Google’s AI creates solutions to fundamental computational problems

Google’s AI team, works on exploring solutions to computational problems, in theory, algorithms, journalism, machine learning, speech, and other data-driven streams with an impact on Google’s products and scientific progress. It focuses on two tools, software libraries to vehicle the research findings to products and services, and publications to make the work known to the community.  Let’s take a look at Google’s AI applications.

Most of the real-world Graph-based learning applications include varied information on relationships between data items. The team’s main aim is to extend Machine learning (ML) approaches to better model the relationships. These are used in many Google products.

Google, has a long history of the building and applying Machine Learning techniques since it has previously developed a Core Google API for supervised machine learning. Recently it has also been into researching and developing tools for the TensorFlow ecosystem. Google’s AI team actively collaborates with other products of Google such as Docs, Search, Ads to deploy ML-based solutions for cutting-edge research.

It also includes supervised learning and semi/unsupervised learning. Its areas of focus are personalization, optimization, data-dependent hashing, privacy learning, and many more. Google AI team has developed principled approaches and has been successful in applying them to Google’s products powering Search and Display Ads, YouTube, and Google Shopping.

The online clustering team provides clustering of the datasets that can extend to billions of data points lining the output of thousands of points per second. The goal behind this is to provide scalable nonparametric clustering without assumptions. The team came up with design techniques to handle data information drifts.

Another interesting sector of research is cross-lingual cross-model access for dynamically organized information for making writing, watching, and reading an immersive experience. The team’s Co-author powers the web content in Google Docs and the team is yet to come up with other new applications as well.

Google’s AI team filters through data to discover, understand and model indirect user behaviors. For this it partners with products like Ads, YouTube, many are yet to get added soon. Since structured data is vital for every Google product such as Fact Check, Search, and Q&A. It uses a wide range of techniques including machine learning, data mining for information retrieval and extraction. The team also develops techniques for fast inferences in ML models improving the speed over 50x along with accurate solutions.

It devises automata, grammars, and other models for speech and keyboard, written-to-spoken transductions, and extractions. These can be merged and optimized to give high accuracy, efficient speech recognition, text normalization, and more. Sensitive content detection helps to create a comprehensive set of classifiers for detecting any kind of offensive content, images, or videos. Google’s AI team has accomplished this using a variety of techniques such as ML models which are trained on images, and text from the web.

Many teams within Google AI have developed algorithms and Machine Learning systems for knowing user preferences through personalized and targeted experiences.   Google’s AI develops systems for transforming cloud-resident ML models that run on resource-constrained mobile devices. Not only this it also enriches electronic conversations by understanding media using multi-modal signals from images, video, text, and web.

Glassbox Learning does Research and Development into making Machine Learning more interpretable without compromising on accuracy. It also provides end-to-end guarantees on the relationship of inputs to outputs. The team has AdaNets that adaptively learns both the structure of the network and its weight. These are based on deep boosting with solid theoretical analysis including data-dependent generalization guarantees.   Google’s AI is doing an amazing job towards research with a varied set of tools and applications.

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Atos launches ThinkAI, its secure end-to-end scalable offering which enables organizations to successfully design, develop, and deliver high-performance AI applications. ThinkAI is for organizations using traditional high-performance computing that want to run more accurate and faster simulations thanks to AI applications, and also for those developing AI applications that need more computing power.

High-performance AI applications augment traditional HPC simulation and are essential to process and analyse massive and complex data sets effectively. Compared to traditional HPC simulation, AI-powered simulation enables researchers to tackle problems faster and more thoroughly, with increased accuracy, improved cost-efficiency and TCO (Total Cost of Ownership), lowering carbon footprint and creating competitive advantage.

AI applications, such as those related to drug discovery, smart cities or autonomous driving for example, are already being developed today, however barriers such as data quality, security and scalability remain – to help overcome these, expert consulting capability is essential and enables users to successfully define an AI roadmap, build scalable AI applications and industrialize these. ThinkAI is the most comprehensive HPC AI solution on the market today to do this and the only one which combines a full offering from consulting, to hardware and software solutions, to orchestration and final integration. It delivers rapid results and insight on data at optimized cost.

“The Atos ThinkAI solution brings together the necessary pieces for HPC users, at all stages of their AI journey, to leverage the significant opportunities of AI in their own research. The Atos ThinkAI solution can help users in both the scientific and industrial sector effectively combine the newest, most performant hardware and software solutions to speed the development of critical AI-based solutions and enhance the value of their simulation workloads.” said Alex Norton, Principal Technology Analyst and Data Analysis Manager, Hyperion.

“AI has created a new paradigm for applications in the scientific and industrial domains, catalyzing the translation of data to actionable insights. As the complexity of the Machine Learning model and its associated costs continue to grow substantially, dedicated high-performance AI infrastructures become crucial for organizations that want to deliver research breakthroughs. ThinkAI provides a holistic and tailor-made solution approach in advising, architecting and accomplishing AI solutions for any industry, so that they may accelerate time to AI operationalization and industrialization.” said Agnès Boudot, Senior Vice President, Head of HPC & Quantum at Atos.

The ThinkAI solution framework is made up of:

• Advise: with industry-contextualized consulting supported by experts at The Atos Center of Excellence in Advanced Computing.
• Architect: using best-of-breed AI hardware and software including partnerships with Graphcore and NVIDIA, supplemented with Atos’ digital security.
• Accomplish: with end-to-end solution orchestration, accelerating time to AI operationalization and industrialization at optimized cost.

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Artificial intelligence (AI) has proven to have several benefits across different industries and businesses. One sector that has benefitted from the use of AI is the healthcare industry. This sector is always full of patient information, health records, and other important data crucial to patients and hospitals. 

Major problems facing healthcare data are cyberattacks, losing the information, and improper handling, leading to mixing up the records. These mistakes always have devastating effects on the healthcare sector as these medical procedures and other treatments are dependent on these data. In addition, there are other procedures outside the health industry that are dependent on these data. Therefore, properly managing healthcare data is fundamental in the healthcare industry.

The importance of these data has led to the adoption of AI in hospitals to help in the management. Here are some of the applications of AI in optimizing data management: 

• Convenient Data Transmission

Health records are constantly subjected to several transfers among patients, hospitals, remote workers, and other legally entitled parties. When transferring this data, there needs to be a convenient and streamlined way to reach all the desired recipients in time. For example, you may opt to use faxing services, like MyFax, and several others to send the faxes digitally without the need for printing and scanning. 

These modes of data transmission ensure that the records are sent faster and securely. This helps reduce cases of alterations or sending to wrong addresses. With AI, the sharing of information is simplified.

• Data Security 

Several cyberattacks are lodged on these records during these transfers as criminals try to steal or change the records. These attacks are a major concern for the healthcare sector. 

Moreover, even when being stored, patient information is always vulnerable to attacks from hackers. Covering all these attack points manually could be next to impossible, considering the amount of data being held by the information system. 

However, with the application of AI, securing health records against any cyberattacks is promising and fruitful. This is because AI can identify possible entry points for hackers and provide possible solutions for correcting them. Moreover, AI can diagnose the system to identify and correct bugs that would otherwise affect the data management system. 

• Automation Of Data Flow

When patients enter a medical facility, their records are always taken by the hospital from time to time. Each process of their treatment is dependent on the information from the previous step to avoid any cases of errors. The number of patients in the hospital could be challenging to handle if the data flow is done manually. Moreover, handling data manually can lead to confusion.

In contrast, AI automates the data flow from one point to the other, streamlining the whole process. Once the information is entered at the first stage, it becomes accessible for authorized personnel in the hospitals. These records are always entered against a patient’s identity, which means very minimal cases of errors. It also becomes easy for return patients to continue their treatment as the complete information is already recorded in the system. 

• Optimizing Data Storage

Traditionally, health records could be stored in paper works and filed for future references. However, this storage has several disadvantages and limitations. 

First, once a record is added, deleting or changing is difficult unless new paperwork is filed. Secondly, paper is limited in storage, and very little information can be stored on a piece of paper. Finally, once you lost these records, it would be difficult to retrieve them due to a lack of backups.

Fortunately, AI changes all these and optimize data storage in many ways. For example, cloud storage can help hospitals store large quantities of data in only one system. In addition, these cloud services have data backup where you can retrieve any lost information. It’s also possible to change any medical data without altering the other record elements when storing it in a system.

• Data Analysis And Decision Making 

Another important use of AI when handling health data, especially in big data, is analyzing and interpreting the data. With AI, it’s possible to deduce important data points from health records, analyze them, and then present them to understand the chart. This can help in decision-making regarding medical procedures or genetic mapping for patients.

Conclusion 

The healthcare sector is crucial due to the information stored in the systems and their value. Therefore, there’s the need to have an efficient data management system that can ensure information security and streamline any process that depends on these data. 

Manual handling of these data has some limitations, unlike AI, which has several applications in health data management. It can be used in automating data flow and aiding in crucial decision making among many others. It’s safe to say that the application of AI in healthcare will improve. 

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Stellenbosch University’s Techpreneurship Centre’s (SUTPC) second Data Science and Entrepreneurship Bootcamp has opened applications for individuals who would like to gain in-depth programming skills in data science and machine learning.

The SU TPC is a new data science and entrepreneurial development upskilling initiative developed by the SU LaunchLab, the University’s technology and entrepreneurship incubator, in collaboration with the SU School for Data Science and Computational Thinking.

The Centre offers courses that bridge the gap between technology and entrepreneurship by providing candidates with both in-depth programming skills in data science and machine learning and how to use these skills in the industry.

According to the university, 90% of the first session’s participants landed interviews with partner companies following the four week, full-time immersive Bootcamp in February..

The second session will be hosted during the July-August winter holiday.

The bootcamp will have experts from the SU LaunchLab and SU School for Data Science and Computational Thinking who will cover a range of topics such as machine learning techniques, algorithms and models, data exploration and analysis in Python, and soft skills such as SCRUM, Agile and Kanban.

SU TPC Coordinator Daniel Maloba said the programme is open to anyone from all academic levels and backgrounds to apply, and that multidisciplinary applicants and backgrounds are very much encouraged.

“The course is coding-intensive; therefore, we recommend that candidates understand programming principles, but it does not matter if you have no specific programming experience,” said Maloba.

One of the participants of the first programme, Tanya Meyer, said the programme was instrumental in fast-tracking progress for her MEng research project in Machine Learning and Data Science.

“It skyrocketed my Python programming skills set whilst developing my previously non-existent, soft tech skills with lots of guest speaker talks, entrepreneurship sessions and colleague collaboration.”

Applications opened on May 3 and will close on June 18. Applicants are admitted on a rolling basis, so interested candidates are encouraged to apply as soon as possible.

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Machine learning is one of the most promising applications of artificial intelligence in businesses. However, almost nine out of ten projects fail before they even go live. DevOps and MLOps can help.

Allowing failure is one of the most basic prerequisites for innovation. If you are not prepared to fail, you will not be able to create anything new. As the German CTO of a Japanese IT service provider with a strong culture focused on innovation, I myself am deeply convinced of this. However, if only one of ten machine learning projects ever go live, something is definitely wrong. After all, machine learning is one of the central applications of artificial intelligence (AI) and the basis of numerous future technologies such as autonomous driving, smart cities, and the Industrial Internet of Things (IIoT). To advance ML and other AI technologies, we therefore need a new form of collaboration between the development and operation of solutions based on DevOps principles – MLOps for short.

Continuous evaluation

Why MLOps? Because AI is different. In traditional IT, the code determines the behavior of the system. The functionality of the system can be evaluated and tested step by step.

In artificial intelligence applications, on the other hand, data determines the behavior of the system. The difficulty here is that the source data is updated in machine learning and other AI processes. Therefore, we need to continuously monitor the behavior of ML models. This process corresponds to the principle of continuous integration (CI) in traditional software development. Experts in MLOps refer to this as continuous evaluation (CE). In addition to the technological know-how for automating evaluation processes, this has to include close collaboration with the company’s data scientists.

MLOps in practice

A typical use case for this type of MLOps is quality improvement. For example, a Japanese automotive company launched a project in which machine learning is to help improve vehicle quality based on complaint letters in natural language. ML analyzes the meaning of the complaint data in the texts. A particular challenge was to maintain the accuracy of the analyses even when introducing new products. Here, we created a simple and fast way to update new classification models based on “bag-of-words” and “gradient boosting”. The immediate result: In the areas of data processing, design, and deployment, the lead time was reduced by a total of six weeks. Among other things, the high speed of checking complaints had a positive impact here. At the same time, the model is much easier and more economical to maintain – throughout the entire lifecycle.

Similarly, in an AI project of an internationally operating insurance company, it was possible to simplify and automate the development and operation of the solution to such an extent that no operational support from IT is required for operation and continuous evaluation. The data scientists can dedicate their time to their data experiments – without any restrictions stemming from the IT infrastructure.

Reliability of AI

Third example: In an Italian bank, the aim was to detect anomalous behavior in gigantic volumes of financial transactions. Experts see this application as a key benefit of artificial intelligence in digital banking. However, the volumes of data involved make manual training of AI models impossible. By using MLOps, an automated system for training the data models was established. Since every analysis and every prediction is reproducible, this model also fulfills the most important requirement for AI, not only in the financial industry: reliability.

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Artificial intelligence applications support doctors’ diagnostic decisions, automate certain tasks, says Turkish social media expert

ANKARA

A Turkish social media expert said artificial intelligence (AI) brings a new vision to the healthcare sector.

“Artificial intelligence brings revolutionary developments in the field of health, as in all areas of life,” Deniz Unay told Anadolu Agency.

“Machine learning and assisted artificial intelligence have features that can develop an entire health system within the framework of a new vision,” Unay said.

Unay said the AI applications support doctors’ diagnostic decisions and automate certain tasks underlining AI’s rise in healthcare applications.

AI can help better and more accurate detection of symptoms, analyzing the side effects of treatments, and processing large amounts of data produced by healthcare facilities, he said.

However, AI in the medical field is an area that is not yet fully sufficient and is considered to be developed especially for automated robotic surgery applications, he warned.

“Artificial intelligence software and robots, which will assist doctors in many areas, enable faster and safer health services,” he said, adding that AI developments in medicine are planned to be used widely on both general health practices and drugs.

Everyone in the field of health, from specialist doctors to first-aid workers, will start to benefit from artificial intelligence technology soon, he added.

The revenue from AI systems in healthcare worldwide in 2021 exceeded $6.6 billion, according to data from Germany-based statistics company Statista.

The investment amount is expected to increase significantly in line with the increasing desire to use artificial intelligence and robots in the health sector, he said, adding that the increase will lead to easier use of health services by large groups.

“Great importance is given to the use of artificial intelligence in the health sector to spread health services to wider areas. Health services offered in various regions are expected to increase, especially with the technological infrastructure,” he continued.

With the spread of medical instruments, doctors are forced to consider more data, he added.

He went on to say that AI is mostly used in medical imaging and interpretation of radiology.

Underlining that some cancers, such as lung or breast cancer, are tough to identify in images produced by scanners, he said programs can identify abnormalities that cannot be detected with the naked eye to detect early tumors more reliably and target better treatments.

Until recently, AI in healthcare was limited to research or predictive analytics, he said. Much focus is now on developing technologies that can improve robot-assisted surgery, he added.

“There are already significant uses of artificial intelligence that prove how it can improve the techniques used for several years in the field of robotic surgery, especially in the field of microsurgery,” Unay noted.

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Artificial intelligence research is increasingly influencing the use of technology

From our smartphones to cars and homes, artificial intelligence is increasingly touching our every walk of life. Applications of artificial intelligence have already proved disruptive across diverse industries, including manufacturing, healthcare, retail, etc. Considering these progresses, we can say artificial intelligence has evolved much impressively in recent years. Research around this technology has also surged and is impacting the way every individual and business interacts with AI technologies. Analytics Insight has listed 10 must look artificial intelligence research papers so far worth looking at now.

Adam: A Method for Stochastic Optimization

Author(s): Diederik P. Kingma, Jimmy Ba

Adam is an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, and it is computationally efficient, invariant to a diagonal rescaling of the gradients, and has little memory requirements. It is well suited for problems that are large in terms of data and parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. Adam has been adopted as a default method of optimization algorithm for all those millions of neural networks that people train nowadays.

Towards a Human-like Open-Domain Chatbot

Author(s): Daniel Adiwardana, Minh-Thang Luong, David R. So, Jamie Hall, Noah Fiedel, RomalThoppilan, Zi Yang, ApoorvKulshreshtha, Gaurav Nemade, Yifeng Lu, Quoc V. Le

This research paper presents Meena, a multi-turn open-domain chatbot that is trained end-to-end on data mined and filtered from public domain social media conversations. This 2.6B parameter neural network is simply trained to minimize the perplexity of the next token. The researchers also propose a new human evaluation metric to capture key elements of a human-like multi-turn conversation, dubbed Sensibleness and Specificity Average (SSA).

Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Author(s): Sergey Ioffe, Christian Szegedy

Training Deep Neural Networks is complicated by the fact that the distribution of each layer’s inputs changes during training, as the parameters of the previous layers change. The researchers refer to this phenomenon as “internal covariate shift”, and address the problem by normalizing layer inputs. Batch Normalization allows the researchers to use much higher learning rates and be less careful about initialization, and in some cases eliminates the need for Dropout. Applied to a state-of-the-art image classification model, Batch Normalization achieves the same accuracy with 14 times fewer training steps and surpasses the original model by a significant margin.

Large-scale Video Classification with Convolutional Neural Networks

Author(s): Andrej Karpathy, George Toderici, Sanketh Shetty, Thomas Leung, Rahul Sukthankar, and Li Fei-Fei

Convolutional Neural Networks (CNNs) have been considered as a powerful class of models for image recognition problems. Encouraged by these results, the researchers provide an extensive empirical evaluation of CNNs on large-scale video classification. This used a new dataset of 1 million YouTube videos belonging to 487 classes. Provided by IEEE Conference on Computer Vision and Pattern Recognition, this research paper has been cited by 865 times with a HIC score of 24 and a CV of 239.

Beyond Accuracy: Behavioral Testing of NLP models with CheckList

Author(s): Marco Tulio Ribeiro, Tongshuang Wu, Carlos Guestrin, Sameer Singh

Through this research paper around artificial intelligence, the authors point out the inadequacies of existing approaches to evaluating the performance of NLP models. The principles of behavioural testing in software engineering inspired researchers to introduce CheckList, a task-agnostic methodology for testing NLP models. It involves a matrix of general linguistic capabilities and test types that facilitate comprehensive test ideation, as well as a software tool to produce a large and diverse number of test cases quickly.

Generative Adversarial Nets

Author(s): Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, SherjilOzair, Aaron Courville, YoshuaBengio

The authors in this AI research paper propose a new framework for estimating generative models via an adversarial process. They simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake.

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

Author(s): Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun

Advances like SPPnet and Fast R-CNN have minimized the running time of state-of-the-art detection networks, exposing region proposal computation as a bottleneck. To this context, the authors introduce a Region Proposal Network (RPN), a fully convolutional network that simultaneously predicts object bounds and abjectness scores at each position. RPN shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals.

A Review on Multi-Label Learning Algorithms

Author(s): Min-Ling Zhang, Zhi-Hua Zhou

Multi-label learning studies the problem where each example is represented by a single instance while associated with a set of labels simultaneously. While there has been a significant amount of progress made toward the machine learning paradigm in the past decade, this paper aims to provide a timely review on this area with an emphasis on state-of-the-art multi-label learning algorithms.

Neural Machine Translation by Jointly Learning to Align and Translate

Author(s): DzmitryBahdanau, Kyunghyun Cho, YoshuaBengio

Neural machine translation is a recently proposed approach to machine translation. Unlike the traditional statistical machine translation, the neural machine translation aims at building a single neural network that can be jointly tuned to maximize the translation performance. The models proposed recently for neural machine translation often belongs to a family of encoder-decoders. It involves an encoder that encodes a source sentence into a fixed-length vector from which a decoder generates a translation.

Mastering the game of Go with deep neural networks and tree search

Author(s): David Silver, Aja Huang, Chris J. Maddison, Arthur Guez, and others

The paper introduces a new approach to computer Go that uses ‘value networks’ to evaluate board positions and ‘policy networks’ to select moves in the game of Go. Go has been perceived as the most challenging of classic games for artificial intelligence. These deep neural networks are trained by a novel combination of supervised learning from human expert games, and reinforcement learning from games of self-play.

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Artificial intelligence has long been a prevalent talking point in the industry, and as consumers and dealers demand more digital capabilities, lenders have set their sights on implementing technology to enable faster, more efficient processes.

In this special preview session of the Auto Finance Innovation Summit, sponsored by Alfa, Blaise Thomson, director of Alfa IQ, discusses the practical applications for artificial intelligence and machine learning in auto finance.

For more content like this, register for the Auto Finance Innovation Summit, taking place March 16-17. The event will cover topics such as compliance in a digital-first era, finding success in startup partnerships, and scaling digital initiatives for improved customer experience. Find the full agenda and more information at AutoFinanceInnovation.com.

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As big data comes with a handful of benefits, let us get to its bottom and learn all the basics of the technology

Today, organizations of all sizes hold vast amounts of data from all aspects of their operations. Companies use the big data accumulated in their systems to improve operations, provide better customer service, create personalized marketing campaigns based on specific customer preferences and, ultimately, increase profitability. Businesses that utilize big data at their best have the potential to outperform others. As big data comes with a handful of great benefits, let us get to its bottom and learn all the basics of the technology.

What is Big Data?

Big data represents the large, diverse sets of information that grows at an exponential rate. Unfortunately, big data is so large that none of the traditional data management tools can store it or process it efficiently. More than the volume of data, the way organizations utilize data matters. Big data can be analyzed for insights that lead to better decisions and strategic business moves. Humans produce 2 quintillions of data every day. The New York Stock Exchange alone creates about one terabyte of new trade data per day. Social media platforms are also big contributors to the surmounting data. Besides, airlines also generate many petabytes of data. In the early 2000s, Doug Laney, an industry analyst listed three V’s that defines the characteristics of big data.

Volume: The amount of data inflow is exponentially high in business organizations. Data from various sources like business transactions, IoT devices, social media, industrial equipment, videos, etc, contribute to the cause. Since it can’t be stored in a physical space, the storage issue was a big deal earlier. However, thanks to emerging technologies like data lakes and Hadoop, the burden is far eased.

Velocity: Besides the exponential amount of data inflow, the data speed also matters. The datasets are put at a tough spot to be handled in a timely manner. RFID tags, sensors and smart meters are driving the need to deal with these torrents of data in real-time.

Variety: There is no assurance that the data we gather are bound to be the same or fall under a similar category. Data comes in all formats like numeric data, text documents, images, videos, emails, audios, financial transaction, etc.

History of big data 

The first trace of big data is seen way back in 1663 when John Graunt dealt with overwhelming amounts of information while he studied the bubonic plague, which was haunting Europe at the time. Graunt was the first-ever person to use statistical data analysis. Later, in the early 1800s, the field of statistics expanded to include collecting and analyzing data.

The world first saw the problem with the overwhelming of data in 1880. The US Census Bureau announced that they estimate it would take eight years to handle and process the data collected during the census program that year. In 1881, a man from the Bureau named Herman Hollerith invented Hollerith Tabulating Machine that reduced the calculation work.

Throughout the 20th century, data evolved at an unexpected speed. Big data became the core of evolution. Machines for storing information magnetically and scanning patterns in messages, and computers were also created at that time. In 1965, the US government built the first data centre, with the intention of storing millions of fingerprint sets and tax returns.

Types of big data

Data comes in different forms. The fact be said, here are the three main categories it falls into.

Structured data

Data that can be stored, accessed and processed in the form of fixed-format is termed as ‘structured data.’ Since this data comes in a similar format, businesses get the maximum out of it by performing analysis. Various advanced technologies are also invented to extract data-driven decisions from structured data. However, the world is going towards an extent where the creation of structured data is ballooning too much as it has already reached the zettabytes mark.

Unstructured data

Any data that comes in an unknown form or structure falls under unstructured data. Processing unstructured data and analyzing them to get data-driven answers is a challenging task as they are from different categories and outing them together will only make things worse. A heterogeneous data source containing a combination of simple text files, images, videos, etc. is an example of unstructured data.

Semi-structured data

Semi-structured data has both structured and unstructured data in it. We can see semi-structured data as structured in form, but it is actually not defined with table definition in relational DBMS. Web application data is an example of semi-structured data. It has unstructured data like log files, transaction history files, etc. OLTP systems are built to work with structured data wherein data is stored in relations.

Applications of big data

Business organisations are leveraging data to reach their maximum potential. Ever since technology took over big data analysis, business decisions are mostly based on predictive outcomes. Besides, big data is also contributing to personalized customer experiences at high-ends. Some of the important business applications of big data are listed below.

• Product development- Companies avail big data to anticipate customer demands. They build predictive models to see customer preference and provide relevant materials.

• Log analytics- Commercial and open-source log analytics provides the ability to collect, process and analyze massive log data without having to dump the data into relational databases and retrieving it through SQL queries.

• Security compliance- Big data helps you identify patterns in data that indicate fraud and aggregate large volumes of information to make regulatory reporting much faster.

• Recommendation engines- Big data, with its scalability and power to processes massive amounts of both unstructured and structured data enables companies to recommend the best option for customers based on their history.

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Today, in this modern era, there is absolutely no shortage in the implementation of data science to address real-world issues. Industries like healthcare, education, banking and finance, e-commerce to name a few make use of data science extensively. The fact that data is enormous and is increasing exponentially calls for a better implementation of data science.

What are the applications of data science in the real world?

Not one, not two but hundreds and thousands of fields make use of data science in ways beyond imagination. Had it not been for data science, the technological world wouldn’t have enjoyed the ease and comfort that we get to see today. Here are a few of the many applications that have changed everything for the better and made life easier than ever.

Medical industry/healthcare sector

This is surely one of those sectors that one cannot do without. This sector has seen probably the best applications of data science. Developing a drug is not as easy as it sounds. It not only involves deep knowledge but also a lot of patience and time in bringing the drug to the market. Sometimes, a single drug might require over 10 years to be developed. But data science in combination with Machine learning can aid in shortening the entire process. Not just this, with Data Science applications, one can also enable an advanced level of treatment through research in genetics and genomics. Integration of different kinds of data with genomic data in disease research is possible using data science. This further implies deeper understanding of genetic issues in reactions to particular drugs and diseases.

These are just two areas that make use of data science on a regular basis. But, what it is to be noted is that the use of data science in the medical industry is endless and can address a lot of issues with great precision.

Search engines

All the search engines like Google, Yahoo, Bing, etc. make use of data science algorithms to deliver the best result for the searched query and that too in a fraction of seconds.

Recommendations

E-commerce industry has been ruling the world for quite some time now. Also, while looking for a particular product, the list of suggestions, recommendations that follow cannot be unnoticed. This is exactly where data science comes into play. This happens in case of OTT platforms as well. Suggestions and recommendations based on the users history is a common scenario these days. Giants like Amazon, Twitter, Google Play, Netflix, Linkedin, etc. all implement data science.

Autocorrect and autocomplete

The number of people using smartphones needs no special mention. While typing anything, it’s quite a common scenario to witness the word getting corrected automatically. Also, while typing something, the rest of the word or sentence is completed automatically. These predictive search techniques employ the usage of data science that thereby help in saving the user’s time.

Virtual assistants

No wonder, the extent to which people are using virtual assistants like Siri, Alexa, etc. is way beyond what was expected. These assistants make use of speech recognition techniques to perform tasks like sending messages, browsing web, playing music, making calls, etc. There cannot be a better way to proceed with this than by employing data science techniques.

Manufacturing industries

The main objective of any business is to cater to the needs of the customers and also make profits. Similar is the case of manufacturing industries. But, what has served to be a game-changer for them is the advent of data science. Firms are making use of data science to optimize their production, reducing costs and boosting profits. It also allows them to predict potential problems, monitor systems and also analyze data.

Finance

The fact that the risks which banks and financial institutions have to deal with is alarming. Using data science, it is possible to identify, monitor as well as prioritize the risks.

In addition to the above applications, there are so many areas that can be dealt with easily using data science. Simply put, the list is endless when it comes to catering areas using data science. The potential that this technology has is immense and is known to address a lot of real-life problems as well.

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