Category Archives: Machine Learning

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

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Machine learning and statistical classification of birdsong link vocal ... - Nature.com

Researchers at the University of Glasgow have published a paper that highlights their so-called ThermoSecure implementation for discovering passwords and PINs. The name ThermoSecure provides a clue to the underlying methodology, as the researchers are using a mix of thermal imaging technology and AI to reveal passwords from input devices like keyboards, touchpads, and even touch screens.

Before looking at the underlying techniques and technology, it's worth highlighting how impressive ThermoSecure is for uncovering password inputs. During tests, the research paper states: "ThermoSecure successfully attacks 6-symbol, 8-symbol, 12-symbol, and 16-symbol passwords with an average accuracy of 92%, 80%, 71%, and 55% respectively." Moreover, these results were from relatively cold evidence, and the paper adds that "even higher accuracy [is achieved] when thermal images are taken within 30 seconds."

How does ThermoSecure work? The system needs a thermal camera, which is becoming a much more affordable item in recent years. A usable device may only cost $150, according to the research paper. On the AI software side of things, the system uses an object detection technique based on Mask RCNN that basically maps the (thermal) image to keys. Across three phases, variables like keyboard localization are considered, then key entry and multi-press detection is undertaken, then the order of the key presses is determined by algorithms. Overall it appears to work pretty well, as the results suggest.

With the above thermal attack looking quite viable option for hackers to spy passwords, PINs, and so on, what can be done to mitigate the ThermoSecure threat? We've gathered the main factors that can impact the success of a thermal attack.

Input factors: Users can be more secure by using longer passwords and typing faster. "Users who are hunt-and-peck typists are particularly vulnerable to thermal attacks," note the researchers.

Interface factors: The thermodynamic properties of the input device material is important. If a hacker can image the input device in under 30 seconds, it helps a lot. Keyboard enthusiasts will also probably be interested to know that ABS keycaps retained touch heat signatures much longer than PBT keycaps.

Erase activity: The heat emitted from backlit keyboards helps disguise the heat traces from the human interaction with the keyboard. A cautious person could sometimes touch keys without actuating them and not leave the input area for at least a minute after they input the username / password.

Go passwordless: Even the best passwords are embarrassingly insecure compared to alternative authentication methods such as biometrics.

In summary, the accuracy of these thermal attacks is surprisingly high, even some time after the user has moved away from the keyboard / keypad. It is worrying but no more so than the other surveillance / skimming techniques that are already widespread. The best solution to these kinds of password and PIN guessing methods appears to be the move to biometrics, and / or two or more factor authentication. Preventing unauthorized access to your device in the first place (i.e. not leaving your laptop or phone unattended), especially not right after typing in your PIN/password, will also help thwart attackers.

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Thermal Cameras and Machine Learning Combine to Snoop Out Passwords - Tom's Hardware

Discover how AI and ML can potentially change the software development industry, and how AI affects software development and minimizes developers workload

Software development is a long, complex, and expensive process. Business owners and developers themselves constantly seek ways to optimize it. Good news for you, using artificial intelligence (AI) and machine learning (ML) is becoming increasingly popular in that regard.

According to a recent survey by Gartner, AI and ML are some of the trends that will shape the future of software development. For instance, early 73 percent of adopters of GitHub Copilot, an AI-driven assistant for engineers, reported that it helped them stay in the flow.

The use of this tool resulted in 87 percent of developers conserving mental energy while performing repetitive tasks. That increased their productivity and performance.

Twinslash and other software vendors and developers, on other hand, build AI-driven tools to help engineers with testing, debugging, code maintenance, and so on.

So: lets learn more about AI and ML and their impact on software development.

The ability to automate monotonous manual tasks is one of the significant benefits of AI. There are several ways to effectively implement AI in the development process that completely replace human intervention or, at least, reduce it enough to remove the tediousness of repetitive tasks and allow your engineers to focus on more critical issues.

One of the common applications of AI in development is utilizing it to reduce the number of errors in the code.

AI-powered tools can analyze historical data to identify recurring errors or faults, spot them, and either highlight them for developers to fix or fix them independently in the background. The latter option will reduce the need to roll back for fixes when something goes wrong during your software development process.

AI improves the quality, coverage, and efficiency of software testing. This is because it can analyze large amounts of data without making mistakes. Eggplant and Test Sigma are two well-known AI-assisted software testing tools.

They aid software testers in writing, conducting, and maintaining automated tests to reduce the number of errors and boost the quality of software code. AI in testing is extremely useful in large-scale projects usually combined with automated testing tools, it helps to check through multi-leveled, modular software faster.

ML software can track how a user interacts with a particular platform and process this data to pinpoint patterns that can be used by developers and UX/UI designers to generate a more dynamic, slick software experience.

AI can also help discover UI blocks or elements of UX people are struggling with, so designers and developers can reconfigure and fix them.

Code security is of utmost importance in software development. You can use AI to analyze data and create models to distinguish abnormal activity from ordinary behavior. This will help software development companies catch issues and threats before they can cause any problems.

Apart from that, tools like Snyk, integrated into engineers Integrated Development Environment (IDE) can help pinpoint security vulnerabilities in the apps before releasing them in production.

Lets talk about the main overall trends that are changing the field of software engineering and product development.

Generative AI is a powerful technology that uses AI algorithms to create any kind of data code, design layouts, images, audio or video files, text, and even entire applications. It studies datasets independently and can help produce a wide range of content.

One of the most significant benefits of generative AI is that it can help developers create software quickly and efficiently. For instance, it assists with:

Code completion. AI-enabled code completion tools in IDEs, such as Microsofts Visual Studio Code, can help developers write code faster. For VS, such a tool is called IntelliCode it analyzes a ton of GitHub repos and searches for code snippets that might be relevant for the developers next step and completes the lines for them.

Layout design. AI-powered design tools can analyze user behavior and preferences to generate optimized layouts for websites and mobile applications. For example, for some AI-powered plugins on the design platform, Canva uses machine learning algorithms to suggest layouts, fonts, and colors for marketing materials.

(Entire) app development. With generative AI, developers can automate the process of creating software or pieces of software by telling the AI the prompts for an app one wants to build. OpenAIs Codex can do that, using natural language processing models both for parsing through conversational language and syntax of a programming language.

Continuous delivery is a software development practice where code updates are automatically built, tested, and deployed to production environments. AI-powered continuous delivery can optimize this process by using machine learning algorithms to identify and address issues before they become critical.

Machine learning algorithms can analyze the performance of production environments and predict potential issues before they occur, reducing downtime and improving software reliability.

Apart from that, ML can parse through different deployment strategies and recommend the best approach based on past performance and current conditions of the system.

Now, that trend isnt directly tied to software development, but it impacts it quite significantly. Product and project managers can use AI tools to plan the project faster.

Of course, tools like ChatGPT wont replace the experience of talking to actual potential users, but it can still help them quickly get a grasp of the market situation, trends, or common concerns users have with the competitors product.

Tools like that one can also be utilized to conduct drafts for SWOT analysis, which is also extra vital for planning out the value proposition of the software and prioritizing features-to-be-built for a roadmap. Now, ChatGPT is also a generative AI, but we thought that its application deserves a separate section.

As Eric Schmidt, former CEO of Google, once said, I think theres going to be a huge revolution in software development with AI. That revolution is now. It is safe to say that the future of software development lies in AI and ML.

With the rise of AI-powered programming assistants and AI-enabled design work and security assessments, software development will become more cost-effective. Utilizing AI and ML in software development will also increase productivity, fasten time-to-market, and improve software quality.

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Software Development Future: AI and Machine Learning - Robotics and Automation News

Q: What are the challenges to undertaking data analytical research? And where have these modes of analysis been successful?

The challenges are many, especially when you want to make a meaningful impact in one of the most complex sectorsthe health care sector. The health care sector involves a variety of stakeholders, especially in the United States, where health care is extremely decentralized yet highly regulated, for example in the areas of data collections and data use. Analytics-based solutions that can help one part of this sector might harm other parts, making finding globally optimal solutions in this sector extremely difficult. Therefore, finding data-driven approaches that can have public impact is not a walk in the park.

Then there are various challenges in implementation. In my lab, we can design advanced machine learning and AI algorithms that have outstanding performance. But if they are not implemented in practice, or if the recommendations they provide are not followed, they wont have any tangible impact.

In some of our recent experiments, for example, we found that the algorithms we had designed outperformed expert physicians in one of the leading U.S. hospitals. Interestingly, when we provided physicians with our algorithmic-based recommendations, they did not put much weight on the advice they got from the algorithms, and ignored it when treating patients, although they knew the algorithm most likely outperforms them.

We then studied ways of removing this obstacle. We found that combining human expertise with the recommendations provided by algorithms not only made it more likely for the physicians to put more weight on the algorithms advice, but also synthesized recommendations that are superior to both the best algorithms and the human experts.

We have also observed similar challenges at the policy level. For example, we have developed advanced algorithms trained on large-scale data that could help the Centers for Disease Control and Prevention improve its opioid-related policies. The opioid epidemic caused more than 556,000 deaths in the United States between 2000 and 2020, and yet the authorities still do not have a complete understanding of what can be done to effectively control this deadly epidemic. Our algorithms have produced recommendations we believe are superior to the CDCs. But, again, a significant challenge is to make sure CDC and other authorities listen to these superior recommendations.

I do not want to imply that policymakers or other authorities are always against these algorithm-driven solutionssome are more eager than othersbut I believe the helpfulness of algorithms is consistently underrated and often ignored in the practice.

Q: How do you think about the role of oversight and regulation in this field of new technologies and data analytical models?

Imposing appropriate regulations is important. There is, however, a fine line: while new tools and advancements should be guarded against misuses, the regulations should not block these tools from reaching their full potential.

As an example, in a paper that we published in the National Academy of Medicine in 2021, we discussed that the use of mobile health (mHealth) interventions (mainly enabled through advanced algorithms and smart devices) have been rapidly increasing worldwide as health care providers, industry, and governments seek more efficient ways of delivering health care. Despite the technological advances, increasingly widespread adoption, and endorsements from leading voices from the medical, government, financial, and technology sectors, these technologies have not reached their full potential.

Part of the reason is that there are scientific challenges that need to be addressed. For example, as we discuss in our paper, mHealth technologies need to make use of more advanced algorithms and statistical experimental designs in deciding how best to adapt the content and delivery timing of a treatment to the users current context.

However, various regulatory challenges remainsuch as how best to protect user data. The Food and Drug Administration in a 2019 statement encouraged the development of mobile medical apps (MMAs) that improve health care but also emphasized its public health responsibility to oversee the safety and effectiveness of medical devicesincluding mobile medical apps. Balancing between encouraging new developments and ensuring that such developments abide by the well-known principle of do no harm is not an easy regulatory task.

At the end, what is needed are two-fold: (a) advancements in the underlying science, and (b) appropriately balanced regulations. If these are met, the possibilities for using advanced analytics science methods in solving our lingering societal problems are endless.

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Big data and machine learning can usher in a new era of policymaking - Harvard Kennedy School

[mat kelcey] was so impressed and inspired by the concept of a very slow movie player (which is the playing of a movie at a slow rate on a kind of DIY photo frame) that he created his own with a high-resolution e-ink display. It shows high definition frames from Alien (1979) at a rate of about one frame every 200 seconds, but a surprising amount of work went into getting a color film intended to look good on a movie screen also look good when displayed on black & white e-ink.

The usual way to display images on a screen that is limited to black or white pixels is dithering, or manipulating relative densities of white and black to give the impression of a much richer image than one might otherwise expect. By itself, a dithering algorithm isnt a cure-all and [mat] does an excellent job of explaining why, complete with loads of visual examples.

One consideration is the e-ink display itself. With these displays, changing the screen contents is where all the work happens, and it can be a visually imperfect process when it does. A very slow movie player aims to present each frame as cleanly as possible in an artful and stylish way, so rewriting the entire screen for every frame would mean uglier transitions, and that just wouldnt do.

So the overall challenge [mat] faced was twofold: how to dither a frame in a way that looked great, but also tried to minimize the number of pixels changed from the previous frame? All of a sudden, he had an interesting problem to solve and chose to solve it in an interesting way: training a GAN to generate the dithers, aiming to balance best image quality with minimal pixel change from the previous frame. The results do a great job of delivering quality visuals even when there are sharp changes in scene contrast to deal with. Curious about the code? Heres the GitHub repository.

Heres the original Very Slow Movie Player that so inspired [mat], and heres a color version that helps make every frame a work of art. And as for dithering? Its been around for ages, but that doesnt mean there arent new problems to solve in that space. For example, making dithering look good in the game Return of the Obra Dinn required a custom algorithm.

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Very Slow Movie Player Avoids E-Ink Ghosting With Machine Learning - Hackaday