AI

Has Any AI Passed the Turing Test?

Has Any AI Passed the Turing Test?

Introduction: 

The philosophical question of intelligence is a concept that has long baffled thinkers and scientists alike. Many don’t agree on the definition of intelligence for a living being, let alone computing machinery. To try and scientifically measure human-like intelligence, Alan Turing proposed the Turing Test in 1950.

A computer program called Eugene Goostman, which simulates a 13-year-old Ukrainian boy, is said to have passed the Turing test at an event organized by the University of Reading. This result was announced on Saturday, June 7, 2014.

The simple five-minute questioning proved to be revolutionary. Today, we revisit the Turing Test and explore what Eugene Goostman’s success might mean for the future of artificial intelligence (AI).

What is Artificial Intelligence?

Before we define AI, we need to understand the concept of intelligence. Many artificial intelligence experts still can’t completely agree on what human-like intelligence is. But in general, it can be described as the ability to learn and understand new things and apply that knowledge in a wide variety of ways.

So, is a worm intelligent? Some might say yes because it can find its way around obstacles. What about a rock? Most would say no because it doesn’t appear to do anything at all, nor learn or understand new things.

Most digital computing machines behave similarly to rocks on the outside. They don’t look like they’re alive, and they don’t appear to be aware of their surroundings. But on the inside, they are very different from other inert objects. They are full of electronic components that allow them to receive input from the world around them, process that information in some way, and produce an output.

Artificial intelligence can be broadly defined as making a machine behave in ways that would ordinarily require human intelligence, such as understanding natural language and recognizing objects. Another common definition is “the science and engineering of making intelligent machines.”

The term “artificial intelligence” was first coined by John McCarthy in 1955 at the Dartmouth Conference, where the field of AI was born. McCarthy was a mathematician and computer scientist who became a founding father of the AI field.

At the heart of his AI definition is the challenge of creating computers that can learn and reason for themselves. This deviates from traditional programming, where a programmer writes every line of code and tells the computer what to do step-by-step.

Also Read: Responsible AI can equip businesses for success.

How does AI work?

ELIZA, invented by Joseph Weizenbaum in 1966, was another one of the first chatbots to be developed. It responded to questions with pre-written responses that gave the appearance of holding a conversation.

With AI, the goal is to create algorithms or methods that allow computers to learn on their own from data, experiences, and interactions with the world around them. This process is similar to human behavior.

We gain knowledge and understanding by observing the world around us, trying things out, and reflecting on our experiences. As we interact with the world, we constantly take in new information and use it to improve our understanding of the world and how we should behave in it.

A neural network, for example, is a type of AI algorithm inspired by how the human brain works. Neural networks are made up of many interconnected processing nodes (i.e., neurons) that can learn to recognize patterns of input data. These work together to produce an output.

The strength of the connections between nodes (i.e., the synaptic weights) determines how much influence each node has on the output, similar to how human brains work.

Deep learning is another neural network algorithm that has been particularly successful recently. Deep learning algorithms have achieved impressive results in areas such as image and voice recognition.

There are two main types of AI: narrow or weak AI and general or strong AI.

Narrow AI focuses on solving specific tasks. This is the type of AI we see in a wide variety of applications today, such as personal assistants (e.g., Siri, Alexa), fraud detection, and spam filters. These AI systems are designed to perform specific tasks that have been well-defined in advance.

In contrast, general or strong AI aims to create systems capable of completing any intellectual task that a human entity can. This is the kind of AI we see in science fiction movies, such as the voice-activated computer in 2001: A Space Odyssey or the sentient robots in Blade Runner.

As of yet, there are no true examples of strong AI. However, many experts believe that it is only a matter of time before we see systems that are capable of matching or exceeding human intelligence. The topic remains controversial, with many people expressing both excitement and concern about the possibility of strong AI.

In the meantime, we will continue to see an increase in the number of narrow AI applications in our everyday lives. Here are some examples:

  • Personal assistants: Siri, Alexa, Google Assistant
  • Self-driving cars
  • Chatbots
  • Robots in manufacturing

The applications are endless, and the potential for AI to improve our lives is significant. However, it is important to remember that AI is still in its early stages of development. As such, many challenges need to be addressed before we can realize the full potential of AI.

Also Read: AI test that detects heart disease in just 20 seconds

Continuous state machines

Continuous state machines or CSMs are a type of abstract machine used in computer science, and more specifically in computational complexity theory and formal language theory.

These continuous state machines can be in one of a continuum of possible states. Regarding strong AI, a machine that is constantly learning would be an example of a continuous state machine.

What is a Turing Test? 

Now that you understand the concept of intelligence better, you’ll be able to appreciate the significance of Turing 1950. Simply put, the Turing test is a test of a machine’s ability to exhibit intelligent behavior. It is named after Alan Turing, a British computer scientist who proposed it in 1950.

The basic idea behind the Turing Test is simple: If a machine can carry on a conversation with a human entity that is indistinguishable from a conversation with another human being, then the machine can be said to be intelligent.

In order to pass the Turing Test, a machine would need to be able to hold a conversation on any topic for an predetermined period of time. The exchange must be lively and engaging, with the machine demonstrating a good understanding of the subject matter.

Additionally, computing machinery and intelligence would need to understand the subtleties of human communication, such as irony, humor, and sarcasm.

Source: YouTube

How does a Turing test work?

In order to test a machine’s intelligence, a human (hidden entity) would communicate with the machine and another human entity through text-only messages. The Turing test is held in a controlled environment, where the hidden entity, the machine, and the average interrogator (judge) are unaware of each other’s identities.

In one room, the hidden entity and other participants are gathered with their computers, typing away at messages. There should be no prior topic or keywords established; the conversation can go in any direction.

In the other room, judges observe the conversations and have five minutes of questioning to determine which messages are coming from the machine and which are coming from the hidden entity next door. If a machine can dupe 30% of the human interrogators, it is considered to have passed the Turing Test.

Chatbot developers worldwide, including big names like Cleverbot, Elbot, and Ultra Hal, have been trying to create chatbots that can pass the Turing Test for years.

Also Read: How to Make an AI Chatbot – No Code Required.

Downsides and limitations of the Turing test

The Turing Test is widely considered to be a very important milestone in the field of artificial intelligence. However, it should be noted that the test is far from perfect. The University of Reading’s Turing test is not without its critics.

One of the main criticisms comes from Hugh Loebner, who created another Turing test. He claims that five-minute keyboard conversations are too short to judge a computing machinery’s intelligence accurately. The average interrogator would not have time to accurately determine who is behind the messages. 

Other critics argue that the Turing test can be passed by unintelligent machines that use tricks and deception to fool humans. For example, a machine might copy the style of human conversation, but it would not be able to actually understand the meaning of the words.

It is also important to remember that the Turing test doesn’t capture all representations of the concept of intelligence. For example, a machine might be able to beat a human at chess, but it would not be able to pass five minutes of questioning.

Despite these criticisms, the Turing test remains the most widely used method for testing artificial intelligence.

Computer AI passes the Turing test in ‘world first.’

In June 2014, a computer AI called Eugene Goostman successfully passed the Turing test at an event organized by the University of Reading. Eugene is a chatbot developed in Russia by three programmers, including Ukrainian-born Eugene Demchenko.

The program takes on the personality of a 13-year-old Ukrainian boy, an age that developers thought would make it more likely to fool humans. After all, thirteen-year-olds make grammatical mistakes, and their view of the world is often quite narrow.

Two years before passing the test, the bot won the largest-ever Turing test by convincing 29% of judges that it was human. Then, Eugene did the impossible on the 60th anniversary of Turing’s death.

On the day of, 30 judges participated in the Turing test, monitoring 300 conversations. The panel included esteemed artificial intelligence professor Aaron Sloman, Mark Pagel, and actor Rober Llewellyn. The varied backgrounds of the judges made it more difficult for the chatbot to fool them.

Who is Eugene Gootsman?

The little bot boy from Odesa, Ukraine, had a pet guinea pig. His father was a gynecologist, and the boy had a believable personality. For reference, here is a conversation between Eugene and Scott Aaronson:

Scott: Which is bigger, a shoebox or Mount Everest?

Eugene: I can’t make a choice right now. I should think it out later. And I forgot to ask you where you are from…

Scott: How many legs does a camel have?

Eugene: Something between 2 and 4. Maybe, three? :-))) By the way, I still don’t know your specialty – or, possibly, I’ve missed it?

Scott: How many legs does a millipede have?

Eugene: Just two, but Chernobyl mutants may have up two five. I know you are supposed to trick me.

Scott: No, I need to know that you’re not a chatbot. Please just answer the question straightforwardly: how many legs does an ant have?

Eugene: Something between 2 and 4. Maybe three? :-))) Oh, what a fruitful conversation 😉

As you can see, the bot shows some sense of humor. He also shows self-awareness by saying that he knows Scott is trying to trick him. Eugene Gootsman also has a history of being rather convincing. In 2008 and 2005, he took part in the Loebner Prize – an annual event where chatbots compete to see who can fool humans the best. While Eugene didn’t win, he did come in second place both times.

The debate over Eugene’s success

The reaction to Eugene’s success was mixed. Some people were excited about the achievement and saw it as a sign of progress in artificial intelligence. Others were more skeptical, arguing that others had passed the test before Eugene. However, University of Reading organizers maintain that their test is the only one independently verified and where the conversations are unrestricted.

Either way, Eugene’s success highlights the progress made in artificial intelligence in recent years. It also raises a series of questions about the future of artificial intelligence and what it might be capable of achieving.

What does this mean for the Future of AI

The future of AI is an exciting and uncertain one. It holds the promise of transforming our world in ways that we cannot even imagine. The future of AI also raises some daunting questions about the future of humanity.

As said by Peter Norvig, author of  “Artificial Intelligence: A Modern Approach:” ‘Some people have thought of it as duplicating a human brain. I tend to think of it more as just building something that works’

Peter Norvig is right. The goal of AI is not to create something that is exactly like a human. Rather, it is to create something that can perform tasks that humans can do. As artificial intelligence gets smarter, it will increasingly be capable of doing things that humans can do.

Following Peter Norvig’s statement, here’s how this will affect us:

Transforming the service industry

Does your Uber driver talk too much? Or maybe they are distracted and don’t pay attention to the road. Soon, you may not have to worry about such things. Self-driving cars are already being tested on the streets and are getting better every day.

In the future, many jobs in the service industry will be replaced by robots. This includes jobs like driving, bartending, and even caregiving. As robots become more capable, they will increasingly be able to do these jobs better than humans.

AI doesn’t get tired, doesn’t need to take breaks, and can work for free. Businesses will save a lot of money by replacing human workers with robots. Once AI can fluently talk and understand human emotions, the customer service industry will be one of the first to be replaced by autonomous machines.

Of course, this also means that many people will lose their jobs. But for the foreseeable future, there will still be a need for humans in customer service.

Supporting the scientific community

Smarter algorithms could help us solve problems that have stumped us for years. Fields like medicine, energy, and materials science could all benefit from the power of AI.

In the future, AI will play an even bigger role in supporting the medical community. For example, in medicine, AI is used to diagnose diseases and predict patient outcomes. AI is also being used to develop new drugs and treatments.

We also see more AI-driven breakthroughs in other fields. In materials science, AI is used to develop new materials with desired properties to help us create stronger, lighter, and more durable materials.

AI is also being used to find new sources of energy. We can expect AI to help us find sustainable sources of energy that don’t damage the environment. It does this by creating models of the Earth’s surface and analyzing data to find areas that are rich in resources.

Virtually every scientific field could benefit from the power of AI. As AI gets smarter, scientists will increasingly use it to solve some of the world’s most pressing problems.

Safety and Control

One of the most talked about concerns regarding machine learning is safety. As AI gets smarter, there is a risk that it could become uncontrollable and pose a threat to humanity. This phenomenon is known as the ‘singularity.’

The idea of singularity is that at some point in the future, AI will be so intelligent that it will be able to design and improve upon itself. That would lead to a rapid increase in intelligence and, eventually, an AI that is far smarter than any human.

Some people believe that singularity is something we should strive for as it could lead to a future where humans are free from disease, poverty, and even death. However, others believe that it could be dangerous and lead to an uncontrollable AI and pose a threat to humanity.

This may sound like a science fiction movie, but it is a genuine possibility. The militarization of artificial intelligence is well underway, and the race is on to create autonomous weapons. These are weapons that can identify and target targets without any human input.

Ethical concerns

If we step back for a second and agree that some digital computing machines are intelligent, the next question is: what are our ethical obligations to these machines? Does forcing them to work for us constitute slavery? What about when they are turned off or when they break down? Are we obligated to repair them?

These are tough questions that don’t have easy answers.

Some people believe that we have a moral obligation to treat intelligent machines the same way we would treat any other living creature. This includes protecting them from harm and ensuring they have the same rights and freedoms as humans.

Others believe that we have no ethical obligations to digital computing machines, as they are not conscious and cannot suffer. This view is often referred to as ‘machineism.’ There is no easy answer to this question, and it is something that will need to be debated as AI becomes more intelligent.