Artificial Intelligence vs Machine Learning vs Deep Learning

Artificial Intelligence, Machine Learning, and Deep Learning are one of the most prominent topics in the domain of technology at the present. Although the three terminologies Artificial Intelligence, Machine Learning, and Deep Learning are used interchangeably, are they really the same? Every technophile is stuck at least once in the beginning whenever there is a mention of artificial learning vs machine learning vs deep learning. Let us try to find out how actually these three terms differ.

The easiest way to think of the relationship between the above terms is to visualize them as concentric circles using the concept of sets with AI — the idea that came first — the largest, then machine learning — which blossomed later, and the most recent being deep learning — which is driving today’s AI explosion — fitting inside both.

Graphically this relation can be explained as in the picture below.

As you can see in the above image consisting of three concentric circles, Deep Learning is a subset of ML, which is also a subset of AI. This gives an idea that AI is the all-encompassing concept that initially erupted, which was then followed by ML that thrived later, and lastly, Deep Learning that is promising to escalate the advances of AI to another level.

Starting with AI, let us have a more in-depth insight into the following terms.

Artificial Intelligence

Intelligence, as defined by Wikipedia, is “Perceiving the information through various sources, followed by retaining them as knowledge and applying them with real-life challenges.” Artificial intelligence is the science that deals with machines that are programmed to think and act like humans. By Wikipedia, it is defined as the simulation of human intelligence in machines using programs and algorithms.

Machines built on AI are of two types – General AI and Narrow AI

General AI refers to the machines capable of using all our senses. We’ve seen these General AI in Sci-Fi movies like The Terminator. In real life, a lot of work has been done on the development of these machines; however, more research is yet to be done to bring them into existence.

What we CAN do falls in the hands of “Narrow AI”. These refer to the technologies that can perform specific tasks as well as, or better than, we humans can. Some examples are – classifying emails as spam and not spam and facial recognition on Facebook. These technologies exhibit some facets of human intelligence.

Where does that intelligence come from? That brings us to our next term -> Machine Learning.

Machine Learning

Learning, as defined by Wikipedia, is referred to as “acquiring information and finding a pattern between the outcome and the inputs from the set of examples given.” ML intends to enable artificial machines to learn by themselves using the provided data and make accurate predictions. Machine Learning is a subset of AI. More importantly, it is a method of training algorithms such that they can learn to make decisions. (Read – AI and ML. Are they one and the same?)

Machine learning algorithms can be classified as supervised and unsupervised depending on the type of problem being solved. In Supervised learning the machine is trained using data which is well labelled that is, some data is already tagged with the correct answer while in unsupervised learning the machine is trained using the information that is neither classified nor labelled and the algorithm is supposed to find a solution to it without guidance. Also, a term called semi-supervised learning exists in which the algorithm learns from a dataset that includes both supervised and unsupervised data.

Training in machine learning requires a lot of data to be fed to the machine which then allows the machine (models) to learn more about the processed information.

Deep Learning 

Deep Learning is an algorithmic approach for the early machine-learning crowd. Neural Networks from the base for Deep Neural Learning and is inspired by our understanding of the biology of the human brain. However, unlike a biological brain where any neuron unit can connect to any other neuron unit within a certain physical distance, these artificial neural networks (ANN) have discrete layers, connections, and directions of data propagation.

For a system designed to recognize a STOP sign, a neural Network model can come up with a “probability score”, which is a highly educated guess, based on the algorithm. In this example, the system might be 86% confident the image is a stop sign, 7% convinced it’s a speed limit sign, and 5% it’s a kite stuck in a tree, and so on.

A trained Neural Networks is one that has been analyzed on millions of samples until it is sampled so that it gets the answer right practically every time.

Deep Learning can automatically discover new features to be used for classification. Machine Learning, on the other hand, requires to be provided these features manually. Also, in contrast to Machine Learning, Deep Learning requires high-end machines and considerably significant amounts of training data to deliver accurate results.

Wrapping up, AI has a bright future, considering the development of deep learning. At the current pace, we can expect driverless vehicles, better recommender systems, and more in the forthcoming time. AI, ML, and Deep Learning (DL) are not very different from each other; but are not the same.

Tableau vs PowerBI: 10 Big Differences

The concept of using pictures to understand patterns in data has been around for centuries. From existing in the form of graphs and maps in the 17th century to the invention of the pie chart in the mid-1800s, the idea has been exquisite. The 19th century witnessed one of the most cited examples of data visualization when Charles Minard mapped Napoleon’s invasion of Russia. The map depicted the size of Napoleon’s army along with the path of Napoleon’s retreat from the city of Moscow – and tied that information to temperature and time scales for a more in-depth understanding of the event.

Read more about data Visualisation in our previous blog – Practices on Data Visualisation.

In the modern world, when it comes to the search for a Business Intelligence (BI) or Data Visualisation tool, we come across two front runners. They are PowerBI and Tableau. These are the top data visualization tools. Both of these products are equipped with a set of handy features like drag-and-drop, data preparation amongst many others. Although similar, each comes with its particular set of strengths and weaknesses, and hence very often articles titled Tableau vs PowerBI are encountered. The following comparisons provide insights into which data visualization tool is best for different purposes.

The tools will be compared on the following grounds:

  • Cost
  • Licensing
  • Visualization
  • Integrations
  • Implementation
  • Data Analysis
  • Functionality

Cost
Cost remains a significant parameter when these products are compared. This is because at one end PowerBI is priced around 100$ a year while Tableau can be rather expensive up to 1000$ a year. PowerBI is more affordable and economical than Tableau and is suitable for small businesses. Tableau, on the other hand, is built for data analysts and offers in-depth insight features. So, when it comes to Tableau vs PowerBI cost comparison, Tableau is a better alternative to PowerBI.

Licensing
Tableau should be the first choice in this case. To explain why Tableau over PowerBI, the final choice is considered that is, whether one wants to pay upfront cost for the software or not. If yes, then Tableau should be chosen else one should opt for PowerBI.

Visualization
When it comes to visualization features, both the products have their strengths. PowerBI can prove to be better if the desired outcome is data with better visuals. PowerBI lets you easily upload datasets. It gives a clear and elegant visualization. However, if the prime focus is visualization, Tableau leads by a fair margin. Tableau performs better with more massive datasets and gives users efficient drill-down features.

Integrations
PowerBI has API access and pre-built dashboards for speedy insights for some of the most widely used technologies and tools like Salesforce, Google Analytics, and Microsoft Products. On the contrary, Tableau has invested heavily in integrations and widely-used connections. A user can view all of the connections included right when he/she logs into the tool.

Implementation
This parameter along with maintenance is primarily dependent on factors like the size of the company, the number of users, and others. Power BI comes out to be fairly more straightforward on the grounds of implementation and requires a low level of expertise. However, Tableau, although is a little more complex, offers more variety. Tableau incorporates the use of quick-start applications for deploying small scale applications.

Data Analysis
Power BI with Excel offers speed and efficiency and establishes relationships between data sources. On the other hand, Tableau provides more extensive features and helps the user in hypothesizing data better.

Functionality
For the foreseeable future, any organization which has users spending more than an hour or two per day using their Business Intelligence tool might want to go with Tableau. Tableau offers a lot of features and minor details that are unmatched.

Feature Power BI Tableau
Date Established 2013 2003
Best Use Case Dashboards & Ad-hoc Analysis Dashboards & Ad-hoc Analysis
Best Users Average Joe/Jane Analysts
Licensing Subscription Subscription
Desktop Version Free $70/user/month
Investment Required Low High
Overall Functionality Very Good Very Good
Visualisations Good Very Good
Performance With Large Datasets Good Very Good
Support Level Low (Or through partner) High

It all depends upon who will be using these tools. Microsoft powered Power BI is built for the joint stakeholder, not necessarily for data analytics. The interface relies on drag and drop and intuitive features to help teams develop their visualizations. It’s a great addition to any organization that needs data analysis without getting a degree in data analysis or any organization having smaller funds.

Tableau is more powerful, but the interface isn’t quite as intuitive, which makes it more challenging to use and learn. It requires some experience and practice to have control over the product. Once this is achieved, Tableau is better than PowerBI and can prove to be much more powerful for data analytics in the long run.

A Short History of Data Science

Over the past two decades, tremendous progress has been made in the field of Information & Technology. There has been an exponential growth in technology and machines. Data and Analytics have become one of the most commonly used words since the past decade. As they are interrelated, it becomes essential to know what is the relation between them and how are they evolving and reshaping businesses.

Data Science was officially accepted as a study since the year 2011; the different or related names were being used since 1962.

There are six stages in which the development of Data Science can be summarised-

Stage 1: Contemplating about the power of Data
This stage witnessed the uprising of the data warehouse where the business and transactions were centralised into a vast repository. This period was embarked at the beginning of the 1960s. In 1962, John Tukey published the article “The Future of Data Analysis” – a source that established a relation between statistics and data analysis. In 1974, another data enthusiast, namely Peter Naur, gained popularity for his article namely “Concise Survey of Computer Methods”. He further coined the term “Data Science” which came into existence as a vast field with lot many applications in the 21st century.

Stage 2: More research on the importance of data
This period was witnessed as a period where businesses started research for finding the importance of collecting vast data. In 1977, the International Association of Statistical Computing (IASC) was founded. In the same year, Tukey published his second major work – “Exploratory Data Analysis” – arguing that emphasis should be laid on using data to suggest the hypothesis for testing and simultaneous exploratory testing for confirmatory data analysis. The year 1989 saw the establishment of the first workshop on Data Discovery which was titled Knowledge Discovery in Databases(KDD) which is now more popularly known as the annual ACM SIGKDD Conference on Knowledge Discovery and Data Mining(KDD).

Stage 3: Data Science gained attention
The early forms of markets began to appear during this phase. Data Science started attracting the attention of businesses. The idea of analysing data was sold and popularised. The Business Week cover story from the year 1994 which was titled ‘Database Marketing” supports this uprise. Businesses started to witness the importance of collecting and applying data for their profit. Various companies started stockpiling massive amounts of data. However, they didn’t know what and how to use it for their benefit. This led to the beginning of a new era in the history of Data Science.

The term Data Science was yet again taken in 1996 in the International Federation of Classification Societies(IFCS) in Kobe, Japan. In the same year, Usama Fayyad, Gregory Piatetsky-Shapiro, and Padhraic Smyth published “From Data Mining to Knowledge Discovery in Databases”. They described Data Mining and stated “Data mining is the application of specific algorithms for extracting patterns from data.

The additional steps in the KDD process, such as data preparation, data selection, data cleaning, incorporation of appropriate prior knowledge, and proper interpretation of the results of mining, became essential to ensure that useful knowledge is derived from the data.

Stage 4: Data Science started being practised
The dawn of the 21st century saw significant developments in the history of data science. Throughout the 2000s, various academic journals began to recognise data science as an emerging discipline. Data science and big data seemed to work ideally with the developing technology. Another notable figure who contributed largely to this field is William S. Cleveland. He co-edited Tukey’s collected works, developed valuable statistical methods, and published the paper “Data Science: An Action Plan for Expanding the Technical Areas of the field of Statistics”.

Cleveland put forward his notion that data science was an independent discipline and named six areas where data scientists should be educated namely multidisciplinary investigations, models and methods of data, computing with data, pedagogy, tool evaluation, and theory.

Stage 5: A New Era of Data Science
Till now, the world has seen enough of the advantages of analysing data. The term data scientist is attributed to Jeff Hammerbacher and DJ Patil as they carefully chose the word. A buzzword was born. The term “data science” wasn’t prevalent yet, but was made incredibly useful and significantly developed. In 2013, IBM shared the statistics that 90% of the world’s data has been created in the last two years alone. By this time, companies had also begun to view data as a commodity upon which they could capitalise. The importance of transforming large clusters of data into usable information and finding usable patterns gained emphasis.

Stage 6: Data Science in Demand
The major tech giants saw significant developments in demand for their products after applying data science. Apple laid out a statement for increased sales giving credit to BigData, and Data Mining. Amazon said that it sold more Kindle online books than ever. Companies like Google, Microsoft used deep Learning for speech and Voice Recognition. Using AI techniques, the usage of data was further enhanced. Data became so precious; companies started collecting all kinds of data from all sorts of sources.

Putting it all together, data science didn’t have a very prestigious beginning and was ignored by the researchers, but once its importance was adequately understood by the researchers and the businessmen, it helped them gain a large amount of profit.

Ethical issues in Artificial Intelligence – Problems and Promises

With the growth of Artificial Intelligence (AI) in the 21st century, the ethical issues with AI grow in importance along with the growth in the technology. Typically, ethics in AI is divided into Robo-ethics and Machine-ethics. Robo-ethics is a concern with the moral behaviour of humans as they design and construct artificially intelligent beings, while Machine-ethics relates to the ethical conduct of artificial moral agents (AMAs). In the modern world today, the countries are stockpiling weapons, artificially intelligent robots and other AI driven machines. So, analysing risks of artificial intelligence like whether it will overtake the major jobs and how can its uncontrolled and unethical usage can affect the humanity also becomes important. And to prevent humanity from the ill-effects and risks of artificial intelligence, these ethics were coined.

AI and robotics are unarguably one of the major topics in the field of artificial intelligence technology. Robot Ethics or more popularly known as roboethics is the morality of how humans interact, design, construct, use, and treat robots. It considers how artificially intelligent beings (AIs) may be used to harm humans and how they may be used to benefit humans. It emphasizes the fact that machines with artificial intelligence should prioritize human safety above everything else and keeping human morality in perspective.

Can AI be a threat to human dignity?

It was the first time in 1976 when a voice was raised against the potential ill-effects of an artificially developed being. Joseph Weizenbaum argued that AI should not be used to replace people in position that require respect and care, such as:

  • A customer service representative
  • A therapist
  • A soldier
  • A Police Officer
  • A Judge

Weizenbaum explains that we require authentic feelings of empathy from people in these positions. If machines replace them, they will feel alienated, devalued, and frustrated. However, there are voices in support of AI when it comes to the matter of partiality, as a machine would be impartial and fair.

Biases in AI System

The most widespread use of AI in today’s world is in the field of voice and facial recognition and thus AI bias cases are also increasing.  Among many systems, some of them have real business implications and directly impact other people. A biased training set will result in a biased predictor. Bias can always creep into algorithms in many ways and it poses one of the biggest threats in AI. As a result, large companies such as IBM, Google, etc. have started researching and addressing bias.

Weaponization of Artificial Intelligence

As questioned in 1976 by Weizenbaum for not providing arms to robots, there stemmed disputes regarding the fact whether robots should be given some degree of autonomous functions.

There has been a recent outcry about the engineering of artificial intelligence weapons that have included ideas of a robot takeover of humanity. In the near future of AI, these AI weapons present a type of danger far different from that of human-controlled weapons. Powerful nations have begun to fund programs to develop AI weapons.

“If any major military power pushes ahead with the AI weapon development, a global arms race is virtually inevitable, and the endpoint of this technological trajectory is obvious: autonomous weapons will become the Kalashnikovs of tomorrow“, are the words of a petition signed by Skype co-founder Jaan Tallinn, and many MIT professors as additional supporters against AI Weaponry.

Machine Ethics or Machine Morality is the field of research concerned with designing of Artificial Moral Agents (AMAs), robots and artificially intelligent beings that are made to behave morally or as though moral. The sci-fi director Isaac Asimov considered the issue in the 1950s in his famous movie – I-Robot. It was here that he proposed his three fundamental laws of machine ethics. His work also suggests that no set of fixed laws can sufficiently anticipate all possible circumstances. In 2009, during an experiment at the Laboratory of Intelligent Systems in the Polytechnique Fédérale of Lausanne, Switzerland, robots that were programmed to cooperate eventually learned to lie to each other in an attempt to hoard the beneficial resource.

Concluding, Artificial Intelligence is a necessary evil. Artificial Intelligence-based beings (friendly AIs) can be a gigantic leap for humans in technological development. It comes with a set of miraculous advantages. However, if fallen into the wrong hands, the destruction can be unimaginable and unstoppable.  As quoted by Claude Shannon, “I visualize a time when we will be to robots what dogs are to humans, and I’m rooting for the machines.”Thus ethics in the age of artificial intelligence is supremely important.