AI is just a catch-all term for machine learning, but I think it’s important to distinguish between the two. Machine learning is about using algorithms to solve problems that are too complex for humans, or by giving simple rules to solve problems that require judgment and decision-making.
So AI is basically just algorithms, but they can be used in a wide range of areas, including finance, marketing, and other more technical disciplines.
People often misunderstand the distinction between these two, which is probably due to the fact that they both have many overlapping applications and problems.
For example, in financial markets there are many different types of algorithms: neural networks use very simple rules (e.g., addition), whereas reinforcement learning uses more complex rules (e.g., making decisions based on past experience).
Machine Learning and Artificial Intelligence
It’s a sad fact that machine learning is often used to make the same mistakes as humans. In fact, it’s a real phenomenon: we all make mistakes. All the time. This is especially true when we are using AI tools where we are trying to solve new problems and going back to our previous mistakes.
Let’s go over a few examples.
I have seen numerous articles on how AI has been used to improve the accuracy of auto insurance claims. This has been done by using highly sophisticated learning algorithms that are trained on data from millions of insurance records and millions of different vehicles, then applied in an automatic mode (where they predict with high accuracy when an accident occurred).
The results were impressive, but what if I told you this works with any kind of data? What if I told you that an algorithm could provide predictions even from day-to-day and minute-to-minute traffic information?
Now, let’s assume that your car runs perfectly well every morning and every night, so you don’t need to pay much for auto insurance (which may be why you bought your car instead of a truck). You might think: “Well, it would be nice if my car ran better than before”.
Then you run into problems again because there is no data available to train your algorithm on (in the form of traffic reports and other kinds of data). So you try something new — training your AI on actual traffic reports, then applying it in automatic mode (where they predict with high accuracy when an accident occurred).
The results were impressive — not only did your car run better than ever before (you can see evidence in these videos), but now there is no way back!
And yet, as important as this improvement was for you personally (there are many other benefits too: lower fuel costs due to reduced emissions; better-driving safety; etc.), it turned out that some people didn’t get their cars repaired at all after having their cars repaired by the same company twice!
This all sounds very simple — training an algorithm to predict at high accuracy when something occurred and then applying it in automatic mode — but there are many pitfalls along the way. We will discuss some of them here: Training an algorithm requires lots of data that cannot be easily obtained from other sources (such as traffic reports) which greatly increases its complexity.
The Impact of Artificial Intelligence
The impact of artificial intelligence on our lives is so profound that many people simply cannot imagine it without experiencing it as a result of computer hardware and software (both hardware and software). It’s not just in the realm of AI applications that we are seeing the impact. Machine learning, neural networks, and other forms of artificial intelligence are now being applied to many different areas of human activity:
- Smartphones, cars, and smart home systems
- Military systems
- Energy efficiency
- Economy & industrial production, distribution, and services
There are several reasons why AI has such a profound impact on our lives. The first is that computers already do most tasks we need to do. Everything is a possibility with us, from shopping to cooking.
Computers can read our email messages or take care of our lawns if we don’t want to think about it. They even can generate very complex financial models for use in the most complex ways imaginable (they don’t have the same capability to make perfect investment decisions as humans but they can still outperform them).
As technology improves so does computing power, which allows more tasks for computers to be automated (which means more work for us). This process is going faster than ever before because of Moore’s law (the observation that computer processing power doubles every 18 months), which states that computer processing power doubles approximately every two years.
That means computers will be able to do more things faster than they could in the past. That also means that computing power will only get better over time. The fastest chip at the moment is roughly 8 years old – a factor of 10 improvements from today’s chips using 1 gigahertz processor – so by 2020 you should be able to buy a machine with 1 gigahertz processor for about $1000 (which doesn’t account for other factors like electricity cost).
And when it comes to computing power in general there is no limit on what you can do with computers – including playing games on your phone! If you think this sounds silly imagine how much fun you would have with a personal computer from seven years ago!
You would have been able to play games like Doom with high-end graphics cards bought at $1500 each just as easily as you could play Candy Crush right now!
There’s also no doubt that crowdsourcing will continue to drive this process forward; internet users make decisions autonomously when applying an algorithm
Artificial Intelligence in Business
There is a growing usage of artificial intelligence in business. In fact, the term AI is used to describe a wide array of technologies that aim at improving processes in different industries and business domains. This is because AI has the ability to learn and make better decisions without human input, which means it can be used to gain superior performance in business problems.
AI technology has been applied successfully to many industries where it can help businesses achieve more efficient operations. For example, artificial intelligence can be used by companies to keep track of their inventory and ensure that they have enough products on hand to fulfill orders while at the same time keeping costs low.
Artificial intelligence is also being applied by companies when they are trying to devise new products or services. For example, companies are using artificial intelligence in areas such as marketing and advertising to help them market their products better than ever before.
So why not add artificial intelligence to your product? If you don’t already have a strong foundation for it (and you should!), you might want to learn more about it here. You’ll need both technical knowledge from computer science, as well as managerial skills from things like programming (especially if you plan on dealing with external customers) and marketing (something that’s not always easy).
Applications of Artificial Intelligence
Artificial intelligence (AI) has been a buzzword for a while. The most recent predictions are that by 2030, more than half of all jobs will be replaced by machines and the majority of jobs will be eliminated entirely. This is happening now, as we speak!
The term “artificial intelligence” was coined and popularized by John McCarthy in 1959 in his paper “Artificial Intelligence: A Critique of Its Supernatural Claims”. In the paper, McCarthy describes two basic approaches to AI: one approach is to create intelligent machines with internal intelligence that mimics human intelligence, while the other approach uses external intelligence to create intelligent machines that mimic human intelligence.
In this post, I’ll explain what machine learning is, how it differs from artificial intelligence, and what the general benefits are to humans from machine learning.
How do we choose the right AI and ML tools for our product?
This is a question I often hear from startups. The answer is, of course, “You can’t possibly do that.” The gist of my answer is: you won’t be able to tell if a particular tool will help you get there at all; but for sure if you are considering using an AI or a machine learning tool, it is wise to have a very well defined strategy around how you will use them and what the parameters of your strategy are.
To me, this seems like the most important part of the whole discussion. It should give you something to think about when talking to people about your product: “Do I want AIs or ML tools?” That might not be as simple as it sounds — but it does give you some guidance on what kind of thinking and planning ahead does for your product.