mark Zuckerberg's Letter From the Facebook Filing

Mark did not intend to build Facebook as a company to make the money, but to accomplish a social mission -- to make the world more open and connected. But he need to enable it profitable so that more talented individuals and more investors could come to flourish this base to provide a solid foundation upon which the revolutionary works can be achieved to push the edges of information society. 

Why revolutionary? Facebook not only changes the way people communicate each other but brings a new fashion (bottom-up) to the society where people own the powers to control the flows of information. The contributions from the people in turns help the industries to build up more advanced services for the society. Facebook here is not the owner of the personal information but the catalyst working on the society and economy transformation.

• Facebook hopes to strengthen how people related to each other
• Facebook hopes to improve how people connect to businesses and the economy
• Facebook hopes to change how people relate to their governments and social institutions
• Facebook don't build services to make money; Facebook make money to build better services
• Facebook act on the Hacker Way

Facebook's core values:

• Focus on Impact
• Move Fast
• Be Bold
• Be Open
• Building Social Value

The full letter  

七 | 点

七......点......

说下午七点，觉得有点晚；

说晚上七点，又觉得还算不上那么晚。

Anyway, that's not the point.

不知道你有没有这样的时候，

在渐渐萧索的某个季节，

坐六七点钟的巴士，从华灯初上的城市里穿行久久。

知道自己的终点还远，

戴起耳机认真看窗外，

偶尔看两眼身边的人，

脑袋里可以慢慢想那些没边没际的事情，

我由此爱上了巴士，

你呢？

正好是街灯开始亮起的时候，

城市的流光溢彩映进了朦朦的车窗，

梦幻极了......

看过巨大的广告牌，上面有最新的数码和最热的明星。

看过挽着手走在街边的母女，女生的手一甩一甩，指向那个明亮又诱惑的橱窗。

看过每个红灯前，红色的汽车尾灯亮成了一片。

看过等在站台的乘客，满脸的疲惫，神色里却有隐隐的喜悦，

是啊，再等一会，就可以回家。

可无奈，自己偏偏是那个到了终点下了车，

回到的地方也仍不是家的，

酸酸的抽抽鼻子，
想戴着耳机应该可以让自己看起来没那么可怜吧，

你看，没有人在身边也还有音乐在耳边......

可是想想自己现在戴着耳机没有表情的样子，最后还是觉得自己有点儿可怜了，

可是那也没有办法呀，

所以......

还是......

【涂鸦于110路巴士】



Stay Hungry. Stay Foolish

"You have to trust in something — your gut, destiny, life, karma, whatever."

"Sometimes life hits you in the head with a brick. Don’t lose faith. I’m convinced that the only thing that kept me going was that I loved what I did. You’ve got to find what you love. And that is as true for your work as it is for your lovers. Your work is going to fill a large part of your life, and the only way to be truly satisfied is to do what you believe is great work. And the only way to do great work is to love what you do. If you haven’t found it yet, keep looking. Don’t settle. As with all matters of the heart, you’ll know when you find it. And, like any great relationship, it just gets better and better as the years roll on. So keep looking until you find it. Don’t settle. "

"Because almost everything — all external expectations, all pride, all fear of embarrassment or failure – these things just fall away in the face of death, leaving only what is truly important. Remembering that you are going to die is the best way I know to avoid the trap of thinking you have something to lose. You are already naked. There is no reason not to follow your heart. "

"Your time is limited, so don’t waste it living someone else’s life. Don’t be trapped by dogma — which is living with the results of other people’s thinking. Don’t let the noise of others’ opinions drown out your own inner voice. And most important, have the courage to follow your heart and intuition. They somehow already know what you truly want to become. Everything else is secondary."

产品经理的14条"军规"

1. 产品经理是一个小总经理。
2. 他（她）必须为一个产品或者产品线的盈亏负责。同时也应该有相应的权限和报酬。
3. 产品经理必须是一个将才，必须有丰富的经验，不仅仅是技术或者仅仅是销售经验。
4. 产品经理下面不宜有太多的兵，最好只有一个兵：产品营销助理。
5. 事业部容易管理，但成本高昂，而且很难发挥合力。产品经理不是事业部经理。
6. 产品经理要会协调各个部门的工作，把各种工作围绕他所负责的产品，使之运转起来。
7. 他要负责产品每周的开发进度和市场推广进度，他要负责销售进度，他要对该产品每周的成本支出和销售收入了如指掌。
8. 每个产品，应该有自己虚拟的账户，不能在企业内部吃大锅饭。不仅人不能吃大锅饭，各个产品也不能吃大锅饭。 
9. 开发部门也好，市场部门也好，销售部门也好，要把产品经理当成自己独立的客户...
10. 产品经理虽然是个好东东，如果运用不当，反而会象邯郸学步，不如传统管理模式有效。如果运用得当，则会发挥100倍威力。能否用好，既要有胆量，也需要虚心学习。
11. 一点不懂技术的人，作不得产品经理，一点不懂成本管理的人，也作不得，不懂沟通之人，作不得，不懂一定销售的人，坐不稳当。
12. 产品经理是从枪林弹雨中打出来的。不是扶出来的。
13. 如果一个总经理下面有两个合格的产品经理，他自己就可以天天去钓鱼，也不用担心公司会有什么麻烦。
14. 产品经理就象一个企业，而企业则象一个挑剔的银行，产品经理自己的产品计划书，就必须象企业的商业计划书一样，详细，有充分的行动计划，该计划书有产品的规划、开发周期安排、目标市场定位、市场推广计划、销售计划、成本与利润的预期...

Human Race

Look at us! Running around; always rushed; always late. Guess that is why they called it the human race. 

But sometimes, it slows down just enough for all the pieces falling into place. Fate works. It is magic! And you are connected. 

Everyone once in a while is in all the randomness. Something unexpected happened, and it pushes us all forward. 

And the truth is what I am starting to think...... What I am starting to feel...... is that maybe human race is not a race at all. 

即将兴起的移动技术？

前段时间读到美国著名科技博客网ReadWriteWeb的创办者Richard MacManus对移动领域的发展趋势进行了阐释。

• 网络应用和本地应用

目前已经有32.6万个针对触屏优化的移动网站；Apple Store里面的数十万个应用；Google Android也有相应的数万个应用。绝大部分应用都需要网络支持。针对手机的浏览器也不断地进步，开发者同样可以在浏览器中开发出色的应用。尽管本地应用的发展很好，但是网络应用必成一个趋势。

• 地理定位

Foursquare的'Location as Platform'的模式正在成为社交网站学习的模型。手机定位'Check-in'迅速成为最热门的在线状态消息。就如同'who you know'在Facebook上大行其道一样，'where you are'迟早也会成为一个提供附加值的平台。定位数据可用于显示周边餐馆、点评、查询电话号码、移动广告、本地新闻、收集本地建筑物的wiki数据等领域。试想如果把这些数据和传感器结合起来一起用，那将兴起一种什么样的应用呢？

• 物联网

除了传感器以外，移动领域的例子还有很多。比如把手机当作RFID阅读器用来扫描条形码，比如把手机当作近距离感应器。iPhone内置加速计实际上就是一个运动感应器，而话筒又可以作为噪音感应器……这些应用在欧美国家应用很广泛，但在国内还未见市场的端倪。

• 新型无线标准

只有装上RFID后，你的手机才能算上真正的智能手机。引用MacManus，RFID芯片将为两种兴起的RFID移动技术带来希望：近场通信(NFC)和DASH7。近场通信在移动支付方面大有可为。而DASH7是一种无线传感器网络版标准，是对近场通信的一个补充。它将推动高级定位服务，远距离移动广告和移动优惠券的发展。很快这两个标准都将成为手机的一部分。NOKIA已经开始使用近场通信技术，Apple和Google据说也在考虑使用近场通信技术。新兴无线标准还有Zigbee,WiMax和4G等等。

• 现实增强(Augmented Reallity)

大概不到一年前AR就已经成为一种热门应用了。例如Junaio是一个iPhone应用，Junaio最近和旧金山湾区快运达成了合作，在应用中提供及时列车信息。这个应用不仅为用户提供及时的列车信息，而且还能估算出每个站台列车到达的时间，然后通过AR技术即使显示。现实应用恰恰就是AR技术应用的核心阵地。

项目经理（PM）的Do and NOT Do

项目经理的几个好习惯（Do）

1. 每天上班第一件事看平台数据，有异常要跟进，建议把平台设为浏览器主页；
2. 欢迎头脑风暴，但事先要把观点准备充分，同时记录别人的观点，最后要整理出确定做的，和下一步要做的list；
3. 需要后续跟进的，用邮件沟通，并加提醒功能；
4. 梳理合并工作的碎片时间，提高效能。

项目经理几个不好的习惯（NOT Do）

1. 执行时过多讨论，偏左偏右，反而需求拟定时缺少调研和互动，执行完缺少回访；
2. 不喜欢随时记录；
3. 不关注数据，想起来就看；
4. 不细心，不严谨；
5. 沟通随意，RTX滥用，导致信息丢失，事后无跟进；
6. 对工作区间划分界限过度在意，不是明确自己做的，尽量不碰。

[Updated: 09-06-2010]

Ten Hints for Proejct Leadership

1. Be Consistent
2. Provide Support
3. Don't make promises you cannot keep
4. Praise in public, criticise in private
5. Be aware of morale danger points
6. Set realistic deadlines
7. Set perceivable targets
8. Explain and show, rather than do
9. Don't rely just on [status report]
10. Encourage a good team spirit