Is Machine Learning the Same As Deep Learning?

Data science is utilized in the field of artificial intelligence (AI) to analyze data, to make smarter business decisions, and to extract meaningful insights. While machine learning and deep learning are carried out within the artificial intelligence umbrella, there are distinctions between the two on how they learn from data inputs and their best use cases. 

Machine learning and deep learning are not the same; however, they are both parts of artificial intelligence, which is a technique to allow computers to imitate human brains. Furthermore, deep learning is a part of machine learning, the difference being how data is analyzed, absorbed, and output. 

In order to differentiate, artificial intelligence, machine learning, and deep thinking are defined in this article. Also, the reasoning behind the differences is described. In the foreseeable future, artificial intelligence will touch most industries and many careers; therefore, read on better to understand this technology and its effects on the world. 

Important Sidenote: We interviewed numerous data science professionals (data scientists, hiring managers, recruiters – you name it) and identified 6 proven steps to follow for becoming a data scientist. Read my article: ‘6 Proven Steps To Becoming a Data Scientist [Complete Guide] for in-depth findings and recommendations! – This is perhaps the most comprehensive article on the subject you will find on the internet!

What Is Artificial Intelligence?

In 1942, a science fiction writer, Isaac Asimov, wrote a moral code for robots entitled Three Laws of Robotics. He referred to robot ethics, not the preciseness that a programmer would need to code exact conditions. What is impressive about his laws is how science has evolved from a writer’s fantasy into actionable steps being used to create automation, which is way beyond Isaac Asimov’s imagination.  

As the years passed, AI research began at a Dartmouth College workshop in 1956. The five founders, Allen Newell, Arthur Samuel, Herbert Simon, John McCarthy, Marvin Minsky, and other attendees, came together to define concepts surrounding thinking machines. 

The nuances of intelligence could be defined and replicated by a machine. According to the Merriam-Webster dictionary, the current definition is that AI is “a branch of computer science dealing with the simulation of intelligent behavior in computers.” 

Correspondingly, artificial intelligence is the science of breaking down intelligence, combining a wide range of human abilities, drawing inferences, learning from the past by trial and error, and applying past experience to similar situations. These newfound perceptions can be utilized by the computer to predict outcomes correctly. 

All artificial intelligence falls into three categories, which have an increased rate of capabilities from small to beyond human ability. Progress has been made in the first category due to facial recognition technology, virtual assistant interactions, and robot-driven cars. And strides have been made in the second category, which includes the existence of supercomputers. 

It is exciting to realize that there is still a long way to evolve into the last stage, artificial superintelligence. Specifically, the three categories are as follows:

1. Artificial Narrow Intelligence (ANI), the weakest form of AI, is working within a predetermined range and data set without human emotional involvement or decision making. Examples are playing Scrabble against the computer or recommending products on an eCommerce website.
2. Artificial General Intelligence (AGI), the strong form of AI, can perform the same tasks as humans, including strategizing or offering creative thoughts. As seen in science fiction movies, strong AI has a conscience, makes judgments, and experiences emotions.
3. Artificial Superintelligence (ASI), will transcend human intelligence in all capacities. Although it’s difficult to imagine, the superintelligence form of AI will outwit all areas of thinking in the human brain and will enhance human experiences and thoughts. 

Resources and Examples of AI

Present-day advances of artificial intelligence are not to be ignored. For instance, cars are increasingly automated, robots are working in manufacturing, AI assistants are used online and on the phone to assist in the customer experience, and healthcare has jumped on board for patient monitoring and testing. 

Famously, in 2011, Jeopardy! Game show contestants battled IBM’s natural language computer, Watson, for the 1 million USD (£768,034.40 GBP) prize. Watson won the prize, and the software system was soon after placed in a New York City hospital to successfully assist with decision making and guidance in lung cancer treatment. 

Also, Amazon, a giant in e-commerce, was an early adopter, still heavily reliant on artificial intelligence. In addition to enhancing the customer experience by connecting search queries into predicting customer behavior and choices. Multiple models were used to train Amazon customer data sets to provide relevant results to the customer, which drives sales. 

Amazon uses AI internally, specifically machine learning, to learn why a customer is searching in a particular way to optimize speed and to create smarter tools. 

For more information, read the book entitled AI Superpowers, according to respected AI expert and venture capitalist, Dr. Kai-Fu Lee. He insists robotic changes, in which jobs are being replaced, are happening all around us, but not everyone is paying attention. 

Also of interest is the White House website, Artificial Intelligence Resources, which provides resources such as strategic documents and fact sheets citing American initiatives and internationally. The reasoning is artificial intelligence is transformational, and keeping the lines of communication open is key to innovation and progress. 

And finally, an Analytics Insights article entitled, 5 Best Artificial Intelligence Videos to Watch, is well worth diving into. In particular, Elon Musk’s thoughts on AI was an interesting 23 minutes of information and awareness.

Machine Learning Defined

Machine learning is an automated analysis method that builds on structured data and algorithms to make decisions without human intervention. That is, a computer can learn from algorithms without specifically being programmed to do so. 

A computer programming language, like Python or R, is commonly used to write the algorithms. After all, you have to tell the computer what you want it to do. Algorithms are not new; they are simply a set of mathematical instructions that can be traced to Alan Turing’s computational theory, as published in 1952.

Although first, there must be data. In the machine learning model, data is captured or created and stored, ready to be loaded when needed.

The data is input, the program or algorithm executes and mechanically follows programming instructions, step by step. The key is in the algorithm technique, which tells the computer what to do and how to accomplish its goal.  

Algorithms are written or retrieved from the libraries on the Internet. Therefore, the process begins with data input, the presentation of an algorithm in which criteria are presented, and if criteria are met, the output is provided. If not completed, feedback is given, and the process is repeated until the computer system learns the correct outcome.

But how does the computer learn? There are three types of learning mechanisms regularly used:

• Supervised – uses labeled data to train the model.
• Unsupervised – uses unlabeled data.
• Reinforcement – learning is based on reward. 

If you are not a programmer, there are alternatives and options. One being is Algorithmia, which is an application that is used to purchase machine learning algorithms used for operations and management. Data scientists can use this app as a tool that integrates nicely with your existing languages, systems, and processes for tons of options.  

Applications of machine learning are endless. In particular, industry usage is exploding in healthcare and e-commerce, but there are many specific applications. For instance, virtual personal assistants, detection of fraudulent activity on credit card accounts, traffic signal regulation, and dynamic pricing models.  

Another specific example, the surge pricing model, is demonstrated by a store selling umbrellas at a higher cost during the rainy season when the product is in high demand. As a branch of artificial intelligence, machine learning is based on the concept that patterns emerge, and a computer can identify, learn, and make decisions from that information by analyzing data. 

Deep Learning Defined

Deep learning is an element of machine learning in which a network of algorithms are created, and the data is interpreted as it runs through them in such a way that mimics the neurons in the brain. Deep learning hones in on the human aspect of artificial intelligence.

Artificial Neural Networks (ANN), used in deep learning, is used similarly to mathematical models such as regression testing and decision making. But the difference is that it mimics how synapses in the brain connect neuron pathways. Using artificial neural networks and patterns can be developed from data so that AI experiences can be customized for future use. 

As an analogy, if you search Amazon using the phrase, “kill bees,” the results are a variety of books, clothing, toys, movies, medicine, food, and more, all related to bees, killer bees, and killing bees. But you must filter through the output and specify criteria until you find a spray can of pesticide in your price range to kill the bees that built a nest outside your door.

With deep learning, you will get there much more quickly because, according to the data set that Amazon has amassed over the past ten years in which you have been a customer, algorithms recognize your preferences and learn to draw conclusions without being told that you had a pest problem and are not a collector of bumblebee mugs. 

Neural networks consist of data inputs connected to inside layers, which do all the information processing. The inside layers examine those inputs, place weights and biases on them, and push appropriately activated neurons to the output.  

Artificial neural networks mimic the brain and use algorithms trained via huge amounts of data collected. The larger the data set, the more accurate the deep learning output will be. Therefore, deep learning only gets better with the upsurge in complex and large data sets and easy data storage.

A demonstration of how neuron networks learn can be found in a 21-minute video using handwritten digit recognition. In other words, the very essence of AI in which the computer analyzes a person’s handwriting and determines the actual number. 

Another example, in the past, engineered facial features were not scalable and time-consuming to create. In the age of improved computing hardware, software, and bigger, more storable data sets, AI, deep learning-based algorithms can be trained to achieve stellar performance due to the detail. Facial recognition is mainstream now, and the progress made in deep learning strategies is the reason. 

Natural Language Processing (NLP) is a specialization that bridges communication between humans and computers. In order to break into this field, a strong understanding of deep learning is your base. For more information, you can watch numerous videos posted on the subject on YouTube. There are many YouTube videos where the experts delve into their current projects and how they perceive the future space to be for AI. 

Differences Between Machine Learning and Deep Learning

While an artificially intelligent computer system is smart, it can’t learn on its own. Machine learning uses algorithms to analyze data, learn from the data, and make decisions based on what is learned. While deep learning is a model in which algorithms are created in artificial neural networks to learn and make decisions accordingly on its own. 

In order for deep learning to work, a massive volume of data must be available to be fed into the algorithms. The more data, the better the decisions will be. 

A huge amount of computational power is measured in Graphical Processing Units (GPU) as opposed to the smaller, cheaper, and less powerful Central Processing Unit (CPU). A GPU is necessary to run the algorithms and process the data but not always available because of the high cost.

In addition, the time it takes to train data can vary from hours to months in length, which may be a constraint. Longer processing time occurs if there are a higher number of neural network layers being used to analyze the data and a large volume of data. Although this may be a disadvantage, deep learning works out the issues related to machine learning limitations and therefore is a step in the right direction. 

In summary, you may be interested in watching a 21-minute video describing the difference between artificial intelligence, machine learning, and deep learning. There are a couple of quizzes at the end to help you determine your level of understanding.

Careers

Machine learning requires many tools, techniques, and experiences that data scientists and other professionals use to decide or resolve a problem. Data science is an exploding field. Therefore, machine learning positions are in high demand. A list of job titles which require experience in machine learning are:

• AI application engineer
• AI data analyst
• AI games engineer
• AI natural language processing
• AI research scientist
• AI User experience (UX) designer
• Data scientist
• Machine learning engineer
• Machine learning scientist

Read the job description very carefully because the content is much more important than the title. The job titles are often mismatched with the actual organization’s expectations and will vary greatly in different companies and industries. But one thing is for sure, the demand for data science expertise in AI is expected to increase in the years to come as more exciting developments occur in this field.

AI will replace repetitive jobs according to this short 1 ½ minute interview on 60 Minutes. The claim is that in 15 years, 40% of jobs will be replaced by robots. Watch the video here:

For further education and certification, MIT offers a well respected Professional Certificate Program in Machine Learning & Artificial Intelligence and is designed for professionals in the data analyst field with a strong desire to create a stronger base in this technical area and to practice by performing hands-on projects and experiments. 

Industry knowledge and real-world examples are taught. In addition to the core courses, a few of the electives are directly applicable:

• Modeling and Optimization for Machine Learning
• Deep Learning for AI and Computer Vision
• Designing Efficient Deep Learning Systems
• Applied Deep Learning Bootcamp

Another excellent skill to have in your repository is to have experience in a computer programming language. Two common choices are R and Python. Both are free, open-source options with extensive libraries and support. Python is more widely used, but they can both handle massive datasets and enhance your marketability and job security.

Conclusion

Artificial intelligence impersonates the cognitive function of the human brain, while machine learning is used to carry out artificial intelligence by training algorithms using vast amounts of incoming data. Deep learning is a type of machine learning that more closely copies natural human intelligence and utilizing artificial neural networks to carry it out. 

Consequently, by taking a deeper dive into AI, it’s evident that machine learning is not the same as deep learning. The difference is evident in the examples discussed which pertain to data science, the difference is evident. By building and fine-tuning your knowledge of machine learning and deep learning, you will set yourself up to be a valuable employee due to the shortage of experienced AI professionals. 

Artificial intelligence is expanding and growing, and it is smart for you to proactively grasp the concepts and to realize how it will affect your world over time.

BEFORE YOU GO: Don’t forget to check out my latest article – 6 Proven Steps To Becoming a Data Scientist [Complete Guide]. We interviewed numerous data science professionals (data scientists, hiring managers, recruiters – you name it) and created this comprehensive guide to help you land that perfect data science job.

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