Study design and participants

This was a retrospective observational study in Japan, and the informed consent for the retrospective study was waived with the Ethical Guidelines for Medical and Biological Research Involving Human Subjects issued by Ministry of Education, Culture, Sports, Science and Technology, Ministry of Health, Labour and Welfare, and Ministry of Economy, Trade and Industry in Japan. We analyzed healthcare insurance claims data obtained from the Japan Medical Data Center (JMDC) in Tokyo. The database contains standardized eligibility and claims data provided by health insurance societies for approximately 4.5 million insured individuals, including data from employees of general corporations and their family members. The database contains all medical treatments received by insured individuals at all treatment facilities and a comprehensive record of all treatments administered to a given patient. We removed the decoding indexes and analyzed the personal data with unlinkable anonymization.

The study protocol was approved by the ethics committee of the National Cerebral and Cardiovascular Center (M22-49, M24-51). On the basis of the Japanese Clinical Research Guidelines, the committee decided that patient informed consent was not essential for inclusion in this study because of the retrospective observational nature of the study. Instead, JMDC made a public announcement in accordance with the ethics committees request and the Japanese Clinical Research Guidelines. The study was performed following the principles of the Declaration of Helsinki and the Japanese Ethical Guidelines for Clinical Research.

In the database of the approximately 4.5 million people, we set the entry criteria of (1) the presence of the complete dataset of the consecutive period of 4years following 2010, (2) no diagnosis of HF at the first year, (3) evidence of the presence or absence of diagnosis of HF during 4years. Under these entry criteria, we narrowed down the database and obtained the complete dataset of 308,205 people. We then randomly allocated 32,547 people for the analysis cohort (Protocol 1) and remaining of 275,658 people for the validation cohort (Protocol 2) using the random numbers table. Because we have had several general experiences that about 30,000 people with 1% of incidence are enough to obtain the significant combinations of factors, and indeed we have experienced that we need several thousands people with about 10% of incidence to obtain the sufficient combinations of factors to identify the worthening of HF10, we selected about 30,000 people to find the meaningful and sufficient combinations of factors to detect the onset of HF for Protocol 1.

In 32,547 Japanese cohort, we obtained 288 clinical, medical, habitual, and physical variables at 2010, including sex; age; urinary sugar levels (borderline, 1+, 2+, 3+, and 4+); urinary protein levels (borderline, 1+, 2+, 3+, and 4+); plasma LDL and HDL cholesterol and triglyceride levels (mg/L); plasma HbA1c levels (%); body mass index; systolic and diastolic blood pressure (mmHg); plasma uric acid levels (mg/L); fasting plasma glucose levels (mg/dL); plasma ALT, AST, and -GTP levels (IU/L); abdominal circumference length (cm); red blood cell number (104/L) and blood Hb levels (g/dL); chest XP findings (A=normal, B=slight changes but no need for observation, C=need for observation, and H=need for treatment); ECG findings (A=normal, B=slight changes but no need for observation, C=need for observation, and H=need for treatment); work using visual display terminals (VDT) (A=need for observation, B=slight changes but no need for observation, and C=normal); interview regarding life habits (smoking at present: yes or no; more than 30min exercise per day: yes or no; changes in body weight more than 2kg over 1year: yes or no; drinking alcohol at present: every day, not every day but sometimes, or none); and prescription details. We carefully performed data cleaning for all data. People were monitored for HF until 2014. HF was diagnosed by cardiologists and general practitioners using the Framingham Criteria of Congestive Heart Failure11, plasma BNP levels12 and echocardiogram13, which seems to be reliable to precisely and accurately diagnose the several types of HF.

We separated people with and without the occurrence of HF over 4years (Table 1).

We employed the novel LAMP method for our data-mining analysis to identify rules consistent with single factors or combinations of factors that significantly affected the occurrence of cardiovascular events8. A person was represented by both individual clinical factors and the class labels of groups with or without the occurrence of HF, and this set of populations was used to form a data table in which each row represented a person. This data table D consisted of N rows, each of which consisted of M factors and a positive or negative class label for each object. LAMP uses Fishers exact tests to draw conclusions from a complete set of statistically significant hypotheses regarding a class label. Here, the hypothesis was based on a combination of class labels and conditions defined as a subset of the M factors in D. As the condition of the uncovered significant hypothesis may include any number of factors from 1 to M, the term limitless-arity has been used to describe this method. Accordingly, LAMP applies a highly efficient search algorithm to quickly and completely derive significant hypotheses from 2M candidates.

If k is the number of all hypotheses for which the conditions exceed or remain equal to objects in D (

$$ f(sigma ) = {{left( {begin{array}{*{20}c} {n_{p} } \ sigma \ end{array} } right)} mathord{left/ {vphantom {{left( {begin{array}{*{20}c} {n_{p} } \ sigma \ end{array} } right)} {left( {begin{array}{*{20}c} N \ sigma \ end{array} } right)}}} right. kern-0pt} {left( {begin{array}{*{20}c} N \ sigma \ end{array} } right)}}. $$

Here, np is the number of objects with positive class labels in D (np/kD (). Because f() is antimonotonic for and /kD () is monotonic, LAMP selects * to balance f(*) and /kD (*). The selected value of * yields the smallest number of candidate hypotheses without applying the tests or missing any significant hypotheses.

For practical reasons, we were interested in a hypothesis that held true for at least 10 people. As all hypotheses involving more than four factors failed to meet this criterion, we limited our LAMP-based search to a maximum of four factors. This limitation further reduced the number kD (*) of the candidate hypotheses and increased the level /kD (*) in LAMP. After all significant hypotheses regarding single clinical factors or combinations of factors were obtained, we excluded each hypothesis for which the condition was a superset of conditions from other simpler hypotheses as the significance of the former would be trivial in comparison with the significance of the latter.

For a larger cohort of 275,658 general people, the clinical characteristics in 2010 were investigated (Table 1) and the occurrence of HF until 2014 was determined. The number of combinations of factors matching the predictive combinations of factors for the onset of HF obtained in Protocol 1 in each of the 275,658 people was determined. To prove the idea that the onset of HF is predictable using clinical variables, we tested the hypothesis that the number of combinations of factors matching the predictive combinations of factors for 2010 is linked to the actual occurrence of HF over 4years. In detail, we checked how many combinations of factors for the prediction of the onset of HF discovered in Protocol I in each of individuals in Protocol II, and we classified 275,658 people of Protocol II into six groups who had 0, 150, 51100, 101150, 151200 or 201250 predictive combinations of factors discovered in Protocol 1 for the onset of HF.

It is a potentially informative censoring due to death from cardiovascular and non-cardiovascular deaths in this study, however this study did not analyze these clinical outcomes because no information of cardiovascular or non-cardiovascular death in the present data set is available.

Descriptive statistics of continuous variables are presented as means with standard deviations. We tested the significant levels of all of the combinations of factors with no more than four clinical mutable variables among 288 variables observed in the present study, and LAMP automatically created multiple comparisons of 470,700 t-tests. We corrected the significance P value using Bonferroni correction in Protocol 1: We multiplied the P values by 470,700 and we used the multiplied P values for statistical analysis. In Protocol 2, since LAMP tests a significant combination based on the binarized data of 0 or 1, we used the Cochran's Q test, one of the nonparametric methods to perform multiple comparison to test whether the probability of the onset of HF increased as the number of combinations of factors matching the predictive combinations of factors increased. We also performed the KaplanMyer Analysis to test the time dependency of the results in Protocol 2. All P values were two-sided, and a P value of<0.05 was considered statistically significant.

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Predicting heart failure onset in the general population using a novel ... - Nature.com

Can TikTok really be banned, as many politicians are now proposing? And what would be lost or gained by a prohibition of the wildly popular video-sharing platform which is used by millions of Americans and billions of people worldwide?

For some answers, Grid turned to David Greene, an attorney with the Electronic Frontier Foundation, a San Francisco nonprofit that promotes online users rights and supported a court challenge to efforts by the Trump administration to shut down TikTok. That effort ultimately floundered. The latest government threat to the social media behemoth comes from a bipartisan bill that gives President Joe Biden the power to ban TikTok, although so far the methodology of any ban is unclear.

It could mean stopping app stores from distributing downloads. It could mean forcing the sale of TikTok to a domestic company. The latest idea surfaced Wednesday from the Committee on Foreign Investment in the United States: force Chinese owners to divest their shares in TikTok. But according to experts, none of these approaches would do away with the app itself. And it beggars the imagination to think that the government would demand that everyone with TikTok on their phones simply stop using it.

Underpinning the regulatory zeal is the presumption that the Chinese-owned company ByteDance, which owns TikTok, would supply user data to the Chinese government or is already doing so. U.S. and European officials have cited national security risks, but so far, as Greene points out, the specifics of that risk have been kept from the public. So we dont know what the Chinese might be looking for or would get from users accounts. Greene and other First Amendment champions are quick to point out that the U.S. government must show a compelling interest to limit free speech as enjoyed by TikTok users, whether they are posting videos of their favorite Friday night outfits or engaging in political advocacy.

This interview has been edited for length and clarity.

Grid: Lets assume that TikTok goes away. Beyond the First Amendment concerns, what do you feel would be lost in terms of users rights and the whole cultural experience that it is brought to the country, particularly young people?

David Greene: TikTok is a major social influence at this particular moment. So its loss would be significant. Im not saying we would never recover; it hardly existed a few years ago. Its used by students. Its used by journalists, especially student journalists, quite broadly. It has academic uses. So it has very quickly become very embedded in our society.

G: Its so-called influencers can drive trends and commerce. TikTok content can be both serious and frivolous. User videos show everything from cute chipmunks, to gum-smacking and lip-syncing teens, to kids who change their outfits five times in one brief video. Beyond that, how would cultural exchange suffer?

DG: Any communications tool that allows people to reach a broad audience around the world, with very little technical skill and very little financial investment, is going to have an impact. There is also a great thirst for cultural exchange for organizing, for advocacy, around rights and around interests, or efforts at democratizing. So as weve seen with every social media platform that gains wide acceptance, these are very beneficial uses and effects that you would expect with people who very easily communicate and spread their ideas and identify community around the world.

G: Lets say the Chinese are hoovering up all this stuff. What is the value to anybody? Or is it a matter of getting embedded data that we dont even know about?

DG: Well, we dont know what the data will be useful for. But it could be a lot of things: Peoples social media habits tell a lot about their interests, and who they associate with, what things theyre likely to buy, what things are likely to catch their attention all of which can be beneficial for companies in terms of directing advertising and finding people were also recommending other content. And you can see how that could be useful to foreign actors, especially if they were trying to influence public opinion and want to make sure that certain people receive certain messages.

G: But TikTok users and other social media users are voluntarily giving this information away. So should the government do more regulation to prevent data mining by all social media companies?

DG: I dont want to downplay the importance of data privacy with respect to governance. Privacy is a human right, and people have a right to autonomy over their private information. I take that part of it very seriously. That being said, each person also has the role of threat modeling; they can determine to what extent, if they have enough knowledge about how their data is used, whether the threat that using the app poses is something thats acceptable for them. For many, many people, it might be a completely reasonable decision to say, you know, I understand that my data is being sucked up and it may end up in the hands of the Chinese government or might end at the hands of other governments. Others would say, I dont want to see it happen.

The important thing is giving them the autonomy to make that choice. And thats why if the government is really concerned about this, it should look at data privacy more broadly and enact more comprehensive data-privacy regulation that would restrict how all companies, not just TikTok, collect and retain and use user data. And so far, in Congress there seems to be bipartisan support for banning TikTok. And then not a lot of support for that type of comprehensive data privacy regulation

G: How could TikTok be effectively banned?

DG: I think the answer is, it depends on what the ban is. But if there were a total ban one of the things commonly discussed is banning the app stores from selling the app I think that would be making it less safe for users to use than more safe. One thing is that apps automatically set updates; if there are user privacy and data privacy vulnerabilities in the app, the company would correct that. Users who already had it on their devices get those updates.

The second thing that pretty likely happens is called using the side door. People will just find a way to install it from a website or otherwise not through the app store. And then you get the same problem that if there are security updates, they wont automatically get to the phones of those people. So you end up with a less-secure service, not a more-secure service. Another thing that could happen is just make it illegal to possess it. And I certainly dont see arresting people who have TikTok on their phone.

G: What else is possible?

DG: One other thing would be to force the sale of the company to U.S. interests. That doesnt necessarily solve data privacy problems, either, although maybe conceivably, it would address issues about closed or open channels with the Chinese government. It could potentially close that. But it doesnt actually mean that youre denying the Chinese government access to user data. So its very hard to see what a ban contributes. And obviously, when youre forcing a sale, that raises a lot of other issues. So it is hard to see exactly how you are putting in a ban.

G: I was talking to another First Amendment lawyer who said, roughly, this is all a publicity stunt by politicians. Would you agree with that?

DG: You know, I dont guess what peoples motives are for doing it. The way I see this is that the First Amendment does require that if youre going to say that U.S. people cant communicate with each other in a certain way, then that restriction, no matter what form it takes if its a ban on the app stores or making it illegal for the company to operate or a decision by Congress that they need to divest from foreign ownership whatever form it takes, that decision is going to have to satisfy First Amendment scrutiny. But no matter what level of scrutiny, the government is going to have to show that theres a real problem that they are trying to address and that this is an appropriately narrow approach. It has to be a real problem and not just be a suspicion or concern. Is this just political theater or is it xenophobia against all things China? Is it to score political points? We have to know what the basis for the restriction is.

G: One congressman described TikTok as a weather balloon into your phone. In other words, spyware.

DG: We dont know, right? We as American consumers dont know. Congress may know; they get a lot more information about spying and national security things. But you would think if they do know something theyd actually tell us instead of just dancing around it. But anyway, we do know theres a pretty legitimate concern for not just TikTok but the way all companies collect user data, and share it both among each other and in commerce, as well as it being available to governments. And this is a serious concern. There are a lot of things that you point to that a lot of social media apps and things we put on our phones could look like spy balloons.

G: The primary point people often jump on is the First Amendment concern, like you said, restricting speech of Americans. And you said theres a strict scrutiny of that. So how does that get resolved in a court?

DG: Presumably, what would happen is that TikTok itself or users would sue to invalidate the law. And some of these laws arent specific to TikTok for example the Restrict Act [the Restricting the Emergence of Security Threats that Risk Information and Communications Technology Act] creates a process thats not limited to TikTok. But whoever is subject to the law would come in and sue. Its true that what they could come in and say is, Look, we have a really, really serious important governmental interest. They could say, Look, theres a real interest, but we cant explain in open court because of national security concerns. They will likely be able to then submit their recommendation to the judge. So the judge can see it even if other parties dont get it or the general public.

G: Do you use TikTok?

DG: I havent actually been on TikTok myself. I dont have an account. Ive seen TikTok videos posted in other places. Theres a lot of things that are funny. There are things that are very political. There are some things I think are stupid and uninteresting to me. As far as I can tell, it has the full range of content.

G: How old are you?

DG: Fifty-eight. But I also want to say the trend with a lot of technology in general is that young people tend to adopt it and drive it into popularity. My choice not to download the app I dont know how much has to do with my age; I certainly know plenty of people my age and older than me who are on TikTok. Its just my personal choice to limit the social media that I spend time on.

G: So once the olds start using it, maybe it will just go away?

DG: If they always see my parents are on it, my grandparents, my third-grade teachers on it, they stop using it or at least stop using it in the way they did before and they find a platform where their peers are on it and people who they dont mind sharing different information with. Thats the trend weve seen with many services.

Thanks to Lillian Barkley for copy editing this article.

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Is a ban on TikTok possible and what would happen if it ... - Grid

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Process mining software is poised to become the trusted, daily digital assistant for every business. For it to take the final step however, we must bring the power of it to find and capture value to everyone in the business, and to do that we must reinvent process mining.

In an earlier article, I wrote about how process mining is being reinvented to deliver an end-to-end map of your business. Through a new approach called object-centric process mining (OCPM), we can produce process models that more accurately reflect the interconnectedness of modern business operations. With these object-centric models, we can visualize the complex relationships between objects as they flow through a single process and even see how multiple processes interact with each other. Analysis that isnt possible with traditional process mining.

OCPM makes process mining more powerful, but that power is only useful if it can be effectively used. For process mining to be the platform that drives performance excellence, we must put the technology in the hands of people across the organization.

For too long, process mining vendors built products that were designed more for IT professionals and data analysts than the non-technical business decision makers who can make actual process improvements. We must change this. We must democratize process mining with a user experience (UX) that enables everyone in the business to analyze and optimize processes right out of the box.

The ubiquitous business systems we use every day (email, word processors, spreadsheets, presentations, internet search) all followed this same tried and true path. They went from complex applications used by a few to simple apps used by almost everyone. And in doing so, these applications drove productivity gains across the organization and society in general.

Process mining technology has the same if not greater potential to improve organizational efficiency and drive business sustainability and in turn promote a more sustainable society.

Building enterprise apps that become essential for every user is no small feat. Successful software, business or consumer, must be powerful, scalable, responsive, secure and provide real value. However, these back-end characteristics often go unnoticed by the end user or are lumped together into a general feeling of the app works pretty well. Running smoothly is just table stakes. As my colleague Bill Detwiler wrote, todays users expect their corporate invoicing system to be as easy-to-use as a ride hailing app.

So the question isnt why, but how. How do you build enterprise software at consumer scale, and in doing so ensure the broad adoption of process mining? It starts by listening to the customer.

During my career, Ive worked in both the consumer space, at YouTube with Google, and the enterprise space, at Hyperion, Oracle and now Celonis. Whether youre building software for enterprise users or consumers, success depends on understanding where your customers are coming from. Thats why Ive always stressed the importance of building bridges between engineers and customers.

When engineers connect with their users, they learn what makes the customer successful, which in turn gets the creative juices flowing and enables innovation as they work to drive more delight with the install base. This is what my team and I are working on at Celonis. Were reimagining the process mining experience and making the technology more accessible for everyone.

At Celonis, were obsessed with understanding who our customers are, the work they are doing and the problems they are trying to solve. From the data analyst or process specialist to the line of business leader or executive, were building a user interface (UI) that flows naturally and guides each persona through what theyre trying to accomplish. The UI were building isnt just a boatload of features, but a series of guided journeys. We call this new version of process mining, Business Miner.

Business Miner is a new capability of Celonis EMS that enables non-technical business users to discover actionable process insights through a unique question-and-answer-based interface. Insights in hand, the interface allows users to share them with others within the organization and collaborate on and discuss them directly within Celonis. Additionally, new users are guided through a streamlined onboarding process.

Heres what this looks like in the real world. While looking at your companys accounts payable process, Business Miner could present your newly onboarded Head of AP with questions, such as: What is the breakdown of payments in early, late, and on-time? Curated visuals would then guide them to key insights and suggest follow-up questions, such as: What is the root-cause of my late payments? Your Head of AP could then share these process insights and discuss them with their team directly within Celonis.

Without Business Miner, process exploration like this would require the work of an analyst and often code, such as Celonis PQL. Likewise, collaborating on the process insights would often mean jumping out of Celonis and sending screenshots through email or your corporate messaging app.

This more powerful, more accessible process mining user experience isnt just theoretical. Companies are already using it to drive value. Alex Moro, Head of Process Re-engineering and Advanced Design at HSBC, says:

We see Celonis as a critical platform to drive business performance at HSBC.Removing the barrier to entry for non-technical users with Business Miner will help us better realize the value of Celonis EMS.

Now imagine if every single process and every single business ran a bit more efficiently. That can create more sustainable businesses and in turn a more sustainable world. As Ive said before, processes can save the world.

Continued here:

Process mining reinvented - why a consumer-grade user experience is a must have - Diginomica

March 20, 2023 10:45 AM | 2 min read

Benzinga Pro data, Comstock Mining (AMEX:LODE) reported Q4 sales of $30 thousand. Earnings fell to a loss of $20.79 million, resulting in a 291.07% decrease from last quarter. In Q3, Comstock Mining brought in $39 thousand in sales but lost $5.32 million in earnings.

Return on Capital Employed is a measure of yearly pre-tax profit relative to capital employed by a business. Changes in earnings and sales indicate shifts in a company's ROCE. A higher ROCE is generally representative of successful growth of a company and is a sign of higher earnings per share in the future. A low or negative ROCE suggests the opposite. In Q4, Comstock Mining posted an ROCE of -0.37%.

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Keep in mind, while ROCE is a good measure of a company's recent performance, it is not a highly reliable predictor of a company's earnings or sales in the near future.

ROCE is a powerful metric for comparing the effectiveness of capital allocation for similar companies. A relatively high ROCE shows Comstock Mining is potentially operating at a higher level of efficiency than other companies in its industry. If the company is generating high profits with its current level of capital, some of that money can be reinvested in more capital which will generally lead to higher returns and, ultimately, earnings per share (EPS) growth.

For Comstock Mining, a negative ROCE ratio of -0.37% suggests that management may not be effectively allocating their capital. Effective capital allocation is a positive indicator that a company will achieve more durable success and favorable long-term returns; poor capital allocation can be a leech on the performance of a company over time.

Comstock Mining reported Q4 earnings per share at $-0.26/share, which did not meet analyst predictions of $-0.05/share.

This article was generated by Benzinga's automated content engine and reviewed by an editor.

2023 Benzinga.com. Benzinga does not provide investment advice. All rights reserved.

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Comstock Mining's Return On Capital Employed Overview - Comstock (AMEX:LODE) - Benzinga

Artificial intelligence (AI) has created new opportunities in recent years. Industries are being affected, making previously unthinkable activities like space travel and melanoma diagnosis viable. As a result, there has also been a continuous rise in AI careers. LinkedIn said AI professionals would be among the 'jobs on the rise' in 2023. The 10 fantastic and well-paying AI jobs you can pursue in 2023 and beyond are discussed in this blog post. Also, do have a look at the online data science certification course if you want to start a career in data science and AI.

The future of AI employment is really bright. The US Department of Labor Statistics anticipates an 11% increase in computer science and information technology employment between 2019 and 2029. The industry will gain roughly 531,200 new employees as a result. It would seem that this is a conservative estimate. "AI and Machine Learning Specialists" are the second most in-demand profession according to the World Economic Forum.

As the industry matures, AI jobs will diversify, increase in quantity, and become more complicated. This will open up opportunities for experts, including novice and senior researchers, statisticians, practitioners, experimental scientists, etc. The future of moral AI is also brightening.

Artificial intelligence is a young and specialized field, yet many different careers exist. There are many different types of careers in AI, each requiring a unique set of qualifications. Let's examine each of the top ten in turn.

Data scientists and software engineers work together to create the field of machine learning engineering. Using big data technologies and programming frameworks, they produce scalable data science models capable of handling terabytes of real-time data and production-ready.

Qualifications suitable for work as a machine learning engineer include data science, applied research, and software engineering. Applicants for AI positions should have a strong mathematical foundation and be knowledgeable in deep learning, neural networks, cloud applications, and Java, Python, and Scala programming. Understanding IDE software development tools like Eclipse and IntelliJ is also beneficial.

Data scientists collect data, analyze it, and make judgments for a number of purposes. They utilize a variety of technical procedures, tools, and algorithms to draw information from data and identify significant patterns. This could entail anything as simple as spotting anomalies in time-series data or something more complicated like making predictions about the future, giving advice. The following qualifications are crucial for a data scientist:

BI developers look into complex internal and external data to find trends. For instance, a business that provides financial services could be a person who keeps track of stock market data to aid in investment selection. This might be someone who keeps an eye on sales patterns for a product company to help with distribution plans.

Business intelligence developers, unlike data analysts, do not construct reports. In order to use dashboards, business users are usually in charge of creating, modeling, and managing complex data on readily accessible cloud-based data platforms. Specific skills are expected of a BI developer:

A research scientist is one of the AI occupations with the highest academic demands. They pose original, thought-provoking queries for AI to respond to. They are specialists in various fields related to artificial intelligence, such as statistics, machine learning, deep learning, and mathematics. Like data scientists, researchers also require a computer science doctorate.

Organizations looking to hire research scientists to anticipate them to be well-versed in these fields, as well as in graphical models, reinforcement learning, and natural language processing. Benchmarking expertise and familiarity with parallel computing, distributed computing, machine learning, and artificial intelligence are preferred.

Big data engineers and architects build ecosystems allowing efficient connectivity among many business verticals and technologies. Although big data engineers and architects are often entrusted with planning, creating, and developing big data environments on Hadoop and Spark systems, this profession may feel more complicated than that of a data scientist.

Most employers prefer professionals with a Ph.D. degree in mathematics, computer science, or a closely related field. Yet, because this is a more practical role than, for example, a research scientist, practical experience is generally regarded as a vital substitute for a lack of academic degrees. Big data engineers require programming knowledge in C++, Java, Python, or Scala. Also, they must learn about data migration, visualization, and mining.

For AI applications, software engineers create software. They combine development operations such as writing code, continuous integration, quality control, API management, and so on for AI employment. They design and administer the software that architects and data scientists utilize. They stay up to date on the latest advances in artificial intelligence technologies.

An AI software engineer must be proficient in software engineering and artificial intelligence. They must have programming capabilities in addition to statistical and analytical abilities. Employers frequently require a bachelor's degree in computer science, engineering, physics, mathematics, or statistics.

Software architects develop and maintain technical standards, platforms, and tools. AI software architects do this for AI technology. They design and maintain the AI architecture, organize and carry out the solutions, select the tools, and ensure the data flow is seamless.

AI-driven organizations require software architects to have a bachelor's degree in computer science, information systems, or software engineering. Experience is just as important as knowledge in terms of actual application. You will be well-positioned if you have practical experience with cloud platforms, data operations, software development, statistical analysis, and so on.

A data analyst collects, cleans, processes, and analyzes data to draw conclusions. In the past, these were primarily routine, monotonous chores. The advent of AI has led to the automation of many routine tasks. As a result, a data analyst must be more familiar with data analytics than just spreadsheets. They must be knowledgeable about the following:

When industrial robots initially gained prominence in the 1950s, robotics engineers were possibly among the first jobs in artificial intelligence. Robotics has come a long way, from manufacturing lines to teaching English. Robotic-assisted surgery is used in healthcare. Personal assistants are being produced using robotic humans. A robotics engineer can do all of this and more.

Robotics engineers design and maintain AI-powered robots. Organizations usually require graduate degrees in engineering, computer science, or a related field for these positions.

Natural language processing (NLP) specialists are artificial intelligence (AI) engineers specializing in spoken and written human language. Engineers who work on voice assistants, speech recognition, document processing, and other projects employ NLP technology. For NLP engineering, organizations require a particular degree in computational linguistics. Companies may also be interested in hiring applicants with computer science, mathematics, or statistics backgrounds.

An NLP engineer would need knowledge of, among other things, sentiment analysis, n-grams, modeling, general statistical analysis, computer capabilities, data structures, modeling, and sentiment analysis. Prior knowledge of Python, ElasticSearch, web programming, and so on can be useful.

The vast majority of contemporary technology jobs are not in AI. Because artificial intelligence is a continuously evolving profession, experts in the area must constantly update themselves and keep up with new advances. AI/ML experts must keep up with the latest research and understand new algorithms on a regular basis; simply learning skills is no longer sufficient.

Furthermore, AI is under severe social and governmental scrutiny. AI professionals must

address the social, cultural, political, and economic consequences of AI and its technical components. The capacity to complete projects distinguishes an AI specialist in the real world. Experience is the only source for this

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10 Best and High-Paying AI Jobs for 2023 - NASSCOM Community