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10 best ways to earn money through machine learning in 2023 are enlisted in this article

10 best ways to earn money through machine learning in 2023 take advantage of the early lifespan and its adoption may then leverage this into other apps.

Land Gigs with FlexJobs: FlexJobs is one of the top freelance websites for finding high-quality employment from actual businesses. Whether you are a machine learning novice or a specialist, you may begin communicating with clients to monetize your skills by working on freelancing projects.

Become a Freelancer or List your Company to Hire a Team on Toptal: Toptal is similar to FlexJobs in that it is reserved for top freelancers and top firms wanting to recruit freelance machine learning programmers. This is evident in the hourly pricing given on the site as well as the caliber of the programmers.

Develop a Simple AI App: Creating an app is another excellent approach to generating money using machine learning. You may design a subscription app in which users can pay to access certain premium features. Subscription applications are expected to earn at least 50% more money than other apps with various sorts of in-app sales.

Become an ML Educational Content Creator: You can make money with machine learning online right now if you start teaching people about machine learning and its benefits. To publish and sell your course, use online platforms that provide teaching platforms, such as Udemy and Coursera.

Create and Publish an Online ML Book: You may create a book to provide extraordinary insights on the power of 3D printing, robots, AI, synthetic biology, networks, and sensors. Online book publication is now feasible because of systems such as Kindle Direct Publication, which provides a free publishing service.

Sell Artificial Intelligence Devices: Another profitable enterprise to consider is selling GPS gadgets to automobile owners. GPS navigation services can aid with traffic forecasting. As a result, it can assist car users in saving money if they choose a different route to work. Based on everyday experiences, you may estimate the places likely to be congested with access to the current traffic condition.

Generate Vast Artificial Intelligence Data for Cash: Because machine learning can aid in the generation of massive amounts of data, you can benefit from providing AI solutions to various businesses. AI systems function similarly to humans and have a wide range of auditory and visual experiences. An AI system may learn new things and be motivated by dynamic data and movies.

Create a Product or a Service: AI chatbots are goldmines and a great method to generate money with machine learning. Creating chatbot frameworks for mobile phones in the back endand machine learning engines in the front end is an excellent way to make money quickly. Making services like sentiment analysis or Google Vision where the firm or user may pay after making numerous queries per month is another excellent approach to gaining money using ML.

Participate in ML Challenges: You may earn money using machine learning by participating in and winning ML contests, in addition to teaching it. If you are a guru or have amassed a wealth of knowledge on this subject, you may compete against other real-world machine-learning specialists in tournaments.

Create and License a Machine Learning Tech: If you can develop an AI technology and license it, you can generate money by selling your rights to someone else. As the licensor, you must sign a contract allowing another party, the licensee, to use, re-use, alter, or re-sell it for cash, compensation, or consideration.

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10 Best Ways to Earn Money Through Machine Learning in 2023 - Analytics Insight

Maximus and Amazon Web Services are working to help federal health agencies leverage the power of artificial intelligence and machine learning to address the needs of citizens and support other missions under a strategic partnership.

As an AWS Alliance Partner, Maximus could help health agencies advance their digital modernization plans by providing capabilities and services across the areas of cloud migration, data analytics, cybersecurity and data management, according to an article published Friday on Maximus website.

AI and ML tools could help revolutionize health care, and agencies need industry partners that could provide technology capabilities that could facilitate the adoption of such tools as well as offer cloud platforms that prioritize data security, management and compliance.

According to the article, Maximus Digital Experience Hub helps agencies respond to large volumes of citizen service requests through web-based content management and patient self-service options while leveraging AWS platforms for mobile-responsive web services and mobile applications.

In delivering modernized AWS cloud architectures, Maximus experts enable agencies to better maintain new technology, achieve continuous authorization to operate, and update applications and services quickly as organization and customer needs evolve, the article reads.

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Maximus, AWS Seek to Help Federal Health Agencies Advance AI ... - ExecutiveBiz

Services like Google Translate can help millions of people communicate in over 100 languages. Users can type or speak words to be translated, or even translate text in photos and videos using augmented reality.

Now, computer science professor Andrea Salgian and Ben Guerrieri 26 are working to add one more language to the list: American Sign Language.

Using computer vision and machine learning, the researchers are setting out to create a program to serve as a Google Translate tool for ASL speakers to sign to the camera and receive a direct translation.

Right now, were looking at recognizing letters and words that have static gestures, Salgian said, referring to letters in the ASL alphabet with no hand movement. The program will act more like a dictionary at first. The pair will then develop the automated translation, she explained.

Salgians research utilizes a free machine-learning framework called Mediapipe, which is developed by Google and uses a camera to detect joint locations in real time. The program tracks the users movements, provides the coordinates of every single joint in the hand, and uses the coordinates to extract gestures that are matched to ASL signs.

Computer science major Ben Guerrieri 26 discovered Salgians project shortly after arriving at TCNJ and is now working alongside her in this AI research.

Its such a hands-on thing for me to do, he said of his contribution to the project, which consists of researching and developing the translator algorithms. We get to incrementally develop algorithms that have super fascinating real-time results.

This project is part of Salgians on-going interest and research into visual gesture recognition that also includes applications to musical conducting and exercising.

ASL is a fascinating application, especially looking at the accessibility aspect of it, Salgian said. To make communication possible for those who dont speak ASL but would love to understand would mean so much, Salgian said.

Kaitlyn Bonomo 23

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From fraud detection to simplified underwriting, machine learning is improving life insurance.

It is a critical component of any financial plan. Life insurance offers financial protection to individuals and their loved ones in case of unforeseen events like illness, disability, or death.

In the past, the life insurance industry has relied on traditional underwriting methods to determine premiums and policy terms.

With the advent of machine learning, the life insurance industry is experiencing a significant digital shift in the way policies are priced, marketed, and underwritten.

One of the most significant advantages of using machine learning in life insurance is its ability to improve pricing and underwriting accuracy. Insurers traditionally relied on static underwriting factors, such as age, gender, and medical history, to assess risk and determine premiums. It's important to state that this approach is often limited in its scope and fails to capture the complex relationships between risk factors.

Machine learning algorithms can analyze large datasets and identify patterns and relationships that were previously unknown. By incorporating non-traditional data sources, such as social media activity or wearable device data, insurers can create a more comprehensive risk profile for policyholders. This approach can lead to more accurate risk assessments and pricing models, reducing errors and improving policyholder satisfaction.

Another area where machine learning is transforming the life insurance industry is in the creation of personalized policies. Personalized policies are tailored to individual policyholders based on their unique characteristics and risk profiles. Traditional underwriting methods often rely on broad categories, such as age or gender, to determine policy terms. However, this approach fails to capture the individual nuances of each policyholder's risk profile.

Machine learning techniques can analyze vast amounts of data to create personalized policies that better reflect a policyholder's individual risk profile. These policies can be tailored to the specific needs and goals of each policyholder, leading to increased customer satisfaction and loyalty.

Another significant area where machine learning is transforming the life insurance industry is in claims processing. Traditional claims processing is often time-consuming and involves significant manual labor. Insurers must review documents, communicate with policyholders and medical professionals, and make complex calculations to determine payouts.

Machine learning techniques can automate many of these processes, leading to faster and more accurate claims processing. By analyzing data from various sources, such as medical records or police reports, machine learning algorithms can assess the validity of claims and calculate payouts accurately.

Insurance fraud is a significant issue for the life insurance industry. Fraudulent claims can lead to significant financial losses for insurers, which can ultimately impact policyholders. Traditional methods of fraud detection often rely on manual review processes or simple rules-based systems, which can miss more complex cases of fraud.

Machine learning algorithms can analyze large amounts of data to identify patterns and anomalies that may indicate fraudulent behavior. By using advanced techniques such as anomaly detection or clustering, insurers can more accurately detect and prevent fraud, reducing losses and improving policyholder satisfaction.

Machine learning is revolutionizing the life insurance industry in many ways. By improving pricing and underwriting accuracy, creating personalized policies, streamlining claims processing, and detecting fraud, insurers can better meet the needs of policyholders and improve the overall customer experience. There are still challenges to overcome, such as data privacy concerns and the need for continued innovation and adoption. As the life insurance industry continues to evolve, it is clear that machine learning will play a critical role in shaping its future.

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Machine Learning in Life Insurance: Applications and Benefits - BBN Times

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