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How Can Artificial Intelligence Help Businesses Monetize More Efficiently In The Digital Economy?

by Designhill Tweet - in Webinar

Artificial Intelligence Help Businesses Monetize More Efficiently

Last updated on August 9th, 2023

Artificial Intelligence is the new revolutionary change the world is witnessing after the Industrial Revolution. In the modern digital world era, almost every simple to complicated task is based on AI. Now, AI is helping companies in monetizing their businesses. But, business owners must first know how to use AI to their advantage. In this webinar transcript, we have shared how artificial intelligence can help businesses monetize more efficiently in the digital economy.

Businesses have started using Artificial Intelligence to advance their interests in highly competitive markets. Global companies like Amazon are actively using AI to deal with customer-related matters effectively. In fact, most businesses are using AI systems to identify high-value customers and target them with new enticing deals.

AI systems are performing many helpful tasks for businesses. These crucial jobs include automated ad placement and customer targeting, quicker workflows, direct site analysis, intelligent screening, product control, and chat systems for direct sales. These are also the ways to help monetize businesses.

However, in the digital economy, businesses must be first well prepared to use Artificial Intelligence. But, businesses can best explore AI to monetize their business only if they are well prepared for it.

Considering that, Designhill, the leading creative marketplace, organized an AMA session on the topic: How Can Artificial Intelligence Help Businesses Monetize in the Digital Economy. The guest speaker was Dr. Michael Wu, an expert in the field.

During the session, he advised on how to intersect AI with monetization and what to expect, opportunity detection for a business, upcoming major trends in AI, machine learning, and data science. He also gave tips on how businesses can prepare and adopt AI.

About Dr. Michael Wu

Dr. Michael Wu is amongst the leading authorities on Artificial Intelligence, Machine Learning, Data Science, and Behavioural Economics. At present, he is serving PROS [NYSE: PRO] as the Chief AI Strategist. He is a Senior Research Fellow at the Ecole des Ponts Business School for his work in Data Science. For a decade, Michael was the Chief Scientist at Lithium.

Here Is The Video of The AMA Session With Dr. Michael Wu


Here Is What Dr. Michael Wu Advised Business Owners About Using AI To Monetize

Michael Wu: I am going to talk to you about how you can use AI to help you monetize more efficiently and effectively in a digital economy. And today, there’s no excuse, because we are under the influence of the pandemic. So, all businesses are trying to go digital. Many statistics show that digital is the way forward. And, we have had a bigger digital shift in the last three to four months in the last decade. This is because people just have no choice but to go on digital. So, AI is very important in the digital economy.

Artificial Intelligence and Business Intelligence

But, what is AI anyway the best way to understand AI is through an example. So AI comes from BI (Business Intelligence). The best way to understand AI is to understand the history of business intelligence. So, business intelligence starts with a lot of data. I’m just going to give you some toy examples here.

When you have a lot of data, the first thing you do is probably just summarize it. How do you compute statistics and indicators to summarize all your data So, you could know what the data is doing without looking at every single data point. These are what we call descriptive statistics and descriptive analysis. But once you do this for a while, you start to see patterns, you could build a model.

Predictive Analytics

Now, the model also summarizes the data. But, what’s the advantage of having a model means that you can now extrapolate to regions where you don’t have data yet. So, that means you can now predict and forecast. Then, you start doing what we call predictive analytics.

Now, predictive analytics is great, but you can go beyond that. When you have AI in the future, you can also tell people what to do. For example, in this case, you are trying to make money from this stock. Then, there is a good time to buy here, and a good time to sell is here. Now you can start to tell people what to do. That’s prescriptive analytic, where you prescribe actions for people to take to achieve some optimal outcome. In business intelligence, you go from descriptive, to predictive to prescriptive.

AI – The Automation of Decisions

Now, what’s next is the beginning of artificial intelligence. Artificial intelligence is just the automation of decisions and subsequent actions that you need to execute. If you automate all that, then you had the inception of AI and it is not truly AI yet, but you had the beginning of it.

To illustrate this picture, if you go from data to decision to action, traditionally, that’s a human role. Regardless of what the data suggests, you write the data, you can use descriptive analytic to show you, say how your stocks perform. You could use predictive analytics and that will show you how the stock is going to perform tomorrow or in a month or the future trajectory. Or, you can even use prescriptive analytics to tell you, which stock to buy and sell.

But at the end of the day, you decide what to buy and what you sell. You say I don’t believe this data. I want to keep the stock. I don’t want to sell it. So, humans decide what to do in the end. But if you had this problem, the test does automate everything. Start directly from data to decision to actions, all automated, then you have the inception of AI.

Now, this is not truly AI yet. It’s just the inception of it and this is already here. Because we have, for example, algorithmic trading. Rather than telling me what to buy, and what to sell, why not just buy and sell for me. If you tell me what to buy and what to sell, then I have to go and click the buy button or the sell button. And that’s when I am busy, I don’t have time, and that is going to be a delay. And when I come back and have time to do that, that may no longer be the optimal sell anymore. It may not be the optimal time to buy or sell anymore, if the optimal, you should just find, buy, and sell for me, these are algorithmic trading.

And, likewise is our automated loan origination. Rather than having a person, check off all the boxes, and when you meet all the criteria, just give him the loan. I want to give you another example of a prescriptive analytics system that we are all very familiar with. And that’s a GPS navigation system. GPS, what do they do? You tell the GPS where you want to go, and the GPS navigation system will prescribe you a sequence of action to get to where you want to go. So, it is a prescriptive analytic system, telling you what to do to get to where you want to go.

But, why tell you what to do to get to where you want to go? Why not just take you there, directly? When that happens, that results in the autonomous vehicle self-driving car using machine learning. So, these are systems that can automate decisions and actions just normally performed by humans.

What really makes AI intelligent?

If you do a little bit of research yourself, you probably will find the figure that deep learning is a kind of machine learning. This is machine learning as a kind of AI. And that’s what makes AI smart. This is because you have deep learning, which is a big buzzword now in the business community.

The problem with this figure is, it’s not quite correct. So, deep learning is indeed a kind of machine learning. But machine learning is not a kind of AI. Machine learning is used in AI, every AI system today has machine learning in it. So, it is a part of AI. It’s not a kind of AI. So, for this reason, whoever created this fingerprint machine learning in AI, but it’s not a kind of AI.

I will give you another example to illustrate what this means. This V-shaped cylinder engine is a kind of engine, and every car uses the engine. So, if you take away these boundaries and descriptors, you may come to the wrong conclusion that an engine is a kind of car, which is ridiculous, as an engine is not a car. So if you say machine learning is a kind of AI, that’s as ridiculous as saying that an engine is a car. It needs to be interpreted correctly.

It all starts with Big Data

Now, how to interpret correctly? So, we should throw this picture away, and give you a better picture. The better picture starts with big data, which is turned into, I would say a model, through machine learning. The people who do this are data scientists. So, now, once you have a model, If you feed it with new data, you have some analytic results. People have to look at those results and decide what to do.

That usually involves some actions in the real world that have consequences and outcomes, and that usually needs to be fed back into your system for it to work well. So, as I said, before, traditionally, humans always made a decision. And actions are, I would say, a lot of the actions in the digital world can already be automated, But in the physical world, a lot of these tasks have to be carried out by humans. Feedback is also traditionally collected by humans. We survey and we do all kinds of ways to collect feedback, But that’s not very fast.

So, in order for the system to work well, this has to happen very quickly, near-real and the decision needs to be made in real-time. The first thing we need to do is we need to track this feedback automatically. We could do this already today. We just have to write code. Engineers have to write code and use instruments to track it.

If this model here is good enough, then we can automate the decision to write. Because, as I showed you before that if your model is good enough, then, you can automate the decision.

How to know if a model is good enough?

So, the model is good enough when the decision, the recommended action, and decision matches a human expert pretty much all the time. So, in machine learning and statistics, nothing’s ever going to be 100%. You’ll always have, in some cases where the human disagrees with the machine, and that’s fine. That’s why there’s a little bit of silver yellow here, humans could still override.

Most of the time, we should just automate it. And, if you are doing this in the digital world, today, we are moving into the digital economy. Everything is and can already be automated. In the future, when we have more advanced robotics, even the physical world can be automated.

Everything is turned into data

But today, most of Digital’s world work can be automated. Now, the feedback loop has almost no human interaction, human involvement, except in rare cases, when humans disagree with the machine. So, there are two implications of this. One is that, like, everything can go very fast. And second is, like, everything can be digitized and turned to data.

Once everything can get turned into data, then this data gets fed back into your big data. And then you could go through this machine learning process again, and update your model, which is a crucial aspect of Machine Learning Operations (MLOps). So, your model is changed because your data has changed. You find out if this model is going to be better than the model before because you have seen more situations in more scenarios.

So basically, you could do this completely automated, but typically, that’s not a good idea. You want data scientists to oversee. But if every time you use this system, you get new data, and then you basically through machine learning, you get a new model. So the model gets better and better every single time you use the system. Eventually, this model becomes so good that you can model this to take decisions and action-outcome very well. And this is what we call the learning loop.

The learning loop is what makes AI smart. Now, we are able to define what AI is basically. But before we define it, I just want to say it with the caveat that the definition of changes. So, some people say that AI is whatever machines can do that they couldn’t do before. There is some truth to that. Therefore, take this definition as the grain of salt.

AI – A Machine Mimicry

But, the way I like to look at AI is it’s basically a machine mimicry of certain aspects of human behavior with two very important characteristics. The first one is automation is the ability to automate decisions and actions. And the second one that I just talked about, is learning. The ability to learn and improve this performance and usage, the more you use it, the better it gets at what you are doing. So if a machine had these systems that could do both of these, it’s considered AI. So now that machine learning is not AI. So just because you used machine learning doesn’t mean, you have AI.

Now, I just want to illustrate this concept of learning is important. Charles Darwin basically says that the strongest species is not the one that survives nor the most intelligent one, not even the smartest one, but the one that’s most adaptable to change. What gives you the ability to adapt is learning, If you don’t learn, you can’t adapt.

I want to give you another example of the power of this learning loop. I am sure a lot of you have probably seen this documentary that AlphaGo is able to beat the world champion in the game of Go. So how do you think it’s able to do that? That is because AlphaGo is a program written by humans, and these humans are no better than this Grandmaster. It is trained with data from other players, but other players are not as good as it is. So, how can it beat this guy?

This is because the speed argument comes in. If you let AlphaGo play a human train AlphaGo to play this game ago, if it’s good enough to play a human, then you can make AlphaGo play against AlphaGo itself. So eventually, you can go faster and faster. When you have AlphaGo playing against a human, you probably take a few hours to finish. But when you get an AlphaGo play against AlphaGo,, they could play thousands of games, in a matter of an hour. They could play like millions of games in a month.

So, what that means is that AlphaGo can basically make all the mistakes that a human could ever make in their entire lifetime. Probably two or three lifetimes learning from those mistakes and never making those mistakes again. And that is how AlphaGo is able to beat this Grandmaster champion in the world. That’s what makes AI so profound, So a lot of time people ask me what artificial intelligence can do for business. Now that is the wrong question as long as we had the data to train the AI then the AI could basically mimic everything that we do.

Digitize Fully

So, the right question is what can you digitize. This is because everything that we can digitize fully, we have to be able to fully digitize everything that we do. Now if we have a process of 10 steps with digitizing, do not expect AI to be able to fill in the last step. But if you digitize all 10 of the steps, you could use those data to give it to the AI. So, you can learn from it what humans have done in a job, And with these 10 steps, then you can mimic what you do. It’s basically mimicry. So it can mimic what you do. So the question is really about the data.

4 Types of AI Applications That Matter

But today, we live in the world of big data and have lots of data. There are literally thousands and thousands of AI applications out there. But, there are four applications that are important today.

01. Perceptional AI

The first one is what we call perceptual AI. Think of them as chatbots and digital assistants. You have seen examples of this in your phone use such as Siri, Google Assistant, Cortana Alexa. All of these are perceptual AI. They are some computer vision systems, or chatbots and digital systems. Their main use cases are in human-computer interaction because now you could talk to a machine as you would talk to a human. You don’t need to write code to talk to them, So, you interact with machines better and improve human-computer interaction.

02. Internet AI

Another familiar example of AI is that of the internet. We spend a lot of time and engineers generate lots of data. When you feed this data into AI, you can figure out what your preferences are, what you like, what you don’t like, and recommend a content product, movies, music, and every authentic stuff for you.

These are basically recommender systems. So internet AI sometimes is also called personalization AI. Perceptual AI is sometimes called cognitive AI, because they try to mimic the higher cognitive function of a human, such as a language, speech, and vision. So the internet is about personalization, because the biggest applications of internet AI is in personalization, through recommending personalized content.

03. Autonomous AI

Autonomous AI is the AI behind the self-driving car. And the biggest application, autonomous AI now is the self-driving car. that in the future, this will change. It will probably become more like robotics and autonomous drones and other autonomous systems as well.

04. Business AI

The last one is business AI. It is the biggest application for business decision automation. This class of AI is very diverse ranging from fraud detection, algorithmic trading, to machine-aided medical diagnosis, drug discovery to machine aided design, All these are business AI. They need to make decisions about what to do in the business. A doctor, for example, needs to decide whether you have cancer or not. But if you give all this data to the AI, it can mimic what a doctor does and is able to decide. So these are called machine aided medical diagnosis and fraud detection.

Normally, you have an expert in a fraud investigator to detect whether something is a fraud. If you keep all this data to the AI, it basically mimics these fraud investigators’ decisions, and it will make those decisions. So essentially, anything that you could automate in a business decision with data, that’s from experts opinion decision, that’s considered business AI.

But, what can these four types of AI do for us?

There are many more AI out there, these are the four major ones the most important ones. I would say that the first one is improving customer experience, increasing efficiency, and helping you with revenue. So, these are the three most important things that AI helps you do. Even though there are many other ones too. So if you want to pick the AI, what you do will depend on what you are trying to accomplish.

01. Improve Customer Experience

If your goal is to improve customer experiences, such as deepen engagement, boost your conversion, gain loyalty, you would probably pick perceptual AI or the internet. Because, as I said before if you can interact with machines better, through perceptual AI, then, you are going to have a much better customer experience. This is because if a machine can know what you like, and what you don’t like, you will improve your customer experience as well.

02. Increase Efficiency

If your goal is to increase efficiency for example reducing the cost, productivity, and increase throughput, what kind of AI can help? Well, perceptually AI can help you because it works as a digital assistant. It can help you do work pretty much anywhere you want. Business AI, sometimes all called decision AI, also can help you do that because they help you make decisions faster. And autonomous AI can help you to some extent automate your physical tasks and robots don’t need to rest.

So as a result, they are much more efficient that way. And if you want to grow revenue, make a better decision. But, that doesn’t mean that I will say that perceptual AI, internet AI, and others cannot help you grow revenue. In fact, every one of these AIs can achieve any one of these results. The big dots here represent the most direct impact. This gives you a sense of what AI is and what it can do for your business.

Designhill: Do you think that AI will eventually circumvent actual thinking and make people dumber and machines smarter?

Dr. Michael Wu: Well, to some extent, that has already happened. This is because we need to do arithmetic and how, in our head, now we have a calculator, so we don’t need to do that as much. So, to some extent, machines have always helped humans. We need physical strength, and since the Industrial Revolution, we don’t need to be so strong and muscular to do our work to survive.

So, to some extent, you will need certain aspects of thinking, maybe automated. But our brain is highly adaptable, we become smarter, in some aspect, we become more empathetic. For example, we are better at establishing a human connection and human relationship. So those are things that are much harder for machines to mimic.

Designhill: Do you think there are any specific areas or industries that are more likely to be managed by artificial intelligence?

Dr. Michael Wu: Again, that is a wrong question to ask. It is where you can get the data. Whatever area of your business generates lots of data, then, that is the area most suitable for AI because you now have the data to train the AI to mimic what you do in those areas. And whatever industry generates a lot of source data, those industries are going to be most suitable for AI automation. This is because you have the data to train the AI.

So, Those are the ones that are more likely to be automated by AI. In the creative space, where and when you’re trying to create some design, those are difficult for the AI because you need to start measuring what’s inside a human’s head. So, those are probably not going to be automated, shortly.

Designhill: What are your thoughts on the cost-effectiveness of AI for small businesses?

Dr. Michael Wu: A full AI system is still expensive. It is just like the cost of computing. Once only big corporations had computers, but now anyone can have a computer. So these things will change. The most expensive part is probably what we call the data acquisition and accumulation of data. That is probably because you can’t speed that up.

Therefore, I would advise that your startup should start collecting data early. Collect data about your processes and everything. Once you have that data, you can start having lots of AI tools. In the future, I would say, even tools on your phone or your personal computer could all start doing it. They are not like these big gigantic AI systems that will automate lots of things.

For example, scheduling meetings with people will be a lot more efficient. If you have data to train it, like when the AI will know like, when would you like to have met people and when people that you’d like to meet are likely to have time. So, you will know these patterns and then AI will be able to help you schedule meetings more efficiently and more effectively.

And this efficiency eventually allows you to be more productive. You can start to gain your investment back. And now you can meet maybe 20 instead of 10 people before. It can make your investment worth it.

Designhill: Do we need to worry about the fear of AI replacing human working?

Dr. Michael Wu: In the short term, no. In the long term, AI will create more jobs. This has happened with every single technology that has ever been invented. There are lots of jobs that don’t exist anymore. The nature of the job changes. But, you may be doing your job completely differently today. You could be still doing the same kind of job. So, in the short term, it will disrupt some jobs and replace some jobs. But later it will create a lot more jobs.

I want to illustrate this a little bit with something that I prepare. Think about how to say to a salesperson, a sales rep, what they need to do, traditionally they need to, for example, analyze the transaction to figure out what opportunity to pursue. Because you can’t pursue every opt-in, and these are b2b sales, probably. And then they need to kind of nurture the prospect, contact them, give them information. Then, eventually, when they nurture certain stages, they need to set up meetings and travel and go meet them. After that maybe close a deal or something like that. That is what a salesperson needs to do traditionally.

AI is there to help you make decisions

But think about this, with AI, instead of the salesperson, analyzing this data. You could just give the data to an AI, which could tell you what opportunity to pursue. Then, you could just decide to pursue them or not. When you say no, this AI is learning about what kind of opportunity you’d like to pursue. Then, eventually, you will be able to recommend the opportunity to so good that you pretty much accept everything. But you still need to write an email to nurture this opportunity,

But why stop there and why not keep all your email to AI, and then I can start to maybe write your email for you. And then, you say this looks good, send it out. It may be that some of them, you say tweak it around a little bit here and there. As you tweak, this AI will start to learn your style, which you like to engage in a certain way. Maybe this day, I will learn that with this particular person, maybe he likes dogs, you want to talk about Dr. Livermore, So he will learn your habits and how you talk to these people.

Automate Processes

And then eventually, he will write the email so good that you pretty much let every one of them go out. Then you automate that. So you automate these first and second processes. After you are able to automate this, you still need to set up a meeting and go meet your prospect. But why stop there? Why not keep your calendar to your AI and let your AI plan itinerary for you. Then, when you plan, it is easier for you to say I like this.

So do this in a book or this fight for this hotel, or you can say no, I don’t like this hotel or this flight. Then when you say no, that is learning how you like to travel, and you basically will learn your travel habits. Eventually, you are able to recommend more and more opportunities that are more relevant to you. So, now you just have to go meet the prospect. Think about this. These processes can be automated because there is data now. If you think of a b2b sales, if you have to research which opportunity to pursue, nurture them and instead of meeting, schedule everything and then go meet how many prospects you can meet.

AI is automating all of this. Then you can probably meet 50 or 100 instead of only say 10. So think about the efficiency gains you get. So you get a lot. We already have a tool that analyzes your transaction and tells you to recommend an opportunity for you. It makes your sales much more effective that way.

Designhill: What job mix do you foresee coming out of the current AI fourth industrial revolution?

Dr. Michael Wu: There are things that we probably could not foresee. Then, there is a profession called emoji translator now because we use so much emoji on our cell phones. And each platform had different emoji. So, in the future, we will have people who design things on their phones and stuff like that. They will say that is impossible. So, for example, 100 years ago, sitting in front of a computer and sending this design across the world was not possible to imagine. So the future is interesting, it is going to be fun.

Designhill: How to apply artificial intelligence to analyze user search behavior on organizations, websites as well as on search engines?

Collect People’s Search Data

Dr. Michael Wu: The first thing you do is that you need to collect people’s search data. What do people search on and all that stuff like that? And then, for example, you need to identify conversion events, a route, router to a human to take care of this person because he may be interested in buying something.

So, ideally, you want to get the data on what humans decided to do after they have seen this type of search data. Then, you give this data to the Artificial Intelligence which will be able to mimic what humans do. It is simple. This process is that you normally have something that a human does. But then you collect data on what humans do, what’s the input, what humans have seen, and what they decide to do.

Then you give this data to AI, and the AI is simply trying to mimic that behavior. And that’s it. If you have that, then basically you will be able to analyze search behavior, and many other ones as well. So, anything else in the business that you could collect data for, and you can give it to the AI.

Designhill: How most companies analyze their data and the current pandemic situation, and how different it is than they usually analyze to know what’s happening in their environment?

Dr. Michael Wu: History is less relevant but I don’t think history is completely irrelevant yet and the world hasn’t changed completely. I mean, we still eat and some things are still the same. There are lots of things that are still relevant, but less relevant. Now, the pattern of how we buy food and eat may change.

But, the best way for companies is to learn fast. What that means is that you look at a shorter amount of history. In this way, you learn from more recent history. You can project the future from more recent history. This is because a long history may no longer be relevant. The long history, more than a year ago, is probably not as relevant, but less relevant.

A problem when you look at less history is that your signal becomes a lot weaker. So, as a result, now, you need many more different and diverse data sources to increase the strength of your signal in your data. Previously, you may have only one or two data sources. For example, you may look at your web search DB to identify enough leads for your sales. But now, because you’re looking at such a short history, you may not identify enough lead.

So, not only do you need your web search, but you may also need your marketing campaign data, social media data, and many sources of data to identify enough leads to power your AI definitely for yourself. So, you need a shorter history that is more diverse and more variety of data sources.

Designhill: How artificial intelligence will work with cryptocurrency considering a lot of countries have now legalized it?

Dr. Michael Wu: Cryptocurrency is one specific application of a technology called the blockchain. How AI is going to be working with cryptocurrency? You could use it to trade cryptocurrency just as you use AI to automate this algorithmic trading. So, you can use it to do that. But I guess the more important thing is to understand how it can work with the more general algorithm behind cryptocurrency and that’s blockchain.

There are two important applications that are very useful in the future. You can use blockchain for example distributed AI capability to trust the agent. So, you can make AI capability more available to smaller businesses in the future. This is because now you are whoever on this kind of blockchain. You can use this to distribute AI capability. Another one is to distribute third trusted parties of data that for AI to consume.

We all know that data have become more and more important even for consumers. How can we distribute our data to parties that we trust and to analyze our data? We don’t want anybody to have access to our data. But what we want is people who we trust should be able to get access to our data and analyze it and give us something back. So, using blockchain technology, we can limit who has access to our data. And, do it that way. Basically, using blockchain AI to distribute AI capability and distribute to share data with trusted agent trusted parties.

Designhill: What would you advise the governments of the countries that they can do to ensure people are early AI adopters?

Get educated on AI

Dr. Michael Wu: The first and most important is probably education. So, there is still a lot of misconception about AI. I would say, fear, as well. So, in education we use the word, to enable people to understand what AI is. Hopefully, I give you some more realistic ideas of what AI is, and what it can do for you. By understanding it, people are less fearful of it. People often fear what they don’t understand. So once you understand how it works, you are not as fearful of it.

And the other thing for the government is that very often governments make sure that everything is perfect and applies to everybody before they kind of roll it out. That is what hinders this process of adapting to the future. But it doesn’t need to be perfect. If a system is not perfect, roll it out on a small scale, It’s more important to use the system when they are not perfect.

So, you have data about you to get people to get feedback, and then you could get data about what you need to fix as well. In this way, you can improve and refine it. This learning loop applies to humans as well. We need to go through this learning loop ourselves. We should learn to get feedback data from the citizens. Learn also what is working, what’s not working, and then use those data to help us figure out where the system is not working. And then we’ll find it that way.

Deploy AI without caring for perfection

So, deploy small AI even if it’s not perfect. And deploy anyway to get data and feedback to fix. Once you fix more you could deploy to a bigger community. Then, you probably see another type of problem as well. You see a bigger scale problem, and then they will give feedback. Once that happens, you fix it. And then you are ready to deploy more. It is about taking baby steps, and that’s the key. But the key thing is that it doesn’t have to be perfect.

If governments hear something bad about anything including AI, they do not want to do that. I am not saying that if you start to hurt the community or something, then you should do it. We just need to be cognizant of the benefit and costs. So, if the cost of a wrong decision is not that great, it’s not that bad, Then you should just deploy it and test it.

Designhill: Can someone from a different background start learning AI or data science and the related concepts? And what are some of the AI apps available for public learning?

Dr. Michael Wu: I would say that to some extent, anyone can learn about AI. You can follow me on LinkedIn. We try to create a YouTube channel that talks about future technology in business. These include AI blockchain, quantum computing, etc. So, keep an eye on that. But it is important to learn about AI to overcome your fear to understand how it works. But I don’t think it’s really necessary to learn enough that you need to.

For example, to build AI yourself, I don’t think that’s needed, for example, do we know everything, what’s going on inside a car to drive it and to use it effectively to bring us to work? We probably don’t. So, to some extent, you could use it better. You could learn to use it and gain benefit from it. Then, there’s not that much more need to do. To understand it, it’s really to understand what you can use it for, to help you know what problem can you solve for you. That is the main goal of learning.

But in the future, AI will be specific. Today you interact with AI all the time. You’re on your phone, So you have Siri, you have all these other AI that I talked about. We need to understand how it worked. But we could still get the benefit from it. And that’s what business needs to do is to be able to know enough about how it works to understand why it’s possible.

Designhill: AI can learn from many cultures and arts and project a new charge of creativity, then AI will be everywhere. What would be the new frontier for mankind?

Dr. Michael Wu: That is not a fair question. With AI, to some extent, you can automate some kind of creative, very limited kind of creativity. So you could give AI or the art that have all been created and asked to create a similar kind of art. But it’s not possible to create something that they have never seen before. And that’s what I’ve seen humans are very good at. We have the ability to create something, out of nothing.

So it is completely a new type of music, a new kind of art, and visual design, etc. But AI is simply mimicking what humans have done, and creating more variety of similar kinds of art. If you give them something like Impressionism art, then it can create something that looks like Impressionism Art.

Designhill: How can AI benefit the industrial development field and digital transformation at the start.

Dr. Michael Wu: For digital transformation, the first step is really what we call digitization. Digital transformation is a long process, You need to essentially reinvent your business, with all the different digital tools, digital technology, and investment. So it changes the course of your business. Where AI can help is that in the first part because AI needs so much data. So to use AI, you have to have data to digitize. Hence, you need data science engineers in your team preferably with a bachelor’s degree in data science to find out critical insights from large data sets.

So it’s a driving force to move you to get you started on digital transformation. In fact, I would say many people will say that digital transformation, the first step of digital transformation is digitization. You need digitization to get the data to train the AI is kind of in reverse. It kind of helps you force you to digitize your processes. You have the data to train AI, but that data can help.

Designhill: If you are analyzing data with AI through social media and social interaction, is there any concern or boundaries in ethical privacy, ethical or privacy matters?

Dr. Michael Wu: Definitely. That is a big debate in the AI community that where is the ethical privacy boundary. I don’t think the human race has resolved this completely yet. There is one side of the argument that says that we need to focus more on innovation. This is because AI, it’s going to help us solve more problems in the future.

But, what if I start hurting and discriminating against people. That is not good, either. So, there are point solutions. For example, we don’t use data that contains race and gender type of attributes. So if you don’t use this data for the AI, then it’s safer. The AI will not make any inference based on those data. That doesn’t solve the problem completely. I would say that these are point solutions.

Obey Ethical Boundaries

There are some ethical boundaries and privacy boundaries that we have to strictly obey. I strongly believe in that and ethical and more use of AI. It’s not only I would say, beneficial, to the AI or the community. But it’s been more beneficial to the greater humanity as well because it takes into more points of consideration. AI is more encompassing and the decision that it makes is no longer based on such a narrow set of data or something like that.

So, these are the key tips that companies can best explore when using AI to monetize their businesses. You can automate the entire process of monetizing your different platforms by targeting customers and other things.

As a business owner, besides the effective use of AI, you will also need impressive visual identities. These include logos, business cards, websites, brochures, etc.

You should explore Designhill to get logos etc. visuals by launching your design contest with a brief for the competing designers. they will create many unique designs out of which you can pick a winning design that best suits your brand personality. So, get started with this site to design unique visuals for your business.

Wrapping Up

Artificial Intelligence is helping companies in monetizing their businesses in many ways. They can use perceptional, internet, autonomous or business AI types depending on their field of business to monetize. You need to collect your customer based data in big numbers and then feed that to the AI systems. then, automate the entire process to have the right machine based calculations regarding your customers and business.

Designhill is the most reliable and fastest-growing custom graphic design crowdsourcing marketplace that connects a thriving community of graphic designers from across the globe with clients looking to source high quality graphic designs such as logo designs, banner designs, packaging designs, merchandise designs, web designs and many other designing works at affordable prices. In just six months of going live, the startup has helped more than 1500 businesses source unique graphic designs and has paid out more than $70000 to its ever-growing community of 29,000+ graphic designers, logo designers, visual artists and illustrators from all over the world. Facebook | Twitter | Google+

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