I would like to dedicate this episode to people that are thinking about new ways to apply ROS and robots in order to provide new solutions to difficult problems. Now let me introduce you Francisco Miranda who is the founder of DSI Metric Solutions. With his company, Francisco is applying ROS to many different sectors in amazing companies like Blue Origin, Disney Research and even Marvel. Let’s hear from Francisco about the amazing projects he has been working using ROS. Welcome to the podcast Francisco!
A couple of months ago, Eric Schmidt, former CEO of Google, was talking to the US congress. He indicated that, in terms of AI, China may be only 1 year behind the USA. Schmidt was worried that the USA may lose in the next years to China the leading position in AI that they are having at present.
In this post, we are going to explore how the world has reached this situation. We will review the plan of China on AI for the next years, what are the reasons behind it, what it involves and what is its current state.
China’s AI awakening moment
According to Kaifu Li, ex-director of Google China, it all started in 2016 when a reinforcement learning program created by DeepMind won the game of Go to the world human champion, Lee Sedol. For Chinese people, a computer winning a person at this ancient game was shocking. It was at this moment when China realized the importance of AI and how their future as a country depended on leading it.
Moved by that realization, in 2017 China’s government released their strategic plan to lead the world of AI by 2030. They called it: New Generation AI Development Plan for 2030.
In this strategic document, China’s government states that being a leader in AI technology is critical for their military and economic position in the world.
Their goal, states the document, is to become an AI power by embedding AI in all aspects of life, industry and commerce.
China’s advantages and drawbacks
Advantages
They plan to leverage their advantage to other countries on four aspects:
First, a large amount of data that they are generating both online and offline. As you probably know, China has already a society that functions based on the mobile internet. Chinese can pay for anything and everything with their phone. This feature generates a lot of data from each transaction that Chinese people do on it. And this data is not only related to online purchases and actions, but also offline payments made at the supermarket, the barber or the cinema.
Second, a race of startup gladiators, as Kaifu Lee calls them in his book AI superpowers. Those are all the startup leaders that are used to compete against each other in the most difficult conditions, trained in an environment where copying others is seen as the normal thing to do. This implies that for a Chinese startup to survive, they have to thrive even more, move faster, innovate faster, and take possession of the market faster. Otherwise, another startup will copy them and take them out of the game.
A committed government, which is willing to put the resources to achieve the success of the plan by providing the infrastructures and the supporting conditions for companies to flourish. In order to better understand this point, let me clarify that the government AI plan acts more like a wish list rather than an actual list of commands. The government indicates a list of technologies they would like to see built, and then incentivises local officials to promote in the private sector the development of those technologies, by using subsidies, public contracts, and AI-friendly policies.
A society hungry for success. People in China want to be rich, and they will go wherever there is a chance to become one, and they will put all their effort into it.
Drawbacks
Having indicated the advantages of China to lead the AI of the world, the document also indicates the drawbacks they are facing:
1. A Lack of cutting-edge AI talent: The Government of China identifies that they lack people trained in AI and related fields. So in order to solve this problem, they launched in 2018 the AI Innovation Action Plan for Colleges and Universities with which they plan to train 500 teachers and 5.000 students in 5 years.
as for their 2020 report, they have achieved 103 non-university scientific research institutions in the field of AI
in their 2020 status report, they indicate they have 215 colleges and universities with undergraduate major on AI
However, there is still a lack of AI talent. Actually, there is at present a war for talent in China by the most important companies, trying to get the best AI talent to their companies with incredible salaries.
2. Lack of major original results and knowledge in relevant sectors required for the success of the plan, which include:
high-end semiconductors: AI runs of top of them, so if you want to be leader on AI you must master the making of semiconductors.
AI Technical standards. Launched the white paper about AI standards in 2018.
Software frameworks and platforms: none of the popular ones used by the AI community, like Tensorflow, Spark, PyTorch, Caffe…, have been developed in China
3. Lack of an ecological niche that allows fruitful interactions between research institutions and enterprises, allowing the application of latest results into products. China has some big companies working on AI like Tencent, Baidu, Alibaba, but their AI level is still behind the level of the big ones from the states. China also has at least ten privately owned AI start-ups valued at more than US$1 billion, including facial-recognition firm SenseTime, but again, that doesn’t make a rich ecosystem for the transfer of good AI research ideas into products. For that, a bigger ecosystem of startups is required.
In their 2021 report, they indicate that they have so far 2205 artificial intelligence enterprises distributed in 20 application fields, with an 8.39% of them applied to intelligent robots.
Key AI areas to develop
The AI plan of China identifies 7 key AI areas they must master, and provide specific expected results:
Medical imaging systems: the commercialization of medical imaging diagnosis support systems for the early detection of diseases, lead by a machine
Goals:
Keep false negatives below 1%
Detect common diseases with 95% accuracy
Audio intelligence: smart devices with speech recognition abilities
Goals
96% accuracy in speech recognition
Connected vehicles: create smart vehicles that are able to autonomously navigate in complex scenarios
Goals
Cover low-level automated driving by 2020
Cover high-level automated driving
Language translation: produce translation solutions that are extremely reliable and accurate in multi-language scenarios.
Goals
Achieve 85% translation accuracy
Service robots: deploy robots that are able to replace humans in sectors like education, caregiving and cleaning
Goals
Overcome challenges of novel scenarios
Unmanned aerial vehicles: vehicles with completely automated cruise control capable of operating in highly complex environments.
Goals
360-degree omni-directional sensitivity
An accuracy margin of 0.005 degrees
Image recognition: this is one of the most ambitious goals. Starting from improving image recognition, they also are pushing into video understanding and summarization, search of specific images inside a video and human-video integration
Goals:
Help build China’s all-encompassing Social Credit System by 2020
What I specially like about the plan for each area is how specific are the goals to achieve and deadlines for them. This is the SMART technique which has been demonstrated to increase the chances of success.
I haven’t found any report that explains the results obtained in each one of those areas. Maybe I wasn’t able to find it, so please let me know in the comments below if you have results for any of those lines of research.
However, I suspect that at this point, the results may not be as good as expected. We can say that we will have 20 new research centers for next year, because for that, the only thing that you need is money, people and somebody that leads the development. If you need to speed up, you put more people, more money and then it speeds up.
But scientific progress doesn’t work like this. To a big degree, research doesn’t depend on the money you pour in it. It depends on the research pace itself. So it is almost impossible to predict when a certain breakthrough will be obtained (that is actually the situation of AI since its infancy (true AI is always 20 years ahead the current time!).
The plan in time frames
China has committed hundreds of billions of dollars to the success of this plan. In order to achieve success, the full plan was divided into three time frames:
First one, by 2020, to put China into the world in each one of those subjects, this means put China up to date in all those fields.
We are already 2021. Can we consider that they have achieved this goal?
So far, based on their 2018, 2020 and 2021 reports, China reported the following achievements:
#1 in both total AI research papers and highly cited AI papers worldwide
#1 in AI patents
#1 in AI venture capital investment
#2 in the number of AI companies
#2 in the largest AI talent pool.
In 2021, a Stanford report indicates that Chinese AI researchers are being more cited than any others.
So yes, we can consider achieved this goal.
Second one, by 2025, Achieve major breakthroughs in AI in all the previously indicated subjects
Third one, by 2030, dominate each of those sectors and become the world AI leader
The dangers of this plan
While China is moving forward with their plan, some voices inside the country have started to point about the dangers of working towards achieving it. Some officials state that there is a chance that global competition over AI could start an AI arms race among countries which could lead to a war. The officials are concerned that states may be more prone to attack other countries with AI military systems due to the lack of casualties. On top of that, the automation of decision systems lead by AIs, can cause many misperceptions and quick conflict escalation, leading again to a war.
In order to prevent those situations, 2018 China’s Academy of Information and Communications Technology published the AI Security White Paper, where they advocate for and international cooperation to create norms that govern the application of those systems at the level of the planet. Then a dialogue between the countries has already started to try to prevent it.
Actually, I consider this point one of the real dangers of artificial intelligence: not the creation of intelligent robots that enslave us, or machines that go wild and convert the whole world into paperclips. But instead, the creation of smart enough machines that take decisions by themselves and escalate conflicts between humans due to misunderstandings. That is, those intelligent devices misinterpret information and then we make fatal decisions based on those suggestions of the AI. That is for me the real possibility where an AI can destroy humanity.
Conclusions
We are only 9 years before 2030.
Previous China’s plans have been on track (like for example, remove poverty from their country or the first phase of the 2030 AI plan). Chances are they will achieve it.
Additionally, other China plans are in the middle which are also pushing this one: Made in China 2025 (we’ll talk about it in another video).
What I’m pretty sure is that this plan of China has revolutionized the AI world and is speeding it up, which is good for all of us.
As a final comment, I would like to stress how China’s AI plan puts a lot of emphasis on using the rules of the market to get results, in terms of commercialized products. This means, the results of the AI research must be closely tied to AI products that the market wants. That is why they stress the interconnection between education, research, investment and building enterprises.
Final thoughts
So given that speed that Artificial Intelligence is getting, the whole field is going to need a lot of experts in AI and robotics.
“The future [of AI] is going to be a battle for data and for talent,”
David Wipf, lead researcher at Microsoft Research in Beijing.
So this is clear: there is a lack of AI and robotics talent
If you want to become part of this revolution, you need to start now and get up to speed about artificial intelligence and how to apply it to robotics. For that, go into The Construct’s Robot Ignite Academy and become one of the pioneers of artificial intelligence applied to robotics. The opportunity is now, because there are not many people doing that right now.
So start now learning about Deep learning, Reinforcement learning, machine learning for robotics at The Construct. Take our step by step learning path on AI for robotics, practice with the simulated robots and then connect to our remote robots located in Barcelona, Spain and practice with them from your location.
All the courses are practice based, including robotics theory learning. So you will understand why you are learning a certain AI algorithm or subject.
Closing
If you liked the post and would like to know more, check below the links to all the documents I examined to build this video.
In the next posts, I will be discussing the response of the USA to this plan, and the plan of Europe… if any!
Let me know what you think about this kind of video in the comments below. Do you want me to do more like this?
Thank you and keep pushing your ROS learning.
References
In order to build this analysis I have read the following documents:
AI Super Powers: China, Silicon Valley and the new world order, by Kai-Fu Lee, 2018
I would like to dedicate this episode to people that are starting to debug programs. You may have learnt how to build code for ROS robots, but when you move from the lessons provided by the courses, for instance the ones that we provide at The Construct academy, now is when it starts to get interesting. Debugging is the difficult part because it is there where you start learning deep ROS. So if you are feeling the struggles of debugging ROS programs, this episode is for you.
Today we are going to be talking about a tool that helps us debug ROS programs in a visual way, but before going into that, let me tell you that his week is the last one for registering to the ROS2 Industrial ready online workshop. This is a workshop of 5 days, that you will be doing online, 6 hours every day. You will be learning from ROS2 basics, to ROS2 navigation and manipulation and grasping. You will practice remotely with these robots that you can see here. You will be connecting remotely to them and make your practice.
Now let me introduce you Adrian Macneil who is a former director of infrastructure at Cruise and currently the CEO and co-founder of Foxglove, a company in the mission to create better robotics visualization tools. Today we are going to talk to him about the tools they are developing to simplify our debugging needs. Welcome to the podcast Adrian!
I would like to dedicate this episode to people that are thinking about building their own robotics startup. Yes the world needs you. Somebody has to build the robots that we saw in the movies. Those robots that help humans in dangerous environments like the Moon or Mars. Those robots that take the hard jobs in their shoulders, those robots that become our must trusted companions.
Today we are going to learn first experience of a person who built a world leader robotics startup based in ROS.
Now let me introduce you Roberto Guzman. Roberto is the founder and CEO of Robotnik company, a ROS based robotics company that produces many types of service robots. He is a ROS expert and has many years experience launching service robots to the market. Real service robots!
Let’s talk with him about the creation of a successful ROS based robotics company.
I would like to dedicate this episode to people that teaches robotics and ROS around the world.
Today we are going to learn how the US military Air Force learn to program robots with ROS.
Want to learn from the best ROS Developers of the world?
Then you cannot miss the 4th ROS Developers Day 2021, a full day of ROS practice delivered by the best ROS programmers of the world.
The conference is completely practical. 12 hours full of ROS practice from the comfort of your home. Check it out here.
Steve Beyer
Now let me introduce you Steve Beyer. Steve is an embedded systems engineer and roboticist that is a member of the United States Air Force developing the US Air Force’s digital communications infrastructure in the Middle East. More recently, he changed his role to senior instructor of embedded systems and robotics at the United States Air Force Academy.
Let’s talk with him about the teaching of robotics in the US Air Force.
DISCLAIMER: The views expressed during this interview are the author’s and do not reflect the official policy or position of the Department of Defense, the United States Government, United States Air Force, or United States Air Force Academy.
S. M. Beyer, “uSAFABOT: A robotics learning platform for a hands-on laboratory approach in an introductory ECE course,” 2021 ASEE Annual Conference and Exposition, Long Beach, CA, US, 2021, accepted.
This episode is about learning the options you have to get some money to start your startup and what is expected you achieve with that money
In this podcast series of episodes we are going to explain how to create a robotics startup step by step.
We are going to learn how to select your co-founders, your team, how to look for investors, how to test your ideas, how to get customers, how to reach your market, how to build your product… Starting from zero, how to build a successful robotics startup.
I’m Ricardo Tellez, CEO and co-founder of The Construct startup, a robotics startup at which we deliver the best learning experience to become a ROS Developer, that is, to learn how to program robots with ROS.
Our company is already 6 years long, we are a team of 15 people working around the world. We have more than 100.000 students, and tens of Universities around the world use our online academy to provide the teaching environment to their students.
We have bootstrapped our startup, but we also (unsuccessfully) tried getting investors. We have done a few pivots and finally ended at the point that we are right now.
With all this experience, I’m going to teach you how to build your own startup. And we are going to go through the process by creating ourselves another startup, so you can see in the path how to create your own. So you are going to witness the creation of such robotics startup.
Money to Start
We have an idea for a cool robotics product and assembled a team to build it. The only thing that remains is to have some money to buy the required parts, computers, and software, or else we may need to build the product. Also, the team building needs to have some payment (or not). If you need to subcontract any service (for example, the design of a logo for the product or a promotional website), or if you need to visit some places to promote it, for all that, you will need money.
The beginning of the startup is always a difficult time because the product still has not been built, so you cannot generate any money. Hence it looks like a circle: you cannot create the product because you don’t have any money being generated, and you don’t generate any money because you don’t have a product. So how do you break that cycle?
Ideas are Worthless
If you have seen all those movies about entrepreneurs, they go to a panel of investors, present their idea, and then everybody gets excited about the idea they propose and gives them the money they need.
Nothing is farther from reality.
You are not going to get money from investors based on your idea. Ideas are worthless.
There is a common misconception about the value of ideas. I myself had this misconception. Several times, I have seen people that say, “Hey, I have a very cool idea for a startup. I tell you the idea, you implement it, and we share ownership 50-50.” I’m pretty sure that your idea is very cool, but what really matters is the implementation of the ideas. The difficult part is to bring into reality an idea. I can assure you that all of us have very cool ideas.
Do you remember the concept of Discmans? Only 24 songs on a CD. Only 24! So a friend of mine shared the idea of the MP3 Discman before the idea was for sale. However, he didn’t become a millionaire selling MP3 Discmans. Instead, it was Sony who implemented that idea (because somebody at Sony had the same idea).
My brother had the idea of Airbnb years before it existed (it was going to be called Travellissimo). I thought of a phone app for dates with locals, even before people had internet on their phones (by creating a local hotspot to communicate with people in the WiFi range). It was going to be called Ligotin.
Ideas per se are worthless. What matters is the implementation of ideas. You can get tons of good ideas. I have seen many people with very good ideas.
However, investors will not invest in your idea unless you have personal connections to important investors (because they are your friends) or you have demonstrated that you can make an idea into a million-dollar business.
Breaking the circle
If just having the idea is worth nothing, how can we break the circle of not having money to finance the initial startup stage?
One option is to run your startup as a side project. This means that you and your co-founders have jobs that pay your bills (house, food, transportation, etc.) and the required materials, travel, and so on for the startup.
At the beginning of The Construct, we each had another job that paid our bills. So we started like that while maturing the idea and building the first Minimum Viable Product (MVP).
The problem with that approach is that it gives you very little time to work on your startup. First, you have to deliver at your job, and then, when you have finished that, you have to go and work on the startup. That is exhausting!
Well, welcome to the startup world! You must also know that if you plan to have a startup for the next few years, that kind of exhaustion will be your life (whether you get financing or not, you will have to work like you never did before!).
So the problem is not that you have to work so many hours, but that very few of those hours are applied to your startup. As a result, your startup will move very slowly. If that is okay with you, your co-founders, and your idea (for example, nobody else in the world can do the same 10 times faster by putting all the effort into it), then this approach is okay.
However, I think that this is never the case in robotics. If you take too much time, somebody else will appear with the same product idea and take you out of business before you even have a minimum viable product.
At The Construct, we started this way, but after a few months of working like that, and once we published our first MVP (which was a complete disaster), we decided that we needed to do more work and move faster. So then, each of us left our jobs and started working for the company. To do that, we had to move to one of the first financing stages of any startup: getting money from fools, friends, and family.
Fools, Friends, and Family, the Three Fs
This stage means you will ask for money from all the people around you who love you and support you in whatever you do. Usually, this means investing the savings you and your co-founders have, asking for money from your close family, like parents, brothers, and lovers, and asking for money from those friends that would do whatever for you. Poor fools!
Then you use this money to build an MVP, a proof of concept that your idea will work, and by work, I don’t mean in the engineering sense (I’m almost 100% sure your idea will work in that sense, that is not the problem). The problem is that it should work in business terms if somebody is willing to pay for it and is willing to pay enough to build a sustainable business.
The chances are that your first iteration of the MVP will not work. So you will have to iterate, modify it, and try again. We will discuss this process in detail in future episodes.
So you will have to take that into account when you are planning your money and the many iterations that you will need before you hit the point where there is business.
How many iterations will you need? Nobody knows. Maybe one, maybe a hundred.
So you have to save as much as possible from that initial money that you have.
Actually, the startup life consists of searching for a sustainable business model in iterations, until you find a good spot or run out of money.
All this means that your money must be managed appropriately to achieve an MVP that shows some business opportunity.
If you reach that point, then you can go to the next point, which is a seed round for your startup from a business angel.
But before going into that, what can you do if you spend all the money and reach no point? Well, you can close your startup and apologize to all those who lend you money. Or you can search for an accelerator.
Accelerators
Accelerators are places that give startups a small amount of money to keep moving, as well as space to work and mentoring from experts to move your idea to the point of MVP faster. In exchange, they usually request a percentage of your company. The amount of money they give and the percentage they take depends on the accelerator.
One of the most famous accelerators is Y Combinator, which has accelerated companies like Dropbox, Stripe, Airbnb, Twitch, and Reddit. They invest $115K for 7% of the company.
I do not recommend accelerators unless you apply to Y combinator or something similar. Or unless you are so desperate and really believe this could fly. The reason is that you can find more accelerators than Starbucks in the last few years. There is one on every corner. And usually, they don’t provide a good service, just a space to stay and a little money. So the acceleration is almost nonexistent. It is more of a survival kit.
By acceleration, I mean a team of experts ready to guide you, spot your false assumptions and errors, and point you in a more successful direction. They basically get some part of the company for a little bit of money and free space (shared with many others).
Business Angels
Business Angels are the ones that invest a small amount of money in your startup after they see some possibilities of success in employing your MVP. They will need to see from you a practical demonstration that your product can make millions in the long run.
Business Angels are usually individuals with a lot of money who want to be part of a project they like and think can be successful. For example, Elon Musk was a Tesla Business Angel. He invested his own money in the early stages of Tesla.
Business Angels will also provide mentorship and guidance based on their contacts and experience. They are directly investing their personal money and want to have a say.
The amount of money invested may be around $50K – $200K
The idea here is to use that money to go beyond the MVP and show that there is real traction in your product – that is, that people would like to buy a lot of it. You don’t have to sell a lot, but just show that if you had enough resources, you would improve the product so much that a large market would like to buy.
Investors
At this point, investors come into the picture. They are there to help you scale your existing business into a big business, optimize the processes, grow in size, or attack adjacent markets.
One point I want to clarify is that investors are not only interested in seeing that you generate money. They could also be interested in traction, that is, that you can engage a lot of people because that usually means that at some point in the future, you must be able to convert those people into money by selling them something.
That is, for example, what happened with Google. Google lived many years with money from investors without producing any decent revenue because they engaged a lot of people (we are talking about millions of people). Then, at some point, one of the employees of Google got the idea of the Google ads system, which made them explode in terms of revenue.
In general, investors do not care very much about your business. It is just a piece in their business game. They may impose demanding conditions for the investment and treat your startup like another piece in their portfolio. This means that if they have to sacrifice it for a greater win (for them), they will. So take care with them.
Sometimes you have no other option, especially in hardware companies that need lots of money to build even something simple (robotics is expensive). But then, you have to be prepared to manage them. So that is another task for you as the founder.
Summary – General Path for Investing
The general way of working is as follows:
You get some initial money from fools, friends, and family to build some pre-idea
You use the good results from that to show to Business Angels, which will help you build the product to another level of results.
With those results, you show investors that there is success and that you need their money to scale to win millions. And they invest in you, several rounds.
Bootstrapping
Another option is to bootstrap your company. This means focusing on generating revenue with your product from the beginning, then using that money to pay your salaries and make your startup grow. This has its advantages and its drawbacks.
Advantages: you don’t dedicate time to get the money, so you will not owe somebody else. Instead, you dedicate your time to building the business. You don’t have to deal with investors that do not care about your business. You are basically free.
Disadvantages: you will grow slower. You are in danger that another company builds the same product as you faster because they have more resources.
Bootstrapping is the method that we used at The Construct. However, with bootstrapping, you must always fight to get enough customers to pay the company’s expenses and grow.
Conclusion
The main point you should take home is that no investors will invest in your idea. Instead, they will invest in some early implementation of your idea that has shown potential in one way or another. Because of that, starting a startup is difficult. You need to cut all expenses to the maximum and ask for money from the people close to you. Then use that money with all the care of the world to demonstrate the
The goal with that money is not to build the business but to demonstrate to investors that your idea has real potential to succeed. And success here means winning in a market the size of billions of dollars.
In the next episode, we will talk about the methodology of building the MVP that will validate your idea and lead you to the next phase.
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