In this article, we will look at some examples of what today’s best-performing AIs can do and why they got those results. Then, we will discuss whether it is possible for future AIs to outperform these current state-of-the-art systems.

Over the past few years, artificial intelligence (AI) has become one of the biggest buzzwords in technology. With every passing day, we see more applications for this technology, from chatbots to self-driving cars.

Even something so small and practical as smartphones have advanced features that use some sort of internal AI software or neural network to function.

The Art of Learning

But does AI think or can it really think beyond its capabilities? Well, we know that there are two main theories about how AI works, so let's get to it. The first is called connectionist theory, which says that an algorithm models relationships between inputs and outputs.

In other words, information that goes in comes out with pre-determined results. This theory, while widely supported, was popularized by psychologist Daniel Kahneman in his book, 'Thinking Fast and Slow'.

The second theory is referred to as computationalism, which assumes that computers work via algorithms just like people do and thus there’s no special 'intelligence' involved.

In short, AI takes the concept of approaching a problem in a pragmatic or common sense way, thus the result is as close to what a human being would do. So regardless of how 'smart' or 'advanced' a computer might be, its limitations will always be with how robust its algorithm is.

With that being said, AI in general, cannot process thoughts the way humans can without giving them more direction, at least for now.

Many skeptics, however, believe that computer programs already possess intelligent behavior, so they argue that it is possible for AIs to be smarter than today’s most sophisticated algorithms. Others think that even if AIs could eventually achieve true general intelligibility, it would take them forever to get there.

While we're on the topic, we will also address the question of whether humans are inherently limited computationally due to our limited biological nature.

No Need to be a Smart

Technically speaking, artificial intelligence (AI) does not allow for AIs to be smarter than humans. This is because an algorithm can never think about thinking; think about that for a second, and how impactful that statement is.

That's because an algorithm must always focus on discrete tasks or functions alone.

An example of this would be if there was an AI program that needed to learn how to play chess. It would have to stop learning how to move pieces and instead start studying concepts like statistics, algebra, and pre-determination, such as what its next move or moves will be.

This isn’t to say that creating intelligent machines is impossible, but it will take much more effort and research in engineering and computer science to make it fruition.

Who's Smarter?

Recent developments in computer science have given rise to something called deep learning. Deep neural networks are computational models that can learn complex patterns from data, making it possible to program computers to perform tasks automatically.

Artificial intelligence (AI) is no longer just about giving computers simple commands to complete specific things. Now we can give them more complicated instructions to recognize patterns and identify logical concepts.

By incorporating algorithms like these into devices, technology becomes increasingly intelligent. For example, when someone uploads a picture onto social media, the software will apply advanced techniques such as deep learning to determine what kind of object or scene the image contains.

This will then produce descriptive features for this object or concept, which others have already researched and can use to understand what was contained in the photo as well.

Another area where deep learning has improved greatly is natural language processing. Technology now exists that can analyze human speech and written material with accuracy far beyond anything available just decades ago

With ever-growing amounts of data, an AI can find correlations and patterns across an unimaginable amount of information. This helps make machines become even smarter, as they’re able to connect ideas and concepts together much faster than the human brain can.

They Are Getting Smarter

Recent developments in artificial intelligence (AI) have brought to light that even computers can now perform tasks beyond what we, as humans, can do or hope to do. This is called deep learning or neural network technology.

Neural networks work by having multiple layers of nodes connected to each other. Nodes at one level learn how to perform simple tasks (think about how your body has neurons that connect to muscles), and then those skills are applied to another layer to achieve more complex functions.

The key difference between this method and earlier AI techniques like symbolic logic or rule-based systems is that it does not require programmed rules or steps to be inputted manually, hence, results are almost always dynamically determined, but with accuracy.

AI learns from examples given to it so that it can figure out its own rules and procedures for performing tasks. So the more problems we ask AI computers to solve, the more competent it becomes in that field. Scientists theorize that at some point, AI will be able to complete a set of tasks on their own, even mundane ones such as making a cup of coffee or watering plants, without being told to.

They (AI) Will Always Be Smarter

Recent developments in artificial intelligence (AI) have raised many questions about whether or not computers are intelligent enough to take over some of our daily tasks. This has got people thinking about how long it will take for machines to completely usurp human dominance, if ever.

One of the most common arguments against this is that humans make up around 2% of all living organisms, while computer programs only account for 0.01% of all life forms here on Earth. Therefore, it is impossible for computers to achieve true intelligence unless they grow beyond this size.

This argument may hold water, but there’s one major problem with it, we already live in a world where technology functions as an integral part of our lives.

According to Harvard Business School, this integration will continue to accelerate at a breakneck pace and calls this phenomenon digital disruption, and he predicts that it will happen within his own lifetime.

So what does this mean for you and for us? Well, eventually your job will be done by a program or at least something equivocal to it. While this may sound scary now, don’t worry, digital disruption won’t necessarily result in mass unemployment. On the contrary, it can open up whole new areas of employment for talented professionals who have advanced training in software.

It's a Hard Question to Answer

Over the past few years, artificial intelligence (AI) has been in the media almost constantly. Almost every major news outlet features at least an article about it at this moment. Technology companies use it for products and services, creating talk about whether or not it is needed.

Many people have their own opinions about what AI will do and how it works. Some believe that it will take over the world, while others think it can be used for good only.

A lot of the time, though, these discussions seem more focused on who made the machine “smart” rather than if they are really smart themselves. This seems weird because we as humans understand how intelligent things are!

We know that dolphins are extremely intelligent and that some birds are clever too. Even some primates show advanced abilities like using tools and learning from experiences.

Some Experts Say Machines will Always be Smarter

On a technical note, this is not an impossible feat as there have been several studies that prove it is possible to create computers that can be as intelligent or even more intelligent than their human creators.

In fact, some researchers claim that this is already happening with something we refer to as “neural networks.” These systems use algorithms and software programs inspired by how humans learn and process information to achieve intelligence.

Neuroscientists, for example, study brain functions so they know what makes people smart. They take these concepts and apply them to computer software programs in order to create technology that mimics human thinking patterns.

A neural network then learns by example just like you or I would. Rather than being taught facts, however, it comes across examples of things that make sense and then picks up additional knowledge from there.

It also uses feedback to determine if new information makes sense and therefore incorporated into its internal data or memory bank.

As This Point, We Will Never Know

Technically speaking, there is already intelligent software that can outperform humans in certain domains, like playing chess or math games, but these programs are designed by human beings and then improved through evolutionary processes.

There is no way to tell if an artificial intelligence program has surpassed its creators in intelligent thinking. If we ever find out, it would be through sheer chance, and perhaps, something that happens very little even in the field of computer science.

We can make some assumptions about what makes someone smarter than another person, but none of them are definitive. People who have won a Nobel Prize for their work are considered smart, so why not use this as our benchmark? After all, Alfred Nobel was clever enough to come up with dynamite.

There’s also Albert Einstein, one of the most well-known physicists of all time. He consistently amazed people with his insights, which made him quite popular. Unfortunately, he always refused to share these secrets because he thought they were too advanced.

A few other famous individuals like Steve Jobs and Bill Gates managed to stay anonymous until later in life when they revealed themselves. They both had strong leadership qualities that allowed them to inspire others around them, making them seem much more knowledgeable, and obviously, more successful.

Joel

Healthcare Professional, Gamer, and Writer all rolled up in one. He once opened up an old Atari 2600 to see if it had a heart ... and it did. Hence, the lifelong love affair with gaming.

www.walawala.gg