Over the last decade, cloud computing has stopped being a concept and has gradually turned into something that has completely “overrun” our digital lives. From mail services to storing intricate workflows, we rely on the cloud to access our personal and professional data. Albeit it is a rather new and radical way of looking at data storage/access, cloud computing is becoming better by the day. And now, with the advent of artificial intelligence and machine learning, the evolution is becoming even more visible. In this article, we shall discuss, at length, whether the ‘wedlock’ between cloud computing and AI is something that can push the boundaries of knowledge even further or not.
Making AI and machine learning a part of everyday life
The desire to begift a machine with intelligence has been burning bright in the human heart. Literature offers an intimate glimpse into the world of intelligent machines that surpassed even their creators: Frankenstein, Talos – the Ancient Greek automaton with wings, Data from the Star Trek franchise, and more.
Unfortunately, we are still far from constructing a machine that is as clever and resourceful as a human, but we have, nevertheless, taken the first steps to what is expected to be a wondrous journey.
Over the past couple of years, artificial intelligence and machine learning have left their incubators, becoming a part of our lives. Take Cortana for instance. A nifty little piece of code that can act as your assistant. You can talk to her, ask her to free up your schedule, find nearby eateries, and more.
However, behind this incredible pulsating circle lies a powerful AI-based algorithm that is capable of learning on the go. Each little mistake, such a misheard vowel, can make Cortana better and more accurate. And Cortana is just one of many examples of how AI became an indispensable tool.
For instance, Watson, an AI developed by IBM, is currently being used to fight cybercrime. Using a large database, one of Watson’s ‘duties’ is to watch out for stuff like ransomware, trojans, or any kind of cues associated with cybercrime. Granted that Watson is still far from being the Sherlock Holmes of cyber-detectives but do remember that failure is the first step to success.
Intelligent machines, as we often like to refer to them, can also be used in other fields. For instance, an AI developed by the Google-owned DeepMind Technologies managed to beat veteran Go players without any human help. The machine ‘simply’ analyzed the outcomes of millions of GO matches to develop new strategies.
Of course, this doesn’t mean that AI can’t go haywire. To name a few AI experiments gone terribly wrong, one can definitely recall the 2016 incident that involved TayTweets, Microsoft’s AI launched or rather unleashed upon Twitter. In just one day, TayTweets managed to go from the ‘lovable’ and innocent AI with a strong ‘desire’ to learn, to something so dark and twisted, that it wrote more than once that Hitler’s actions were not wrong.
Same goes for one of Facebook’s AI programs. After being curious about what happens when two AI-powered bots start to chat, the company had to pull the plug on the project after the duo went so far off tracks that they developed their own language.
Artificial intelligence is interesting, to say the least. But how can this technology improve or even add to what we came to call cloud computing?
Combining AI with cloud computing – what is the right way?
As you have probably figured out by now, nearly every application we use today is paired with a cloud backup service – Facebook, Google’s Mail, Sheets, Office, each sports a cloud backup feature. The migration toward cloud computing is obvious and, from our point of view, inevitable. Big and small companies have turned their attention to cloud computing since it offers a far ‘cleaner’ and cheaper solution to data access and storage than traditional backup methods do.
On-site storage devices, by far the most preferred method before the cloud took over, are now considered both obsolete and costly. This is one of the many reasons so many company owners have begun migrating data and workloads into the cloud. Stuff stored in the cloud can be accessed in real-time from anywhere in the world and, of course, from any device.
By combining artificial intelligence with cloud computing, you basically get a vast network capable of storing unimaginable amounts of data but with a capacity to learn and improve on the go. IBM’s Watson is a good example of how the cloud combined with AI should work.
Working as a full-time cyber investigator means that Watson has to store its clues and evidence somewhere in order to review them. Based on its findings, the supercomputer can make various predictions and determinations as to where a certain cybercrime might occur. Furthermore, IBM’s AI can discover if there are any patterns to how cybercriminals act or think.
Another great example of AI and cloud computing is, without a shadow of a doubt, Google’s Cloud Machine Learning. Harnessing the full power of what we know today as machine learning, Google’s comprehensible environment offers scientists a suitable platform to create powerful models and to test them.
Even though Google’s Cloud ML lacks the deep learning framework of IBM’s machine, it is still more than capable of making accurate determinations. For example, the web search giant’s AI is presently being used to make weather predictions or to ensure that the food we put on the table is safe to eat. Companies handling many emails from customers can also take advantage of Google’s ML to increase the response time.
To wrap everything up nice and tidy, we have to say that there’s great potential in combining cloud computing with artificial intelligence. However, some ironing out still needs to be done before we can enjoy a product that can be truly useful. AI has still got a lot of learning to do before it can handle things like a human. It is great to figure out the inside of the box, but it is much better to learn how to think outside of it.