Taking into consideration the positive trends of Data Science from previous years, there lies an immense well of possibilities that awaits us in the future, that is, the upcoming year 2020. Some of these Data Science Trend Forecast for 2020 can be foreseen as follows:
Complicated code and extensions will no longer be required to get deep insights from data. The augmented analysis helps layman users/analysts (in machine learning/data science) to make use of AI to analyze data. This will change the way data is consumed, created and shared across all data-intensive fields. Already several BI and analytics tools are trying to implement AI assistance full force in their platforms.
Continuous/ Real-time Intelligence
There is intensive activity ongoing every second in real-time platforms. If through some method, one can plug into this data, real-time user experience can be enhanced manifold. Continuous or real-time intelligence aims to do just that by analyzing data in real-time so that instant results can be provided to the user while he is still surfing the platform. It can also help increase profit margins by re-aligning the platform as per the observed interaction of the user.
Natural Language Processing is a very important segment of Artificial Intelligence since most real-world data are in text or voice format. To process such data, advanced NLP techniques are required which are being innovated with each passing data. Today, we can read, understand, classify and even create unique text documents with the help of machines. Further developments like intelligent summarization, entity recognition and task management using text input and much more are expected to happen, owing to the intense research and increasing data-science experts choosing NLP specialisation.
There has already been a visible surge in the performance of voice assistants in 2019. In 2020, it is expected to further improve such that the conversational systems become more sensitive to the human language and also more humane in their response. By more humane, it will mean that the systems can keep track of previous responses and questions (which is not a very developed feature in any voice assistant in the market to this day). Also, most client interactions are expected to be taken over by conversational technology, thus, increasing response rate and efficiency.
The last decade has seen massive growth in AI aided decisions for sure, but it has been a persistent problem to be able to explain these decisions or why the AI wants to go a certain way instead of another. Recently, however, a lot of research has increased the scope of explainable AI. 2020 can further be invested in understanding problems like say, how and why a certain neural network arrived at a certain decision. This will indefinitely increase the faith of clients on adolescent technology.
Persistent memory/ In-memory computation
In-memory computing or IMC can deliver extremely high-performance tasks due to optimized memory architecture. It also has become more feasible due to the decreasing expense of memory which owes credit to constantly emerging innovations.
Data Fabric helps in the smooth access and sharing of data in distributed environments. It is usually custom made and helps in the transfer and store of data, data pipelines, APIs and previously used data services that have a chance of being re-invoked. Trusted and efficient data fabric can help to catalyze data science pipelines and reduce delays in customer-client interaction/iterations.
Advances in Quantum Computing
The research in Quantum Computing has a very high momentum at the moment. Even though the whole architecture of Quantum computing is at a very basic stage, increased investments and research are helping the field to grow by inches every passing day. A quantum computer is said to perform calculations which will take general computers a few years, in just a few seconds! As remarkable as it sounds, it can bestow superpowers to mankind! Imagine munching on years and years of historical data to arrive at conclusions about the future in just a few seconds. A whole lot of astonishing things await us, and we must be blessed to be a part of this century.
It is expected that India’s job openings in the analytics sector will double to about 200000 or two lakh jobs in 2020. Here is what 2020 for job seekers in data science will look like:
Fields like finance, IT, professional services and insurance will see a boom in demand for data science and analytics.
Having analytics skills like MapReduce, Apache Pig, Machine learning and Hadoop can provide an edge over other competitors in the field. The most fundamental in-demand skills will be Python and Machine Learning. Statistics is an added advantage.
Vacancies for roles like data developers, data engineers and data scientists will go over 700,000 by 2020.
The most promising sectors that will tend to create increasing opportunities include Aviation, Agriculture, Security, Healthcare and Automation.
The average salaries in India in development roles like Data Scientist or Data Engineer will range from 5 to 8 Lakh per annum.
The average salaries in India in management/strategizing roles like data architect or business intelligence manager will range from 10 to 20 Lakh per annum.
As exciting as all of it sounds, there is always a bag of unforeseen advancements that are bound to take us all by surprise, as has always happened with Data Science and AI in the past. So, hold tight for yet another mind-boggling ride through the lanes of technology this 2020!
Data Science has seen a massive boom in the past few years. It has also been claimed that it is indefinitely one of the fastest-growing fields in the IT/academic sector. One of the most hyped Trends in Data Science this year was that the sector saw a major hike in jobs as compared to the past years!
Such an unprecedented growth owes all its dues to the unimaginable benefits that artificial intelligence has brought to the plate of mankind for the very first time. It was never before imagined that external machines could aid us with such sophistication as is present today. Owing to this, it is imperative that an individual, irrespective of his/her calling, must have at least a superficial knowledge about the past advances and future possibilities of this field of study. Even if it is the job of scientists and engineers to figure out solutions using machine learning and data science, the solutions, undoubtedly is bound to affect all our lives in the upcoming years. Moreover, if you are planning to plug into the huge well of job openings in data science, exploring the past and upcoming trends in this field will surely take you a step ahead.
Looking back on the achievements of the year 2019, there is much which has happened. Here is a brief glimpse of what Trends in Data Science of 2019 looked like:
The once-popular belief that AI technology was only meant for high-scale and high-tech industries, is now an old wives’ tale. AI has spread so rapidly across every phase of our lives, that sometimes we do not even realize that we are being aided by AI. For instance, recommendations that we get on online forums are something we have become very used to in recent times. However, very few have the conscious knowledge that the recommendations are regulated by AI technology. There are also several instances where a layman can use AI to get optimized outputs, like in automated machine learning pipelines. We even have improvised AI-aided security systems, music systems and voice assistants in our very homes! Overall, the impact of AI in everyday lives saw a massive boost in 2019, and it is only bound to increase.
The rapid growth of IoT products
As was already forecasted, the number of machines/devices which came online in 2019 was immense. Billions were invested in research to back the uprising IoT industry. Today it is nothing out of the ordinary to control home appliances like television and air conditioners with our smartphones or lock our and unlock our cars from even the opposite end of the globe. Bringing devices online not only makes the user experience far smoother but also generates crucial data for analysis. With such data, several unopened gates can be explored across several domains. The investments and count of IoT devices are expected to go up at an increasing rate in the upcoming years.
Evolution of Predictive Analysis
The concept of predictive analysis is to use past data to learn recurring patterns, such that it can predict outcomes of future events based on the patterns learnt. Today, with increasing data it becomes extensively important to make use of optimized predictive solutions. Big data comes into picture here and significant advancements have been made in 2019 about it. Tools like PySpark and MLLib have helped scale simple predictive solutions to extensive data.
Migration of Dark Data
Dark data is very old data which has probably been sitting in obsolete archives like old systems or even files in storage rooms! There is a general understanding that such unexplored data can show us the way to crucial insights about past trends which can help grab useful opportunities and even avoid unwanted loopholes. Therefore, there has been visible initiatives to make dark data more available to present-day systems with the help of efficient storage and migration tools.
Implementation of Regulations
In 2018, General Data Protection Regulation (GDPR) brought in a few data governance rules to emphasize the importance of data governance. The rules were laid down so fast that even at the year-end, several companies dealing with data are still trying to comply wholly with all the principles laid down. These principles have not only created a standard for data consumption and data handling domains but are also bound to shape the future of data handling with great impact.
DataOps is an initiative to bring in some order in the way the data science pipeline functions. It is essentially a reflection of agile and DevOps methods in the field of data science. In 2019, it has been one of the major concerns of management in data science to integrate DataOps into their respective teams. Previously, such integration was not possible since the generic pipeline was still in making or under research. However, now, with a more robust structure, integrating DataOps can mean wonders for data science teams.
As stated by Gartner, Inc. cloud computing and edge computing has evolved to become a complementary model in 2019. Edge computing goes by the concept of “more the proximity (or closeness to the source of computation), better is the efficiency”. Edge computing allows workloads to be located closer to the consumers and thus, reduces latency several-fold.
There is, however, a huge recurring gap when it comes to the need and availability of skilled people who can launch and contribute to these developments significantly. India contributed to 6% of job openings worldwide in 2019, which scales to around 97000 jobs!
The job trends of 2019 looked as follows:
BFSI sector had a massive demand for analytics professionals, followed by the e-commerce and telecom sectors. The banking and financial sectors continued to have high demand throughout.
Python served as a great skill to attract employers to skilled job seekers
A 2% increase in jobs offering over 15 Lakh per annum was observed
Also, 21% of jobs demanded young talent in data science, a great contrast to all previous years. 70% of job openings were for professionals with less than 5 years of experience.
The top in-demand designations were Analytics Manager, Business Analyst, Research Analyst, Data Analyst, SAS Analyst, Analytics Consultants, Statistical Analyst and Hadoop Developer
Big data skills like Hadoop and Spark were extremely in demand due to the growing rate of data.
Telecom industry saw a fall in demand for data science professionals.
The median salary of analytics jobs was just over 11 Lakh per annum.
As we move towards a data-driven world, we tend to realize how the power of analytics could unearth the most minute details of our lives. From drawing insights from data to making predictions of some unknown scenarios, both small and large industries are thriving under the power of big data analytics.
There are various terms, keywords, concepts that are associated with Analytics. This field of study is broad, and hence, it could be overwhelming to know each one of it. This blog covers some of the critical concepts in analytics from A-Z, and explain the intuition behind that.
A: Artificial Intelligence – AI is the field of study which deals with the creation of intelligent machines that could behave like humans. Some of the widespread use cases where Artificial Intelligence has found its way are ChatBots, Speech Recognition, and so on.
There are two main types of Artificial Intelligence –Narrow AI, and Strong AI. A poker game is an example for the weak or the narrow AI where you feed all the instructions into the machines. It is trained to understand every scenario and incapable of performing something on their own.
On the other hand, a Strong AI thinks and acts like a human being. It is still far-fetched, and a lot of work is being done to achieve ground-breaking results.
B: Big Data – The term Big Data is quite popular and is being used frequently in the analytical ecosystem. The concept of big data came into being with the advent of the enormous amount of unstructured data. The data is getting generated from a multitude of sources which bears the properties of volume, veracity, value, and velocity.
Traditional file storage systems are incapable of handling such volumes of data, and hence companies are looking into distributed computing to mine such data. Industries which makes full use of the big data are way ahead off their peers in the market.
C: Customer Analytics – Based on the customer’s behavior, relevant offers delivered to them. This process is known as Customer Analytics. Understanding the customer’s lifestyle and buying habits would ensure better prediction of their purchase behaviors, which would eventually lead to more sales for the company.
The accurate analysis of customer behavior would increase customer loyalty. It could reduce campaign costs as well. The ROI would increase when the right message delivered to each segmented group.
D: Data Science – Data Science is a holistic term which involves a lot of processes which includes data extraction, data pre-processing, building predictive models, data visualization, and so on. Generally, in big companies, the role of a Data Scientist is well defined unlike in startups where you would need to look after all the aspects of an end-to-end project.
source: Towards Data Science
To be a Data Scientist, you need to be fluent in Probability, and Statistics as well, which makes it a lucrative career. There are not many qualified Data Scientists out there, and hence mastering the relevant skills could put you in a pole position in the job market.
E: Excel –An old, and yet the most used after visualization tool in the market is Microsoft Excel. Excel is used in a variety of ways while presenting the data to the stakeholders. The graphs and charts lay down the proper demonstration of the work done, which makes it easier for the business to take relevant decisions.
Moreover, Excel has a rich set of utilities which could useful in analyzing structured data. Most companies still need personnel with the knowledge of MS Excel, and hence, you must master it.
F: Financial Analytics – Financial Data such as accounts, transactions, etc., are private and confidential to an individual. Banks refrain from sharing such sensitive data as it could breach privacy and lead to financial damage of a customer.
However, such data if used ethically could save losses for a bank by identifying potential fraudulent behaviors. It would also be used to predict the loan defaulting probability. Credit scoring is another such use case of financial analytics.
G: Google Analytics – For analyzing website traffic, Google provides a free tool known as Google Analytics. It is useful to track any marketing campaign which would give an idea about the behavior of customers.
There are four levels via which the Google Analytics collects the data – User level which understands each user’s actions, Session level which monitors the individual visit, Page view level which gives information about each page views, and Event level which is about the number of button clicks, views of videos, and so on.
H: Hadoop –The framework most commonly used to store, and manipulate big data is known as Hadoop. As a result of high computing power, the data is processed fast in Hadoop.
Moreover, parallel computing in multiple clusters protects the loss of data and provides more flexibility. It is also cheaper, and could easily be scaled to handle more data.
I: Impala – Impala is a component of Hadoop which provides a SQL query engine for data processing. Written in Java, and C++, Impala is better than other SQL engines. Use SQL; the communication enabled between users and the HDFS, which is faster than Hive. Additionally, different formats of a file could also be read using Impala.
J: Journey Analytics – A sequential journey related to customer experience, which meets a specific business referred to as Journey Analytics. Over time, a customer’s interaction with the company compiled from its journey analytics.
K: K-means clustering – Clustering is a technique where you group a dataset into some small groups based on the similar properties shared among the members of the same group.
K-Means clustering is one such clustering algorithm where an unsupervised dataset split into k number of groups or clusters. K-Means clustering could be used to group a set of customers or products resembling similar properties.
L: Latent Dirichlet Allocation – LDA or Latent Dirichlet Allocation is a technique used over textual data in use cases such as topic modeling. Here, a set of topics imagined by the LDA representing a set of words. Then, it maps all the documents to the topics ensuring that those imaginary topics capture words in each text.
M: Machine Learning – Machine Learning is a field of Data Science which deals with building predictive models to make better business decisions.
A machine or a computer is first trained with some set of historical data so that it finds patterns in it, and then predict the outcome on an unknown test set. There are several algorithms used in Machine Learning, one such being K-means clustering.
N: Neural Networks – Deep Learning is the branch of Machine Learning, which thrives on large complex volumes of data and is used to cases where traditional algorithms are incapable of producing excellent results
Under the hood, the architecture behind Deep Learning is Neural Networks, which is quite similar to the neurons in the human brain.
O: Operational Analytics –The analytics behind the business, which focuses on improving the present state of operations, referred to as Operational Analytics.
Various data aggregation and data mining tools used which provides a piece of transparent information about the business. People who are expert in this field would use operational software provided knowledge to perform targeted analysis.
P: Pig –Apache Pig is a component of Hadoop which is used to analyze large datasets by parallelized computation. The language used is called Pig Latin.
Several tasks, such as Data Management could be served using Pig Latin. Data checking and filtering could be done efficiently and quickly with Pig.
Q: Q-Learning –It is a model-free reinforcement learning algorithm which learns a policy by informing an agent the actions to be taken under specific certain circumstances. The problems handled with stochastic transitions and rewards, and it doesn’t require adaptations.
R: Recurrent Neural Networks –RNN is a neural network where the input to the current step is the output from the previous step.
It used in cases such as text summarization was to predict the next word, the last words are needed to remember. The issue of the hidden layer was solved with the advent of RNN as it recalls sequence information.
S: SQL –One of the essential skill in analytics is Structured Query Language or SQL. It is used in RDBMS to fetch data from tables using queries.
Most companies use SQL for their initial data validation and analysis. Some of the standard SQL operations used are joins, sub-queries, window functions, etc.
T: Traffic Analytics –The study of analyzing a website’s source of traffic by looking into its clickstream data is known as traffic analytics. It could help in understanding whether direct, social, paid traffic, etc., are bringing in more users.
U: Unsupervised Machine Learning –The type of machine learning which deals with unlabeled data is known as unsupervised machine learning.
Here, no labels provided for a corresponding set of features, and information is grouped based on the similarity in the properties shared by the members of each group. Some of the unsupervised algorithms are PCA, K-Means, and so on.
V: Visualization –The analysis of data is useless if not presented in the forms of graphs and charts to the business. Hence, Data visualization is an integral part of any analytics project and also one of the key steps in data pre-processing and feature engineering.
W: Word2vec –It is a neural network used for text processing which takes in a text as input and output are a set of feature vectors of the words.
Some of the applications of word2vec are in genes, social media graphs, likes, etc. In a vector space, similar words are grouped using word2vec without the need for human intervention.
X: XGBoost –Boosting is a technique in machine learning by which a strong learner strengthens a weak learner in subsequent steps.
XGBoost is one such boosting algorithm which is robust to outliers, or NULL values. It is the go-to algorithm in Machine Learning competitions for its speed and accuracy.
Y: Yarn –YARN is a component of Hadoop which lies between HDFS, and the processing engines. In individual cluster nodes, the processing operations monitored by YARN.
The dynamic allocation of resources is also handled by it, which improves application performance and resource utilization.
Z: Z-test –A type of hypothesis testing used to determine whether to reject or accept the NULL hypothesis. By how many standard deviations, a data point is further away from the mean could be calculated using Z-test.
In this blog post, we covered some of the terms related to the analytics starting with each letter in the English.
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Reports suggest that around 2.5 quintillion bytes of data are generated every single day. As the online usage growth increases at a tremendous rate, there is a need for immediate Data Science professionals who can clean the data, obtain insights from it, visualize it, train model and eventually come up with solutions using Big data for the betterment of the world.
By 2020, experts predict that there will be more than 2.7 million data science and analytics jobs openings. Having a glimpse of the entire Data Science pipeline, it is definitely tiresome for a single human to perform and at the same time excel at all the levels. Hence, Data Science has a plethora of career options that require a spectrum set of skill sets.
Let us explore the top 5 data science career options in 2019 (In no particular order).
1. Data Scientist
Data Scientist is one of the ‘high demand’ job roles. The day to day responsibilities involves the examination of big data. As a result of the analysis of the big data, they also actively perform data cleaning and organize the big data. They are well aware of the machine learning algorithms and understand when to use the appropriate algorithm. During the due course of data analysis and the outcome of machine learning models, patterns are identified in order to solve the business statement.
The reason why this role is so crucial in any organisation is that the company tends to take business decisions with the help of the insights discovered by the Data Scientist to have an edge over the company’s competitors. It is to be noted that the Data Scientist role is inclined more towards the technical domain. As the role demands a wide range of skill set, Data Scientists are one among the highest paid jobs.
Core Skills of a Data Scientist
Database and querying
Data warehousing solutions
Machine learning algorithms
2. Business Intelligence Developer
BI Developer is a job role inclined more towards the Non-Technical domain but has a fair share of Technical responsibilities as well (if required) as a part of their day to day responsibilities. BI developers are responsible for creating and implementing business policies as a result of the insights obtained from the Technical team.
Apart from being a policymaker involving the usage of dedicated (or custom) Business Intelligence analytics tools, they will also have a fair share of coding in order to explore the dataset, present the insights of the dataset in a non-verbal manner. They help in bridging the gap between the technical team that works with the deepest technical understanding and the clients that want the results in the most non-technical manner. They are expected to generate reports from the insights and make it ‘less technical’ for others in the organisation. It is noted that the BI Developers have a deep understanding of Business when compared to Data Scientist.
Core Skills of a Business Analytics Developer
Business model analysis
Design of business workflow
Business Intelligence software integration
3. Machine Learning Engineer
Once the data is clean and ready for analysis, the machine learning engineers work on these big data to train a predictive model that predicts the target variable. These models are used to analyze the trends of the data in the future so that the organisation can take the right business decisions. As the dataset involved in a real-life scenario would involve a lot of dimensions, it is difficult for a human eye to interpret insights from it. This is one of the reasons for training machine learning algorithms as it easily deals with such complex dataset. These engineers carry out a number of tests and analyze the outcomes of the model.
The reason for conducting constant tests on the model using various samples is to test the accuracy of the developed model. Apart from the training models, they also perform exploratory data analysis sometimes in order to understand the dataset completely which will, in turn, help them in training better predictive models.
Core Skills of Machine Learning Engineers
Machine Learning Algorithms
Data Modelling and Evaluation
4. Data Engineer
The pipeline of any data-oriented company begins with the collection of big data from numerous sources. That’s where the data engineers operate in any given project. These engineers integrate data from various sources and optimize them according to the problem statement. The work usually involves writing queries on big data for easy and smooth accessibility. Their day to day responsibility is to provide a streamlined flow of big data from various distributed systems. Data engineering differs from the other data science careers as in, it is concentrated on the system and hardware that aids the company’s data analysis, rather than the analysis of data itself. They provide the organisation with efficient warehousing methods as well.
Core Skills of Data Engineer
Machine Learning algorithm
5. Business Analyst
Business Analyst is one of the most essential roles in the Data Science field. These analysts are responsible for understanding the data and it’s related trend post the decision making about a particular product. They store a good amount of data about various domains of the organisation. These data are really important because if any product of the organisation fails, these analysts work on these big data to understand the reason behind the failure of the project. This type of analysis is vital for all the organisations as it makes them understand the loopholes in the company. The analysts not only backtrack the loophole and in turn provide solutions for the same making sure the organisation takes the right decision in the future. At times, the business analyst act as a bridge between the technical team and the rest of the working community.
Core skills of Business Analyst
The data science career options mentioned above are in no particular order. In my opinion, every career option in Data Science field works complimentary with one another. In any data-driven organization, regardless of the salary, every career role is important at the respective stages in a project.
After decades of a heavy slog with no promise of success, quantum computing is suddenly buzzing! Nearly two years ago, IBM made a quantum computer available to the world. The 5-quantum-bit (qubit) resource they now call the IBM Q experience. It was more like a toy for researchers than a way of getting any serious number crunching done. But 70,000 users worldwide have registered for it, and the qubit count in this resource has now quadrupled. With so many promises by quantum computing and data science being at the helm currently, are there any offerings by quantum computing for the AI? Let us explore that in this blog!
What is Quantum Computing?
A traditional computer works on bits of data that are binary, or Boolean, with only two possible values: 0 or 1. In contrast, a quantum bit has possible values of 1, 0 or a superposition of 1 and 0. According to scientists, qubits are more like physical atoms and molecular structures. However, many find it helpful to theorize a qubit as a binary data unit with superposition.
The use of qubits makes the practical quantum computer model quite difficult. Traditional hardware requires altering to read and use these unknown values. Another idea, known as entanglement, uses quantum theory to suggest that accurate values cannot be obtained in the ways that traditional computers read binary bits. It also has been suggested that a quantum computer is based on a non-deterministic model, where the computer has more than one possible outcome for any given case or situation. Each of these ideas provides a foundation for the theory of actual quantum computing, which is still problematic in today’s tech world.
Use of Quantum Computing
Let us look at some of the use cases of the quantum computing below. This will help you understand the scale of the application of quantum computing currently.
Use cases can be:
The most common area people associate quantum computing with is advanced cryptography. The ordinary computers we use today make it infeasible to break the encryption that uses very large prime number factorization (300+ integers). With quantum computers, this decryption could become trivial, leading to much stronger protection of our digital lives and assets. Of course, we’ll also be able to break traditional encryption much faster.
Quantum technology could enable much more complex computer modelling like aeronautical scenarios. Aiding in the routing and scheduling of aircraft has enormous commercial benefits for time and costs. Large companies like Airbus and Lockheed Martin are actively researching and investing in the space to take advantage of the computing power and the optimization potential of the technology.
3. Data Analytics
Quantum mechanics and quantum computing can help solve problems on a huge scale. A field of study called topological analysis where geometric shapes behave in specific ways describes computations that are simply impossible with today’s conventional computers due to the data set used.
NASA is looking at using quantum computing for analyzing the enormous amount of data they collect about the universe, as well as research better and safer methods of space travel.
Predicting and forecasting various scenarios rely on large and complex data sets. Traditional simulation of, for example, the weather is limited in the inputs that can be handled with classical computing. If you add too many factors, then the simulation takes longer than for the actual weather to evolve.
5. Pattern Matching
Finding patterns in data and using these to predict future patterns is highly valuable. Volkswagen is currently looking into how they can use quantum computing to inform drivers of traffic conditions 45 minutes in advance. Matching traffic patterns and predicting the behaviour of a system as complex as modern day traffic is so far not possible for today’s computers, but this is going to change with quantum computers.
6. Medical Research
There are literally billions of possibilities to how something could react across the human body and even more when you consider that this could be a drug administered to billions of people, each with slight differences in their makeup.
Today, it takes pharmaceutical companies up to 10+ years and often billions of dollars to discover a new drug and bring it to market. Improving the front end of the process with quantum computing can dramatically cut costs and time to market, repurpose pre-approved drugs more easily for new applications, and empower computational chemists to make new discoveries faster that could lead to cures for a range of diseases.
7. Self-Driving Cars
Car companies like Tesla and tech companies like Apple and Google are actively developing driverless cars. Not only will these improve the standard of living for most people, but also cut pollution, reduce congestion and bring about a bunch of other benefits.
AI and Quantum Computing
Quantum computing is not a replacement for AI but you can see it more like an enhancement. AI is a major task which we are trying to solve and quantum computing helps us in optimising the sub-tasks of it. Currently, we have a limited scope of quantum computing in AI as technology is still currently new. But on a broad level, quantum computing affects the following tasks in AI
1. Simulation Simulation modelling is the process of creating and analyzing a digital prototype of a physical model to predict its performance in the real world. It is used to help designers and engineers understand whether, under what conditions, and in which ways a part could fail and what loads it can withstand. This modelling can also help to predict fluid flow and heat transfer patterns. It analyses the approximate working conditions by applying the simulation software.
2. Optimisation An optimization problem is a problem of finding the best solution from all feasible solutions. Optimization problems can be divided into two categories depending on whether the variables are continuous or discrete. An optimization problem with discrete variables is known as a discrete optimization. In a discrete optimization problem, we are looking for an object such as an integer, permutation or graph from a countable set. Problems with continuous variables include constrained problems and multimodal problems.
3. Sampling Data sampling is a statistical analysis technique used to select, manipulate and analyze a representative subset of data points to identify patterns and trends in the larger data set being examined. It enables data scientists, predictive modelers and other data analysts to work with a small, manageable amount of data about a statistical population to build and run analytical models more quickly, while still producing accurate findings.
Benefits of Quantum Computing in AI
1. Less time in training The big advantage of quantum computing is that it allows an exponential increase in the number of dimensions it can process. While a classical perceptron can process an input of N dimensions, a quantum perceptron can process 2N dimensions.
2. Better Results It turns out that quantum perceptron can easily classify the patterns in these simple images. We use the quantum model of perceptron as an elementary nonlinear classifier of simple patterns
3. Achieving parallelism The earliest examples of a quantum algorithm are exponentially faster than any possible deterministic classical algorithm. Quantum computing allows solving the problem since it is capable of simultaneously evaluating f(0)and f(1). This possibility stems from ‘quantum parallelism’. The quantum parallelism allows computing 2n entries for a state consisting of n-qubits. That is: from a linear growth in the number of qubits, we can achieve exponential growth in computing space.
Sensitivity to interaction with the environment Quantum computers are extremely sensitive to interaction with the surroundings since any interaction (or measurement) leads to a collapse of the state function. This phenomenon is called decoherence. It is extremely difficult to isolate a quantum system, especially an engineered one for a computation, without it getting entangled with the environment. The larger the number of qubits the harder is it to maintain the coherence.
Error-correction Quantum error correction (QEC) is used in quantum computing to protect quantum information from errors due to decoherence and other quantum noise. Quantum error correction is essential if one is to achieve fault-tolerant quantum computation that can deal not only with noise on stored quantum information, but also with faulty quantum gates, faulty quantum preparation, and faulty measurements. Copying quantum information is not possible due to the no-cloning theorem. This theorem seems to present an obstacle to formulating a theory of quantum error correction
Constraints on state preparation State preparation is the essential first step to be considered before the beginning of any quantum computation. In most schemes, the qubits need to be in a superposition state for the quantum computation to proceed correctly. We have a variety of problems due to the nature of superposition and entanglements, and state transition using local transformations is not realistic in a large system. Macrosystems that have been used as model quantum computing systems [14, 33,34] appear to implement not pure states but mixtures. Thus it appears that the NMR experiments do not validate the quantum algorithm.
Three decades after they were first proposed, quantum computers remain largely theoretical. Even so, there’s been some encouraging progress toward realizing a quantum machine. There’s no doubt that these are hugely important advances. and the signs are growing steadily more encouraging that quantum technology will eventually deliver a computing revolution. The potential of quantum computing in artiﬁcial intelligence will be evident soon, but still, we do not know how to translate that potential into reality. Undoubtedly, time will put things in place