You've probably heard that A, I and ML are the same thing, or that ai ai algorithms consort out even the messiest sets of data or that superintelligent robots are taking over.

And those are just a fraction of all the myths we've come across.

So how can you tell facts from fiction?

Easy, keep watching and see how we debunk the top 10 misconceptions about a I with solid evidence.

Here we go.

Misconception.

Number one, eh?

I works like the human brain despite a eyes mind blowing progress.

This notion is incorrect.

It seems we can communicate directly with programs by speaking in English to Siri or by typing Russian words into Google Search engine.

But although Google finds associations between words or texts, it struggles with content and grammar.

Yes, there are a I novels and TV shows, but their plots are far from masterpieces.

Self.

Unless you enjoy reading computer generated reports, you will most probably find a I writing efforts boring or unintelligible.

What about Siri Alexa, Google Duplex and the like, sure, if they could make appointments, but they start giving chaotic answers of the conversation goes off track.

Watson, for instance, beat the two top human champions in the game show Jeopardy, But it doesn't always win.

Once that tripped up on a question about the Olympic gymnast George Kaiser's anatomical oddity missing a leg, Watson specified, the part of the body that was odd the leg would have failed to comprehend was that this person had a leg missing.

Thus the answer leg was incorrect.

So if anything, A.

I has taught us that the processes in the human brain, or even harder to recreate than we have previously thought.

Misconception.

Number two intelligent machines can learn on their own.

Well, they congrats.

How to perform a task in a better way.

Nevertheless, we the human programmers, data administrators users provide the necessary input for their learning and improvement.

You could argue that ML makes it possible for A I programs like Deep mines Alfa zero to achieve a superhuman level of chess play in a mere four hours.

Sure, but Alfa zero success would be impossible without the data engineers who fed it with the initial data What about reasoning?

Once again, computer scientists enable A i technologies to interpret human languages being English or Chinese.

With that said, our beloved technologies can do without us, at least not in the foreseeable future.

All right, we're starting to make our way through the list.

Misconception.

Number three.

Hey, I could be 100% objective.

Hardly so algorithms are only a spare is the people who create them.

So a prejudiced data scientists will create prejudice algorithms based on their intentional or unintentional preferences.

Finally, enough, these may remain unexposed until the algorithms are used publicly.

An interesting example is Amazon's recruiting tool, which showed bias against women.

The company's experimental hiring tool used a I to rate job candidates by giving them 1 to 5 stars.

Looks like you rate products on Amazon.

But by 2015 it was strikingly obvious that candidates for software developer jobs and other technical positions were not rated in a gender neutral way.

As it turned out, Amazons computer models were trained to scan applicants based on patterns and resumes received by the company over a 10 year period due to the male dominance across the tech industry.

Most came from men.

So what happened is that Amazon system taught itself that men were the preferable candidates.

It penalized resumes that included the word women's and undervalued graduates from all women's colleges.

Sure enough, Amazon made the program's neutral to these specific terms.

But does that guarantee the machines would not come up with other ways of scoring candidates that could prove discriminatory?

Misconception number four.

Aye, aye, and ML are interchangeable terms.

A.

I and ML are often wrongly used as substitutes for one another.

So let's clarify what's what machine learning is.

A sub field of a I of sorts.

Ml is the ability of machines to predict outcomes and give recommendations without explicit instructions from programmers.

Aye, aye.

On the other hand, is the science of making technology operate through traits of human intelligence?

Hey, I is an ever changing concept due to the constant technological advancements.

For instance, in the 19 eighties, the Gemini home robot was revolutionary with his ability to take voice commands and keep a map of your home for navigation purposes.

But today it would be considered.

Maura's a charming relic than a I.

Currently, ML is the only feasible path to a I that we're aware off.

All right.

Moving on to misconception number five A.

I will take your job.

People have had the same fear during any major revolution in history.

Consider the industrial revolution, for example.

However, this is far from grounded.

A.

I is currently designed to work with humans rather than against them.

So, eh, I could do boring and repetitive tasks while you concentrated more creative and challenging work.

Even if some roles were taken over by eye, this would most likely generate the demand for new types of jobs based on new capabilities and needs.

Well, in case you're still anxious about robots replacing you at the workplace checkout, will robots take my job dot com link in the description and to your job title and see the percentage of risk for your possession?

Then finally breathe a sigh of relief or not misconception.

Number six A.

I cannot be creative.

A.

I is not autonomous, but it can be creative when combined with human understanding and intuition.

In CG art, the program Aaron, written by artists Harold Cohen, creates original artistic images, although new styles or imagery must be hand coded by the artists.

Thus, excluding 100% human free creativity, Cohen compares the relationship between him and his program to that of Renaissance painters and their assistance in music.

David Cope Develop the E.

M.

Ai program, which analyzes the musical genre and compositions and re combines patterns into new original works in the styles of Beethoven, Mozart, Chopin, Bach and more.

How many humans could do that?

Huh?

So although a I isn't an independent artist, it definitely poses some important questions like What is the essence of art?

Is it created in the mind of the artist or in the eye of the beholder?

Who knows, Maybe a.

I will give us some creative answers in the future.

Misconception Number seven.

All the eyes are created equal, not at all.

In fact, there are three types of Ai ai, ai and AI artificial narrow intelligence, a G I artificial General intelligence and A S ai artificial Super intelligence.

And I's basically the artificial technologies we incorporate in our lives.

Today they perform single tasks like playing chess or predicting the weather.

A G.

I, on the other hand, hasn't come into existence quite yet.

In theory, a G I should be able to completely mimic human intelligence and behaviour.

It should be a creative problem solver that could make decisions under pressure.

Now, it is widely believed that once we reach a G, I will be on the fast lane to A s I or Artificial Super Intelligence, a mighty and sophisticated program that surpasses human brain power and will lead us to our demise.

Fortunately for now, this could only happen in your favorite sci fi movies.

Misconception number eight.

Aye, aye.

Algorithms can figure out any and all your messy data.

Now, that would be fantastic if it were true.

Right.

But in reality, A I needs our help to figure out data.

That's where data engineers come to the rescue.

They take the raw data, clean it and organize it for machines to ingest from pharmaceutical companies using various data to improve their patient centric service is toe automobile companies using data to predict the performance of their new engine designs.

Clean data is a must for productive.

Aye, aye solutions.

So if we want perfect results, we'd better make sure we've provided perfect training data.

First.

Misconception.

Number nine A.

I is new.

Although a I seems like the latest thing.

It was first foreseen in the 18 forties.

That's right.

English mathematician and writer lady at a Love lace predicted part of it.

In her words, a machine might compose elaborate and scientific pieces of music of any degree of complexity or extent.

A century later, Alan Turing laid the foundations for machine learning with the bomb machine used to crack German code for sending secure messages.

During World War two.

A prominent 19 fifties milestone was Arthur Samuels draughts Player, which learned to beat Samuel himself.

Imagine the headlines that made back then.

In the 19 sixties, computer scientists were developing algorithms for math problems, solutions and machine learning and robots.

And although a I research funding was scarce in the 19 seventies, in the 19 eighties, things change for the better in the nineties, leading to the highest achievements in a I today.

So the initial idea behind the terms A, I and ML goes way back, although over time the concepts of change from what they used to mean misconception number 10 cognitive, Aye, Aye.

Technologies can understand and solve new problems the way the human brain can cognitive, eh, I can identify an image or analyze the message of a sentence, but they definitely need human intervention.

Facebook, for example, has an image recognition application that analyzes photos and offers.

The user adds Taylor to the content they interact with.

The AP also helps identify band content.

However, when Facebook tried to identify relevant news to present to users, Theo animated process failed to distinguish real from fake news.

In fact, Russian hackers managed to post deliberately false news on Facebook without detection by automated filters.

That's one fine example of security lagging behind.

I wonder why.

Here's one of the reasons.

Turns out, there are certain patterns develop to trick algorithms into misclassifying objects when layered on images.

If you're curious for more details, check out the link in the description.

There's a pretty cool article at the end of it, so cognitive technologies are a great tool, but your brain is still far superior.

That's it.

We managed to explode 10 minutes surrounding a I in just a few minutes.

We hope you enjoyed it as much as we did.