Have you ever heard about the famous Beer and Diaper theory?
Walmart, the world’s largest retailer, “supposedly” created this theory to understand the correlation among the products and identify patterns.
Men, within the age group 30-40 years, who bought diapers somewhere in between 5 PM and 7 PM on Fridays, tend to have beer on their trucks. This theory motivated the grocery stores to keep beer carats beside diapers, thereby increasing the sales of both the items geometrically.
Now, how this theory works? After a long week of tough grind, working-class men tend to get tired. Along with their daily responsibilities, their weekend often involves buying diapers for their babies and grabbing a beer for themselves from the adjacent aisle.
This is a perfect example of a correlation. This theory explains how large chains of supermarket associate products. Correlation can be an important part of building machine learning models.
Machine learning is anything that makes a task easier. We aren’t talking just about the big tasks, but the manual colouring of black & white images and manually finding someone on social media as well. Now imagine a machine that understands the task itself and evolves with the new, current and past requirements.
What is machine learning?
Machine learning is a sub-part of AI (Artificial Intelligence). It is the science of creating an algorithm that can learn on its own. It works by recognizing patterns from the data rather than applying specific programming. Once designed, it does not require any manual operation. Machine learning is intelligent enough to learn by itself. It finds patterns from the original data and predicts future patterns by using statistical analysis.
For a better understanding, here are some examples:
1) Siri, Alexa, and Google Assistant are some of the famous examples of machine learning. They are Virtual Voice Assistants, they assist in finding information when asked over voice, and all you need to do is activate them. Some more examples of integrated Virtual Voice Assistants are:
- Amazon Echo
- Samsung Bixby
- Google Allo
2) Image Recognition
Image Recognition is one of the most common examples of machine learning. It is the ability to identify objects, places, people, etc. People share a large amount of data through apps, social media, websites, etc. and Facebook is capable enough to perform facial recognition at 98% accuracy putting amounts of data at risk. There is a lot of controversy regarding how image recognition will affect privacy and security around the world.
Machine Learning: Why it matters?
Traditionally, data scientists used to build finished models to gain insights instead of training computers to do so. This seems to be an impossible approach now, as data is abundant and heterogeneous. Machine learning comes into play here as it breaks an enormous volume of data cleverly and proposes smart algorithms to provide meaningful solutions.
Google processes 20 petabytes (1 petabyte= 10^15 bytes) of data per day. The search engine giant has a data centre where it keeps a record of all the information it crawls. You may not remember what you searched on Google 2 years ago, but Google does. It’s like a vast library where billions of books are available covering almost every bit of data on the planet.
There are softwares available in the market which can track everyday schedules and help you in your daily tasks like booking a cab, turning on the air conditioner before reaching home or turning on the coffee maker in the morning.
Regardless of whether we want to or not, we leave behind a behavioural pattern every time we perform a simple task; these patterns are decoded by algorithms to understand our needs and find efficient alternatives to basic standard processes.
Are Artificial Intelligence, Machine Learning & Deep Learning the same?
No, they are not. You can think about them as a set nested within each other. The easiest way to understand this is by visualizing them in concentric circles. Deep learning is the sub-set of Machine Learning which is also the subset of Artificial Intelligence.
Let’s have a look at how they are different from each other.
Artificial Intelligence- According to John McCarthy, “Artificial Intelligence is the science and engineering of making intelligent machines, especially intelligent computer programs”.
Artificial intelligence is the process of creating a machine, a robot-controlled computer machine, or a product which think intelligently as a human. Artificial Intelligence is the combination of two words “Artificial” and “Intelligence” where artificial means unnatural or created by human and intelligence means the ability to think & understand.
Some key points about Artificial Intelligence:
- The primary motive is to increase the chance of success.
- It is a program that does all the smart work.
- AI can solve complex problems.
- It develops a “human-like” system, to respond based on the circumstances.
Machine Learning- As defined above, Machine learning is the science of creating an algorithm that can learn on its own. It is a sub-part of artificial intelligence.
Some key points about Machine Learning:
- The primary aim is to increase the accuracy.
- It studies the data and learns from it.
- It also learns about processed information.
- It goes for one solution, whether it is optimal or not.
Deep Learning– Deep Learning is a sub-part of Machine Learning. It deduces patterns from the provided data and helps in extracting solutions from it. It is capable of learning from unstructured or unlabeled data, which could take decades to discover the patterns.
Some key points about Deep Learning:
- The primary aim is to discover patterns in the given data.
- It observes patterns and predicts from it.
- Uses a multi-levelled dimension of artificial neural systems to complete the procedure of machine learning.
Influence of Machine Learning
Machine learning is the next level technology where machine meets human knowledge, which has a great deal of importance in changing our lives. Let’s take a look at various areas of daily life affected by Machine Learning:
Fifteen years ago, we would have never thought about how convenient communication is going to be in the future. But now, we can communicate with anyone in and around the world in seconds and somewhere we all are relying on it.We rely so much on computers for communication, navigation, obtaining information, etc. This is where Machine Learning comes into play and helps our daily activities.
The process of healthcare management, like the planning of public healthcare, starts with the classification based on history. This helps in examining, investigating and monitoring to deliver a future outcome. These assumptions help in finding out the needs in areas that require it the most.
We already know about the recent advancements like self-driving cars, or Tesla’s new semi-autonomous trucks where AI has taken transportation to a different level. Observers analyze the data to predict decisions appropriately like public safety, helping in traffic management or crime details in real-time. It also helps in finding the paths for pedestrians and cyclists which leads to a falling number of traffic accidents.
Earlier, there was only one method of learning between the teacher and students. But with the addition of machine learning, many institutions have started utilizing it by maximizing the teacher-student interaction and increasing efficiency by creating proper schedules for them. It also helped challenge students by providing adaptive learning, using personalized learning to give each student individualized attention.
Machine Learning as an SEO Partner
Are you unsure of how Machine Learning and SEO can go hand in hand?
Every search engine is learning how to look at things in a better way which allows them to provide better results.
An appropriate example of how machine learning is changing the world of SEO is seeing how filtering of emails is done now, which is quite significant. The success rate of Google filtering out spam is 99.9% in a subtle way, This process of machine learning was adopted by Google to get rid of spam in TensorFlow specifically. This entire process has been taking place for years now.
Along with this, Google has also been using artificial intelligence with rule-based filters that are capable of blocking obvious spam. These patterns are detected by the sites where these spams are linked, the types of unwanted links they get, etc.
Machine Learning also impacts content SEO. Let’s see how:
Since 10 years, Google has been working on the problem – matching phrases and ejecting out a result. To rectify this problem they introduced a machine learning system in September 2016 named Google Neural Machine Translation System (GNMT). This accomplishes efficiency in understanding the phrase by encoding it and then decoding it to display the required results.
Machine learning: Why it matters for the future?
Soon there won’t be any stone left unturned by artificial intelligence and machine learning. In some years, there would be a significant change in how people work. Dependencies would be more on computers rather than humans. Most of the labour energies would be automated by computers.
You might think that this evolution in machine learning and artificial intelligence might lead to a loss of jobs around the world. But, that is not true.
According to BBC, Machine Learning is taking over so that routine and repetitive tasks could be done quickly and efficiently by the algorithms written by humans. It might affect the labour market, but they may acquire jobs requiring more complex and less routine skills.
A Study from Mckinsey suggests that by 2030, AI & ML would replace 30% of the world’s labour.
Despite these fears, every technological revolution has ended up creating more jobs than were pulverized.