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Artificial Intelligence (AI) and Machine Learning (ML)are two groundbreaking concepts that have changed the way we perceive computer science and data analysis. Cortana, Siri, Google’s Home Assistant are only a couple of examples of how AI’s leading the innovation crusade. Cloud Computing, one of the greatest gifts technology has bestowed upon us, has recently joined the AI and Machine Learning race. In this article, we are going to discuss at length the benefits of this motley mixture and how AI alone will be capable of achieving more that IoT ever could.

How Artificial Intelligence Has Changed the World

We are still far from the cybernetic doomsday as imagined by cinema or literature. However, Artificial Intelligence or the so-called rise of the machines does pose some very interesting questions – what are the limits to what a machine can do? Will there be a point in time where a machine will be no longer be called a machine?

As you can see, it’s not only a question of how we can make Artificial Intelligence happen. It’s too late for that. AI is already here and, by the looks of it, seems more benign than bent on chaos or destruction. Over the years, what has been a footnote on a blueprint, is now something palpable, tangible, and, why not, with a potential to change the way we interact with our environment.

“To make robots practical, flaws must be removed. To make robots endearing, flaws must be added.” Khang Kijaro Nguyen

Don’t think as Artificial Intelligence as a member of a nomadic freak show, which puts on display an individual so insightful that he could probably be able to see atoms move in real time. AI is much more than that. Of course, to demonstrate one’s potential for greatness, you just need to put on a good show, as they say.

AI and Deep Learning frameworks have been pitted against human players in time-honored games such as chess or GO. Even more interesting is the fact that popular video games such as StarCraft II have been used to train AIs to acquire the same resourcefulness as a human given various circumstance.

Moving on to more serious stuff, for the past couple of years, big time tech titans like Amazon, IBM, Google, and Microsoft, have developed AIs capable of analyzing weather patterns, molecular movements or to make assessments and plot the appropriate course of treatment given a patient’s condition and tests.

Mixing AI, Machine Learning, and Cloud Storage Solutions

Perhaps AIs greatest achievement is ‘taking over’ the Cloud. How would that end well, you ask? Well, Cloud Computing is, without a shadow of a doubt, a step in the right direction – that direction being getting rid of traditional data storage/accessing machines and migrate every byte of to a virtual entity which, as it would appear has no point of beginning or an end for that matter.

Of course, one would be inclined to believe that such a system which, at the same time is and isn’t, must be managed somehow. Otherwise, we would end up with chaos. Sure, smartly designed algorithms can help the users access and retrieve data from the cloud anytime and anywhere. But, we ask you, is that enough? This is where the AI comes into the picture.

“I don’t need a hard disk in my computer if I can get to the server faster…carrying around these non-connected computers is Byzantine by comparison.” – Steve Jobs

Artificial Intelligence and anything pertaining to it (Machine Learning, Deep Learning) is, indeed, a great piece of tech, but, without the proper marketing and its promise of making businesses even more profitable, the product would have been dead in the womb. Fortunately, this wasn’t the case here. In fact, the idea of having a little tiny man behind the ‘wheel’ taking care of everything, so you don’t have to sound fantastic, to say the least.

But now comes the question – is it easy to put this idea into practice? Not so much, but that didn’t stop AI developers from ‘messing around’ with the Cloud.

With that in mind, for the time being, we have two types of AI-based actors that have begun to reshape the notion of Cloud Computing. We have the so-called Cloud Machine Learning Platforms and AI Cloud Services.

The chief distinction between the two is that the first one was engineered to empower existing technologies with Machine Learning techniques. To name a few, we have Google’s Cloud Machine, AWS Learning Machine, and, of course, Azure Machine Learning. The major disadvantage of Cloud Machine Learning solutions is that they’re incompatible with Deep Learning algorithms.

However, that’s how AI Cloud Services were born. Natural Language APIs, IBM’s Watson or Microsoft’s Cognitive Services, to name just a few, are frameworks capable of ‘summoning’ complex AIs which are capable of working with complex and, most of the time, abstract models.

The fact that AI changed cloud computing would be a serious understatement. It’s probably too early to assume this, in the foreseeable future, what we came to know as a cloud will cease to be considered that place where we store our pictures, movies or documents and become a sentient entity, capable of taking its own decisions and act upon them.

Everything sounds very promising, but there’s still lots of work to do before we can genuinely enjoy this outstanding product. For instance, as the cloud grows under the baton of the AI conductor, the need for power management tools becomes even visceral. Another benefit of implementing Artificial Intelligence in Public Cloud would be improved security and, of course, a far safer environment for storing sensitive data.

Conclusion

Is Artificial Intelligence a game-changer in the Public Cloud? Most definitely and the fact that the tech companies are pushing the boundaries of ML and AI even further means that this technology’s potential has yet to tap. Are we anywhere near that day when the machines will take over? Perhaps but Artificial Intelligence is still far off from being capable of taking life and death decisions.

Image source: Pixabay