DATA SCIENCE: A BRIEF HISTORY

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.

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VIDEO ANALYTICS – KEEPING YOUR EYES OPEN

Video Analytics was invented with a motive – to help in reviewing the growing hours of surveillance video that a security guard or a system manager (or a human) may never have time to watch. Video Surveillance systems equipped with Video Analytics can help us in finding those minor details that can’t be perceived by naked eyes. Video Analytics or Video Content Analysis is computerized video footage analysis that uses algorithms to differentiate between object types and identify specific behavior or action in real-time, providing alerts and insights to users. Since Video Analytics is based on the technology of Artificial Intelligence, experience plays a significant role. A highly trained model can see through very minute details in video footage.

This technical capability is being used in a wide range of domains, including entertainment, health-care, retail, transport, home automation, flame, and smoke detection, safety, and security.

Video Analytics relies on useful video input. To make the video useful, following techniques are implemented for increasing the quality of the video recorded:
1. Video Denoising
2. Image Stabilisation
3. Unsharp Masking
4. Super-Resolution

What are the Commercial Applications of Video Analytics?

CCTV Systems – This is the most widespread application of Video Analytics. VCA(Video Content Analysis) is distributed on the cameras (at the edge) or centralized on dedicated processing systems. These CCTV’s, for example, can be used to detect and report any suspicious activities of shoppers in a store. Another popular example, is the PIDS(Perimeter Intrusion Detection System). It is deployed in areas whose perimeter can extend to a large radius, like airports, seaports, and the railways. With this technology, we are able to track any intrusions in real-time, giving us sufficient time to react.

  • Traffic Systems – Deployment of Video Analytics on busy squares in crowded cities of the world can be a massive time-saver for the people and the government. At peak times of the day, when the traffic is very high, specialized use of analytics can be used to avoid congestion.

 

  • Counter-Flow Detection – Walking against the flow in specific locations, such as the airport security checkpoint and gates, can be a sign of some danger. It can potentially result in terminal shutdowns. The wrong entry of vehicles in a one-way can lead to congestion affecting a large number of people. These faults can be quickly responded to by the uses of video Analytics.
  • Suspect Search – The data of facial-recognition can be aggregated along with video Analytics for the detection of criminals at high-security places like the airports’ immigration counter, the baggage collection facility, taxi stands, etc. This can lead to the smooth and swift arrest of such people or elimination of such objects. Time is the essence when looking for something critical.

 

  • Long Queue Problems at the Shopping Centres – In the densely populated countries like China, India, the crowd increases significantly in stores during the festive season. Trends in data can be used to analyze the crowd and arrange for particular changes for a short duration of time, increasing the store efficiency, and saving the time of the people.
  • Reducing Retail Shrinkage – Retail and logistics companies can use video surveillance analytics to minimize inventory loss significantly. The model is trained to detect unusual activities like unexpected times of presence, unauthorized access, or any suspicious movement of inventory and more.

 

  • Improving Patient Satisfaction – Video analytics can help hospitals and dispensaries to improve the overall patient experience. Artificially engineered cameras can continuously monitor patients waiting to meet the doctor and ensure they are checked-in within a given time duration. Even an alert can be sent to the staff regarding a patient who has been left unattended for a long time.

Video Analytics is the smart way of engaging customers, reducing wastage of time and improve security. Video data collected is massive and it would be practically impossible for a human to replace a computer. With the fast pace of life and the amount of video content today, using video analytics is a lifesaver for different fields.

THE RISE OF AUTOMATED MACHINE LEARNING (AML)

What is AutoML?

Automated machine learning (AutoML) is the term used for the technology automating the end-to-end process of applying machine learning to real-world problems. A typical machine learning problem requires a dataset that consists of some input data on which a training model is needed to be built. The input data may not be in such a form that all machine learning algorithms may be applied to it. An ML expert needs to implement the appropriate procedures (including data preprocessing steps, feature scaling, feature extraction), resulting in a dataset suitable for machine learning. Building the model involves the selection of the best algorithm for maximizing performance from the dataset. Many of these steps are often beyond the abilities of non-experts. Considering this in mind, AutoML was proposed as an Artificial Intelligence-based solution to the gruesome challenge of applying machine learning.

What is the Need for AutoML?

The idea of AutoML took off with the development in the field of Artificial Intelligence. It all took shape when Jeff Dean, Google’s Head of AI, suggested that “100x computational power could replace the need for machine learning expertise”. This raised several questions:

Do hundreds of thousands of developers need to “design new neural nets for their particular needs,” or is there an effective way for Neural Networks to generalize similar problems? Or can a large amount of computation power replace machine learning expertise?

Clearly, the answer is NO. Many factors support the idea of AutoML:

  • Shortage of machine learning expertise
  • Machine-Learning expertise is cost-inefficient

For large organizations requiring high efficiency, AutoML cannot replace a machine learning expert, but it can be cost-effective and can be useful for smaller organizations.

Applications of AutoML

AutoML can be used for the following tasks:

  • Automated Data Preparation
    It Involves column type detection, intent detection, and automated task detection within the dataset.
  • Feature Engineering
    It includes Feature Scaling, meta-learning, and feature selection.
  • Automated Model Selection
    AutoML can help in model selection.
  • Automated problem checking
    Problem checking and debugging can be automated.
  • Automated analysis of results obtained
    Applying wonders of AI can save time and capital.

Popular AutoML Libraries like Featuretools, Auto-sklearn, MLBox, TPOT, H2O, Auto-Keras are the ones contributing to enhanced AutoML experience.

Advantages of AutoML

  • The installation of the libraries is effortless.
  • The introduction of Cloud AutoML has speeded up the development of AutoML.
  • Cost-effective, and Labour-efficient.
  • Require a lower level of expertise.

Limitations of AutoML

Although coming with a set of advantages, advanced AutoML introduces the concept of hyperparameters, which are itself needed to be learnt. AutoML can be usefully incorporated for doing a task that can be generalized, but for functions that are unique and require some level of expertise, AutoML turns out to be a disaster.

Future of AutoML

Automated Machine Learning (AutoML) has been gaining traction within the Data Science community. This surge of interest is reflected in the development and release of numerous open-source AutoML libraries, which are mentioned above, and on the emergence of businesses focused on building and commercializing AutoML systems (like DataRobot, DarwinAI, H2O.ai, OneClick.ai). AutoML is a hot topic for the industry, but it is not all-set for replacing data scientists from existence. Besides the difficulty of automating many of the data science tasks, its sole purpose is to assist data scientists and free them from the burden of repetitive, and less demanding jobs that can be generalized, so they can invest their time on tasks that are more challenging, creative, and harder to automate.

Concluding, we live in an era where the growth of data beats our ability to make sense of it. AutoML is an exciting technological field that has been in the spotlight and which promises to mitigate this problem through the development in the sector of Artificial Intelligence.

We expect significant strides of progress in this field in the near future, and we recognize the help of AutoML systems in solving many of the challenges that we face out there.

QUANTUM COMPUTING – THE UNEXPLORED MIRACLE

What is Quantum Computing?
Quantum computing is the use of quantum-mechanical phenomena such as superposition and entanglement to perform computation. A quantum computer is specifically used to perform such calculation, which can be implemented theoretically or physically. The field of quantum computing is a sub-field of quantum information science, which includes quantum cryptography and quantum communication. The idea of Quantum Computing took shape in the early 1980s when Richard Feynman and Yuri Manin expressed the idea that a quantum computer had the potential to simulate things that a classical computer could not.

The year 1994 saw further development of Quantum Computing when Peter Shor published an algorithm that was able to efficiently solve problems that were being used in asymmetric cryptography that were considered very hard for a classical computer. There are currently two main approaches to physically implementing a quantum computer: analog and digital. Analogue methods are further divided into the quantum simulation, quantum annealing, and adiabatic quantum-computation.

Basic Fundamentals of Quantum Computing
Digital quantum computers use quantum logic gates to do computation. Both approaches use quantum bits or qubits. These qubits are fundamental to Quantum Computing and are somewhat analogous to bits in a classical computer. Like a regular bit, Qubit resides in either 0 or 1 state. The specialty is that they can also be in the superposition of 1 and 0 states. However, when qubits are measured, the result is always either a 0 or a 1; the probabilities of the two outcomes depends on the quantum state they were in.

Principle of Operation of Quantum Computing
A quantum computer with a given number of quantum bits is fundamentally very different from a classical computer composed of the same number of bits. For example, representing the state of an n-qubit system on a traditional computer requires the storage of 2n complex coefficients, while to characterize the state of a classical n-bit system it is sufficient to provide the values of the n bits, that is, only n numbers.

A classical computer has a memory made up of bits, where each bit is represented by either a one or a zero. A quantum computer, on the other hand, maintains a sequence of qubits, which can represent a one, a zero, or any quantum superposition of those two qubit states; a pair of qubits can be in any quantum superposition of 4 states, and three qubits in any superposition of 8 states. In general, a quantum computer with n qubits can be in any superposition of up to different states. Quantum algorithms are often probabilistic, as they provide the correct solution only with a certain known probability.

What is the Potential that Quantum Computing offers?
Quantum Computing is such a unique field that very few people show their interest in it. There is a lot of room for development. It has a lot of scope. Some of the areas in which this is penetrating today are:

  • Cryptography – A quantum computer could efficiently solve this problem using multiple algorithms. This ability would allow a quantum computer to break many of the cryptographic systems in use today
  • Quantum SearchQuantum computers offer polynomial speedup for some problems. The most well-known example of this is quantum database search, which can be solved by Grover’s algorithm using quadratically fewer queries to the database than that is required by classical algorithms.
  • Quantum Simulation – Since chemistry and nanotechnology rely on understanding quantum systems, and such systems are impossible to simulate efficiently classically, many believe quantum simulation will be one of the most important applications of quantum computing.
  • Quantum Annealing and Adiabatic Optimization
  • Solving Linear Equations – The Quantum algorithm for linear systems of equations or “HHL Algorithm,” named after its discoverers Harrow, Hassidim, and Lloyd, is expected to provide speedup over classical counterparts.
  • Quantum Supremacy

In conclusion, Quantum computers could spur the development of breakthroughs in science, medication to save lives, machine learning methods to diagnose illnesses sooner, materials to make more efficient devices and structures, financial strategies to live well in retirement, and algorithms to direct resources such as ambulances quickly.  The scope of Quantum Computing is beyond imagination. Further developments in this field will have a significant impact on the world.

NATURAL LANGUAGE PROCESSING – GIVING MACHINES A VOICE

What is Natural Language Processing (NLP)?

Natural Language Processing commonly abbreviated as NLP is a subfield of computer science and artificial intelligence. It is mainly concerned with the interaction between computers and the languages humans speak, like English, Italian, French, among various others. It is used in particular to program machines to process and analyze large amounts of natural language data.

The development of NLP applications is quite challenging because computers traditionally require human beings to communicate to them through a programming language or a high-level language. Human speech, however, is not always precise, is often ambiguous and is dependent on factors like the emphasis on a particular word or expression. These are the factors that the computer finds very difficult to understand.

How does Natural Language Processing work?

Syntax and Semantic analysis are two main techniques that are used with NLP. The  Syntax is the arrangement of words in a sentence to make some grammatical sense. Different Syntax methods used are:

  • Parsing
  • Word segmentation
  • Sentence breaking
  • Morphological segmentation and
  • Stemming

The Semantic involves the use and meaning behind the words. NLP applies the algorithms to understand the grammar and meaning of the sentences. The techniques used by NLP in semantic Analysis are:

  • Named Entity Recognition
  • Natural Language Generation

The current approaches to NLP are mainly based on Deep Learning, which is a type of AI that examines and uses the patterns in data to improve programs understanding. It is basically dependent on supervised learning, which consists of a training set and a test set.

Three tools very commonly used for NLP are NLTK, Gensim, and Intel NLP Architect. Natural Language Toolkit(NLTK), is an open-source python module with data sets and tutorials. Gensim is a Python library for topic modeling and document indexing. Intel NLP Architect is also another Python library for deep learning topologies and techniques.

What are the Uses of Natural Language Processing?

Although, NLP came into existence for the first time by Alan Turing when he published an article titled “Computer Machinery and Intelligence,”. The vast use came into effect only from the 80s, after the introduction of Machine Learning. Before 1980, the most natural language processing systems were based on complex sets of hand-written rules. Writing these rules included a lot of labor and was inaccurate due to diversity in the pronunciation of a language. Introduction of Machine Learning speeded up the development of Natural Language Processing.

Natural language Processing is very widely used today in our daily routine. It finds its application in:

  • Chatbots – Chatbots handle various clients and answer their query without considerable human effort. Chatbots are trained on a vast set of data and hence process only the essential part from a conversation. Companies like Uber, Zomato use the Chatbots to minimize human involvement.
  • Voice Assistants – The most significant use of NLP is implemented for this purpose. Technological giants like Google, Microsoft, Amazon, etc. use their own personal voice assistant to help in communicating with smart devices quickly. Amazon assigns over 1000 personnel globally for enhancing its voice assistant.
  • Very brilliant use of NLP came with the name Grammarly, which is a tool that keeps on a check on writers’ write-ups, and points out grammatical errors and suggests better phrases.
  • Google translate also uses NLP to translate a webpage from one language to another by understanding its content.

 

What are the Challenges faced by NLP?

NLP, though a new technology with a lot of advantages isn’t completely developed. For Example – Semantic and Grammar Analysis is still a challenge for NLP. Other difficulties includes, NLP not relating to sarcasm easily, since NLP cannot figure the changing meaning of words on the basis of speaker emphasis. NLP is also challenged by the fact that the dialect of people changes with regions.

On a final note, Natural Language Processing is a very handy tool, although it is in the developing state and faces some difficulties. The recent development in the NLP has made it a gem for the Technological Giants. The future of NLP through Machine and Deep Learning seems quite bright.

REINFORCEMENT LEARNING

What is Reinforcement Learning?
Reinforcement Learning (commonly abbreviated as RL) is an area and application of Machine Learning. Reinforcement, as described from its meaning, is about taking suitable actions to maximize reward in a particular situation. It is implemented after rigorous testing by various machines and complex software to find the best possible behavior or path that it should take in a specific condition.

The primary specifics of reinforcement learning are summarized as follows:

  • Input: The input is defined to be an initial state from which the model will start.
  • Output: There are many possible outputs as there are a variety of solutions for a particular task.
  • Training: The training is wholly based upon the input provided, in return, the model returns a state, and then it is the users decision to decide whether to reward or punish the model based on its output.
  • The model keeps learning.
  • The maximum award determines the best solution.

How is it different from Supervised Learning?
Supervised Learning is implemented based on a training set that acts as an answer key, so the model is trained according to the correct answer itself. In Reinforcement Learning, there is no answer, but the work is done by the Reinforcement Agent who decides what to do to perform the given task. In the absence of a dataset, it is bound to learn from experience. In Reinforcement Learning, each right step gives a reward while each wrong step subtracts the award.

 

REINFORCEMENT LEARNING

SUPERVISED LEARNING

Reinforcement learning is about making decisions sequentially. In more simpler words, we can say that the output depends on the state of the current input, and the next input depends on the output of the previous information. In Supervised learning, the decision is made on the initial data or the feedback given.
Reinforcement learning is decision dependent. So, labels are given to sequences of dependent decisions. Supervised learning the choices are independent of each other, so labels are assigned to each decision.
Example: Chess game Example: Object recognition

 

Applications and Use Cases of Reinforcement Learning
In the era of Convolutional Neural Network (CNN), Reinforcement Learning as a framework seems to be undervalued. After all, this branch of Machine Learning is unique and has its own importance. The uses of Reinforcement Learning are as follows:

  • Resource Management in Computer Clusters:

Reinforcement Learning can be used to automatically learn to allocate and schedule the computer resources for waiting jobs, with the primary objective to minimize the average job slowdown.

  • Traffic Light Control:

Researchers found a way to solve the traffic congestion problem using Reinforcement Learning. Though tested only on a simulated environment, a significant improvement is seen over conventional traffic methods.

Example: The below figure depicts five agents. These were put in a five-transaction traffic network, with a Reinforcement Learning agent at the central intersection to control traffic signaling. The state was defined as an eight-dimensional vector with each element representing the relative traffic flow of each lane. Eight choices were available to the agent, each representing a phase combination, and the reward function was defined as a reduction in delay compared with the previous time step.

  • Robotics:

Robotics witnesses a tremendous work on applying Reinforcement Learning. It can be used to help the robot to learn policies to map raw video images to the robot’s action.

  • Web System Configuration:

The Reinforcement Learning helps in achieving the targeted response time and measured response time.

  • Chemistry:

Reinforcement Learning can also be applied in optimizing chemical reactions. Combined with LSTM to model the policy function, the Reinforcement Learning agent can optimize the chemical reaction with the Markov decision process (MDP) characterized by {S, A, P, R}, where S was the set of experimental conditions (like temperature, pH, etc.), A was the set all possible actions that can change the experimental conditions, P was the transition probability from current experiment condition to the next term, and R was the reward which is a function of the state.

As a conclusion, Reinforcement Learning is highly helpful in industry and daily life, though it is criticized for its industrial use. Although it has its weaknesses, Reinforcement Learning is useful in the space of corporate research given its vast potential in decision making.

In the future, Reinforcement Learning is assumed to be assisting human and evolve into Artificial General Intelligence (AGI). Imagine about a robot assisting you in your work. Amazing. Isn’t it?

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