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Artificial Intelligence Restores Mutilated Rembrandt Painting The Night Watch – ARTnews

One of Rembrandts finest works, Militia Company of District II under the Command of Captain Frans Banninck Cocq (better known as The Night Watch) from 1642, is a prime representation of Dutch Golden Age painting. But the painting was greatly disfigured after the artists death, when it was moved from its original location at the Arquebusiers Guild Hall to Amsterdams City Hall in 1715. City officials wanted to place it in a gallery between two doors, but the painting was too big to fit. Instead of finding another location, they cut large panels from the sides as well as some sections from the top and bottom. The fragments were lost after removal.

Now, centuries later, the painting has been made complete through the use of artificial intelligence. The Rijksmuseum in the Netherlands has owned The Night Watch since it opened in 1885 and considers it one of the best-known paintings in its collection. In 2019, the museum embarked on a multi-year, multi-million-dollar restoration project, referred to as Operation Night Watch, to recover the painting. The effort marks the 26th restoration of the work over the span of its history.

In the beginning, restoring The Night Watch to its original size hadnt been considered until the eminent Rembrandt scholar Erst van der Wetering suggested it in a letter to the museum, noting that the composition would change dramatically. The museum tapped its senior scientist, Rob Erdmann, to head the effort using three primary tools: the remaining preserved section of the original painting, a 17th-century copy of the original painting attributed to Gerrit Lundens that had been made before the cuts, and AI technology.

About the decision to use AI to reconstruct the missing pieces instead of commissioning an artist to repaint the work, Erdmann told ARTnews, Theres nothing wrong with having an artist recreate [the missing pieces] by looking at the small copy, but then wed see the hand of the artist there. Instead, we wanted to see if we could do this without the hand of an artist. That meant turning to artificial intelligence.

AI was used to solve a set of specific problems, the first of which was that the copy made by Lundens is one-fifth the size of the original, which measures almost 12 feet in length. The other issue was that Lundens painted in a different style than Rembrandt, which raised the question of how the missing pieces could be restored to an approximation of how Rembrandt would have painted them. Erdmann created three separate neural networks, a type of machine learning technology that trains computers to learn how to do specific tasks to address the problems.

The first [neural network] was responsible for identifying shared details. It found more than 10,000 details in common between The Night Watch and Lundenss copy. For the second, Erdmann said, Once you have all of these details, everything had to be warped into place, essentially by tinkering with the pieces by scoot[ing one part] a little bit to the left and making another section of the painting 2 percent bigger, and rotat[ing another] by four degrees. This way all the details would be perfectly aligned to serve as inputs to the third and final stage. Thats when we sent the third neural network to art school.

Erdmann made a test for the neural network, similar to flashcards, by splitting up the painting into thousands of tiles and placing matching tiles from both the original and the copy side-by-side. The AI then had to create an approximation of those tiles in the style of Rembrandt. Erdmann graded the approximationsand if it painted in the style of Lundens, it failed. After the program ran millions of times, the AI was ready to reproduce tiles from the Lundens copy in the style of Rembrandt.

The AIs reproduction was printed onto canvas and lightly varnished, and then the reproduced panels were attached to the frame of The Night Watch over top the fragmented original. The reconstructed panels do not touch Rembrandts original painting and will be taken down in three months out of respect for the Old Master. It already felt to me like it was quite bold to put these computer reconstructions next to Rembrandt, Erdmann said.

As for the original painting by Rembrandt, it may receive conservation treatment depending on the conclusions of the research being conducted as part of Operation Night Watch. The painting has sustained damaged that may warrant additional interventions. In 1975, the painting was slashed several times, and, in 1990, it was splashed with acid.

The reconstructed painting went on view at the Rijksmuseum on Wednesday and will remain into September.

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Banking on AI: The Opportunities and Limitations of Artificial Intelligence in the Fight Against Financial Crime and Money Laundering – International…

By Justin Bercich, Head of AI, Lucinity

Financial crime has thrived during the pandemic. It seems obvious that the increase in digital banking, as people were forced to stay inside for months on end, would correlate with a sharp rise in money laundering (ML) and other nefarious activity, as criminals exploited new attack surfaces and the global uncertainty caused by the pandemic.

But, when you consider that fines for money-laundering violations have catapulted by 80% since 2019, you begin to realise just how serious and widespread the situation is. Consequently, the US Government is making strides to re-write its anti-money laundering (AML) rulebook, having enacted its first major piece of AML legislation since 2004 earlier this year.New secretary of the treasury Janet Yellen, with her decades of financial regulation experience, adds further credence to the fact the AML sector is primed for more significant reform in the coming months and years.

Yet, despite the positives and promises of technological innovation in the AML space, there still remains great debate and scepticism about the ethics and viability of incorporating artificial intelligence (AI) and machine learning deeply into banks and the broader financial ecosystem. What are the opportunities and limitations of AI, and how can we ensure its application remains ethical for all?

Human AI A banks newest investigator

While AI isnt a new asset in the fight against financial crime, Human AI is a ground-breaking application that has the potential to drastically improve compliance programs among forward-thinking banks. Human AI is all about bringing together the best tools and capabilities of people and machines. Together, human and machine help one another unearth important insights and intelligence at the exact point when key decisions need to be made forming the perfect money laundering front-line investigator and drastically improve productivity in AML.

The most powerful aspect of Human AI is that its a self-fulfilling cycle. Insights are fed back into the machine learning model, so that both human and technology improve. After all, the more the technology improves, the more the human trusts it. As we gain trust in technology we feed more relevant human-led insights back into the machine, ultimately resulting in a flowing stream of synergies that strengthens the Human-AI nexus, therefore empowering users and improving our collective defenses against financial crime. That is Human AI.

An example of this in action is Graph Data Science (GDS) an approach that is capable of finding hidden relationships in financial transaction networks. The objective of money launderers is to hide in plain sight, while AML systems are trying to uncover the hidden connections between a seemingly normal person/entity and a nefarious criminal network. GDS helps uncover these links, instead of relying on a human to manually trawl through a jungle of isolated spreadsheets with thousands of fields.

Human AI brings us all together

Whats more, a better understanding of AI doesnt just benefit the banks and financial institutions wielding its power on the frontline, it also strengthens the relationship between bank and regulator. Regulatorus need to understand why a decision has been made by AI in order to determine its efficacy and with Human AI becoming more accessible and transparent (and, therefore, human), banks can ensure machine-powered decisions are repeatable, understandable, and explainable.

This is otherwise known as Explainable AI, meaning investigators, customers, or any user of an AI system have the ability to see and interact with data that is logical, explainable and human. Not only does this help build a bridge of trust between humans and machines, but also between banks and regulators, ultimately leading to better systems of learning that help improve one another over time.

This collaborative attitude should also be extended to the regulatory sandbox, a virtual playground where fintechs and banks can test innovative AML solutions in a realistic and controlled environment overseen by the regulators. This prevents brands from rushing new products into the market without the proper due diligence and regulatory frameworks in place.

Known as Sandbox 2.0, this approach represents the future of policy making, giving fintechs the autonomy to trial cutting-edge Human AI solutions that tick all the regulatory boxes, and ultimately result in more sophisticated and effective weapons in the fight against financial crime and money laundering.

Overhyped or underused? The limitations of AI

Anti-money laundering technology has, in many ways, been our last line of defence against financial crime in recent years a dam that is ready to burst at any moment. Banks and regulators are desperately trying to keep pace with the increasing sophistication of financial criminals and money launderers. New methods for concealing illicit activity come to surface every month, and technological innovation is struggling to keep up.

This is compounded by our need to react quicker than ever before to new threats. This leaves almost no room for error, and often not enough time to exercise due diligence and ethical considerations. Too often, new AI and machine learning technologies are prematurely hurried out into the market, almost like rushing soldiers to the front line without proper training.

Increasing scepticism around AI is understandable, given the marketing bonanza of AI as a panacea to growth. Banks that respect the opportunities and limitations of AI will use the technology to focus more on efficiency gains and optimization, allowing AI algorithms to learn and grow organically, before looking to extract deeper intelligence used to driverevenue growth. It is a wider business lesson that can easily be applied to AI adoption: banks must learn their environment, capabilities, and limitations beforemastering a task.

What banks must also remember is that AI experimentation comes withdiminishing returns. They should focus on executing strategic, production-readyAI micro-projects in parallel with human teams to deliver actionable insights and value. At the same time, this technology can be trained to learn from interactions with their human colleagues.

But technology cant triumph alone

Application of AI and machine learning is now being used across most major aspects of the financial ecosystem, areas that have traditionally been people-focussed, such as issuing new products, performing compliance functions, and customer service. This requires an augmentation of thinking, where human and AI work alongside one another to achieve a common goal, rather than just throwing an algorithm at the problem.

But of course, we must recognise that this technology cant win the fight in isolation. This isnt the time to keep our cards close to our chests the benefits of AI against financial crime and ML must be made accessible to everyone affected.

Data must be tracked across all vendors and along the entire supply chain, from payments processors to direct integrations. And, the AI technology being used to enable near-real time information sharing must go both ways: from bank to regulator and back again. Only then suspicious activity can be analysed effectively, meaning everyone can trust the success of AI.

Over the next few years, the potential of Human AI will be brought to life. Building trust between one another is crucial to addressing blackbox concerns, along with consistent training of AI and machines to become more human in their output, which will ultimately make all our lives more fulfilling.

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Acceleration of Artificial Intelligence in the Healthcare Industry – Analytics Insight

Healthcare Industry Leverages Artificial Intelligence

With the continuous evolvement of Artificial Intelligence, the world is being benefited to the utmost level, as the applications of Artificial Intelligence is unremitting. This technology can be operated in any sector of industry, including the healthcare industry.The advancement of technology and the AI (Artificial Intelligence), as a part of modern technology have resulted in the formation of a digital macrocosm. Artificial Intelligence, to be precise, is a programming where, there is a duplication of human intelligence incorporated in the machines and it works and acts like a human.

Artificial Intelligence is transmuting the system and methods of the healthcare industries. Artificial Intelligence and healthcare, were found together over half a century. The healthcare industries use Natural Language Process to categorise certain data patterns.Natural Language Process is the process of giving a computer, the ability to understand text and spoken words just like the same way human beings can. In the healthcare sector, it gives the effect to the clinical decision support. The natural language process uses algorithms that can mimic like human responses to conversation and queries. This NLP, just like a human can take the form of simulated mediator using algorithms to connect to the health plan members.

Artificial Intelligence can be used by the clinical trials, to hasten the searches and validation of medical coding. This can help reduce the time to start, improve and accomplish clinical trainings. In simple words medical coding is transmitting medial data about a patient into alphanumeric code.

Clinical Decisions All the healthcare sectors are overwhelmed with gigantic volumes of growing responsibility and health data. Machine learning technologies as a part of Artificial Intelligence, can be applied to the electronic health records, with the help of this the clinical professionals can hunt for proper, error-free, confirmation-based statistics that has been cured by medical professionals. Further, Natural Language Process just like the chatbots, can be used for everyday conversation where it allows the users to type questions as if they are questioning a medical professional and receive fast and unfailing answers.

Health Equity Artificial Intelligence and Machine learning algorithms can be used to reduce bias in this sector by promoting diversities and transparency in data to help in the improvement of health equity.

Medication Detection Artificial Intelligence can be used by the pharma companies, to deal with drug discoveries and thus helping in reducing the time to determine and taking drugs all the way to the market. Machine Learning and Big Data as a part of Artificial Intelligence do have the great prospective to cut down the value of new medications.

Pain Management With the help of Artificial Intelligence and by creating replicated veracities the patients can be easily distracted from their existing cause of pain. Not only this, the AI can also be incorporated for the for the help of narcotic crisis.

System Networked Infirmaries Unlike now, one big hospital curing all kind of diseases can be divided into smaller pivots and spokes, where all these small and big clinics will be connected to a single digital framework. With the help of AI, it can be easy to spot patients who are at risk of deterioration.

Medical Images and Diagnosis The Artificial Intelligence alongside medical coding can go through the images and X-rays of the body to identify the system of the diseases that is to be treated. Further Artificial Intelligence technology with the help of electronic health records is used in healthcare industry that allows the cardiologists to recognize critical cases first and give diagnosis with accuracy and potentially avoiding errors.

Health Record Analysing With the advance of Artificial Intelligence, now it is easy for the patients as well as doctors to collect everyday health data. All the smart watches that help to calculate heart rates are the best example of this technology.

This is just the beginning of Artificial Intelligence in the healthcare industry. Making a start from Natural Language process, Algorithms and medical coding, imaging and diagnosis, there is a long way for the Artificial Intelligence to be capable of innumerable activities and to help medical professionals in making superior decisions. The healthcare industry is now focusing on technological innovation in serving to its patients. The Artificial Intelligence have highly transmuted the healthcare industry, thus resulting in development in patient care.

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Data Privacy Is Key to Enabling the Medical Community to Leverage Artificial Intelligence to Its Full Potential – Bio-IT World

Contributed Commentary by Mona G. Flores, MD

June 24, 2021 | If theres anything the global pandemic has taught healthcare providers, it is the importance of timely and accurate data analysis and being ready to act on it. Yet these same organizations must move within the bounds of patient rights regulations, both existing and emerging, making it harder to access the data needed for building relevant artificial intelligence (AI) models.

One way to get around this constraint is de-identify the data before curating it into one centralized location where it can be used for AI model training.

An alternative option would be to keep the data where it originated and learn from this data in a distributed fashion without the need for de-identification. New companies are being created to do this, such as US startup Rhino Health. It recently raised $5 million (US) to connect hospitals with large databases from diverse patient populations to train and validate AI models using Federated Learning while ensuring privacy.

Other companies are following suit. This is hardly surprising considering that the global market for big data analytics in health care was valued at $16.87 billion in 2017 and is projected to reach $67.82 billion by 2025, according to a report from Allied Market Research.

Federated Learning Entering the Mainstream

AI already has led to disruptive innovations in radiology, pathology, genomics, and other fields. To expand upon these innovations and meet the challenge of providing robust AI models while ensuring patient privacy, more healthcare organizations are turning to federated learning.

With Federated Learning, Institutions hide their data and seek the knowledge. Federated Learning brings the AI model to the local data, trains the model in a distributed fashion, and aggregates all the learnings along the way. In this way, no data is exchanged whatsoever. The only exchange occurring is model gradients.

Federated Learning comes in many flavors. In the client-server model employed by Clara FL today, the server aggregates the model gradients it receives from all of the participating local training sites (Client-sites) after each iteration of training. The aggregation methodology can vary from a simple weighted average to more complex methods chosen by the administrator of the FL training.

The end result is a more generalizable AI model trained on all the data from each one of the participating institutions while maintaining data privacy and sovereignty.

Early Federated Learning Work Shows Promise

New York-based Mount Sinai Health Systems recently used federated learning to analyze electronic health records to better predict how COVID-19 patients will progress using the AI model and data from five separate hospitals. The federated learning process allowed the model to learn from multiple sources without exposing patient data.

The Federated model outperformed local models built using data from each hospital separately and it showed better predictive capabilities.

In a larger collaboration among NVIDIA and 20 hospitals, including Mass General Brigham, National Institutes of Health in Bethesda, and others in Asia and Europe, the work focused on creating a triage model for COVID-19. The FL model predicted on initial presentation if a patient with symptoms suspicious for COVID-19 patient will end up needing supplemental oxygen within a certain time window.

Considerations and Coordination

While Federated learning addresses the issue of data privacy and data access, it is not without its challenges. Coordination between the client sites needs to happen to ensure that the data used for training is cohesive in terms of format, pre- processing steps, labels, and other factors that can affect training. Data that is not identically distributed at the various client sites can also pose problems for training, and it is an area of active research. And there is also the question of how the US Food and Drug Administration, European Union, and other regulatory bodies around the world will certify models trained using Federated Learning. Will they require some way of examining the data that went into training to be able to reproduce the results of Federated Learning, or will they certify a model based on its performance on external data sets?

In January, the U.S. Food and Drug Administration updated its action plan for AI and machine learning in software as a medical device, underscoring the importance of inclusivity across dimensions like sex, gender, age, race, and ethnicity when compiling datasets for training and testing. The European Union also includes a right to explanation from AI systems in GDPR.

It remains to be seen how they will rule on Federated Learning.

AI in the Medical Mainstream

As Federated Learning approaches enter the mainstream, hospital groups are banking on Artificial Intelligence to improve patient care, improve the patient experience, increase access to care, and lower healthcare costs. But AI needs data, and data is money. Those who own these AI models can license them around the world or can share in commercial rollouts. Healthcare organizations are sitting on a gold mine of data. Leveraging this data securely for AI applications is a golden goose, and those organizations that learn to do this will emerge the victors.

Dr. Mona Flores is NVIDIAs Global Head of Medical AI. She brings a unique perspective with hervaried experience in clinical medicine, medical applications, and business. She is a boardcertified cardiac surgeon and the previous Chief Medical Officer of a digital health company.She holds an MBA in Management Information Systems and has worked on Wall Street. Herultimate goal is the betterment of medicine through AI. She can be reached at mflores@nvidia.com.

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What is Multiple Regression? – Built In

Linear regression, while a useful tool, has significant limits. As its name implies, it cant easily match any data set that is non-linear. It can only be used to make predictions that fit within the range of the training data set. And, most importantly for our purposes, linear regression can only be fit to data sets with a single dependent variable and a single independent variable.

This is where multiple regression comes in. While it cant overcome all of linear regressions weaknesses, its specifically designed to create regressions on models with a single dependent variable and multiple independent variables.

Multiple regression is an extension of linear regression models that allow predictions of systems with multiple independent variables.

To start, lets look at the general form of the equation for linear regression:

y = B * x + A

Here, y is the dependent variable, x is the independent variable, and A and B are coefficients dictating the equation. The difference between the equation for linear regression and the equation for multiple regression is that the equation for multiple regression must be able to handle several inputs, instead of only the single input of linear regression. To account for this change, the equation for multiple regression looks like this:

y = B_1 * x_1 + B_2 * x_2 + + B_n * x_n + A

In this equation, the subscripts denote the different independent variables. For example, x_1 is the value of the first independent variable, x_2 is the value of the second independent variable, and so on. It keeps going as we add more independent variables until we finally add the last independent variable, x_n, to the equation. (Note that this model allows you to have any number, n, independent variables and more terms are added as needed.) The B coefficients employ the same subscripts, indicating they are the coefficients linked to each independent variable. A, as before, is simply a constant stating the value of the dependent variable, y, when all of the independent variables, the xs, are zero.

As an example, imagine that youre a traffic planner in your city and need to estimate the average commute time of drivers going from the east side of the city to the west. You dont know how long it takes on average, but you do know that it will depend on a number of factors like the distance driven, the number of stoplights on the route, and the number of other cars on the road. In that case you could create a linear multiple regression equation like the following:

y = B_1 * Distance + B_2 * Stoplights + B_3 * Cars + A

Here y is the average commute time, Distance is the distance between the starting and ending destinations, Stoplights is the number of stoplights on the route, and A is a constant representing other time consumers (e.g. putting on your seat belt, starting the car, maybe stopping at a coffee shop).

Now that you have your commute time prediction model, you need to fit your model to your training data set to minimize errors.

Similarly to how we minimize the sum of squared errors to find B in linear regression, we minimize the sum of squared errors to find all of the B terms in multiple regression. The difference here is that since there are multiple terms, and an unspecified number of terms until you create the model, there isnt a simple algebraic solution to find A and B. This means we need to use stochastic gradient descent. You can find a good description of stochastic gradient descent in Data Science from Scratch by Joel Gros or use tools in the Python scikit-learn package. Fortunately, we can still present the equations needed to implement this solution before reading about the details.

The first step is summing the squared errors on each point. This takes the form:

Error_Point = (Actual Prediction)

In this instance, Error is the error in the model when predicting a persons commute time, Actual is the actual value (or that persons actual commute time), and Prediction is the value predicted by the model (or that persons commute time predicted by the model). Actual Prediction yields the error for a point, then squaring it yields the squared error for a point. Remember that squaring the error is important because some errors will be positive while others will be negative and if not squared these errors will cancel each other out making the total error of the model look far smaller than it really is.

To find the error in the model, the error from each point must be summed across the entire data set. This essentially means that you use the model to predict the commute time for each data point that you have, subtract that value from the actual commute time in the data point to find the error, square that error, then sum all of the squared errors together. In other words, the error of the model is:

Error_Model = sum(Actual_i Prediction_i)

Here i is an index iterating through all points in the data set.

Once the error function is determined, you need to put the model and error function through a stochastic gradient descent algorithm to minimize the error. The stochastic gradient descent algorithm will do this by minimizing the B terms in the equation.

Once youve fit the model to your training data, the next step is to ensure that the model fits your full data set well.

To make sure your model fits the data use the same r value that you use for linear regression. The r value (also called the coefficient of determination) states the portion of change in the data set predicted by the model. The value will range from 0 to 1, with 0 stating that the model has no ability to predict the result and 1 stating that the model predicts the result perfectly. You should expect the r value of any model you create to be between those two values. If it isnt, retrace your steps because youve made a mistake somewhere.

You can calculate the coefficient of determination for a model using the following equations:

r = 1 (Sum of squared errors) / (Total sum of squares)

(Total sum of squares) = Sum(y_i mean(y))

(Sum of squared errors) = sum((Actual_i Prediction_i))

Heres where testing the fit of a multiple regression model gets complicated. Adding more terms to the multiple regression inherently improves the fit. Additional terms give the model more flexibility and new coefficients that can be tweaked to create a better fit. Additional terms will always yield a better fit to the training data whether the new term adds value to the model or not. Adding new variables which do not realistically have an impact on the dependent variable will yield a better fit to the training data, while creating an erroneous term in the model. An example of this would be adding a term describing the position of Saturn in the night sky to the driving time model. The regression equations will create a coefficient for that term, and it will cause the model to more closely fit the data set, but we all know that Saturns location doesnt impact commute times. The Saturn location term will add noise to future predictions, leading to less accurate estimates of commute times even though it made the model more closely fit the training data set. This issue is referred to as overfitting the model.

Additional terms will always improve the model whether the new term adds significant value to the model or not.

This fact has important implications when developing multiple regression models. Yes, you could keep adding more terms to the equation until you either get a perfect match or run out variables to add. But then youd end up with a very large, complex model thats full of terms which arent actually relevant to the case youre predicting.

One way to determine which parameters are most important is to calculate the standard error of each coefficient. The standard error states how confident the model is about each coefficient, with larger values indicating that the model is less sure of that parameter. We can intuit this even without seeing the underlying equations. If the error associated with a term is typically high, that implies the term is not having a very strong impact on matching the model to the data set.

Calculating the standard error is an involved statistical process, and cant be succinctly described in a short article. Fortunately there are Python packages available that you can use to do it for you. The question has been asked and answered on StackOverflow at least once. Those tools should get you started.

After calculating the standard error of each coefficient, you can use the results to identify which coefficients are highest and which are lowest. Since high values indicate that those terms add less predictive value to the model, you can know those terms are the least important to keep. At this point you can start choosing which terms in the model can be removed to reduce the number of terms in the equation without dramatically reducing the predictive power of the model.

Another method is to use a technique called regularization. Regularization works by adding a new term to the error calculation that is based on the number of terms in the multiple regression equation. More terms in the equation will inherently lead to a higher regularization error, while fewer terms inherently lead to a lower regularization error. Additionally, the penalty for adding terms in the regularization equation can be increased or decreased as desired. Increasing the penalty will also lead to a higher regularization error, while decreasing it will lead to a lower regularization error.

With a regularization term added to the error equation, minimizing the error means not just minimizing the error in the model but also minimizing the number of terms in the equation. This will inherently lead to a model with a worse fit to the training data, but will also inherently lead to a model with fewer terms in the equation. Higher penalty/term values in the regularization error create more pressure on the model to have fewer terms.

Read More From Our ExpertsWhat is Linear Regression? Explaining Concepts and Applications With Tensorflow 2.0

The model youve created is not just an equation with a bunch of numbers in it. Each one of the coefficients you derived states the impact an independent variable has on the dependent variable assuming all others are held equal. For instance, our commute time example says the average commute will take B_2 minutes longer for each stoplight in a persons commute path. If the model development process returns 2.32 for B_2, that means each stoplight in a persons path adds 2.32 minutes to the drive.

This is another reason its important to keep the number of terms in the equation low. As we add more terms it gets harder to keep track of the physical significance (and justify the presence) of each term. Anybody counting on the commute time predicting model would accept a term for commute distance but will be less understanding of a term for the location of Saturn in the night sky.

Note that this model doesnt say anything about how parameters might affect each other. In looking at the equation, theres no way that it could. The different coefficients are all connected to only a single physical parameter. If you believe two terms are related, you could create a new term based on the combination of those two. For instance, the number of stoplights on the commute could be a function of the distance of the commute. A potential equation for that could be:

Stoplights = C_1 * Distance + D

In this case, C_1 and D are regression coefficients similar to B and A in the commute distance regression equation. This term for stoplights could then be substituted into the commute distance regression equation, enabling the model to capture this relationship.

Another possible modification includes adding non-linear inputs. The multiple regression model itself is only capable of being linear, which is a limitation. You can however create non-linear terms in the model. For instance, say that one stoplight backing up can prevent traffic from passing through a prior stoplight. This could lead to an exponential impact from stoplights on the commute time. You could create a new term to capture this, and modify your commute distance algorithm accordingly. That would look something like:

Stoplights_Squared = Stoplights

y = B_1 * Distance + B_2 * Stoplights + B_3 * Cars + B_4 * Stoplights_Squared + C

These two equations combine to create a linear regression term for your non linear Stoplights_Squared input.

Multiple regression is an extension of linear regression models that allow predictions of systems with multiple independent variables. We do this by adding more terms to the linear regression equation, with each term representing the impact of a different physical parameter. When used with care, multiple regression models can simultaneously describe the physical principles acting on a data set and provide a powerful tool to predict the impacts of changes in the system described by the data.

This article was originally published on Towards Data Science.

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What is Multiple Regression? - Built In

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Grab the Opportunity: Top AI and Data Science Jobs to Apply Today – Analytics Insight

AI and data science jobs are already seen as a rewarding career path for professionals

Artificial intelligenceis a promising technology, that has made significant changes in the 21st century. Starting from self-driving cars and robotic assistants to automated disease diagnosis and drug discovery, the stronghold ofartificial intelligenceis no joke. Along withartificial intelligence,data sciencehas also shifted the way we live and work. With the demand fordata scienceandartificial intelligence spiralling, the job market is opening its door toAI and data science jobs. The tech sphere has ensured thatartificial intelligence jobsanddata science jobsprovide limitless opportunities for professionals to explore cutting edge solutions. According to a Gartner report,Artificial intelligence jobsrose to over 2.3 million in 2020. While the competition in the industry is heating up,AI and Data science jobsare already seen as the rewarding career path. Analytics Insight has listed topAI and Data science jobs that aspirants should apply for today.

Location: Bengaluru, Karnataka, India

About the company: IBM, also known as International Business Machines Corporation is a leading American computer manufacturer. The company has developed a thoughtful, comprehensive approach to corporate citizenship that they believe aligns with IBMs values and maximized the impact they can make as a global enterprise.

Roles and responsibilities: As a data scientist at IBM, the candidate is expected to develop, maintain, and evaluate AI solutions. He/she will be involved in the design of data solutions using artificial intelligence-based technologies like H2O, Tensorflow. They are responsible for designing algorithms and implementation including loading from disparate datasets, pre-processing using Hive and Pig. The candidate should scope and deliver solutions with the ability to design solutions independently based on high-level architecture. They should also maintain the production systems like Kafta, Hadoop, Cassandra, and Elasticsearch.

Qualifications

Applyherefor the job.

Location: Bengaluru, Karnataka, India

About the company: Accenture is a global professional services company that provides a range of services and solutions in strategy, consulting, digital, technology, and operations. Combining deep experience and specialized skills across 40 industries and business functions, Accenture works at the intersection of business and technology to help clients improve performance and create sustainable value for stakeholders.

Roles and responsibilities: As a senior analyst- artificial intelligence innovation, the candidate will be aligned with Accentures insights and intelligence vertical and help them generate insights by leveraging the latest artificial intelligence and analytics techniques to deliver value to its clients. Generally, the artificial intelligence innovation team at Accenture is responsible for the creation, deployment, and managing of the operations of projects. In this role, the candidate will need to analyze and solve increasingly complex problems. He/she should frequently interact with their peers at Accenture and clients to manage the development well.

Qualifications

Applyherefor the job.

Location: Azcapotzalco, Mexico City, Mexico

About the company: AT&T is a US-based telecom company and the second largest provider of mobile services. AT&T operates as a carrier of both fixed and mobile networks in the US but offers telecoms services elsewhere. The company also provides pay-TV services through DirecTV.

Roles and responsibilities: The artificial intelligence engineer at AT&T is responsible for designing and implementing artificial intelligence and machine learning packages, including data pipelines, to process complex, large-scale datasets used for modelling, data mining, and research purposes. He/she is expected to design, develop, troubleshoot, debug, and modify software for AT&T services or the management and monitoring of these service offerings. They should interact with systems engineers t realize the technical design and requirements of the service, including management, systems, and data aspects.

Qualifications

Applyherefor the job.

Location: Bengaluru, Karnataka, India

About the company: LinkedIn is a social networking site designed to help people make business connections, share their experience and resumes, and find jobs. LinkedIn is free, but a subscription version called LinkedIn Premium offers additional features like online classes and seminars.

Roles and responsibilities: As a data engineer at LinkedIn, the candidate is expected to work with a team of high-performing analytics, data science professionals, and cross-functional teams to identify business opportunities, optimize product performance or go to market strategy. He/she should build data expertise and manage complex data systems for a product or a group of products. They should perform all the necessary data transformation tasks to serve products that empower data-driven decision-making. The candidate should establish efficient design and programming patterns for engineers as well as for non-technical partners.

Qualifications

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Location: Bengaluru, Karnataka, India

About the company: Google is an American search engine company founded in 1998. Began as an online search firm, the company now offers more than 50 internet services and products including email, online document creation, software for mobile phones and tablet computers, etc.

Roles and responsibilities: As a data scientist at Google, the candidate will evaluate and improve Googles products. They will collaborate with a multi-disciplinary team of Engineers and Analysts on a wide range of problems, bringing analytical rigour and statistical methods to the challenges of measuring quality, improving consumer products, and understanding the behaviour of end-users, advertisers, and publishers. He/she should work with large, complex data sets and solve difficult, non-routine analysis problems by applying advanced methods. The candidate should conduct end-to-end analysis, including data gathering and requirements specification, processing, analysis, ongoing deliverables, and presentations.

Qualifications

Applyherefor the job.

Location: Melbourne, Victoria, Australia

About the company: Tata Consultancy Services, also known as TCS, is a global leader in IT services, digital, and business solutions. The company partners with clients to simplify, strengthen, and transform their business.

Roles and responsibilities: As a data engineer, the candidate should design and build production data pipelines from ingestion to consumption within a big data architecture, using Java, Python, or Scala. He/she should design and implement data engineering, ingestion, and curation functions on AWS cloud using AWS native or custom programming.

Qualifications

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Women in Data Science event will showcase the versatility of a career in data – Siliconrepublic.com

Communication, engagement and creativity are key to fostering the next generation of data scientists, said event organiser Aine Lenihan.

Aine Lenihan, also known as Data Damsel, will host a panel of world-leading women working in data science as part of a free virtual event she has organised.

The event, which coincides with International Women in Engineering Day on 23 June, aims to support women and girls entering the male-dominated field of data science.

All genders are welcome to attend the event, which is part of the Women in Data Science (WiDS) Worldwide conference series run by Stanford University in more than 150 locations worldwide.

In her role as WiDSs Irish ambassador, Lenihan said she curated the events speakers carefully with the aim of highlighting to attendees the versatility of a data science career.

Unfortunately, the people shaping the data that shapes the world are a homogeneous bunch AINE LENIHAN

We have something for sports fans and entrepreneurs with the amazing Hlne Guillaume straight from her experience on Dragons Den. We will have the head of data science at Starling Bank, Harriet Rees, as well as TEDx speaker and clinical neuroscientist Rebecca Pope. Well also have the managing director of Accenture Labs, the renowned Medb Corcoran, and Jennifer Cruise, who has just joined EY as director of analytics.

Having mentored and lectured in data and database management systems in Trinity College Dublin, as well as working in the industry for over 20 years, Lenihan is passionate about education and increasing the visibility of women role models for the next generation.

Real-world applications of data science, from combatting cybersecurity to climate change, are shaping todays world and tomorrows. Unfortunately, the people shaping the data that shapes the world are a homogeneous bunch, she said.

Somewhere upwards of 78pc to 85pc of data scientists are male. Bringing women into data science is critical for ensuring accurate and unbiased data is available for todays data-driven businesses.

Lenihans own personal experience as a woman in data science also drives her push for greater gender diversity in her industry. She currently works as a senior analytics architect in IBMs Watson division and previously work in various software and data roles at AIB.

A challenge I have found throughout my career has been missing the company of having other women on my team. Thats a really important factor for me, she said.

Thankfully, there are now lots of active communities, including WiDS, bridging those gaps.

So, what advice would Lenihan give to people hoping to pursue a career in data science and the STEM industry in general?

She predicts the sector will be one of the fastest-growing through to the rest of the decade, and it will be subject to continual evolution of trends in artificial intelligence. AI will also create completely new jobs we havent even dreamed up yet, she said.

My advice is not gender-specific, but a call to those who do not consider STEM for them because they are creative rather than analytical. To those people, I want them to know that creativity is the secret sauce in STEM. Creativity and STEM are no longer chalk and cheese. There is a place for all skillsets and talents in STEM careers. Being creative rather than analytical does not rule out a career in STEM for anyone. In fact, the magic happens when both work together.

Lenihan has herself learned to appreciate the broad expanse of data science and the multitude of backgrounds data scientists can have in her role mentoring second and third-level students on IBMs Pathways to Technology (P-Tech) programme.

As a mentor with P-Tech, students share their career goals and dreams for the future with me, some of which they think are unrelated to AI. But it really gives me the perfect opportunity for me to demonstrate how AI will touch every industry. So you want to be a footballer? Well, AI is being used to create future superstars by boosting performance, minimising injury, predicting recovery time. Want to work as a makeup artist? Well check out the handheld makeup printer, or the NASA-backed skincare micro-mist.

The dynamic Data Damsel who was given the nickname by her former colleagues due to her relatively rare status as a young, female data expert is confident about the industrys future.

There really is so much happening to pique the interest of future data damsels, but communication and engagement is key. There will no doubt come a time I will be more data dame than damsel but hopefully if I continue my advocacy, there will be many generations of data damsels behind me.

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Top Data Visualization Tools of 2021 – Analytics Insight

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 wouldnt want to miss out on.

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.

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.

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 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 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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Analytics Insight is an influential platform dedicated to insights, trends, and opinions from the world of data-driven technologies. It monitors developments, recognition, and achievements made by Artificial Intelligence, Big Data and Analytics companies across the globe.

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MLOps: The Latest Shift in the AI Market in Israel – Geektime

Written by Asaf Somekh, CEO & Founder at iguazio

We are witnessing a technological revolution that is dramatically changing the way we live and work. The speed at which technological breakthroughs are occurring has no precedent in previous periods of transformation. This revolution is disrupting almost every industry in every country.

Most of the technological revolutions now taking place or about to take place are based on AI. Adoption of AI in businesses across the US, Europe and China has risen sharply over the past year, to 34 percent. AI technology uses algorithms to analyze large data sets, detect patterns, extract insights, and make decisions accordingly. Israel is widely celebrated as an AI powerhouse, despite its population size.

AI technologies make it possible to use the massive amounts of accumulated data and make use of them. The growing market around AutoML solutions has made data science accessible to a larger segment of organizations. However, according to industry analysts an estimated 85% of data science projects, which have shown great promise in the lab, never make it to production. This is due to the challenges of transforming an AI model, which is functional and shows great promise in lab conditions, to a fully operational AI application that can deliver business impact at scale and in real business environments.

The potential use cases for data science are truly exciting. But the elaborate challenges of operationalizing machine learning can--and often does--impede companies from bringing innovative solutions to market. Software development has by now become a repeatable efficient practice, but for the AI industry, the complexities involved in machine learning applications means there is still a lack of standards and widespread best practices.

This is changing. MLOps (Machine Learning Operations) is an emerging discipline that echoes DevOps practices for machine and deep learning. MLOps decreases time and effort by automating the tasks involved with deploying, monitoring and managing AI applications in production. As the MLOps field evolves, new technologies like feature stores are emerging to break down silos between data scientists, data engineers and DevOps practitioners, by allowing everyone on the team to build, share, reuse, analyze and monitor features in production. This unified approach to feature engineering accelerates the path from research to production and enables companies to develop, deploy and manage AI at scale with ease. As companies across industries weave AI/ML applications into their processes, IT leaders must invest in MLOps to drive real business impact.

The ARC Innovation Center at Sheba Medical Center, ranked one of the top ten hospitals worldwide by Newsweek, is a standout example of how AI can be operationalized to dramatically improve healthcare. Sheba Medical Center is the largest hospital in Israel and the MEA region, and possesses one of the worlds largest reservoirs of health data. ARC recently launched a new project that brings urgent real-time predictions to the ICU floor. Harnessing data from various sources, such as real-time vital signs, x-rays and historic patient records, they use advanced machine learning algorithms to optimize patient care, predict COVID-19 patient deterioration and even control the flow of cars to parking spaces . Real-time dashboards surface alerts for doctors and prioritize patient intake, so the medical center can respond quickly and dramatically improve outcomes for all involved.

The massive strain placed on companies and health organizations by COVID-19 has only emphasized what we knew before: it is absolutely vital for businesses who want to survive the current situation to create a competitive advantage by bringing AI innovations to market quickly. Companies must adapt to a rapidly shifting environment by focusing on developing and deploying AI more efficiently, without the excessive costs and lengthy timeframes that might have seemed reasonable just a year ago. And now more than ever, monitoring AI applications for concept drift is critical, as human behavior changes dramatically from week to week during these unpredictable times, leaving AI models unusable due to a change in the very things they were built to predict.

With the new Israeli government setting a goal to increase the percentage of high tech employees from 10% to 15% of the overall workforce, AI technologies will be a critical growth engine for the Israeli economy. Israels past success establishing thought leadership and market dominance in the cybersecurity market bodes well for its ability to overcome the current obstacles it faces on the path to global AI leadership. MLOps will be a big facilitator in this path, enabling more and more companies to see real business value from their AI endeavors, in a short timeframe and with a lean team.

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Quantum Theory: A Scientific Revolution that Changed Physics Forever – Interesting Engineering

To many, quantum physics, or quantum mechanics, may seem an obscure subject, with little application for everyday life, but its principles and laws form the basis for explanations of howmatter and light work on the atomic and subatomic scale. If you want to understand how electrons move through acomputerchip, how photons of light travel in a solar panel or amplify themselves in alaser, or even why the sun keeps burning, you will need to use quantum mechanics.

Quantum mechanics is the branch of physics relating to the elementary components of nature, it is the study of the interactions that take place between subatomic forces. Quantum mechanics was developed because many of the equations ofclassical mechanics, which describe interactions at larger sizes and speeds, cease to be useful or predictive when trying to explain the forces of nature that work on the atomic scale.

Quantum mechanics, and the math that underlies it, is not based on a single theory, but on a series of theories inspired by new experimental results, theoretical insights, and mathematical methods which were elucidated beginning in the first half of the 20th century, and together create a theoretical system whose predictive power has made it one of the most successful scientific models created.

The story of quantum mechanics can be said to begin in 1859, a full 32 years before the discovery of the electron. Many physicists were concerned with a puzzling phenomenon: no matter what an object is made of, if it can survive being heated to a given temperature, the spectrum of light it emits is exactly the same as for any other substance.

In1859, physicistGustav Kirchhoff proposed a solution when he demonstrated thatthe energy emitted by a blackbody objectdepends on the temperature and the frequencyof the emitted energy, i.e

E=J(T,v)

A blackbody is a perfect emitter - an idealized object that absorbs all the energy that falls on it (because it reflects no light, it would appear black to an observer).Kirchhoff challenged physicists to find the function J, which would allow the energy emitted by light to be described for all wavelengths.

In the years following, a number of physicists would work on this problem. One of these was Heinrich Rubens, who worked to measurethe energy of black-body radiation. In 1900, Rubens visited fellow physicist Max Planckand explained his results to him. Within a few hours of Rubens leavingPlanck's house,Planckhad come up with an answer to Kirchoff's function whichfitted the experimental evidence.

Planck sought to use the equation to explain the distribution of colors emitted over the spectrum in the glow of red-hot and white-hot objects. However, when doing this, Planck realized the equation implied that only combinations of certain colors were emitted, and ininteger multiples of a small constant (which became known as Plank's Constant) times the frequency of the light.

This was unexpected because, at the time, light was believed to act as a wave, which meant that the values of color emitted should be a continuous spectrum. However,Planck realized that his solution gave different values at different wavelengths.

In order toexplain howatomswere being prevented from producing certain colors, Planck made a novel assumption - thatatoms absorb and emit energy in the form of indistinguishable energy units - what came to be called quanta.

At the time, Planck regarded quantization as a mathematical trick to make his theory work. However, a few years later, physicists provedthat classical electromagnetism couldneveraccount for the observed spectrum. These proofs helped to convincephysicists that Planck's notion of quantized energy levels may in fact be more than a mathematical "trick".

One of the proofs was given by Einstein, who published a paper in 1905 in which he envisioned light traveling not as a wave, but as a packet of "energy quanta" which could be absorbed or generated when an atom "jumps" between quantized vibration rates. In this model, the quanta contained the energy difference of the jump; when divided by Plancks constant, that energy difference determined the wavelength of light given off by those quanta.

In 1913 Niels Bohrapplied Planck's hypothesis of quantization to Ernest Rutherford's 1911 "planetary" model of the atom. This model, which came to be called the Rutherford-Bohr model, postulated that electrons orbited the nucleus in a similar way to how planets orbit the sun. Bohr proposed that electrons could only orbit at certain distances from the nucleau, and could "jump" between the orbits; doing so would give off energy at certain wavelengths of light, which could be observed as spectral lines.

It now appeared that light could act as a wave and as a particle. However, what about the matter?

In 1924, French physicist Louis de Broglie used the equations of Einstein'stheory of special relativityto show that particles can exhibit wave-like characteristics, and vice-versa.

German physicist Werner Heisenberg met with Neils Bohr at the University of Copenhagen in 1925, and after this meeting, he applied de Broglie's reasoning to understand the spectrum intensity of an electron.At the same time, Austrian physicist ErwinSchrdinger, working independently, also used de Broglie's reasoning to explain how electrons moved around in atoms.The following year, Schrdinger demonstrated that the two approaches were equivalent.

In 1927, Heisenberg reasoned that if matter can act as a wave, there must be a limit to how precisely we can know some properties, such as an electron's position and speed. In what would later be called "Heisenberg'suncertainty principle," he reasoned that the more precisely an electron's position is known, the less precisely its speed can be known, and vice versa. The proved an important piece of the quantum puzzle.

In the Heisenberg-Schrdingerquantum mechanical model of the atom, each electron acts as a wave, or "cloud") around the nucleus of an atom, with the ability to measure only the speed or position of an electron to a particular probability. This model replaced the Rutherford-Bohr model.

All these revelations regarding quantum theory revolutionized the world of physics and revealed important details about universal actions at atomic and subatomic levels.

Quantum mechanics further combined with other phenomena in physics such as relativity, gravitation, electromagnetism, etc. also increased our understanding of the physical world and how construction and destruction occur within it.

For their exceptional contributions, Planck, Einstein, Bohr, Heisenberg, and Schrdinger were awarded the Nobel Prize in Physics in 1918, 1921, 1922, 1932, and 1933 respectively.

While it may seem as though quantum mechanics progressed in a fairly straightforward series of theoretical leaps, in reality, there was a lot of disagreement among physicists over its relevance.

These disagreements reached a peak at the 1927 Solvay Conference in Brussels, where 29 of the world's most brilliant scientists gathered to discuss the many seemingly contradictory observations inquantum theory that could not be reconciled. One major point of contention had to do with the theory that, until they are observed, the location and speed of entities such as electrons, can only exist as a "probability".

Bohr, in particular,emphasized that quantum predictions founded on probability are able to accurately describe physical actions in the real world. In what later came to be called the Copenhagen interpretation, he proposed that while wave equations described the probability of where entities like electronscouldbe found,theseentities didn't actually exist as particles unless they were observed. In Bohr's words, they had no "independent reality" in the ordinary physical sense.

He described that the events that take place on atomic levels can alter the outcome of quantum interaction.According to Bohr, a system behaves as a wave or a particle depending on context, but you cannot predict what it will do.

Einstein, in contrast, argued thatan electron was an electron, even if no one was looking at it, that particles like electrons had independent reality, and prompting his famous claim that God does not play dice with the universe.

Einstein and Bohr would debate their views until Einstein's death three decades later, but remained colleagues and good friends.

Einstein argued that the Copenhagen interpretation was incomplete. He theorized that there might be hidden variables or processes underlying quantum phenomena.

In 1935, Einstein, along with fellow physicists Boris Podolsky and Nathan Rosen published a paper on what would be known as the Einstein-Boris-Podolsky (EPR) paradox. The EPR paradox described in the paper again raised doubts on the quantum theory.

The EPR paper featured predetermined values of momentum and particle velocity and suggested that the description of physical reality provided by the wave function in quantum theory is incomplete, and therefore, physical reality can not be derived from the wave function or in the context of quantum-mechanical theory.

The same year, Bohr replied to the claims made by Einstein. In his response, published in the Physical Review, Bohr proved that the predetermined values of the second particles velocity and momentum, as per the EPR paradox were incorrect. He also argued that the paradox failed to justify the inability of quantum mechanics to explain physical reality.

The understanding of elementary particles and their behavior helped to create groundbreaking innovations in healthcare, communication, electronics, and various other fields. Moreover, there are numerous modern technologies that operate on the principles mentioned in quantum physics.

Laser-based equipment

Laser technology involves equipment that emits light by the means of a process called optical amplification. Laser equipment work on the principle of photon emission and they release the light with a well-defined wavelength in a very narrow beam. Hence, the laser beams function in alignment with theories (such as the photoelectric effect) mentioned in quantum mechanics.

A report published in 2009 reveals that extreme ultraviolet lasers when hit a metal surface can cause electrons to move out of the atom, this outcome is said to further extend Einsteins photoelectric effect in the context of super-intense lasers.

Electronic Devices and Machines

From flash memory storage devices like USB drives to complex lab equipment such as electron microscopes, an understanding of quantum mechanics led to countless modern-day inventions. Light-emitting diodes, electric switches, transistors, quantum computers, etc are examples of some highly useful devices that resulted from the advent of quantum physics.

Let us understand this from the example of Magnetic Resonance Imaging (MRI) machine, this medical equipment is very useful in diagnosing the brain and other body organs. MRI works on the principle of electromagnetism, it has a strong magnetic field that uses the spin of protons in hydrogen atoms to analyze the composition of different tissues.

MRI aligns all the protons in the body as per their spin, due to the magnetic field, the protons absorb energy and emit the same (quantum theory), the MRI scanner uses the emitted energy signals received from all the water molecules to deliver a detailed image of the internal body parts.

X-Rays

Used in medical diagnosis, border inspection, industrial tomography, cancer treatment, and for many other purposes, X-rays are a form of electromagnetic radiation. While the discovery of X-rays predates quantum mechanics, quantum mechanical theory has allowed the use of X-rays in a practical way.

A beam of X-rays can be regarded as consisting of a stream of quanta. These quanta are projected out from the target of the X-ray tube, and, on penetrating tissue, there is an effect produced that is proportional to the number of the quanta multiplied by the energy carried by each quantum.

The emitted electrons also emit photons, whichare able to penetrate the matter and form its image on the X-ray screen. Therefore, the elementary particles mentioned in quantum mechanics interact with X-ray energy to deliver the inside look of an object.

Fluorescence-based Applications

Fluorescence is referred to the emission of light under UV exposure that takes place when an electron achieves a higher quantum state and emits photons, fluorescent lamps and spectrometers work on basis of quantum theory. Various minerals such as Aragonit, Calcite, and Fluorite are also known to exhibit fluorescence.

Fluorescence is also used to lit synthetic gems and diamonds, jewelry manufacturers use this phenomenon to create artificial imitation stones that look brighter and more beautiful than the naturally occurring original stones.

Apart from these applications, quantum mechanics has contributed to our understanding of many areas of technology, biological systems, and cosmic forces and bodies. While there are several important questions remaining inquantum physics, the core concepts, which define the behavior of energy, particles, and matter have continued to hold constant.

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