AI and machine learning are hot topics these days, but the exact differentiation can be hard to find if you don’t have a degree in computer science to begin with.
These two subjects are tightly interwound, however, and separating them can help you make a more realistic appraisal of the possible uses of each.
All Machine Learning is AI
Machine learning is an approach to the encompassing field of artificial intelligence. That is to say: all machine learning is within the AI field, but not all AI utilizes machine learning.
Machine learning is a particular approach which is used in order to “teach” machines how to solve problems, resulting in an algorithm which increases in effectiveness over time. Machine learning can be separated into two categories:
- With an expected result and an intervening “teacher”, supervised ML is able to seperate things efficiently into categories. If you’re familiar with Gmail’s interface, for instance, the algorithm which keeps your inbox free of spam is based on a machine learning program. It’s also used in regressive analysis, which can make predictions and process data on a constant basis when working with things like fluctuating electrical power or even stocks.
- Unsupervised learning allows the program to parse data and separate it on its own. The most common application here is “cluster analysis” which finds patterns hidden in large amounts of data and is commonly used in marketing programs and other large scale applications.
Machine learning relies almost entirely on the input data and the skill of the program operator at ensuring that everything is going well. This is doubly true with unsupervised cluster analysis, where the chance of picking up the “wrong” pattern in the data is a real possibility.
It’s a far-cry from the science fiction AIs that many of us were raised on, but it’s useful and available.
The Limitations of Machine Learning
Machine learning has some inherent limitations, and it’s important to understand them if you’re planning on using them.
You can safely ignore the claims of those selling the products most of the time. More so than any other technique currently being used it’s reached an almost legendary status in the eyes of those who are trying to sell it to you.
Using a machine learning program isn’t a simple process: you still need the background knowledge of analytics and the time to parse the output data in the beginning.
While machine learning programs rarely make actual errors, the data still needs to be examined by someone who knows what they’re doing. This is particularly the case for unsupervised ML, where the categorization of the output data may lead to really odd results.
Machine learning is also inhibited by the input data. If you’re using it for marketing and you only have access to your own data, this can be a big drawback.
If you’re only making a couple of sales a day, then chances are you’re not going to learn anything new.
That’s not to say it’s a bad technology, just that the usage is a bit limited compared to what people want you to believe.
Advantages of Machine Learning
One of the most common uses of ML technology is in parsing huge amounts of data and making recognizable categories out of it.
The easiest way to understand this for most people is in the marketing sector: if you have access to a lot of data and a skilled operator then the amount of conclusions which can be drawn far outstrip what can be drawn with even a team of great analysts in a shorter amount of time.
Unsupervised learning, in particular, is great for discovering patterns in information which aren’t readily apparent to the human mind.
As time goes on, we can expect to see more and more applications of this technology. It’s already being used in many fields with great results.
When you’re working with a lot of data with a narrow focus, machine learning is generally able to beat out any other form of technology which is why it stands poised as a world changing technology at this point.
Whether something better or more complete comes along in the next couple of decades is kind of up in the air but at the moment it’s one of the most commonly applied subsets of artificial intelligence.
A Growing Technology
Even a couple of years ago the uses were pretty limited.
And the truth is that even most of the machine learning programs we use today are fairly invisible. Whether it’s your spam filter, the personalized results which now typify Google and other search engines, or even just product recommendations popping up on your social media feed.
For most people it’s become a running joke that Facebook ads pretty much read their minds, but the truth is that our generalized behavior is getting pretty close to it. Our online information isn’t quite as secure as we’d like it to be, but there’s a growing group of people who simply don’t mind because they’re actually receiving relevant information.
Machine learning is a constantly improving technology, even if we’re still a bit off from SkyNet levels of artificial intelligence.
General AI: The Sci-Fi Dream
What we currently have when it comes to AI is known as narrow AI.
These are awesome for specific applications, and machine learning is one of the best known implementations of this technology although there are others such as the reactions of enemies in video games to stimuli.
Simply put artificial intelligence is just the replication of human intellect in a machine. The applications have been in place since the early days of computers, starting with chess playing computers like Deep Blue and continuing to the sophisticated enemies in modern day games.
Practical applications of AI in data science began occurring in 2015, which was a landmark year as Google began to use their algorithms in nearly anything.
As we pointed out, however, these artificial intelligence programs are still limited to a specific usage. The idea of general AI is still something of a pipe dream.
A general AI should be able to handle just about anything you throw at it, which is what makes their use in fiction so compelling. Things like Skynet from the Terminator series, or the overarching AIs in neon-drenched cyberpunk novels aren’t quite on the menu just yet.
There’ve been some vague attempts at a general AI, including a machine which learned to play several Atari games, but the closest implementation is probably voice responsive systems like Alexa and Cortana. They’re still a bit specific to be called general AIs however.
Currently, the main race for artificial intelligence is coming in the form of machine learning. With a comprehensive enough set of algorithms the ideal of a program that can improve itself and run largely without supervision becomes increasingly closer to reality.
True general AIs may only be a few decades in the future, although at this point it’s kind of up in the air. There’s also the concern of artificial intelligence which misinterprets goals and ends up doing something devastating, which is why there’s been some amount of resources allocated to ensuring AI safety as the idea becomes more of a reality.
The future here remains uncertain, since it would seem that at this point we can already create narrow AIs which are better than even the fastest humans at some tasks but the idea of being able to spread it into an actual, functioning intelligence still seems to be pretty far off.
Bridging the Gap With Deep Learning
Deep learning is looking to be the way forward to general AI programs.
While machine learning tends to depend on rather easy to understand processesses like boundary definitions and if-then style inputs and outputs, deep learning seeks to mimic the structure of the human brain.
Through the creation of an artificial neuron network, relatively abstract concepts can be grasped on a deep level.
One of the leading deep learning machines currently is Google’s AlphaGo, a program which… well, it plays Go.
The thing is, unlike chess, Go has been notoriously hard to produce AIs which could play at a high level. Without being guided with an if-then style of proposition or by a human teacher, the program quickly learned to play at a master level after playing with some of the world leading players.
These artificial neural networks operate on a basic principle: by layering algorithms, the ability of the program to learn over time becomes much deeper and more abstract than with simpler methods of artificial intelligence.
Deep learning has become a big thing in some unexpected sectors.
Customer service, especially the technical side, is quickly becoming more and more automated due to it for instance. Since the machine actively learns what causes certain problems, it can then reply with increasing certainty as to the source of problems.
It’s looking likely that deep learning is going to be the beginning of general AIs and the future holds a lot of promise for this technology even if a better method is eventually found.
With the advent of machine learning becoming more and more available, and the ubiquitous nature of Big Data in the modern world, the entire face of marketing looks like it’s soon going to be due for a change.
Modern services are being offered which are rapidly changing the nature of the game.
These range from relatively simple if-then programs to retarget individuals who’ve entered a sales funnel or abandoned their online cart to pinpoint targeting of customers based on the information which is held on them.
Machine learning is also great at finding untapped markets for products.
The real point is this: as machine learning advances it’s going to be harder and harder to get by without integrating at least some form of it into your marketing plans.
Big Data + Machine Learning = Big Profit
There’s definitely a few hard limits to machine learning.
One of the biggest: if you don’t have enough input data then you’re not going to get adequate output.
There are a ton of ways to acquire access to large amounts of data, and with the right algorithms laid in place you’ll be able to parse through the data and get the answers that you need in order to get ahead.
Of course, chances are that most of us also need an analyst on our side and with more advanced applications even someone with experience in coding can be necessary.
That’s not to say it can’t be done, just that you’ll need to figure it into your business’ expenses and find a way to make it fit properly. As long as you’re asking the right questions, chances are you’ll get the answers you need to get ahead.
Artificial intelligence and machine learning are intimately intertwined, and understand this technology is going to become more and more necessary in the coming years. While we aren’t quite at the level of complete automation just yet, the quality of life increases afforded by both of these reach along the entire spectrum from developer to consumer.
Machine learning is AI, and it’s one of the fastest growing sectors of the technology at this time, but it’s not the only way in which artificial intelligence is beginning to surface in our everyday lives.
Since there are so many applications being developed for this technology it’s set to be world changing, even if the pipedream of general AI still stands decades in the future. For those of us who may find it soon to be a vital part of our everyday living it’s important to understand the how’s and why’s.
It’s also important to separate both from the sometimes magical visions given to us by those who sell these programs and applications. There are still a lot of very real limitations in place before it becomes the “one true path to the future.”
What remains is useful, solid, world-changing technology and even a cursory understanding can help us make sense of an increasingly digital world.