Machine learning- Data mining, optimization, statistics, algorithms, limitations, and software… Wait, a machine that learns?
That’s correct. Machine learning is the science behind making computers act like humans and learn norms or rules based on data! In-depth, Machine Learning uses mathematical algorithms to parse data, learn from it based on certain “averages” and calculations, and then make a determination out of it, depending on a set of rules.
Imagine that you’re searching for “Carpenter” on Google. Usually, the world “Carpenter” is searched more together with the word “hammer”, than “nail polish” for instance. The Machine Learning algorithmcollects the search data and improves on the information it gives you, so that when you look up “Carpenter” you get pictures of planks, workers, and hammers- instead of nail polish, cars or mobile phones.
Until now we have managed to research scientific studies and complex algorithms that will help build a so-called “model” of the data that we want to predict which you can see a bit further into the article. We have come pretty far, but there’s still tons of information to learn- for both the machines and us. It is quite interesting to imagine how much further we will have come in 20 years. But where does the machine get the information from and how does it learn?
The Learning Process
As there are many mathematicians, as are there many mathematical algorithms- which means that there are many maaany different learning “processes” in machine learning. The algorithms are usually grouped into two different sections: Learning style
(Supervised learning, unsupervised learning, semi-supervised learning) or by a similarity in form of function
(classification, regression, decision tree, clustering, etc.). I saw a video
not too many days ago about someone programming a self-driving racing car. An explanation more in-depth; It’s a simple network with a
fixed number of nodes where a parent node is the “main” algorithm that is being followed. The network evolves through random mutation as the evaluation is done manually by the programmer. The manual evaluation takes into consideration distance traveled, time and optimal path. Some nodes would crash into the wall and fail, others would go far and crash with a longer distance- which would then be picked to be the next parent of the algorithm.
Regardless of the learning style of function, all combinations of machine learning algorithms consist of the following:
- Representation (a set of classifiers or the language that a computer understands)
- Evaluation (objective/scoring function, for example, the time, path and distance)
- Optimization (search method; often the highest-scoring classifier, for example, the parent node in the car racing program)
The goal with the learning process is to take the data in consideration and learn from the data it has never “seen” before, and make a new norm based on the new “general” or “normality” in the algorithm. Below is some visual representation of what it is and what it does.
How Important is it?
We have just begun sniffing on the term of algorithms and machine learning, and barely have enough resources to call it a “mainstream” topic. However, machine learning is running in the background of numerous platforms that people are on daily, kind of like the Microsoft Word program I’m in right now. Even if Microsoft Word uses a predictable determination method to find better words and structures, it is still the same concept. Word helps me correct sentences and suggest better ones judging by my data and my text. It is super helpful because I don’t think when I write- I just write and let the program think for me, and that’s the whole point of Machine Learning!
Machine learning is useful because of the processing power and ability to handle precise requests, highlight patterns or find data that should be modified in my word document. Machine learning is like working with a cup of coffee without having coffee in the morning. However, on the business side of it, machine learning drives real business results- such as time and money savings- that have the potential to dramatically improve the efficiencyor impact the future operations of a business. We are talking automating tasks that would normally be done by a live agent, mitigate accidents from happening that will free up time and resources. Below is a simple list of the top 3 things Machine Learning in AI is used for in everyday life that might be more essential than you’d think.
In China where there aren’t enough radiologists to keep up the pace of reviewing 1.4 billion CT scans each year, neuroscience is the inspiration for creating a machine that can mimic the thought process of our own Brains. Google’s DeepMind helps us reduce the time it takes to plan treatments and diagnoses by allowing algorithms to help diagnose cancer more accurately and efficiently.
Cars are increasingly connected and generate data that can be used in various ways. Volvo uses data to help predict when parts would fail or when vehicles need servicing. This helps us upholding safety by monitoring vehicle performance to prevent situations happening and improving passenger convenience.
Cyber Bullying has become a broad term, and something difficult to fight against. Twitter, Facebook, and Instagram utilize Deep learning and artificial intelligence to fight cyberbullying and delete offensive comments, along with enhancing products and help increase user activity by having deep neural networks run in the background to process data.
How aware are you?
We have talked forth and back about Google and Microsoft Word and specific learning tasks that operate. The awareness of machine learning itself is quite narrow as people take things for granted and don’t give the background processes time of thought. Not too long ago, Facebook had an incident where safety and user data was a big topic. When scrolling through Facebook, we communicate, like, view and share things we are interested in. 1.2 billion people uploading 136,000 photos and updating their status 293,00 times per minute- and all that data being highly valuable to enhancement and improvement of their platform. However, Mark Zuckerberg shot himself in the foot when user data was something he didn’t clarify well enough. Facebook uses all this data to give you more relevant posts,understand what you like and who you are close to– so they can send you fun birthday videos with your closest friends and family or help notify you when someone close has posted something. I’m sure you’re aware of Facebook’s “People you may know”.
Ask yourself “is this something a normal machine would do or are there human traits behind this that would mimic a possible machine learning or AI?” next time you search for something or pick up your phone. Google Assistant, Google Maps, Google search- All improved and enhanced for user convenience.
Positive vs Negative sides of Machine Learning
|1. Great Opportunity for Progress|
Machine learning has made tremendous progress in the recent decade. The growth is enormous and very diverse, slipping its tentacles into a variety of industries – from finance and healthcare to transport and education. As a machine learning engineer, you will not only witness the technological revolution but be a part of it! Additionally, machine learning engineering as a career is an extremely lucrative job. The starting salary of a machine learning engineer in India is INR 6,70,000 per year! The mid-career salary of a person working in this field is a whopping Rs 1,568,971.
|1. Demanding Job|
Training models, handling data as well as making and testing prototypes on a daily basis can lead to mental exhaustion. As a machine learning engineer, data munging will also be a painful part of your job. Data munging simply means converting raw, unprocessed data into a more appropriate, usable form. Sometimes you might even have to scrape data from a paginated website and integrate it with your client’s internal data while simultaneously dealing with date-time and data type errors. Doing this is no walk in the park and it could get frustrating for some.
picture credits: pexels.com
|2. Work that matters|
Machine learning engineering will allow you to work and build real-world products, right from autonomous cars to security drones. These are not some numbers you crunch into a spreadsheet only to never hear about them again. Everything you create has a real-world application. Imagine the satisfaction of seeing something you’ve created help someone in their everyday life! To put it simply, the efforts you put in day in and day out is for work that matters.
|2. It takes time and resources for machine learning to yield tangible results|
Machine learning occurs over time. So, there will be a period when your interface or algorithm won’t be developed enough for your company’s needs. The precise amount of time required will depend upon the nature of data, data source and how it is to be used. You’ll simply need to wait as new data is generated — sometimes this can take days, weeks, months or even years!
|3. Direct link to Data Science|
As a machine learning engineer, you will also develop the skills needed to be a data scientist. Becoming competent in both fields will make you a hot commodity for employers. As a data scientist, you’ll be able to analyze data and extract value from it. As a machine learning engineer, you’ll be able to make use of that information to train a machine learning model to predict results. In several organizations, machine learning engineers work with data scientists for better synchronization of work products.
|3. Need to stay updated|
As mentioned earlier, machine learning is a rapidly evolving field. Due to this, machine learning engineers are required to spend a considerable amount of time learning about the latest updates in the field. Reading and learning research papers from various universities and organizations will have to become a regular part of your life if you want to pursue this field. So, unless the idea of continuous learning does not appeal to you, you should rethink your decision of being a machine learning engineer.
The text is taken from https://careerninja.in/explore/explore-machine-learning/pros-and-cons-of-machine-learning/
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