Can AI create award-winning music, painting masterpieces, and captivating novels? So far, AI and art combine has shown incredible potential across industries and has made powerful strides in assisting in any given task. However, people mainly argue whether the impact of AI could be seen in creative industries, in which success relies heavily on human intuition.
This article explores the relationship between AI and artistic fields, the uses of AI in different art forms, and whether it can be counted as an assisting tool or a threat to artists.
AI is already transforming industries like music, design, and literature. However, the use of AI in creative industries has raised several concerns among art producers and audiences. We started to hear questions like: Can AI truly replicate the soul that breathes life into art? And, can AI capture the spark of originality that sets human creations apart?
In fact, AI plays a significant role in enhancing the outcomes of different art forms when artists employ it correctly. It can analyze existing styles and help discover new ones. It is also a great assistant for generating innovative ideas beyond traditional artistic practices.
AI is still far from creating genuine art pieces unless an established artist with a unique perspective provides input tailored to elicit favorable outcomes. However, combining AI with human creativity opens untapped venues for art creators to explore and innovate.
AI powerful capabilities have the potential to reshape the creative industry, as it opens new possibilities for artists, designers, filmmakers, and musicians. Here is a breakdown of the essential uses of AI and its benefits in each of these fields:
AI is a valuable addition to the graphic design toolbox. It offers efficiency, creativity, and accessibility, enabling designers to create even more impactful and engaging visuals. Performing the following tasks is made way easier with AI:
AI is revolutionizing the world of filmmaking, demonstrating remarkable results as it augments and supports human talent. The following are three prominent uses of AI in filmmaking:
So far, AI is demonstrating the great potential and endless capabilities, unlocking promising opportunities for the creative industry. However, it’s important to address the fact that AI remains a tool that cannot replace unique human creativity. The best results it can achieve primarily depend on the tailored input artists provide.
Recently, HR departments have been integrating AI to automate time-consuming tasks to focus on strategic work. AI provides HR professionals with insights and data, helping them make better decisions and develop personalized, engaging employee experiences.
This article explores the impact of Artificial Intelligence and its role in the Human Resources department, studying the transformation of HR through AI.
A Human resources (HR) division is typically responsible for finding, recruiting, screening, and training job applicants. It administers employee job descriptions and their performance and benefit programs.
When recruiting for new positions, HR departments are responsible for understanding the organization’s needs, functions, sectors, and services and ensuring that employees meet the company’s needs when hired.
The HR division provides employees with guidance to help build their career paths within the company. HR periodically checks on the progress of each employee and determines their skill set to build a personalized training program to ensure that departments and teams have an optimal function.
Performance management assesses tools, coaching, and counseling and collects continuous feedback to maintain or improve job performance. Typically, the evaluation varies depending on the unique characteristics of the workforce distribution, size, and other factors.
HR activities build rapport, a two-way dialogue, and engagement between employees, HR, and senior leadership. It builds the company culture to encourage employees. To do so, HR divisions recognize individual and group achievements by organizing company-wide gatherings.
HR departments use AI-powered tools and platforms to streamline and automate manual tasks, improve employee experience, and reduce bias in the hiring process. Unlike usual recruitment processes, AI recruitment can automate time-consuming tasks like resume screening and scheduling interviews.
AI can help recruiters screen resumes quickly and accurately by extracting essential information from resumes, like skills, experience, and education, and matching it to job requirements.
Chatbots automate many time-consuming tasks in initial candidate interaction, like answering questions and scheduling interviews. It also reduces bias in the screening process by evaluating candidates based on pre-set criteria rather than subjective impressions.
AI predictive analytics help companies identify candidates more likely to succeed in a specific post. It analyzes historical data on past hires to help businesses reduce bias in the hiring process by objectively assessing candidates’ qualifications and potential.
AI algorithms analyze employee data, like job descriptions and performance reviews, to identify the required skills and knowledge for a job post. The algorithms then recommend courses tailored to each employee’s needs. AI algorithms can also track employee progress to adjust the difficulty of the content to ensure that each employee isn’t overwhelmed.
VR can create work environments for employees to learn in a safe environment without the risk of accidents or errors. VR can also help employees develop soft skills like customer service, communication, and leadership.
HR departments can use AR to provide employees with instructions and feedback while performing their jobs. AR can also create gamified learning experiences and other interactive activities that make training fun and engaging for employees.
AI can analyze employee data like employee performance, skills assessments, and career goals to identify strengths and weaknesses and recommend tailored learning paths that are relevant and effective for each employee.
AI can also analyze employee feedback from surveys and performance reviews to identify patterns and trends in performance data. It helps HR professionals identify employees who may need additional support.
AI can analyze data like employee task completion rates and performance levels to track employee progress toward their goals. HR departments can then use this information to provide feedback and help coach employees to achieve their goals.
AI predictive analytics uses artificial intelligence (AI) to analyze data and determine patterns and trends to predict future results. HR can use predictive analytics to identify potential employee performance tracking and feedback issues.
For negative sentiment, AI-driven sentiment analysis can scan employee feedback to understand what employees are engaged with and are not. AI-driven sentiment analysis can assess and address areas lacking work culture to help enhance employee experience.
Chatbots can proactively contact employees regularly to collect feedback on their work experience and manager satisfaction. Chatbots offer a confidential and convenient way for employees to provide feedback without fear of retaliation.
AI can analyze employee data, like health insurance claims, biometric data, and survey results, to identify employees with health problems. HR departments can then use this information to proactively generate personalized wellness recommendations for employees. Personalized wellness programs show employees that the company cares about their health and well-being.
Overall, the integration of AI in HR can transform how companies practice by automating tasks and providing insights. It enhances the employee experience and helps HR professionals be more effective and efficient.
Artificial intelligence (AI) is transforming our perspective on education. Cultivating an environment where students learn from AI-driven machines and not just textbooks, AI offers a more immersive experience that allows students to explore different paths of knowledge.
From providing access to personalized learning materials to aiding teachers in assessing student performance more efficiently, AI has much to offer educational institutions. This article will look at the various ways AI can be used in education and discuss its future benefits, challenges, and potential.
Nowadays, the educational sector’s landscape is rapidly shifting due to tech-driven innovations like AI. With a wide set of benefits, it has the potential to make learning more efficient, effective, and personalized with customized content based on each student’s individual needs and capabilities.
Personalized learning emerged as an approach to tailoring educational content and activities to each student’s needs, abilities, and interests. With AI, this process is quickly becoming more efficient and accurate.
Based on the contextual data from students’ interactions with digital learning tools, teachers can customize lessons and assessments. AI-based systems can gather data on each student’s academic performance. Later, teachers can utilize that data to create personalized curriculums tailored to each student’s needs.
With the boost in personalized learning, AI can provide personalized learning experiences for students with disabilities and other special needs, making class participation and access to educational resources much easier.
AI-based tools such as voice recognition, machine learning, natural language processing, and other AI innovations can promote an equitable learning environment for all learners, regardless of their needs or abilities.
For instance, automatic lip reading can help individuals with hearing impairments understand audio content more easily. At the same time, text-to-speech applications provide spoken translations of written material for those who struggle with visual impairments or learning disabilities.
On the other hand, AI-assisted closed captioning and subtitling facilitate access to audio content for individuals with hearing impairments. Hence, they won’t need to rely on sign language interpreters or other third parties. This also comes in handy for students who cannot speak the course instruction language, allowing them to access numerous educational materials in their native language.
AI can have a profound impact on student achievement. It can identify areas where students need help, generate individualized study plans, provide feedback on student performance, and detect any issues hindering progress.
Tools like chatbots and virtual assistants allow students to stay on track with their studies by providing personalized support, guidance, and feedback. Teachers and educators, on the other hand, can adopt these tools to monitor academic progress. This allows them to easily track student progress and recognize any needed changes to expedite progress and achieve learning goals.
Thanks to its automation capacity, automating certain tasks, such as grading exams or tracking progress, allows teachers to focus more on developing creative teaching strategies and engaging with students. In doing so, AI will foster an environment conducive to higher student achievement.
AI-driven tools allow teachers to use data better, analyze student performance more accurately, and provide personalized learning experiences. Consequently, teachers gain a holistic view of their student’s progress and create tailored lesson plans that meet their needs.
Data-driven insights gathered from AI also give teachers a better understanding of how they should approach teaching each class and anticipate student needs and trends, helping them adjust their teaching style accordingly to prepare for future challenges.
For instance, AI-driven automated grading and feedback systems provide teachers with detailed reports on student performance that can be used to assess student progress and individualize instruction. They also have the potential to reduce time spent grading assessments, freeing up educators’ time for other tasks such as lesson planning or professional development.
AI is a powerful tool for improving the quality of education and learning. However, several challenges and limitations must be addressed before it can be fully integrated into educational systems.
Nowadays, we have access to a whole world of information anytime, anywhere, making learning easier and more efficient. However, this reliance on technology and AI also comes with its challenges.
Over-reliance on technology could eventually result in decreased creativity and problem-solving skills for learners. Indeed, with so much information readily available online, students may not feel the need to think critically or develop creative solutions on their own.
This can also lead to a lack of understanding of the material being taught. Students may be unable to think independently or process information when they are not aided by technology or AI.
Building, maintaining, training, and updating AI systems is often too expensive for small institutions. They require large amounts of data for training and expensive hardware for processing power, which puts much strain on resources.
In addition, many schools and universities lack access to skilled personnel to manage these systems properly. Hence, hiring professionals to create and maintain systems is quite costly and could limit many institutions’ adoption of AI technologies.
Not to mention that, as more complex algorithms are developed, the deployment costs increase exponentially, making it difficult for institutions to keep up with the rapid pace at which technology is advancing.
While the promise of AI-enhanced learning and personalized instruction is exciting, there are valid concerns about how student data is collected and used, including who can access it and the purposes for which it can be used.
AI systems collect large amounts of student activity data, including sensitive information such as health records or financial information. Clearly, this data must be kept secure to ensure it is not misused or accessed by unauthorized individuals.
There are also questions about whether the use of AI in education will ultimately result in unequal outcomes for students based on their race or socioeconomic status. Unfortunately, this can lead to unintended consequences such as unfair grading or algorithmic discrimination in the classroom since AI systems absorb and perpetuate societal biases in their training data sets. Hence, educators and administrators must consider the ethical implications of using AI-based educational tools and systems and mitigation measures for potential privacy risks.
Innovation has become the norm today, with incredible innovations and technological advancements pouring in. AI is at the forefront of this remarkable breakthrough, a game-changing technology impacting various sectors in a way that is nothing short of extraordinary, and education is no exception.
AI will enable teachers to create more personalized learning experiences for their students by tailoring their teaching methods to different learners. It will also reduce the administrative workload by automating tasks such as grading assignments, analyzing student data, and detecting plagiarism. In the near future, we expect it to take on a bigger role in the education sector.
However, as AI applications become more common in education, recognizing the critical role that humans will play in this dynamic is essential. Contrary to the common narrative, Humans will still be needed, even with full AI deployment in the future.
Humans still need to facilitate interactions between students and machines and provide guidance on how best to use AI technologies for educational purposes. They can also design effective curriculums incorporating AI-based tools into their teachings and assess their effectiveness.
Although there is still much to learn about the potential benefits and challenges of utilizing AI in education, one thing is certain; it will continue to play an important role in learning environments as we advance. With responsible use and thoughtful implementation of this technology, educators can create better learning experiences for students of all levels worldwide.
Recently, Davinci’s team of experts has been extremely busy developing innovative software. Being an IT engineering company that employs artificial intelligence breakthroughs to help businesses of all kinds to stand out, Davinci took the challenge of implementing AI in the pricing processes of the transportation industry.
Auto transportation is one of the business fields that took advantage of this technology the most. It is because the auto transport industry is extensive, and its clients are many; serving them on short notice and generating accurate auto transport estimations requires a lot of time and effort.
Therefore, companies use online auto transport price calculators to provide real-time average cost estimates to their clients—an efficient software tool but only to a certain level due to its limitations and lack of accuracy. However, artificial intelligence provided a way to improve it significantly, and that was Davinci’s approach in this regard.
This article will demonstrate everything about the AI-based auto transportation cost calculator: its definition, benefits, and future.
What is an online transportation cost calculator?
A transportation cost calculator is a traditional software tool that generates instant, inaccurate transportation quotes after examining many expense factors. These factors include the cargo type and weight, the distance from point A to point B, and the shipping speed.
Davinci, however, has improved this primitive cost calculator to a more sophisticated one that employs artificial intelligence in all the calculating processes. The new tool utilizes artificial intelligence, machine learning, and big data to ensure accuracy in real-time calculation.
Top advantages of AI auto transport cost calculators
Producing an auto transport quote is a very complex process that requires high expertise and a long time. A customer service agent has to research the market, check fuel prices, calculate the distance from the pickup to the delivery time, and the list goes long until they can finally come up with a quote.
A human-generated quote, however, will never be accurate, as no human mind can carry out all these assignments in a little while. On the other hand, Davinci’s AI auto transport cost calculator provides quotes immediately based on variable factors and the latest market updates.
Auto transport companies usually hire customer service agents to work around the hour and provide price quotes to interested prospects. Implementing Davinci’s AI auto transport cost calculator saves considerable monthly expenses and operates at different time zones to provide accurate price quotes.
Despite the initial investment required to implement the technology in a company’s website, it’s cost-efficient and saves operational costs, including rentals, equipment, and monthly salaries.
Instead of manually organizing clients’ names, orders, pickup and delivery locations, shipment dates, and all the details needed for placing an auto transport order, everything will be listed and arranged by Davinci’s calculator.
It also follows up with clients the way they prefer: text messages or emails. This calculator handles the process from A to Z, so no human intervention is required.
The AI cost calculator Davinci improved can evolve automatically by experience and use of available data on the internet. It also uses algorithms and statistics to analyze and draw deductions from patterns in data.
The future of AI transportation cost calculator
Davinci’s AI calculator has achieved great success. All the US auto transport companies collaborating with Davinci have witnessed improvements in all aspects. Revenues have risen, clients number have increased, and work efficiency notably boosted. That’s why Davinci expanded its horizons and started laying out a plan to serve more transport industry sectors.
Air freight and sea freight companies will soon get a share of Davinci’s breakthrough. A new, more advanced, and sophisticated version of the AI transport cost calculator will be able to solve air and sea shipping companies’ quote calculation dilemma.
Calculating air or sea shipping costs is way more demanding and complicated than land shipping, as they’re greatly affected by the weather, the exchange rate in different countries, fuel costs, etc. However, Davinci’s cost calculator (check at tempuslogix.com) will be able to learn about all those flirtations and provide immediate, real-time cost estimations.
A new AI cost calculator that will serve air and sea freight sectors is under study and development; Davinci, in collaboration with many leading partners, will officially launch this advanced tool.
Conclusion
Davinci, the leading IT engineering company, has no limits and is always improving its current services and coming up with novel strategies to keep up with the world’s developments and updates. This company’s solutions have found a way to many businesses worldwide and will keep improving to reach and aid a wider scale of clients.
“Interpretability methods” seek to shed light on how machine-learning models make predictions, but researchers say to proceed with caution.
About a decade ago, deep-learning models started achieving superhuman results on all sorts of tasks, from beating world-champion board game players to outperforming doctors at diagnosing breast cancer.
These powerful deep-learning models are usually based on artificial neural networks, which were first proposed in the 1940s and have become a popular type of machine learning. A computer learns to process data using layers of interconnected nodes, or neurons, that mimic the human brain.
As the field of machine learning has grown, artificial neural networks have grown along with it.
Deep-learning models are now often composed of millions or billions of interconnected nodes in many layers that are trained to perform detection or classification tasks using vast amounts of data. But because the models are so enormously complex, even the researchers who design them don’t fully understand how they work. This makes it hard to know whether they are working correctly.
For instance, maybe a model designed to help physicians diagnose patients correctly predicted that a skin lesion was cancerous, but it did so by focusing on an unrelated mark that happens to frequently occur when there is cancerous tissue in a photo, rather than on the cancerous tissue itself. This is known as a spurious correlation. The model gets the prediction right, but it does so for the wrong reason. In a real clinical setting where the mark does not appear on cancer-positive images, it could result in missed diagnoses.
With so much uncertainty swirling around these so-called “black-box” models, how can one unravel what’s going on inside the box?
This puzzle has led to a new and rapidly growing area of study in which researchers develop and test explanation methods (also called interpretability methods) that seek to shed some light on how black-box machine-learning models make predictions.
What are explanation methods?
At their most basic level, explanation methods are either global or local. A local explanation method focuses on explaining how the model made one specific prediction, while global explanations seek to describe the overall behavior of an entire model. This is often done by developing a separate, simpler (and hopefully understandable) model that mimics the larger, black-box model.
But because deep learning models work in fundamentally complex and nonlinear ways, developing an effective global explanation model is particularly challenging. This has led researchers to turn much of their recent focus onto local explanation methods instead, explains Yilun Zhou, a graduate student in the Interactive Robotics Group of the Computer Science and Artificial Intelligence Laboratory (CSAIL) who studies models, algorithms, and evaluations in interpretable machine learning.
The most popular types of local explanation methods fall into three broad categories.
The first and most widely used type of explanation method is known as feature attribution. Feature attribution methods show which features were most important when the model made a specific decision.
Features are the input variables that are fed to a machine-learning model and used in its prediction. When the data are tabular, features are drawn from the columns in a dataset (they are transformed using a variety of techniques so the model can process the raw data). For image-processing tasks, on the other hand, every pixel in an image is a feature. If a model predicts that an X-ray image shows cancer, for instance, the feature attribution method would highlight the pixels in that specific X-ray that were most important for the model’s prediction.
Essentially, feature attribution methods show what the model pays the most attention to when it makes a prediction.
“Using this feature attribution explanation, you can check to see whether a spurious correlation is a concern. For instance, it will show if the pixels in a watermark are highlighted or if the pixels in an actual tumor are highlighted,” says Zhou.
A second type of explanation method is known as a counterfactual explanation. Given an input and a model’s prediction, these methods show how to change that input so it falls into another class. For instance, if a machine-learning model predicts that a borrower would be denied a loan, the counterfactual explanation shows what factors need to change so her loan application is accepted. Perhaps her credit score or income, both features used in the model’s prediction, need to be higher for her to be approved.
“The good thing about this explanation method is it tells you exactly how you need to change the input to flip the decision, which could have practical usage. For someone who is applying for a mortgage and didn’t get it, this explanation would tell them what they need to do to achieve their desired outcome,” he says.
The third category of explanation methods are known as sample importance explanations. Unlike the others, this method requires access to the data that were used to train the model.
A sample importance explanation will show which training sample a model relied on most when it made a specific prediction; ideally, this is the most similar sample to the input data. This type of explanation is particularly useful if one observes a seemingly irrational prediction. There may have been a data entry error that affected a particular sample that was used to train the model. With this knowledge, one could fix that sample and retrain the model to improve its accuracy.
How are explanation methods used?
One motivation for developing these explanations is to perform quality assurance and debug the model. With more understanding of how features impact a model’s decision, for instance, one could identify that a model is working incorrectly and intervene to fix the problem, or toss the model out and start over.
Another, more recent, area of research is exploring the use of machine-learning models to discover scientific patterns that humans haven’t uncovered before. For instance, a cancer diagnosing model that outperforms clinicians could be faulty, or it could actually be picking up on some hidden patterns in an X-ray image that represent an early pathological pathway for cancer that were either unknown to human doctors or thought to be irrelevant, Zhou says.
It’s still very early days for that area of research, however.
Words of warning
While explanation methods can sometimes be useful for machine-learning practitioners when they are trying to catch bugs in their models or understand the inner-workings of a system, end-users should proceed with caution when trying to use them in practice, says Marzyeh Ghassemi, an assistant professor and head of the Healthy ML Group in CSAIL.
As machine learning has been adopted in more disciplines, from health care to education, explanation methods are being used to help decision makers better understand a model’s predictions so they know when to trust the model and use its guidance in practice. But Ghassemi warns against using these methods in that way.
“We have found that explanations make people, both experts and nonexperts, overconfident in the ability or the advice of a specific recommendation system. I think it is very important for humans not to turn off that internal circuitry asking, ‘let me question the advice that I am
given,’” she says.
Scientists knowexplanations make people over-confident based on other recent work, she adds, citing some recent studies by Microsoft researchers.
Far from a silver bullet, explanation methods have their share of problems. For one, Ghassemi’s recent research has shown that explanation methods can perpetuate biases and lead to worse outcomes for people from disadvantaged groups.
Another pitfall of explanation methods is that it is often impossible to tell if the explanation method is correct in the first place. One would need to compare the explanations to the actual model, but since the user doesn’t know how the model works, this is circular logic, Zhou says.
He and other researchers are working on improving explanation methods so they are more faithful to the actual model’s predictions, but Zhou cautions that, even the best explanation should be taken with a grain of salt.
“In addition, people generally perceive these models to be human-like decision makers, and we are prone to overgeneralization. We need to calm people down and hold them back to really make sure that the generalized model understanding they build from these local explanations are balanced,” he adds.
Zhou’s most recent research seeks to do just that.
What’s next for machine-learning explanation methods?
Rather than focusing on providing explanations, Ghassemi argues that more effort needs to be done by the research community to study how information is presented to decision makers so they understand it, and more regulation needs to be put in place to ensure machine-learning models are used responsibly in practice. Better explanation methods alone aren’t the answer.
“I have been excited to see that there is a lot more recognition, even in industry, that we can’t just take this information and make a pretty dashboard and assume people will perform better with that. You need to have measurable improvements in action, and I’m hoping that leads to real guidelines about improving the way we display information in these deeply technical fields, like medicine,” she says.
And in addition to new work focused on improving explanations, Zhou expects to see more research related to explanation methods for specific use cases, such as model debugging, scientific discovery, fairness auditing, and safety assurance. By identifying fine-grained characteristics of explanation methods and the requirements of different use cases, researchers could establish a theory that would match explanations with specific scenarios, which could help overcome some of the pitfalls that come from using them in real-world scenarios.
Source: MIT News