As machine learning has advanced throughout time, a multitude of sectors has utilized it to innovate and streamline corporate processes. AI and machine learning have been used to improve client experiences in a variety of industries, including healthcare, commerce, industrial, defense, and academia. Machine learning has revolutionized the way tiny data is processed. It has sped up the processing to seconds. 

Professor Gabriel Gomila’s microscopic bioelectrical classification group at Catalonia’s Institute for Bioengineering has been studying a cell type using a sort of microscope called scanning dielectric force volume microscopy. They created this technique in recent years to construct maps of the dielectric constant, an electrical physical parameter. Researchers used this method to speed up the processing of nanoscale information. In this article, let us explore more on how machine learning is used to reduce data time processing.

What can this study on machine learning provide?

When Hans and Zacharias Janssen — a Dutch father and son — built the world’s first microscope in 1590, our interest in what happens at the tiniest levels has resulted in the development of extremely powerful equipment. In 2021, researchers can create precise maps of a variety of physical and chemical characteristics using non-optical approaches like scanning force microscopes, besides optical microscopy technologies that allow us to view microscopic particles in higher definition than it’s ever been. Here’s what this study can provide.

• Because each of the macromolecules that make up cells—lipids, proteins, and nucleic acids—has distinctive dielectric properties, a mapping of this feature is effectively a representation of cell constitution.
• They created an approach that outperforms the existing conventional optical approach, which entails the use of a fluorescent dye that can disturb the cell investigation.
• Their method eliminates the need for any highly destabilizing external agents.
• However, the implementation of this technique necessitates a lengthy post-processing step to translate the observed data points into physical magnitudes, which takes a long time in eukaryotic cells.
• A workstation computer can take months to process a single image. That is because it uses locally recreated geometrical prototypes and calculates the dielectric constant as pixel by pixel.

The researchers used a novel methodology to speed up the microscopic processing of data in this new work, which was a recent issue of the journal Small Methods. Rather than using traditional computational approaches, they applied machine learning models this time. The outcome was stunning after being instructed; the ML algorithm could generate a composition map of the cells with dielectric biochemical within seconds. No foreign compounds were used in the experiment, which is a long-sought objective in cell biology composition characterization. They were able to accomplish these quick results by employing a complex algorithm known as neural networks, which simulate the way human brain neurons function. The key points to be considered are:

• The investigators employed dried-out cells in their concrete evidence work to avoid the tremendous impact of water in dielectric observables owing to its increased dielectric constant.
• They also focused on fixed cells that are in a fluid state. They could accurately map the biomolecules that resulted in eukaryotic cells by comprehensively comparing the dry and liquid versions.
•  Plants, animals, fungi, and other creatures comprise these multi-structured cells. The approach will be used to electrically responsive live cells, such as neurons, where significant electrical impulses happen as its next phase in this project. 

Biomedical Application

The researchers confirmed their observations by comparing them to well-known aspects of cell architecture, like the lipid-rich structure of the cell membrane and the extensive amount of nucleic acids found in the nucleus. They’ve made it possible to analyze enormous numbers of cells in record time thanks to this effort. This research study provides biologists with a powerful tool for doing fundamental research and also prospective practical diagnostics. 

Variations in the cell’s dielectric properties are being investigated as potential indicators for disorders like cancer and neurological diseases. This is the first experiment to produce a microscopic biological composition model from dielectric measurements of dried eukaryotes, which are notoriously difficult to trace owing to their complicated three-dimensional geometry.

Finally, with such progression in the research and experimentation, it is needless to say we are transforming into the new phases of machine learning, with grace, intelligence, and facts. While the work on this nanoscale dielectric constant has just filled few gaps, the future is more dynamic in the aspects of data processing. What took months is now taking seconds, and that is undeniably a revolution of its own. With such applications in the biomedical industry, who would guess it can turn on a real-time diagnosis of many deadly diseases? 

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Business intelligence techniques help understand trends and identify patterns from big data

In the digital world, modern businesses generate big data on daily basis. The recent advancement in technology has opened the door for companies to effectively store and process big data to unleash data-driven decisions and insights. Unfortunately, there is a void between data storage and usage. Many companies, starting from small to big, collect huge data but only use very little of it to make business decisions. In order to mitigate this data gap, business intelligence is being deployed. With the rise in the need for real-time data processing, business intelligence techniques have exploded, making data and analytics accessible for more than just analysts. While business intelligence technology helps decision-makers to analyze data and make informed decisions, top business intelligence techniques drive the initiatives. They help analysts understand trends and aid them to identify patterns in the mountains of big data that businesses build up. The need for more disruption in decision-making and the growing demand for business intelligence has opened the door for a surplus amount of business intelligence techniques. In this Article, Analytics Insight has listed top business intelligence techniques that help companies to get the maximum out of big data.

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Top Business Intelligence Techniques

OLAP

Online Analytical Processing (OLAP) is an important business intelligence technique, that is used to solve analytical problems with different dimensions. A major benefit of using OLAP is that its multi-dimensional nature provides leniency for users to look at data issues from different views. By doing so, they can even identify hidden problems in the process. OLAP is mainly used to complete tasks like budgeting, CRM data analysis, and financial forecasting.

Data Visualization

Data is often stored in form of numbers that are put together as a matrix. But interpreting the matrix to make business decisions is a critical task. A commoner, or even an analyst, can find the progress of data when it is stored as a set. To untangle the knot, data visualization is used. Data visualizations help professionals look at data from more than one dimension and help them make informed decisions. Therefore, visualization of data in charts is an easy and convenient way to understand the stance.

Data Mining

Data mining is the process of analyzing large quantities of data to discover meaningful patterns and rules by automatic or semi-automatic means. In a corporate data warehouse, the amount of data stored is very huge. Finding the actual data that could drive business decisions is quite critical. Therefore, analysts use data mining techniques to unravel the hidden patterns and relationships in data. Knowledge discovery in databases is the whole process of using the database along with any required selection, processing, sub-sampling, choosing the proper way for data transformation.

Reporting

Reporting in business intelligence represents the whole process of designing, scheduling, generating the performance, sales, reconciliation, and saving the content. It helps companies to effectively gather and present information to stand by the management, planning, and decision-making process. Business leaders get to view the reports at daily, weekly, or monthly intervals as per their needs.

Analytics

Analytics in Business Intelligence defines the study of data to extract effective decisions and figure out the trends. Analytics is famous among business companies as it lets analysts and business leaders deeply understand the data they have and drive value from it. Many business perspectives, from marketing to call centers to use analytics in different forms. For example, call centers leverage speech analytics to monitor customer sentiments and improve the way answers are presented.

Multi-Cloud

Following the outbreak of the pandemic and the lockdown that came to effect, companies across the globe started moving their routine working into cloud modes. The rise of cloud technology has greatly impacted many businesses. However, even after the restrictions are lifted, companies still prefer to work over the cloud because of its lenient accessibility and easy-to-use attributes. Moving a step forward, even Research & Development initiatives are being moved to the cloud, thanks to its cost-saving and easy-to-use nature.

ETL

Extraction-Transaction-Loading (ETL) is a unique business intelligence technique that takes care of the overall data processing routine. It extracts data from storage, transforms it into the processor, and loads it into the business intelligence system. They are mainly used as a transaction tool that transforms data from various sources to data warehouses. ETL also moderates the data to address the need of the company. It improves the quality level by loading it into the end targets such as databases or data warehouses.

Statistical Analysis

Statistical analysis uses mathematical techniques to create the significance and reliability of observed relations. It also grasps the change of behavior in people that are visible in data with its distribution analysis and confidence intervals. Post data mining, analysts carry out statistical analysis to devise and get effective answers.

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The chances of partiality, in the process of data analysis, are extreme and it can vary from how a question is hypothesized and explored to how the data is sampled and organized. Bias can be introduced at any stage from defining and capturing the data set to run the analytics or AI or ML system. Hariharan Kolam, CEO, and founder of Findem, a people intelligence company stated in an interview, “Avoiding bias starts by recognizing that data bias exists, both in the data itself and in the people analyzing or using it,” Actually it is kind of impossible to be completely unbiased and biasedness is an existing element of human nature.

The Human Catalyst

Bias in data analysis can come from human sources because they use unrepresentative data sets, leading questions in surveys, and biased reporting and measurements. Often bias goes unnoticed until some decision is made based on the data, such as building a predictive model that turns out to be wrong. Although data scientists can never completely eliminate bias in data analysis, they can take countermeasures to look for it and mitigate issues in practice.

The Social Catalyst

Bias is also a moving target as societal definitions of fairness evolve. Reuters has reported an instance when the International Baccalaureate program had to cancel its annual exams for high school students in May due to COVID-19. Instead of using exams to grade students, the IB program used an algorithm to assign grades that were substantially lower than many students and their teachers expected.

Biasedness from Existing Data

Amazon’s previous recruiting tools showed preference toward men, who were more representative of their existing staff. The algorithms didn’t explicitly know or look at the gender of applicants, but they ended up being biased by other things they looked at that were indirectly linked to gender, such as sports, social activities, and adjectives used to describe accomplishments. In essence, the AI was picking up on these subtle differences and trying to find recruits that matched what they internally identified as successful.

Under-representing populations

Another big source of bias in data analysis can occur when certain populations are under-represented in the data. This kind of bias has had a tragic impact in medicine by failing to highlight important differences in heart disease symptoms between men and women, said Carlos Melendez, COO, and co-founder of Wovenware, a Puerto Rico-based nearshore services provider. Bias shows up in the form of gender, racial or economic status differences. It appears when data that trains algorithms do not account for the many factors that go into decision-making.

Cognitive biases

Cognitive bias leads to statistical bias, such as sampling or selection bias. Often analysis is conducted on available data or found in data that is stitched together instead of carefully constructed data sets. Both the original collection of the data and an analyst’s choice of what data to include or exclude creates sample bias. Selection bias occurs when the sample data that is gathered isn’t representative of the true future population of cases that the model will see. In times like this, it’s useful to move from static facts to event-based data sources that allow data to update over time to more accurately reflect the world we live in. This can include moving to dynamic dashboards and machine learning models that can be monitored and measured over time.

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Often the words such as data, information, and insight are used interchangeably, but these words are not similar, they have different meanings. Understanding those differences can help you tailor your program to benefit your business.

What is data? How does that turn into information and what kind of insights does it yield?

These differences are confusing right! But the distinctions are the simple ones you see how they work together. Let’s see what their definitions actually mean.

What is Data?

Data is raw with unprocessed facts that we capture according to some agreed standards. Data can be in the form of numbers, images, audio, transcriptions, etc. While working on analytics projects, the first task is to go through the client’s data structure or even normalize it.

What is Information?

Information is a collection of data points that we can use to understand something that needs to be measured. It is data that is processed, aggregated into a manner humans could read and understand. The common ways to present information are through data visualization, reports, and dashboards.

What is Insight?

Insights are gained by analyzing data and information to understand and draw conclusions that can benefit the organization while decision making.  These are the final outputs of the data in a usable form.

How they work together

The data, information, and insight work together to yield a complete analytics package. In an organization the data is collected in a raw manner, later it is converted into a readable format that is the information, this information is later processed to insights which are highly valuable in making big decisions of the firm. If one is absent or inconsistent that means it can affect the overall functioning of the company.

Impact of Insight on Business Decisions

Data-driven marketing is the latest word that is heard everywhere, but insights are more powerful than that. Most of the brands already have data but couldn’t find the right insights. Insight can help in talking about the value of your business more than data does.

Any business decisions depend on insights not the raw form of data. These insights can make a wide difference in the strategies and performance of businesses. Great insights can help you overcome any kind of market problems such as competitors, and consumer behavior.  All kinds of evolutionary changes can occur only with the insights that can help decide better in the market.

According to Global Web Index, insights are four kinds, they are Human, Universal, True to Brand, and Targeted. In other words, a great insight reveals a story that is unique to the brands. Insights help the company and customers by coming up with new and fresh ideas.

The road to getting out valuable insights is through adequate research. Here are a few steps that can help you put information for the benefit of your business.

Goal: Set the right goal and objectives for the organization to achieve through the research. This can ensure you understand better what the company actually needs.

Collect: Once you’re done researching, the next step comes a collection of the data, whether through quantitative or qualitative.

Analyze: Here the collected data is analyzed to get valued insights. It can be about your customers, employees, products, and satisfaction.

Action: The last and final step comes where the insights are gained from analysis and these are put into action by the businesses which can help in a better decision-making process.

Now, hope you got what is the difference between data vs information vs insights and also understood how insights can impact business decisions.

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Artificial intelligence is a data hog; effectively building and deploying AI and machine learning systems require large data sets. “The development of a machine learning algorithm depends on large volumes of data, from which the learning process draws many entities, relationships, and clusters,” says Philip Russom of TDWI. “To broaden and enrich the correlations made by the algorithm, machine learning needs data from diverse sources, in diverse formats, about diverse business processes.”

At the same time, AI itself can be instrumental in identifying and preparing the data needed to increase the value of AI-driven or analytics-driven systems. Companies have needed cadres of data scientists or high-level analysts to put AI and machine learning algorithms in place, AI itself may ultimately help automate such roles to a large degree.

“A new generation of enterprise analytics is emerging, and it incorporates some degree of both automation and contextual information,” according to Tom Davenport and Joey Fitts, writing in Harvard Business Review. AI-enhanced analytics systems “can prepare insights and recommendations that can be delivered directly to decision makers without requiring an analyst to prepare them in advance.”

Business intelligence analysts and quantitative professionals “will still have important tasks to perform, but many will no longer have to provide support and training to amateur data users,” according to Davenport and Fitts. “Small to mid-size businesses that haven’t been able to afford data scientists will be able to analyze their own data with higher precision and clearer insight. All that will matter to organizations’ analytical prowess will be a cultural appetite for data, a set of transactional systems that generate data to be analyzed, and a willingness to invest in and deploy these new technologies.”

Of course, the ability to effectively automate data science tasks depends on industry and circumstances. As Matt Przybyla, senior data scientist and author of Toward Data Science, points out, there often still needs to be trained human guidance to AI and machine learning initiatives, especially if the output is critical to the tasks at hand. “Sure, use an automated data science platform if you already have a data analyst on your team. Or, use the automated solution for predictions that are not harmful if incorrect. Categorizing clothes incorrectly is not the worst thing that can happen, but when you are in the health or finance industry and you classify a disease or large sums of money incorrectly, the harm is undeniable.”

While automated AI data science tools or platforms may be easy and powerful, they also may leave businesses with unanswered questions. “Imagine you are not a data scientist and have not had an academic background in the various types of machine learning algorithms,” Przybyla continues. “You will have to explain these platform model results and implement the suggestions or predictions with regards to your company’s integrations, which could prove to be time-consuming and difficult.”

The post The Data Paradox: Artificial Intelligence Needs Data; Data Needs AI appeared first on Artificial Intelligence.

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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No wonder, data science has emerged out to be the most sought-after profession. Obtaining insights from data, as data science is rightly defined, has proven to be no less than a blessing in almost every sector that one can think of. Making the best of data is what data scientists are expected to do. Data visualization is thus a critical aspect of data science and when done well can yield the desired results. That being said, a question that is seeking answers is – how to achieve efficient data visualization so that the organization is in a position to make better decisions? Well, data visualization tools to the rescue it is!

In order to make the whole data visualization process smooth and to achieve valuable results, having the right data visualization tools that are worth relying on – is the need of the hour. Here is a list of top data visualization tools for 2021 that you wouldn’t want to miss out on.

Tableau

Tableau is one of the most widely used data visualization tools What sets it apart from the rest is its ability to manage the data using the combination of data visualization and data analytics tools. From a simple chart to creative and interactive visualizations, you can do it all using Tableau. One of the many remarkable features of this tool is that the data scientists do not have to write custom code in this tool. Additionally, the tasks are completed fast and with ease because of the drag and drop feature supported by this tool. All in all, Tableau is interactive software that is compatible with a lot of data sources.

Sisense

If looking for a data visualization tool that is used to create dashboards and visualise large amounts of data, then Sisense is the one for you! From health, manufacturing to social media marketing, Sisense has proved to be beneficial. The best part about Sisense is that the dashboard can be created in the way the user wants to according to their needs.

PowerBI

This is yet another interactive data visualization tool that helps in converting data from various data sources into interactive dashboards and reports. In addition to providing real-time updates on the dashboard, it also provides a secure and reliable connection to your data sources in the cloud or on-premise. Enterprise data analytics as well as self-service is something that you get on a single pl PowerBI, being available for both mobile and desktop versions has, without a doubt, benefitted many. Why PowerBI gets all the attention is because even non-data scientists can easily create machine learning models.

E charts

E charts is one of the most sought after enterprise-level chart data visualization tool. E charts are compatible with a majority of browsers, runs smoothly on various platforms and are referred to as a pure JavaScript chart No matter what size the device is, charts would be available. This data visualization tool being absolutely free to use provides a framework for the rapid construction of web-based visualizations and boasts of multidimensional data analysis.

DataWrapper

DataWrapper is an excellent data visualization tool for creating charts, maps and tables. With this, you can create almost any type of chart, customizable maps and also responsive tables. Additionally, printing and sharing the charts is not at all an issue to be bothered about. From students to exerts, everyone can make use of DataWrapp This data visualization tool gives away the message that charts and graphs stand the potential to look great even without coding or any design skills. The free version of this tool has many features that are definitely worth giving a try.

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Data annotation is the process of labeling data to make it easy for machines to access it.

Why did humans start making machines? The immediate answer would be to make a mechanical and computerised model that works like humans. Yes, humans wanted machines to imitate whatever they do. The purpose of artificial intelligence is no different. If we look at the things that artificial intelligence-powered machines are doing for us today, most of them try to minimize our work by taking over the routine, time-consuming jobs. In order to make machine learning models advanced, they should be trained with datasets. That is where data annotation makes its debut.

Artificial intelligence and machine learning have changed the way we live. Starting from product recommendations and search engine results to self-driving cars and autonomous drones, everything is powered by artificial intelligence. However, this would be impossible without data annotation. Today, we are building a future where automation and autonomous-powered working is everything. To create such automated applications and machines, the datasets need to be trained properly. However, since the datasets are very huge and the human mode of training won’t help, artificial intelligence companies use data annotation to label the content and use it for machine learning models’ training. By implying data annotation, machine learning models get to be fed with well trained and labelled datasets. In this article, we take you through the basics of data annotation, explain its types, and list the use cases.

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What is data annotation?

In simple terms, data annotation is the process of labelling data to make it easy for machines to access it. Data annotation is specifically important for supervised machine learning as the models rely on labelled datasets to process, understand, and learn from input patterns to arrive at desired outputs.

Data comes in various forms like text, image, video, documents, etc. But such diverse types can’t be fed into a machine learning model without segregating and sorting it according to their varieties. Therefore, data annotation acts as an intermediary tool to mitigate training issues. By using data annotation, companies can train their machine learning models with the right tools and techniques. In a machine learning model, data annotation takes place before the information gets fed to a system. The process is similar to how we teach kids. For example, in order to teach them about a ball, we either show the picture or a real ball. Similarly, data annotation labels the object as ‘ball’ in the dataset and feeds it to the machine learning model. Some of the uses of data annotation are listed as follows,

• While using annotated data to train a machine learning model, the accuracy of its mechanism will be higher.
• Machine learning models trained with annotated data leverages a seamless experience for end-users.
• Even virtual assistants or chatbots use the trained dataset to answer users’ queries.
• In search engine recommendation, a machine learning model trained with annotated data provides comprehensive results.
• Besides helping on large scale, data annotation can help with localized labelling based on geolocations. It locally labels information, images, and other content.

What is human-annotated data?

Despite the sophistication technology is enjoying, they will be nothing without humans help. It is no different while training a machine learning model. Human help big time in making machines learn about the way the world functions. Therefore, data annotation loops humans in the training process to improve performance.

But why is human-annotated data important in machine learning? Humans have a special talent called judgement and hunch, which machines don’t possess. The recent developments in the technology industry are pointing to developing machines that can think like humans. That is where human-annotated data comes into the picture. Human-annotated data introduces subjectivity, intent, and clarification, making machines determine whether a search result is relevant.

Types of data annotation

Text annotation: Today, most companies are moving to automatic models, especially, text-based to power their working system. Owing to the increasing adoption, text annotation has become the centre of attention recently. Text annotation includes a wide variety of annotations like sentiment, intent, and query.

Video annotation: When it comes to video annotation, humans are seen as a good source to train the datasets. For example, companies use human assistance in search engine results. They collect the input from many people in terms of their preferences and promote similar content to others.

Image annotation: Image annotation is very important in training a dataset. Many technologies including computer vision, robotic vision, facial recognition, etc. rely on image annotation to label and interpret image forms. To train the models with image data, metadata must be assigned to the images in form of identifiers, captions, or keywords.

Audio annotation: Audio annotation is quite different from the other types of annotation. Unlike others, audio annotation takes an in-depth step to transcribe and time-stamp the speech data, including transcription of specific pronunciation and intonation.

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Gary M. Shiffman, Ph.D. is the Founder and CEO of Giant Oak and Co-Founder and CEO of Consilient. He is the creator of GOST and Dozer. 

According to my company’s research, a full 25% of PPP fraud casesbrought by the Department of Justice could have been easily prevented. The fraud is so obviously clumsy that it is embarrassing to whomever approved the loans.

Decision-makers consume a lot of data. The world is awash in data, and data is there to be used — or not used — like at no other time. As a result, risk measurement systems today perform far better than the systems of even just three years ago. But what if yesterday’s performance was poor, in absolute terms? Can improvement over last year justify missing obviously blatant threats to your organization? I want to focus this article on obvious but undiscovered risk and the data not used in analytics.

Artificial Intelligence and Machine Learning (AI/ML) enable qualitative changes to risk management, which deliver large step increases in quantitative performance, leaving a gaping question. If asked, “How much improvement is enough?,” then “any improvement” might sufficiently answer the question. But “any” feels like an inattentive answer. The very existence of data demands decisions most executives have not been trained to make: What data can be excluded from the analysis? And yet these decisions on what data to use and exclude require great care, like receiving a double-edged razor in an unprotected hand. 

About a decade ago, when “big data” was the buzz, I remember joining industry discussions as executives rushed to formulate initiatives and responses. Leaders would often clench their fists while arguing that there is such a thing as too much data. 

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Too much data overwhelms humans, so the reaction of the 2010s made sense at the time. However, data also creates more accurate ML models. Amazon’s market capitalization in 2011 was $78 billion and grew to an astounding $1.7 trillion by 2021; the growth came from understanding the value of more data, not less. Risk professionals in 2021 similarly understand that happiness with less data can cut a career short.

Machine Learning tools are available, posing a new “big data” challenge for the 2020s: missing threats because of data not used. Market leaders have moved from fearing too much data to too little data in analytics. 

To limit the use of data in risk discovery leaves threats undiscovered, exposing decision-makers to ex post facto criticism: “How did you miss that? It was so obvious!” The data is free and publicly available. Read news reports of PPP fraud cases, for example. People who did not have companies or employees received large amounts in Covid-19 relief dollars. “How did they miss that?” you might think. The bank and government screeners used too little data and missed obvious information. They erred in selecting the data not used. 

Critics of using more data, even in 2021, rightly complain that added data still creates too many “false positives,” especially in unstructured data. Like oiling a blade in a sawmill, data helps for a while but eventually gums up the moving parts. Data has a history of gumming up the risk discovery process. 

To prevent these big-data frustrations in the past, data-as-a-service vendors emerged. Firms in these markets use hundreds or thousands of people to filter data, creating highly curated data sets, and they sell this high-cost data at a high price to risk professionals in many industries, financial institutions and law enforcement agencies.  

Unfortunately, human-based filtering absolutely separates risk management professionals from massive amounts of valuable data. For example, financial services firms spend $180.9 billion on financial crime compliance worldwide, according to a 2020 LexisNexis study, and yet financial institutions capture less than 1% of the criminal proceeds. Fifty-seven percent of that $180.9 billion is spent on labor. The large effort masks the lack of progress.

To protect oneself from the double-edged sword of data availability in 2021, use more data in risk measurement to decrease the universe of unused data and use AI/ML to decrease the false positives challenges which vex human screeners and investigators. This is the balance to keep in mind: Use more data and reduce errors by replacing manual human curation with machine learning. 

AI/ML can solve much of the double-edged nature of data abundance. Technology delivers effectiveness with efficiency. The key is reindexing the publicly available information on the internet, a task too massive for a human but easy enough for well-trained ML models, and then to perform entity resolution (ER) on that massive mess of unstructured data.

In addition, organizational changes can be implemented — for example, routine testing of ML model output with measurements of efficiency and effectiveness, such as precision and recall against a known set of test data. To do this, organizations may want to consider training management to better understand the measurement of ML systems. Including someone fluent in AI/ML performance on your company’s board also makes sense in today’s world of important data exclusion decisions. 

If this technology exists, why is it not pervasive across every bank in the U.S.? The answer is that it takes time for the widespread adoption of new technology. There is no villain. There is no government branch or bank CEO fighting adamantly against it — in fact, joint regulatory agencies, FinCEN and the Bank Policy Institute are encouraging it. AI/ML, which is already so pervasive in our cell phones and homes, will soon start impacting the risk world, such as AML/CFT and Customer Due Diligence.

Decision-makers consume a lot of data but need the ability to use more. Entity resolution across massive public and unstructured data will soon be a part of every risk management organization. The most successful risk management managers of the 2020s will find innovative ways to utilize more data, protect privacy and improve both effectiveness and efficiency. 

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As machine learning has progressed over the years, several industries adopted this technology to innovate and simplify business processes. Many industrial sectors like healthcare, retail, manufacturing, defense, and education have taken up AI and machine learning to enhance customer experiences.

Machine learning has worked wonders for microscopic data processing. It has reduced the processing time from months to seconds.

The nanoscale bioelectrical characterization group of the Institute for Bioengineering of Catalonia, led by Professor Gabriel Gomila, has been analyzing a type of cell using a special kind of microscopy called scanning dielectric force volume microscopy. This technique is developed in recent years to create maps of an electrical physical property called the dielectric constant.

Researchers have chosen this technique to reduce the microscopic data processing time. To increase efficiency, they are using machine learning algorithms instead of traditional computing methods, which took months to deliver accurate results earlier. The machine-learning algorithm can build the dielectrically composition map in just seconds. It functions with the help of deep neural networks that mimics the functions of a human brain.

Researchers have certified their findings by analyzing them with different facts about cell composition like the lipid nature of the cell membrane, the nucleic acids present in the nucleus, and others. This recent development opens unprecedented opportunities to study large quantities of cells in a short amount of time.

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