Ted Talk: “How AI Could Empower Any Business” Speech Summary, Text, & Analysis

The power of AI has become more apparent within the past few years. In the Ted Talk: “How AI Could Empower Any Business”, Andrew Ng discusses the powerful implications of artificial intelligence.

Ted Talk: “How AI Could Empower Any Business” Speech Summary

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• Widespread literacy enables a much richer society than not everyone having to read and write
• AI is mainly concentrated in big tech companies, due to the high cost of building an AI system and the potential for a large payoff from having hundreds of millions of users
• AI may not always need massive data sets, as seen with a small pizza store
• Half a million independent restaurants in the US serve tens of millions of customers, but no one-size-fits-all AI will work for them
• AI development platforms shift the focus from code to data and let more people participate
• These platforms can empower an inspector to build an AI system to detect fabric defects, a baker to check the quality of cakes, a farmer to check the quality of vegetables, and a furniture maker to check the quality of wood
• Democratizing access to AI will ensure the wealth created by it is spread more evenly across society

Ted Talk: “How AI Could Empower Any Business” Speech Text

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"When I think about the rise of ai, I’m reminded by the rise of literacy. A few hundred years ago, many people in society thought that maybe not everyone needed to be able to read and write. Back then many people were attending Fuse or herding sheeps, so maybe there was less need for written communication. And all that was needed was for the high priests and priestesses and monks to be able to read the Holy Book and the rest of us could just go to the temple or church or the holy building and sit and listen to the high priests and priestesses read to us. Fortunately, we’re since figured out that we can build a much richer society if lots of people can read and write today, AI is in the hands of the high priests and processes. These are the highly skilled AI engineers, many of whom work in the big tech companies, and most people have access only to the AI that they build for them.

I think that we can build a much rich society if we can enable everyone to help to write the future. But why is AI largely concentrated in the big tech companies? Because many of these AI projects have been expensive to build, they may require dozens of highly skill engineers and it may cost millions or tens of millions of dollars to build an AI system. And the large tech companies, particularly the ones with hundreds of millions or even billions of users, have been better than anyone else at making these investments pay off because for them, a one size fits all AI system such as one that improves web search or that recommends better products for online shopping can be applied to this very large number of users to generate a massive amount of revenue. But this recipe for AI does not work once you go outside the tech and internet sectors to other places where for the most part, there are hardly any projects that apply to a hundred million people or that generates comparable economics. Let me illustrate an example.

Many weekends I drive a few minutes from a house to a local pizza store to buy a slice of Hawaiian pizza from the gentleman that owns his pizza store, um, and his pizza sprayed. But he always has a lot of coat pizza sitting around and every weekend some different flavor of pizza is other star. But when I watch him operate his store, I get excited because by selling pizza, he is generating data and this is data that he can take advantage of if he had access to ai. AI systems are good at smarting patterns when given access to the right data and perhaps an AI system could spot if Mediterranean Pizzas sell really well on a Friday night, maybe could suggest to him to make more of it on a Friday afternoon. Now you might say to me, Hey Andrew, this is a small pizza store. What’s the big deal? And I say to the gentleman that owns this pizza store, something that could help him improve his revenues by a few thousand dollars a year.

That will be a huge deal to him. I know that there is a lot of hype about AI’s need for massive data sets and having more data does help. But contrary to the hype AI can often work just fine even on modest amounts of data such as the data generated by a single pizza store. So the real problem is not that there isn’t enough data from the pizza store, the real problem is that the small pizza store could never serve enough customers to justify the cost of hiring an AI team. I know that in the United States there are about half a million independent restaurants and collectively these restaurants do serve tens of millions of customers.

But every restaurant is different with a different menu, different customers, different ways of recording sales that no one size fits all. AI will work for all of them. What would it be like if we could enable small businesses and especially local businesses to use ai? Let’s take a look at what it might look like at a complete that makes and sells t-shirts. I would love if an accountant working for the t-shirt company can use AI for demand forecasting. Say, figure out what funny memes the print on t-shirts that would drive sales by looking at what’s trending on social media or for product placement.

Why can’t a front to store manager take pictures of what the store looks like and shorten AI and have an AI recommend where to place products to improve sales supply chain? Can an AI recommend to buyer whether or not they should pay $20 per yard for a piece of fabric now or if they should keep looking because they might be able to find it cheaper elsewhere or quality control? A quality inspector should be able to use AI to automatically scan pictures of the fabric being used to make t-shirts to check if there are any tears or discolorations in the calf. Today, large tech companies routinely use AI to solve problems like these and to great effect. But a typical t-shirt company or a typical auto mechanic or retailer or school or local farm will be using AI for exactly zero of these applications. Today, every t-shirt maker is sufficiently different from every other t-shirt maker that there is no one size vial AI that will work for all of them. And in fact, once you go outside the internet and tech sectors in other industries, even large companies such as the pharmaceutical companies, the car makers, the hospitals also struggle with this.

This is the long tail problem of ai. If you were to take all current and potential AI projects and sort them in decreasing order of value and plot them, you get drafted, looks like this. Maybe the single most valuable AI system is something that decides what ads to show people on the internet. Maybe the second most valuable is a web search engine. Maybe the third most valuable is a online shopping product recommendation system. But when you go to the right of this curve, you then get projects like t-shirt product placements or t-shirt demand forecasting or pizzeria demand forecasting.

And each of these is a unique project that needs to be custom built. Even T-shirt demand forecasting if it depends on trending memes on the on social media is a very different project than pizzeria. Um, demand forecasting if that depends on the sales data. So today there are millions of projects sitting on the tail of this distribution that no one is working on, but whose aggregate value is massive. So how can we enable small businesses and individuals to build AI systems that matter to them for most of the last few decades? If you want to build an AI system, this is what you have to do. You have to write pages and pages of code.

And while I would love for everyone to learn to code and in fact online education and also offline education are helping more people than ever learn to code. Unfortunately, not everyone has the time to do this, but there is an emerging new way to build AI systems that will let more people participate just as pen and paper, which are a vastly superior technology to stones, tablet and chisel where instrumental to widespread literacy, there are emerging new AI development platforms that shift the focus from asking you to write lots of code to asking you to focus on providing data. And this turns out to be much easier for a lot of people to do today. There there are multiple companies working on platforms like these.

Let me illustrate a few of the concepts using one that my team has been building. Take the example of an inspector wanting AI to help detect defects in fabric. An inspector can take pictures of the fabric and upload it to a platform like this and they can go in to show the AI what tears in the fabric looks like by drawing rectangles. And they can also go in to show the AI what discolorations and the fabric looks like by drawing rectangles. So these pictures together with the green and pink rectangles that the inspectors drawn are data created by the inspector to explain to AI how to find tears and discolorations.

After the AI examines this data, we may find that it has seen enough pictures of tears, but not yet enough pictures of discolorations. This is akin to if a junior inspector had learned to rely these spot terrorists but still needs to further hone their judgment about discolorations so the inspector can go back and take more pictures of discolorations to show to the AI to help it deepen its understanding. By adjusting the data you give to the ai, you can help the AI get smarter. So an inspector using an accessible platform like this can in a few hours to a few days, um, and with a, with purchasing a suitable camera setup, be able to build a custom AI system to detect defects discovers in all the fabric being used to make t-shirts throughout the factory.

And once again, you may ask, you may say, Hey Andrew, this is one factory. Like why is this a big deal? And I say to you, this is a big deal to that inspector whose life this makes easier. And equally, this type of technology can empower a baker to use AI to check for the quality of the cakes they’re making or a organic farmer to check the quality of the vegetables or a furniture maker to check the quality of the wood they’re using.

Platforms like these will probably still need a few more years before they’re easy enough to use for every pizzeria owner, but many of these platforms are coming along and some of them are getting to be quite usable to someone that is tech savvy today with just a bit of training. But what this means is that rather than relying on the high S and processes the right AI systems for everyone else, we can start to empower every accountant, every store manager, every buyer, and every quality inspector to build their own AI systems. I hope that the Zare owner and many other small business owners like him will also take advantage of this technology because AI is creating tremendous wealth and will continue to create tremendous wealth. And it’s only by democratizing access to AI that can ensure that this wealth is spread far and wide across society. Hundreds of years ago, I think hardly anyone understood the impact that widespread literacy will have today. I think hardly anyone understands the impact that democratizing access to AI will have. Building AI systems has been other reach for most people, but that does not have to be the case in the coming era for AI will empower everyone to build AI systems for themselves and I think that will be an incredibly exciting future. Thank you very much. Thank."

Ted Talk: “How AI Could Empower Any Business” Speech Analysis

With any speech you upload or record, Yoodli provides immediate feedback in the categories of Word Choice and Delivery. Through simple speech analysis, you can receive suggestions for various components of speech.

Word Choice

In the Word Choice category, Andrew Ng ranked well. His Ted Talk was absent of both filler words and inclusive language. Throughout his 11-minute speech, Ng used the top keyword “AI” 49 times total! Surprisingly, one of Ng’s top keywords also included “pizza” at a total of 12 times.

Despite the strong use of language in Ng’s Ted Talk, Yoodli detected 20 weak words, including “so” and “just”.

Delivery

In the Delivery category, Andrew Ng ranked well in both pacing and the appropriate use of pauses. However, Yoodli provided suggestions for both centering and eye contact.

Wrapping Up

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